U.S. patent application number 11/048148 was filed with the patent office on 2005-07-21 for methods, software and apparati for identifying genomic regions harboring a gene associated with a detectable trait.
This patent application is currently assigned to Genset S.A.. Invention is credited to Blumenfeld, Marta, Cohen-Akenine, Annick, Cohen, Daniel, Essioux, Laurent, Schork, Nicholas J..
Application Number | 20050158788 11/048148 |
Document ID | / |
Family ID | 26805417 |
Filed Date | 2005-07-21 |
United States Patent
Application |
20050158788 |
Kind Code |
A1 |
Schork, Nicholas J. ; et
al. |
July 21, 2005 |
Methods, software and apparati for identifying genomic regions
harboring a gene associated with a detectable trait
Abstract
The present invention relates to methods, software, and apparati
for determining whether a genomic region harbors a gene associated
with a detectable trait. In one embodiment, the present invention
relates to a method of confirming that a genomic region harbors a
gene associated with a detectable trait comprising the steps of
identifying a candidate genomic region suspected of harboring the
gene associated with the detectable trait, constructing a
trait-associated distribution of association values using the
biallelic markers in the candidate genomic region, identifying a
plurality of biallelic markers in random genomic regions which are
not suspected of harboring the gene associated with the detectable
trait, constructing a random distribution of association values
using the biallelic markers in the random genomic regions,
comparing the trait-associated distribution of association values
to the random distribution of association values, and determining
whether the trait-associated distribution of association values and
the random distribution of association values are significantly
different from one another. In other embodiments, the present
invention comprises software for performing the above method and
devices comprising the software in a retrievable form.
Inventors: |
Schork, Nicholas J.; (San
Diego, CA) ; Essioux, Laurent; (Paris, FR) ;
Cohen-Akenine, Annick; (Paris, FR) ; Blumenfeld,
Marta; (Paris, FR) ; Cohen, Daniel; (Le
Vezinet, FR) |
Correspondence
Address: |
SALIWANCHIK LLOYD & SALIWANCHIK
A PROFESSIONAL ASSOCIATION
PO BOX 142950
GAINESVILLE
FL
32614-2950
US
|
Assignee: |
Genset S.A.
Evry
FR
|
Family ID: |
26805417 |
Appl. No.: |
11/048148 |
Filed: |
February 1, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11048148 |
Feb 1, 2005 |
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09858289 |
May 15, 2001 |
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09858289 |
May 15, 2001 |
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09438016 |
Nov 10, 1999 |
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6291182 |
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60107986 |
Nov 10, 1998 |
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60140785 |
Jun 23, 1999 |
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Current U.S.
Class: |
435/6.11 ;
702/20 |
Current CPC
Class: |
G16B 40/20 20190201;
G16B 20/40 20190201; G16B 30/00 20190201; G16B 30/20 20190201; G16B
40/00 20190201; C12Q 1/6827 20130101; G16B 20/00 20190201 |
Class at
Publication: |
435/006 ;
702/020 |
International
Class: |
C12Q 001/68; G06F
019/00; G01N 033/48; G01N 033/50 |
Claims
We claim:
1. A method of confirming that a candidate genomic region harbors a
gene associated with a disease comprising the steps of:
constructing a candidate region distribution of test values using a
plurality of biallelic markers in a candidate genomic region
suspected of harboring said gene associated with said detectable
trait, said candidate region distribution of test values being
indicative of the difference in the frequencies of said plurality
of biallelic markers in said candidate region in individuals who
possess said detectable trait and control individuals who do not
possess said detectable trait; constructing a random region
distribution of test values using a plurality of biallelic markers
in random genomic regions which are not suspected of harboring said
gene associated with said detectable trait, said random region
distribution of test values being indicative of the difference in
the frequencies of said plurality of biallelic markers in said
random genomic regions in individuals who possess said detectable
trait and control individuals who do not possess said detectable
trait; and determining whether said candidate region distribution
of test values and said random region distribution of test values
are significantly different from one another.
2. The method of claim 1, wherein said step of constructing a
candidate region distribution of test values comprises performing a
haplotype analysis on each possible combination of biallelic
markers in each group in a series of groups of biallelic markers in
said candidate region, calculating test values for each possible
combination, and including the test value for the haplotype which
has the greatest association with said trait in said candidate
region distribution of test values for each group in said series of
groups of biallelic markers in said candidate genomic region and
wherein said step of constructing a random region distribution of
test values comprises performing a haplotype analysis on each
possible combination of biallelic markers in each group in a series
of groups of biallelic markers in said random genomic regions,
calculating test values for each possible combination, and
including the test value for the haplotype which has the greatest
association with said trait in said random region distribution of
test values for each group in said series of groups of biallelic
markers in said random genomic regions.
3. The method of claim 2, wherein said steps of performing a
haplotype analysis on each possible combination of biallelic
markers in each group in said series of groups of biallelic markers
in said candidate genomic region and calculating said test values
for each combination comprises the steps of: calculating the
frequencies for each combination of biallelic markers in each group
in said series of groups of biallelic markers in said candidate
genomic region in individuals expressing said detectable trait;
calculating the frequencies for each combination of biallelic
markers in each group in said series of groups of biallelic markers
in said candidate genomic region in individuals who do not express
said detectable trait; and comparing the haplotype frequencies in
individuals who express said trait and individuals who do not
express said trait by performing a chi-squared analysis to yield
said test values.
4. The method of claim 3, wherein said steps of performing a
haplotype analysis on each possible combination of biallelic
markers in each group in said series of groups of biallelic markers
in said random genomic regions and calculating said test values for
each combination comprises the steps of: calculating the
frequencies for each combination of biallelic markers in each group
in said series of groups of biallelic markers in said random
genomic regions in individuals expressing said detectable trait;
calculating the frequencies for each combination of biallelic
markers in each group in said series of groups of biallelic markers
in said random genomic regions in individuals in individuals who do
not express said detectable trait; and comparing the haplotype
frequencies in individuals who express said trait and individuals
who do not express said trait by performing a chi-squared analysis
to yield said test values.
5. The method of claim 4, wherein said step of comparing said
candidate region distribution of test values to said random region
distribution of test values comprises performing a Wilcoxon rank
test.
6. The method of claim 4, wherein said step of comparing said
candidate region distribution of test values to said random region
distribution of test values comprises performing a
Kolmogorov-Smirnov test.
7. The method of claim 4, said step of comparing said candidate
region distribution of test values to said random region
distribution of test values comprises performing both a Wilcoxon
rank test and a Kolmogorov-Smirnov test.
8. The method of claim 4, wherein each of said groups of biallelic
markers in said series of groups of biallelic markers in said
candidate genomic region and each of said groups of biallelic
markers in said series of groups of biallelic markers in said
random genomic regions comprises 3 biallelic markers.
9. The method of claim 4, wherein each of said groups of biallelic
markers in said series of groups of biallelic markers in said
candidate genomic region and each of said groups of biallelic
markers in said series of groups of biallelic markers in said
random genomic regions comprises at least 3 biallelic markers.
10. The method of claim 4, wherein said biallelic markers in each
of said groups in said series of groups of biallelic markers in
said candidate genomic region have an average intermarker distance
selected from the group consisting of one marker every 3 kb, one
marker every 5 kb, one marker every 10 kb, one marker every 20 kb,
and one marker every 30 kb.
11. The method of claim 10, wherein said biallelic markers in each
of said groups in said series of groups of biallelic markers in
said random genomic regions have an average intermarker distance
selected from the group consisting of one marker every 3 kb, one
marker every 5 kb, one marker every 10 kb, one marker every 20 kb,
and one marker every 30 kb.
12. The method of claim 4, further comprising selecting random
genomic regions for use in said haplotype analysis which have at
least 3 biallelic markers therein.
13. The method of claim 12, further comprising selecting random
genomic regions for use in said haplotype analysis in which said
biallelic markers have an average intermarker distance sufficient
conducting a haplotype analysis.
14. The method of claim 13, further comprising selecting random
genomic regions for use in said haplotype analysis wherein said at
least 3 biallelic markers are in Hardy-Weinberg equilibrium in
individuals expressing said detectable trait and control
individuals who do not express said detectable trait.
15. The method of claim 14, further comprising selecting random
genomic regions for use in said haplotype analysis in which said at
least 3 biallelic markers are not in complete linkage
disequilibrium to be useful in conducting a haplotype analysis.
16. The method of claim 3, further comprising selecting biallelic
markers in said candidate genomic region which are in
Hardy-Weinberg equilibrium in individuals expressing said
detectable trait and control individuals who do not express said
detectable trait for use in said haplotype analysis.
17. The method of claim 16, further comprising determining the
total number of markers in said candidate genomic region.
18. The method of claim 4, further comprising the step of verifying
that the biallelic markers in said random genomic regions are
appropriate for use in the haplotype analysis by: randomly dividing
said biallelic markers in said random genomic regions into a first
verification group and a second verification group, wherein said
first verification group and said second verification group contain
a substantially identical number of biallelic markers; constructing
a first verification distribution of test values for the biallelic
markers in said first verification group by performing a haplotype
analysis on each possible combination of biallelic markers in each
group in a series of groups of biallelic markers in said first
verification group, calculating test values for each possible
combination, and including the test value for the haplotype which
has the greatest association with said trait in said first
verification distribution of test values for each group in said
series of groups of biallelic markers in said first verification
group; constructing a second verification distribution of test
values for the biallelic markers in said second verification group
by performing a haplotype analysis on each possible combination of
biallelic markers in each group in a series of groups of biallelic
markers in said second verification group, calculating test values
for each possible combination, and including the test value for the
haplotype which has the greatest association with said trait in
said second verification distribution of test values for each group
in said series of groups of biallelic markers in said second
verification group; determining whether said first verification
distribution and said second verification distribution are
significantly different from one another, wherein said biallelic
markers in said random genomic regions are appropriate for use in
the haplotype analysis if said first verification distribution and
said second verification distribution are not significantly
different from one another.
19. The method of claim 18, wherein said steps of performing a
haplotype analysis on each possible combination of biallelic
markers in each group in said series of groups of biallelic markers
in said first and second verification groups and calculating said
test values for each combination comprises the steps of:
calculating the frequencies for each combination of biallelic
markers in said first verification group in each group in said
series of groups of biallelic markers in individuals expressing
said detectable trait; calculating the frequencies for each
combination of biallelic markers in said first verification group
in each group in said series of groups of biallelic markers in
individuals who do not express said detectable trait; comparing the
haplotype frequencies of said biallelic markers in said first
verification group in individuals who express said trait and
individuals who do not express said trait by performing a
chi-squared analysis to yield said test values; calculating the
frequencies for each combination of biallelic markers in said
second verification group in each group in said series of groups of
biallelic markers in individuals expressing said detectable trait;
calculating the frequencies for each combination of biallelic
markers in said second verification group in each group in said
series of groups of biallelic markers in individuals who do not
express said detectable trait; comparing the haplotype frequencies
of said biallelic markers in said second verification group in
individuals who express said trait and individuals who do not
express said trait by performing a chi-squared analysis to yield
said test values
20. The method of claim 1, wherein said disease is selected from
the group consisting of breast cancer, Alzheimer's disease, and
prostate cancer.
21. A method of determining whether a candidate genomic region
harbors a gene associated with a detectable trait comprising
determining whether the association of a plurality of biallelic
markers located in said candidate genomic region with said
detectable trait is significantly different than the association of
a plurality of biallelic markers located in a plurality of random
genomic regions.
22. The method of claim 21, wherein the determination of whether
the association of said plurality of biallelic markers located in
said candidate genomic region with said detectable trait is
significantly different than the association of said plurality of
biallelic markers located in a plurality of random genomic regions
comprises: constructing a candidate region distribution of test
values using said biallelic markers in said candidate genomic
region, said candidate region distribution of test values being
indicative of the difference in the haplotype frequencies of said
biallelic markers in said candidate region in individuals who
possess said detectable trait and control individuals who do not
possess said detectable trait; constructing a random region
distribution of test values using said biallelic markers in said
genomic region said random region distribution of test values being
indicative of the difference in the haplotype frequencies of said
biallelic markers in said random genomic regions in individuals who
possess said detectable trait and control individuals who do not
possess said detectable trait; and comparing said candidate region
distribution of test values with said random region distribution of
test values.
23. The method of claim 22, wherein said step of constructing a
candidate region distribution of test values comprises performing a
haplotype analysis on each possible combination of biallelic
markers in each group in a series of groups of biallelic markers in
said candidate region, calculating test values for each possible
combination, and including the test value for the haplotype which
has the greatest association with said trait in said candidate
region distribution of test values for each group in said series of
groups of biallelic markers in said candidate genomic region and
wherein said step of constructing a random region distribution of
test values comprises performing a haplotype analysis on each
possible combination of biallelic markers in each group in a series
of groups of biallelic markers in said random genomic regions,
calculating test values for each possible combination, and
including the test value for the haplotype which has the greatest
association with said trait in said random region distribution of
test values for each group in said series of groups of biallelic
markers in said random genomic regions.
Description
RELATED APPLICATIONS
[0001] This application claims priority under 35 U.S.C.
.sctn.119(e) to U.S. Patent Application Ser. No. 60/107,986, filed
Nov. 10, 1998, and to Application Ser. No. 60/140,785, filed Jun.
23, 1999, the entire disclosures of which are incorporated herein
by reference.
FIELD OF THE INVENTION
[0002] The present invention relates to methods and apparati using
nucleic acid markers having a statistical association with a
detectable trait to identify one or more genes responsible for the
trait or for a predisposition for expressing the trait.
BACKGROUND OF THE INVENTION
[0003] Recent advances in genetic engineering and bioinformatics
have enabled the manipulation and characterization of large
portions of the human genome. While efforts to obtain the full
sequence of the human genome are rapidly progressing, there are
many practical uses for genetic information which can be
implemented with partial knowledge of the sequence of the human
genome.
[0004] As the full sequence of the human genome is assembled, the
partial sequence information available can be used to identify
genes responsible for detectable human traits, such as genes
associated with human diseases, and to develop diagnostic tests
capable of identifying individuals who express a detectable trait
as the result of a specific genotype or individuals whose genotype
places them at risk of developing a detectable trait at a
subsequent time. Each of these applications for partial genomic
sequence information is based upon the assembly of genetic and
physical maps which order the known genomic sequences along the
human chromosomes.
[0005] The present invention relates to methods and apparati using
nucleic acid markers having a statistical association with a
detectable trait to identify one or more genes responsible for the
trait or for a predisposition for expressing the trait.
SUMMARY OF THE INVENTION
[0006] The present invention relates to methods and apparati for
identifying one or more genes associated with a detectable
phenotype. As described in more detail below, the present invention
involves the use of biallelic markers, which are polymorphic
nucleic acid sequences which differ from one another at a single
nucleotide. The allelic frequencies of the biallelic markers are
compared in nucleic acid samples derived from individuals
expressing the detectable trait and individuals who do not express
the detectable trait. In this manner, candidate genomic regions
suspected of harboring a gene associated with the detectable trait
under investigation are identified.
[0007] The existence of one or more genes associated with the
detectable trait within the candidate region is confirmed by
identifying more biallelic markers lying in the candidate region. A
first haplotype analysis is performed for each possible combination
of groups of biallelic markers within the genomic region suspected
of harboring a trait-associated gene. For example, each group may
comprise three biallelic markers. For each of the groups of
markers, the frequency of each possible haplotype (for groups of
three markers there are 8 possible haplotypes) in individuals
expressing the trait and individuals who do not express the trait
is estimated. For example, the haplotype frequencies may be
estimated using the Expectation-Maximization method of Excoffier L
and Slatkin M, Mol. Biol. Evol. 12: 921-927 (1995), the disclosure
of which is incorporated herein by reference and which is described
in more detail below. In some embodiments, the
Expectation-Maximization method may be performed using the EM-HAPLO
program (Hawley M E, Pakstis A J & Kidd K K, Am. J. Phys.
Anthropol. 18: 104 (1994), the disclosure of which is incorporated
herein by reference). Alternatively, the frequency of each allele
of individual biallelic markers may be determined in nucleic acid
samples from individuals who express the trait under investigation
and control individuals who do not express the trait.
[0008] The frequencies of each of the possible haplotypes of the
grouped markers (or each allele of individual markers) in
individuals expressing the trait and individuals who do not express
the trait are compared. For example, the frequencies may be
compared by performing a chi-squared analysis. Within each group,
the haplotype (or the allele of each individual marker) having the
greatest association with the trait is selected. This process is
repeated for each group of biallelic markers (or each allele of the
individual markers) to generate a distribution of association
values, which will be referred to herein as the "candidate region"
distribution.
[0009] A second haplotype analysis is performed for each possible
combination of groups of biallelic markers within random genomic
regions. For example, each group may comprise three biallelic
markers. For each of the groups of markers, the frequency of each
possible haplotype (for groups of three markers there are 8
possible haplotypes) in individuals expressing the trait and
individuals who do not express the trait is estimated. For example,
the haplotype frequencies may be estimated using the
Expectation-Maximization method of Excoffier L and Slatkin M, as
described above. In some embodiments, the Expectation-Maximization
method may be performed using the EM-HAPLO program as described
above. Alternatively, the frequency of each allele of individual
biallelic markers may be determined in nucleic acid samples from
individuals who express the trait under investigation and control
individuals who do not express the trait.
[0010] The frequencies of each of the possible haplotypes of the
grouped markers (or each allele of individual markers) in
individuals expressing the trait and individuals who do not express
the trait are compared. For example, the frequencies may be
compared by performing a chi-squared analysis. Within each group,
the haplotype (or the allele of each individual marker) having the
greatest association with the trait is selected. This process is
repeated for each group of biallelic markers (or each allele of the
individual markers) to generate a distribution of association
values, which will be referred to herein as the "random region"
distribution.
[0011] The "candidate region" distribution and the "random region"
distribution of are then compared to one another to determine if
there are significant differences between them. For example, the
candidate region distribution and the random region distribution
can be compared using either the Wilcoxon rank test (Noether, G. E.
(1991) Introduction to statistics: "The nonparametric way",
Springer-Verlag, New York, Berlin, the disclosure of which is
incorporated herein by reference) or the Kolmogorov-Smirnov test
(Saporta, G. (1990) "Probalites, analyse des donnees et
statistiques" Technip editions, Paris, the disclosure of which is
incorporated herein by reference) or both the Wilcoxon rank test
and the Kolmogorov-Smirnov test.
[0012] If the candidate region distribution and the random region
distribution are found to be significantly different, the candidate
genomic region is highly likely to contain a gene associated with
the detectable trait. Accordingly, the candidate genomic region is
evaluated more fully to isolate the trait-associated gene.
Alternatively, if the candidate region distribution and the random
region distribution are equal using the above analyses, the
candidate genomic region is unlikely to contain a gene associated
with the detectable trait. Accordingly, no further analysis of the
candidate genomic region is performed.
[0013] The present invention solves the need for empirical
assessments of the statistical significance of the association of
biallelic markers with detectable traits. The present invention
considers the trait being investigated as well as the populations
of individuals utilized to determine the significance of the
association. In particular, the present invention allows the
reference points (i.e. the controls) for evaluating significance to
be derived from the same populations as those used to detect the
association between the biallelic markers and the trait. In
addition, in some embodiments, the present invention allows all the
data available for candidate genomic regions suspected of harboring
a gene associated with a detectable trait to be utilized in the
determination of whether the candidate region does in fact harbor
such a gene. Accordingly, the present invention avoids the risk of
failing to detect a significant association between the markers and
the trait as a consequence of selecting non-optimal markers or
haplotypes for the analysis.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 is a cytogenetic map of chromosome 21.
[0015] FIG. 2A shows the results of a computer simulation of the
distribution of inter-marker spacing on a randomly distributed set
of biallelic markers indicating the percentage of biallelic markers
which will be spaced a given distance apart for 1, 2, or 3
markers/BAC in a genomic map (assuming a set of 20,000 minimally
overlapping BACs covering the genome are evaluated).
[0016] FIG. 2B shows the results of a computer simulation of the
distribution of inter-marker spacing on a randomly distributed set
of biallelic markers indicating the percentage of biallelic markers
which will be spaced a given distance apart for 1, 3, or 6
markers/BAC in a genomic map (assuming a set of 20,000 minimally
overlapping BACs covering the genome are evaluated).
[0017] FIG. 2C shows the results of a linkage disequilibrium
analysis in a random French caucasian population.
[0018] FIG. 3 shows, for a series of hypothetical sample sizes, the
p-value significance obtained in association studies performed
using individual markers from the high-density biallelic map,
according to various hypotheses regarding the difference of allelic
frequencies between the T+ and T- samples.
[0019] FIG. 4 is a hypothetical association analysis conducted with
a map comprising about 3,000 biallelic markers.
[0020] FIG. 5 is a hypothetical association analysis conducted with
a map comprising about 20,000 biallelic markers.
[0021] FIG. 6 is a hypothetical association analysis conducted with
a map comprising about 60,000 biallelic markers.
[0022] FIG. 7 is a haplotype analysis using biallelic markers in
the Apo E region.
[0023] FIG. 8 is a simulated haplotype analysis using the biallelic
markers in the Apo E region included in the haplotype analysis of
FIG. 7.
[0024] FIG. 9 shows a minimal array of overlapping clones which was
chosen for further studies of biallelic markers associated with
prostate cancer, the positions of STS markers known to map in the
candidate genomic region along the contig, and the locations of
biallelic markers along the BAC contig harboring a genomic region
harboring a candidate gene associated with prostate cancer which
were identified using the methods of the present invention.
[0025] FIG. 10 is a rough localization of a candidate gene for
prostate cancer which was obtained by determining the frequencies
of the biallelic markers of FIG. 9 in affected and unaffected
populations.
[0026] FIG. 11 is a further refinement of the localization of the
candidate gene for prostate cancer using additional biallelic
markers which were not included in the rough localization
illustrated in FIG. 10.
[0027] FIG. 12 is a haplotype analysis using the biallelic markers
in the genomic region of the gene associated with prostate
cancer.
[0028] FIG. 13 is a simulated haplotype using the six markers
included in haplotype 5 of FIG. 12.
[0029] FIG. 14 shows the results of a linkage disequilibrium
analysis indicating that rare biallelic markers may be in linkage
disequilibrium with more frequent markers or with other rare
markers.
[0030] FIG. 15 shows the results of a linkage disequilibrium
analysis indicating that non-exonic markers may be in linkage
disequilibrium with exonic markers or other non-exonic markers.
[0031] FIG. 16A depicts the estimated distribution function in
random BACs and a candidate BAC harboring a first gene associated
with prostate cancer.
[0032] FIG. 16B compares the random region distribution and the
candidate region distribution of FIG. 16A.
[0033] FIG. 17A depicts the estimated distribution function in
random BACs and a candidate BAC harboring a second gene associated
with prostate cancer.
[0034] FIG. 17B compares the random region distribution and the
candidate region distribution of FIG. 17A.
[0035] FIG. 18 is a flow diagram illustrating the process for
identifying a genomic region harboring a gene associated with a
detectable trait.
[0036] FIG. 19 illustrates a process for identifying random genomic
clones.
[0037] FIG. 20 illustrates a process for determining the test
values of haplotype frequency differences between control and
trait-associated populations within random clones.
[0038] FIG. 21 illustrates a process for determining the test
values of haplotype frequency differences between control and trait
associated populations within a candidate clone.
[0039] FIG. 22 illustrates the process for identifying markers in
random clones which are in H-W equilibrium in the case and control
populations.
[0040] FIG. 23 illustrates the process for identifying markers in
candidate clones which are in H-W equilibrium in the case and
control populations.
[0041] FIG. 24 illustrates the process for comparing the two
distributions of test values.
[0042] FIG. 25 is a flow diagram illustrating the process for
identifying a genomic region harboring a gene associated with a
detectable trait.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
[0043] The human haploid genome contains an estimated 80,000 to
100,000 or more genes scattered on a 3.times.10.sup.9 base-long
double stranded DNA shared among the 24 chromosomes. Each human
being is diploid, i.e. possesses two haploid genomes, one from
paternal origin, the other from maternal origin. The sequence of
the human genome varies among individuals in a population. About
10.sup.7 sites scattered along the 3.times.10.sup.9 base pairs of
DNA are polymorphic, existing in at least two variant forms called
alleles. Most of these polymorphic sites are generated by single
base substitution mutations and are biallelic. Less than 10.sup.5
polymorphic sites are due to more complex changes and are very
often multi-allelic, i.e. exist in more than two allelic forms. At
a given polymorphic site, any individual (diploid), can be either
homozygous (twice the same allele) or heterozygous (two different
alleles). A given polymorphism or rare mutation can be either
neutral (no effect on trait), or functional, i.e. responsible for a
particular genetic trait.
[0044] Genetic Maps
[0045] The first step towards the identification of genes
associated with a detectable trait, such as a disease or any other
detectable trait, consists in the localization of genomic regions
containing trait-causing genes using genetic mapping methods. The
preferred traits contemplated within the present invention relate
to fields of therapeutic interest; in particular embodiments, they
will be disease traits and/or drug response traits, reflecting drug
efficacy or toxicity. Traits can either be "binary", e.g. diabetic
vs. non-diabetic, or "quantitative", e.g. elevated blood pressure.
Individuals affected by a quantitative trait can be classified
according to an appropriate scale of trait values, e.g. blood
pressure ranges. Each trait value range can then be analyzed as a
binary trait. Patients showing a trait value within one such range
will be studied in comparison with patients showing a trait value
outside of this range. In such a case, genetic analysis methods
will be applied to subpopulations of individuals showing trait
values within defined ranges.
[0046] Genetic mapping involves the analysis of the segregation of
polymorphic loci in trait positive and trait negative populations.
Polymorphic loci constitute a small fraction of the human genome
(less than 1%), compared to the vast majority of human genomic DNA
which is identical in sequence among the chromosomes of different
individuals. Among all existing human polymorphic loci, genetic
markers can be defined as genome-derived polynucleotides which are
sufficiently polymorphic to allow a reasonable probability that a
randomly selected person will be heterozygous, and thus informative
for genetic analysis by methods such as linkage analysis or
association studies.
[0047] A genetic map consists of a collection of polymorphic
markers which have been positioned on the human chromosomes.
Genetic maps may be combined with physical maps, collections of
ordered overlapping fragments of genomic DNA whose arrangement
along the human chromosomes is known. The optimal genetic map
should possess the following characteristics:
[0048] the density of the genetic markers scattered along the
genome should be sufficient to allow the identification and
localization of any trait-related polymorphism,
[0049] each marker should have an adequate level of heterozygosity,
so as to be informative in a large percentage of different
meioses,
[0050] all markers should be easily typed on a routine basis, at a
reasonable expense, and in a reasonable amount of time,
[0051] the entire set of markers per chromosome should be ordered
in a highly reliable fashion.
[0052] However, while the above maps are optimal, it will be
appreciated that individual marker and haplotype association
analyses such as those described below may be performed without the
necessity of determining the order of biallelic markers derived
from a single BAC with respect to one another.
Genetic Maps Based on RFLPs or VNTRs
[0053] The analysis of DNA polymorphisms has relied on the
following types of polymorphisms. The first generation of genetic
markers were restriction fragment length polymorphisms (RFLPs),
single nucleotide polymorphisms which occur at restriction sites,
thereby modifying the cleavage pattern of the corresponding
restriction enzyme. Though the original methods used to type RFLPs
were material-, effort- and time-consuming, today these markers can
easily be typed by PCR-based technologies. Since they are biallelic
markers (they present only two alleles, the restriction site being
either present or absent), their maximum heterozygosity is 0.5. The
theoretical number of RFLPs distributed along the entire human
genome is more than 10.sup.5, which leads to a potential average
inter-marker distance of 30 kilobases. However, in reality, the
number of evenly distributed RFLPs which occur at a sufficient
frequency in the population to make them useful for tracking of
genetic polymorphisms is very limited.
[0054] The second generation of genetic markers was VNTRs (Variable
Number of Tandem Repeats), which can be categorized as either
minisatellites or microsatellites. Minisatellites are tandemly
repeated DNA sequences present in units of 5-50 repeats which are
distributed along regions of the human chromosomes ranging from 0.1
to 20 kilobases in length. Since they present many possible
alleles, their polymorphic informative content is very high.
Minisatellites are scored by performing Southern blots to identify
the number of tandem repeats present in a nucleic acid sample from
the individual being tested. However, there are only 10.sup.4
potential VNTRs that can be typed by Southern blotting.
[0055] Microsatellites (also called simple tandem repeat
polymorphisms, or simple sequence length polymorphisms) constitute
the most developed category of genetic markers. They include small
arrays of tandem repeats of simple sequences (di-, tri-,
tetra-nucleotide repeats) which exhibit a high degree of length
polymorphism and thus a high level of informativeness. Slightly
more than 5,000 microsatellites easily typed by PCR-derived
technologies, have been ordered along the human genome (Dib et al.,
Nature 380: 152 (1996), the disclosure of which is incorporated
herein by reference).
[0056] A number of these available microsatellites were used to
construct integrated physical and genetic maps containing less than
5,000 markers. For example, CEPH (Chumakov et al., Nature 377:
175-298 (1995) and Cohen et al., Nature 366: 698-701 (1993), the
disclosures of which are incorporated herein by reference), and
Whitehead Institute and Gnthon (Hudson et al., 1995), constructed
genetic and physical maps covering 75% to 95% of the human genome,
based on 2500 to 5000 microsatellite markers.
[0057] However, the number of easily typed informative markers in
these maps was too small for the average distance between
informative markers to fulfill the above-listed requirements for
genetic maps.
Biallelic Markers
[0058] Biallelic markers are genome-derived polynucleotides which
exhibit biallelic polymorphism. As used herein, the term biallelic
marker means a biallelic single nucleotide polymorphism. As used
herein, the term polymorphism may include a single base
substitution, insertion, or deletion. By definition, the lowest
allele frequency of a biallelic polymorphism is 1% (sequence
variants which show allele frequencies below 1% are called rare
mutations). There are potentially more than 10.sup.7 biallelic
markers which can easily be typed by routine automated techniques,
such as sequence- or hybridization-based techniques, out of which
10.sup.6 are sufficiently informative for mapping purposes.
However, a biallelic marker will show a sufficient degree of
informativeness for use in genetic mapping only if the frequency of
its less frequent allele is not less than about 10% (i.e. a
heterozygosity rate of at least 0.18) (the heterozygosity rate for
a biallelic marker is 2 P.sub.a (1-P.sub.a), where P.sub.a is the
frequency of allele a). Preferably, the frequency of the less
frequent allele of the biallelic markers in the present maps is at
least 20% (i.e. a heterozygosity rate of at least 0.32). More
preferably, the frequency of the less frequent allele of the
biallelic markers in the present maps is at least 30% (i.e. its
heterozygosity rate is higher than about 0.42).
[0059] Initial attempts to construct genetic maps based on non-RFLP
biallelic markers have focused on identifying biallelic markers
lying within sequence tagged sites (STS), pieces of genomic DNA
having a known sequence and averaging about 250 bases in length.
More than 30,000 STSs have been identified and ordered along the
genome (Hudson et al., Science 270: 1945-1954 (1995); Schuler et
al., Science 274: 540-546 (1996), the disclosures of which are
incorporated herein by reference). For example, the Whitehead
Institute and Gnthon's integrated map contains 15,086 STSs.
[0060] These sequence tagged sites can be screened to identify
polymorphisms, preferably Single Nucleotide Polymorphisms (SNPs),
more preferably non RFLP biallelic markers therein. Generally
polymorphisms are identified by determining the sequence of the
STSs in 5 to 10 individuals.
[0061] Wang et al. (Cold Spring harbor laboratory: Abstracts of
papers presented on genome Mapping and sequencing p. 17 (May 14-18,
1997), the disclosure of which is incorporated herein by reference)
recently announced the identification and mapping of 750 Single
Nucleotide Polymorphisms issued from the sequencing of 12,000 STSs
from the Whitehead/MIT map, in eight unrelated individuals. The map
was assembled using a high throughput system based on the
utilization of DNA chip technology available from Affymetrix (Chee
et al., Science 274: 610-614 (1996), the disclosure of which is
incorporated herein by reference).
[0062] However, according to experimental data and statistical
calculations, less than one out of 10 of all STSs mapped today will
contain an informative Single Nucleotide Polymorphism. This is
primarily due to the short length of existing STSs (usually less
than 250 bp). If one assumes 10.sup.6 informative SNPs spread along
the human genome, there would on average be one marker of interest
every 3.times.10.sup.9/10.sup.6, i.e. every 3,000 bp. The
probability that one such marker is present on a 250 bp stretch is
thus less than {fraction (1/10)}.
[0063] While it could produce a high density map, the STS approach
based on currently existing markers does not put any systematic
effort into making sure that the markers obtained are optimally
distributed throughout the entire genome. Instead, polymorphisms
are limited to those locations for which STSs are available.
[0064] The even distribution of markers along the chromosomes is
critical to the future success of genetic analyses. In particular,
a high density map having appropriately spaced markers is essential
for conducting association studies on sporadic cases, aiming at
identifying genes responsible for detectable traits such as those
which are described below.
[0065] As will be further explained below, genetic studies have
mostly relied in the past on a statistical approach called linkage
analysis, which took advantage of microsatellite markers to study
their inheritance pattern within families from which a sufficient
number of individuals presented the studied trait. Because of
intrinsic limitations of linkage analysis, which will be further
detailed below, and because these studies necessitate the
recruitment of adequate family pedigrees, they are not well suited
to the genetic analysis of all traits, particularly those for which
only sporadic cases are available (e.g. drug response traits), or
those which have a low penetrance within the studied
population.
[0066] Association studies offer an alternative to linkage
analysis. Combined with the use of a high density map of
appropriately spaced, sufficiently informative markers, association
studies, including linkage disequilibrium-based genome wide
association studies, will enable the identification of most genes
involved in complex traits.
[0067] The present invention relates to a method for generating a
high density linkage disequilibrium-based genetic map of the human
genome which will allow the identification of sufficiently
informative markers spaced at intervals which permit their use in
identifying genes responsible for detectable traits using
genome-wide association studies and linkage disequilibrium
mapping.
Construction of a Physical Man
[0068] The first step in constructing a high density genetic map of
biallelic markers is the construction of a physical map. Physical
maps consist of ordered, overlapping cloned fragments of genomic
DNA covering a portion of the genome, preferably covering one or
all chromosomes. Obtaining a physical map of the genome entails
constructing and ordering a genomic DNA library.
[0069] Physical mapping in complex genomes such as the human genome
(3,000 Megabases) requires the construction of DNA libraries
containing large inserts (on the order of 0.1 to 1 Megabase). It is
crucial that such libraries be easy to construct, screen and
manipulate, and that the DNA inserts be stable and relatively free
of chimerism.
[0070] Yeast artificial chromosomes (YACs; Burke et al., Science
236: 806-812 (1987), the disclosure of which is incorporated herein
by reference) have provided an invaluable tool in the analysis of
complex genomes since their cloning capacity is extremely high (in
the Mb range). YAC libraries containing large DNA inserts (up to 2
Mb) have been used to generate STS-content maps of individual
chromosomes or of the entire human genome (Chumakov et al. (1995),
supra; Hudson et al. (1995), supra; Cohen et al., Nature 366:
698-701 (1993; Chumakov et al., Nature 359: 380-387 (1992); Gemmill
et al., Nature 377: 299-319 (1995); Doggett et al., Nature 377:
335-365 (1995); the disclosures of which are incorporated herein by
reference).
[0071] The present genetic maps may be constructed using currently
available YAC genomic libraries such as the CEPH human YAC library
as a starting material. (Chumakov et al. (1995), supra).
Alternatively, one may construct a YAC genomic library as described
in Chumakov et al., 1995, the disclosure of which is incorporated
herein by reference, or as described below.
[0072] Once a YAC genomic library has been obtained, the genomic
DNA fragments therein are ordered. Ordering may be performed
directly on the genomic DNA in the YAC library. However, direct
ordering of YAC inserts is not preferred because YAC libraries
often exhibit a high rate of chimerism (40 to 50% of YAC clones
contain fragments from more than one genomic region), often suffer
from clonal instability within their genomic DNA inserts, and
require tedious procedures to manipulate and isolate the insert
DNA. Instead, it is preferable to conduct the mapping and
sequencing procedures required for ordering the genomic DNA in a
system which enables the stable cloning of large inserts while
being easy to manipulate using standard molecular biology
techniques.
[0073] Accordingly, it is preferable to clone the genomic DNA into
bacterial single copy plasmids, for example BACs (Bacterial
Artificial Chromosomes), rather than into YACs. Bacterial
artificial chromosomes are well suited for use in ordering genomic
DNA fragments. BACs provide a low rate of chimerism and fragment
rearrangement, together with relative ease of insert isolation.
Thus BAC libraries are well suited to integrate genetic, STS and
cytogenetic information while providing direct access to stable,
readily-sequenceable genomic DNA. An example of bacterial
artificial chromosome is the BAC cloning system of Shizuya et al.,
which is capable of stably propagating and maintaining relatively
large genomic DNA fragments (up to 300 kb long) as single-copy
plasmids in E. coli (Shizuya et al., Proc. Natl. Acad. Sci. USA 89:
8794-8797 (1992), the disclosure of which is incorporated herein by
reference).
[0074] Example 1 below describes the construction of a BAC library
containing human genomic DNA. It will be appreciated that the
source of the genomic DNA, the enzymes used to digest the DNA, the
vectors into which the genomic DNA is inserted, and the size of the
DNA inserts which are cloned into said vectors need not be
identical to those described in Example 1 below. Rather, the
genomic DNA may be obtained from any appropriate source, may be
digested with any appropriate enzyme, and may be cloned into any
suitable vector. Insert size may vary within any range compatible
with the cloning system chosen and with the intended purpose of the
library being constructed. Typically, using BAC vectors to
construct DNA libraries covering the entire human genome, insert
size may vary between 50 kb and 300 kb, preferably 100 kb and 200
kb.
[0075] To construct a physical map of the genome from genomic DNA
libraries, the library clones have to be ordered along the human
chromosomes. In a preferred embodiment, a minimal subset of the
ordered clones will then be chosen that completely covers the
entire genome.
[0076] For example the genomic DNA in the inserts of the BAC
vectors may be ordered using STS markers whose positions relative
to one another and locations along the genome are known using
procedures such as those described herein. The STS markers used to
order the BAC inserts may be the STS markers contained in the
integrated maps described above. Alternatively, the STSs may be
STSs which are not contained in any of the physical maps described
above. In another embodiment, the STSs may be a combination of STSs
included in the physical maps described above and STSs which are
not included in the integrated maps described above.
[0077] The BAC vectors are screened with STSs until there is at
least one positive BAC clone per STS. Preferably, a minimally
overlapping set of 10,000 to 30,000 BACs having genomic inserts
spanning the entire human genome are identified. More preferably, a
minimally overlapping set of 10,000 to 30,000 BACs having genomic
inserts of about 100-300 kb in length spanning the entire human
genome are identified. In a preferred embodiment, a minimally
overlapping set of 10,000 to 30,000 BACs having genomic inserts of
about 100-150 kb in length spanning the entire human genome is
identified. In a highly preferred embodiment, a minimally
overlapping set of 15,000 to 25,000 BACs having genomic inserts of
about 100-200 kb in length spanning the entire human genome is
identified. Alternatively, a smaller number of BACs spanning a set
of chromosomes, a single chromosome, a particular subchromosomal
region, or any other desired portion of the genome may be ordered.
The BACs may be screened for the presence of STSs as described in
Example 2 below.
[0078] Alternatively, a YAC (Yeast Artificial Chromosome) library
can be used. The very large insert size, of the order of 1
megabase, is the main advantage of the YAC libraries. The library
can typically include about 33,000 YAC clones as described in
Chumakov et al. (1995, supra). The YAC screening protocol may be
the same as the one used for BAC screening.
[0079] The known order of the STSs is then used to align the BAC
inserts in an ordered array (contig) spanning the whole human
genome. If necessary new STSs to be tested can be generated by
sequencing the ends of selected BAC inserts. Subchromosomal
localization of the BACs can be established and/or verified by
fluorescence in situ hybridization (FISH), performed on metaphasic
chromosomes as described by Cherif et al. 1990 and in Example 8
below. BAC insert size may be determined by Pulsed Field Gel
Electrophoresis after digestion with the restriction enzyme
NotI.
[0080] Finally, a minimally overlapping set of BAC clones, with
known insert size and subchromosomal location, covering the entire
genome, a set of chromosomes, a single chromosome, a particular
subchromosomal region, or any other desired portion of the genome
is selected from the DNA library. For example, the BAC clones may
cover at least 100 kb of contiguous genomic DNA, at least 250 kb of
contiguous genomic DNA, at least 500 kb of contiguous genomic DNA,
at least 2 Mb of contiguous genomic DNA, at least 5 Mb of
contiguous genomic DNA, at least 10 Mb of contiguous genomic DNA,
or at least 20 Mb of contiguous genomic DNA.
Identification of Biallelic Markers
[0081] In order to generate polymorphisms having the adequate
informative content to be used as biallelic markers for genetic
mapping, the sequences of random genomic fragments from an
appropriate number of unrelated individuals are compared. Genomic
sequences to be screened for biallelic markers may be generated by
partially sequencing BAC inserts, preferably by sequencing the ends
of BAC subclones. Sequencing the ends of an adequate number of BAC
subclones derived from a minimally overlapping array of BACs such
as those described above will allow the generation of biallelic
markers spanning the entire genome, a set of chromosomes, a single
chromosome, a particular subchromosomal region, or any other
desired portion of the genome with an optimized inter-marker
spacing. For example, portions of the BACs in the selected ordered
array may be subcloned and sequenced using, for example, the
procedures described in Examples 3 and 4 below.
[0082] To identify biallelic markers using partial sequence
information derived from subclone ends, such as the ends of the BAC
subclones prepared above, pairs of primers, each one specifically
defining a 500 bp amplification fragment, are designed using the
above mentioned partial sequences. The primers used for the genomic
amplification of fragments derived from the subclones, such as the
BAC subclones prepared above, may be designed using the OSP
software (Hillier L. and Green P., Methods Appl., 1: 124-8 (1991),
the disclosure of which is incorporated herein by reference). The
GC content of the amplification primers preferably ranges between
10 and 75%, more preferably between 35 and 60%, and most preferably
between 40 and 55%. The length of amplification primers can range
from 10 to 100 nucleotides, preferably from 10 to 50, 10 to 30 or
more preferably 10 to 20 nucleotides. Shorter primers tend to lack
specificity for a target nucleic acid sequence and generally
require cooler temperatures to form sufficiently stable hybrid
complexes with the template. Longer primers are expensive to
produce and can sometimes self-hybridize to form hairpin
structures.
[0083] All primers may contain, upstream of the specific target
bases, a common oligonucleotide tail that serves as a sequencing
primer. Those skilled in the art are familiar with primer
extensions which can be used for these purposes.
[0084] To identify biallelic markers, the sequences corresponding
to the partial sequences determined above are determined and
compared in a plurality of individuals. The population used to
identify biallelic markers having an adequate informative content
preferably consists of ca. 100 unrelated individuals from a
heterogeneous population. In such procedures, DNA samples, such as
peripheral venous blood samples, are obtained from each donor using
methods such as those described in Example 5 below. The DNA
obtained from peripheral blood as described above is amplified
using amplification primers. The sequences of the amplicons are
determined and biallelic markers within the amplicons are
identified as provided in Example 6 below.
[0085] In some embodiments, the biallelic markers are identified by
sequencing pools of DNA samples from 100 individuals. The detection
limit for the frequency of biallelic polymorphisms detected by
sequencing pools of 100 individuals is about 10% for the minor
allele, as verified by sequencing pools of known allelic
frequencies. However, more than 90% of the biallelic polymorphisms
detected by the pooling method have a frequency for the minor
allele higher than 25%. Therefore, the biallelic markers selected
by this method have a frequency of at least 10% for the minor
allele and 90% or less for the major allele, preferably at least
20% for the minor allele and 80% or less for the major allele, more
preferably at least 30% for the minor allele and 70% or less for
the major allele, thus a heterozygosity rate higher than 0.18,
preferably higher than 0.32, more preferably higher than 0.42.
[0086] In an initial study to determine the frequency of biallelic
markers in the human genome that can be obtained using the above
methods the following results were obtained. 300 different
amplicons derived from 100 individuals, and covering a total of 150
kb obtained from different genomic regions, were sequenced. A total
of 54 biallelic polymorphisms were identified, indicating that
there is one biallelic polymorphism with a heterozygosity rate
higher than 0.18 (frequency of the minor allele higher than 10%),
preferably higher than 0.38 (frequency of the minor allele higher
than 25%), every 2.5 to 3 kb. Given that the human genome is about
3.106 kb long, this indicates that, out of the 10' biallelic
markers present on the human genome, approximately 10.sup.6 have
adequate heterozygosity rates for genetic mapping purposes.
[0087] Using the procedures of Examples 1-6 below, sets containing
increasing numbers of biallelic markers may be constructed. For
example, in some embodiments, the procedures of Examples 1-6 are
used to identify 1 to about 50 biallelic markers. In some
embodiments, the procedures of Examples 1-6 are used to identify
about 50 to about 200 biallelic markers. In other embodiments, the
procedures of Examples 1-6 are used to identify about 200 to about
500 biallelic markers. In some embodiments, the procedures of
Examples 1-6 are used to identify about 1,000 biallelic markers. In
other embodiments, the procedures of Examples 1-6 are used to
identify about 3,000 biallelic markers. In further embodiments, the
procedures of Examples 1-6 are used to identify about 5,000
biallelic markers. In another embodiment, the procedures of
Examples 1-6 are used to identify about 10,000 biallelic markers.
In still another embodiment, the procedures of Examples 1-6 are
used to identify about 20,000 biallelic markers. In still another
embodiment, the procedures of Examples 1-6 are used to identify
about 40,000 biallelic markers. In still another embodiment, the
procedures of Examples 1-6 are used to identify about 60,000
biallelic markers. In still another embodiment, the procedures of
Examples 1-6 are used to identify about 80,000 biallelic markers.
In a still another embodiment, the procedures of Examples 1-6 are
used to identify more than 100,000 biallelic markers. In a further
embodiment, the procedures of Examples 1-6 are used to identify
more than 120,000 biallelic markers.
[0088] As discussed above, the ordered nucleic acids, such as the
inserts in BAC clones, which contain the biallelic markers of the
present invention may span a portion of the genome. For example,
the ordered nucleic acids may span at least 100 kb of contiguous
genomic DNA, at least 250 kb of contiguous genomic DNA, at least
500 kb of contiguous genomic DNA, at least 2 Mb of contiguous
genomic DNA, at least 5 Mb of contiguous genomic DNA, at least 10
Mb of contiguous genomic DNA, or at least 20 Mb of contiguous
genomic DNA.
[0089] In addition, groups of biallelic markers located in
proximity to one another along the genome may be identified within
these portions of the genome for use in haplotyping analyses as
described below. The biallelic markers included in each of these
groups may be located within a genomic region spanning less than 1
kb, from 1 to 5 kb, from 5 to 10 kb, from 10 to 25 kb, from 25 to
50 kb, from 50 to 150 kb, from 150 to 250 kb, from 250 to 500 kb,
from 500 kb to 1 Mb, or more than 1 Mb. It will be appreciated that
the ordered DNA fragments containing these groups of biallelic
markers need not completely cover the genomic regions of these
lengths but may instead be incomplete contigs having one or more
gaps therein. As discussed in further detail below, biallelic
markers may be used in single maker and haplotype association
analyses regardless of the completeness of the corresponding
physical contig harboring them.
Ordering of Biallelic Markers
[0090] Biallelic markers can be ordered to determine their
positions along chromosomes, preferably subchromosomal regions,
most preferably along the above-described minimally overlapping
ordered BAC arrays, as follows.
[0091] The positions of the biallelic markers along chromosomes may
be determined using a variety of methodologies. In one approach,
radiation hybrid mapping is used. Radiation hybrid (RH) mapping is
a somatic cell genetic approach that can be used for high
resolution mapping of the human genome. In this approach, cell
lines containing one or more human chromosomes are lethally
irradiated, breaking each chromosome into fragments whose size
depends on the radiation dose. These fragments are rescued by
fusion with cultured rodent cells, yielding subclones containing
different portions of the human genome. This technique is described
by Benham et al. (Genomics 4: 509-517, 1989) and Cox et al.,
(Science 250: 245-250, 1990), the entire contents of which are
hereby incorporated by reference. The random and independent nature
of the subclones permits efficient mapping of any human genome
marker. Human DNA isolated from a panel of 80-100 cell lines
provides a mapping reagent for ordering biallelic markers. In this
approach, the frequency of breakage between markers is used to
measure distance, allowing construction of fine resolution maps as
has been done for ESTs (Schuler et al., Science 274: 540-546, 1996,
hereby incorporated by reference).
[0092] RH mapping has been used to generate a high-resolution whole
genome radiation hybrid map of human chromosome 17q22-q25.3 across
the genes for growth hormone (GH) and thymidine kinase (TK) (Foster
et al., Genomics 33: 185-192, 1996), the region surrounding the
Gorlin syndrome gene (Obermayr et al., Eur. J. Hum. Genet. 4:
242-245, 1996), 60 loci covering the entire short arm of chromosome
12 (Raeymaekers et al., Genomics 29: 170-178, 1995), the region of
human chromosome 22 containing the neurofibromatosis type 2 locus
(Frazer et al., Genomics 14: 574-584, 1992) and 13 loci on the long
arm of chromosome 5 (Warrington et al., Genomics 11: 701-708,
1991). These publications are all incorporated herein by
reference.
[0093] Alternatively, PCR based techniques and human-rodent somatic
cell hybrids may be used to determine the positions of the
biallelic markers on the chromosomes. In such approaches,
oligonucleotide primer pairs which are capable of generating
amplification products containing the polymorphic bases of the
biallelic markers are designed. Preferably, the oligonucleotide
primers are 18-23 bp in length and are designed for PCR
amplification. The creation of PCR primers from known sequences is
well known to those with skill in the art. For a review of PCR
technology see Erlich, H. A., PCR Technology; Principles and
Applications for DNA Amplification. 1992. W.H. Freeman and Co., New
York, incorporated herein by reference.
[0094] The primers are used in polymerase chain reactions (PCR) to
amplify templates from total human genomic DNA. PCR conditions are
as follows: 60 ng of genomic DNA is used as a template for PCR with
80 ng of each oligonucleotide primer, 0.6 unit of Taq polymerase,
and 1 mCi of a .sup.32P-labeled deoxycytidine triphosphate. The PCR
is performed in a microplate thermocycler (Techne) under the
following conditions: 30 cycles of 94.degree. C., 1.4 min;
55.degree. C., 2 min; and 72.degree. C., 2 min; with a final
extension at 72.degree. C. for 10 min. The amplified products are
analyzed on a 6% polyacrylamide sequencing gel and visualized by
autoradiography. If the length of the resulting PCR product is
identical to the length expected for an amplification product
containing the polymorphic base of the biallelic marker, then the
PCR reaction is repeated with DNA templates from two panels of
human-rodent somatic cell hybrids, BIOS PCRable DNA (BIOS
Corporation) and NIGMS Human-Rodent Somatic Cell Hybrid Mapping
Panel Number 1 (NIGMS, Camden, N.J.).
[0095] PCR is used to screen a series of somatic cell hybrid cell
lines containing defined sets of human chromosomes for the presence
of a given biallelic marker. DNA is isolated from the somatic
hybrids and used as starting templates for PCR reactions using the
primer pairs from the biallelic marker. Only those somatic cell
hybrids with chromosomes containing the human sequence
corresponding to the biallelic marker will yield an amplified
fragment. The biallelic markers are assigned to a chromosome by
analysis of the segregation pattern of PCR products from the
somatic hybrid DNA templates. The single human chromosome present
in all cell hybrids that give rise to an amplified fragment is the
chromosome containing that biallelic marker. For a review of
techniques and analysis of results from somatic cell gene mapping
experiments. (See Ledbetter et al., Genomics 6: 475-481(1990),
incorporated herein by reference.)
[0096] Example 7 below describes a preferred method for positioning
of biallelic markers on clones, such as BAC clones, obtained from
genomic DNA libraries.
[0097] Using such procedures, a number of BAC clones carrying
selected biallelic markers can be isolated. The position of these
BAC clones on the human genome can be defined by performing STS
screening as described in Example 2. Preferably, to decrease the
number of STSs to be tested, each BAC can be localized on
chromosomal or subchromosomal regions by procedures such as those
described in Examples 8 and 9 below. This localization will allow
the selection of a subset of STSs corresponding to the identified
chromosomal or subchromosomal region. Testing each BAC with such a
subset of STSs and taking account of the position and order of the
STSs along the genome will allow a refined positioning of the
corresponding biallelic marker along the genome.
[0098] If the DNA library used to isolate BAC inserts or any type
of genomic DNA fragments harboring the selected biallelic markers
already constitutes a physical map of the genome or any portion
thereof, using the known order of the DNA fragments will allow the
order of the biallelic markers to be established.
[0099] As discussed above, it will be appreciated that markers
carried by the same fragment of genomic DNA, such as the insert in
a BAC clone, need not necessarily be ordered with respect to one
another within the genomic fragment to conduct single point or
haplotype association analyses. However, in other embodiments of
the maps, the order of biallelic markers carried by the same
fragment of genomic DNA may be determined.
[0100] The positions of the biallelic markers used to construct the
maps of the present invention may be assigned to subchromosomal
locations using Fluorescence In Situ Hybridization (FISH) (Cherif
et al., Proc. Natl. Acad. Sci. U.S.A., 87: 6639-6643 (1990), the
disclosure of which is incorporated herein by reference). FISH
analysis is described in Example 8 below. This procedure was used
to confirm the subchromosomal location of numerous biallelic
markers obtained using the methods described above. Simple
identification numbers were attributed to each BAC from which the
markers were derived. FIG. 1 is a cytogenetic map of chromosome 21
indicating the subchromosomal regions therein. Amplification
primers for generating amplification products containing the
polymorphic bases of these markers and microsequencing primers for
use in determining the identities of the polymorphic bases of these
biallelic markers were also designed.
[0101] The rate at which biallelic markers may be assigned to
subchromosomal regions may be enhanced through automation. For
example, probe preparation may be performed in a microtiter plate
format, using adequate robots. The rate at which biallelic markers
may be assigned to subchromosomal regions may be enhanced using
techniques which permit the in situ hybridization of multiple
probes on a single microscope slide, such as those disclosed in
Larin et al., Nucleic Acids Research 22: 3689-3692 (1994), the
disclosure of which is incorporated herein by reference. In the
largest test format described, different probes were hybridized
simultaneously by applying them directly from a 96-well microtiter
dish which was inverted on a glass plate. Software for image data
acquisition and analysis that is adapted to each optical system,
test format, and fluorescent probe used, can be derived from the
system described in Lichter et al. Science 247: 64-69 (1990), the
disclosure of which is incorporated herein by reference. Such
software measures the relative distance between the center of the
fluorescent spot corresponding to the hybridized probe and the
telomeric end of the short arm of the corresponding chromosome, as
compared to the total length of the chromosome. The rate at which
biallelic markers are assigned to subchromosomal locations may be
further enhanced by simultaneously applying probes labeled with
different fluorescent tags to each well of the 96 well dish. A
further benefit of conducting the analysis on one slide is that it
facilitates automation, since a microscope having a moving stage
and the capability of detecting fluorescent signals in different
metaphase chromosomes could provide the coordinates of each probe
on the metaphase chromosomes distributed on the 96 well dish.
[0102] Example 9 below describes an alternative method to position
biallelic markers which allows their assignment to human
chromosomes.
[0103] The ordering analyses described above may be conducted to
generate an integrated genome wide genetic map comprising about
20,000 biallelic markers (1 biallelic marker per BAC if 20,000 BAC
inserts are screened). In another embodiment, the above procedures
are conducted to generate a map comprising about 40,000 markers (an
average of 2 biallelic markers per BAC if 20,000 BAC inserts are
screened). In a further embodiment preferred embodiment, the above
procedures are conducted to generate a map comprising about 60,000
markers (an average of 3 biallelic markers per BAC if 20,000 BAC
inserts are screened). In a further embodiment preferred
embodiment, the above procedures are conducted to generate a map
comprising about 80,000 markers (an average of 4 biallelic markers
per BAC if 20,000 BAC inserts are screened). In yet another
embodiment, the above procedures are conducted to generate a map
comprising about 100,000 markers (an average of 5 biallelic markers
per BAC if 20,000 BAC inserts are screened). In a further
embodiment, the above procedures are conducted to generate a map
comprising about 120,000 markers (an average of 6 biallelic markers
per BAC if 20,000 BAC inserts are screened).
[0104] Alternatively, maps having the above-specified average
numbers of biallelic markers per BAC which comprise smaller
portions of the genome, such as a set of chromosomes, a single
chromosome, a particular subchromosomal region, or any other
desired portion of the genome, may also be constructed using the
procedures provided herein.
[0105] In some embodiments, the biallelic markers in the map are
separated from one another by an average distance of 10-200 kb. In
further embodiments, the biallelic markers in the map are separated
from one another by an average distance of 15-150 kb. In yet
another embodiment, the biallelic markers in the map are separated
from one another by an average distance of 20-100 kb. In other
embodiments, the biallelic markers in the map are separated from
one another by an average distance of 100-150 kb. In further
embodiments, the biallelic markers in the map are separated from
one another by an average distance of 50-100 kb. In yet another
embodiment, the biallelic markers in the map are separated from one
another by an average distance of 25-50 kb. Maps having the
above-specified intermarker distances which comprise smaller
portions of the genome, such as a set of chromosomes, a single
chromosome, a particular subchromosomal region, or any other
desired portion of the genome, may also be constructed using the
procedures provided herein.
[0106] FIG. 2, showing the results of computer simulations of the
distribution of inter-marker spacing on a randomly distributed set
of biallelic markers, indicates the percentage of biallelic markers
which will be spaced a given distance apart for a given number of
markers/BAC in the genomic map (assuming 20,000 BACs constituting a
minimally overlapping array covering the entire genome are
evaluated). One hundred iterations were performed for each
simulation (20,000 marker map, 40,000 marker map, 60,000 marker
map, 120,000 marker map).
[0107] As illustrated in FIG. 2A, 98% of inter-marker distances
will be lower than 150 kb provided 60,000 evenly distributed
markers are generated (3 per BAC); 90% of inter-marker distances
will be lower than 150 kb provided 40,000 evenly distributed
markers are generated (2 per BAC); and 50% of inter-marker
distances will be lower than 150 kb provided 20,000 evenly
distributed markers are generated (1 per BAC).
[0108] As illustrated in FIG. 2B, 98% of inter-marker distances
will be lower than 80 kb provided 120,000 evenly distributed
markers are generated (6 per BAC); 80% of inter-marker distances
will be lower than 80 kb provided 60,000 evenly distributed markers
are generated (3 per BAC); and 15% of inter-marker distances will
be lower than 80 kb provided 20,000 evenly distributed markers are
generated (1 per BAC).
[0109] As already mentioned, high density biallelic marker maps
allow association studies to be performed to identify genes
involved in complex traits.
[0110] Association studies examine the frequency of marker alleles
in unrelated trait positive (T+) individuals compared with trait
negative (T-) controls, and are generally employed in the detection
of polygenic inheritance.
[0111] Association studies as a method of mapping genetic traits
rely on the phenomenon of linkage disequilibrium, which is
described below.
Linkage Disequilibrium
[0112] If two genetic loci lie on the same chromosome, then sets of
alleles on the same chromosomal segment (called haplotypes) tend to
be transmitted as a block from generation to generation. When not
broken up by recombination, haplotypes can be tracked not only
through pedigrees but also through populations. The resulting
phenomenon at the population level is that the occurrence of pairs
of specific alleles at different loci on the same chromosome is not
random, and the deviation from random is called linkage
disequilibrium (LD).
[0113] If a specific allele in a given gene is directly involved in
causing a particular trait T, its frequency will be statistically
increased in a T+ population when compared to the frequency in a T-
population. As a consequence of the existence of LD, the frequency
of all other alleles present in the haplotype carrying the
trait-causing allele (TCA) will also be increased in T+ individuals
compared to T- individuals. Therefore, association between the
trait and any allele in linkage disequilibrium with the
trait-causing allele will suffice to suggest the presence of a
trait-related gene in that particular allele's region. Linkage
disequilibrium allows the relative frequencies in T+ and T-
populations of a limited number of genetic polymorphisms
(specifically biallelic markers) to be analyzed as an alternative
to screening all possible functional polymorphisms in order to find
trait-causing alleles.
[0114] LD among a set of biallelic markers having an adequate
heterozygosity rate can be determined by genotyping between 50 and
1000 unrelated individuals, preferably between 75 and 200, more
preferably around 100. Genotyping a biallelic marker consists of
determining the specific allele carried by an individual at the
given polymorphic base of the biallelic marker. Genotyping can be
performed using similar methods as those described above for the
generation of the biallelic markers, or using other genotyping
methods such as those further described below.
[0115] LD between any pair of biallelic markers comprising at least
one of the biallelic markers of the present invention
(M.sub.i,M.sub.j) can be calculated for every allele combination
(M.sub.i1,M.sub.j1; M.sub.i1,M.sub.j2; M.sub.i2,M.sub.j1 and
M.sub.i2,M.sub.j2), according to the Piazza formula:
.DELTA.M.sub.ik,M.sub.jl={square root}.theta.4-{square
root}(.theta.4+.theta.3)(.theta.4+.theta.2), where:
[0116] .theta.4=--=frequency of genotypes not having allele k at
M.sub.i and not having allele l at M.sub.j
[0117] .theta.3=-+=frequency of genotypes not having allele k at
M.sub.i and having allele l at M.sub.j
[0118] .theta.2=+-=frequency of genotypes having allele k at
M.sub.i and not having allele l at M.sub.j
[0119] Linkage disequilibrium (LD) between pairs of biallelic
markers (Mi, Mj) can also be calculated for every allele
combination (Mi1,Mj1; Mi1,Mj2; Mi2,Mj1; Mi2,Mj2) according to the
maximum likelihood estimate (MLE) for delta (the composite linkage
disequilibrium coefficient), as described by Weir (B. S. Weir,
Genetic Data Analysis, (1996), Sinauer Ass. Eds, the disclosure of
which is incorporated herein by reference). This formula allows
linkage disequilibrium between alleles to be estimated when only
genotype, and not haplotype, data are available. This LD composite
test makes no assumption for random mating in the sampled
population, and thus seems to be more appropriate than other LD
tests for genotypic data.
[0120] Another means of calculating the linkage disequilibrium
between markers is as follows. For a couple of biallelic markers,
Mi (a.sub.i/b.sub.i) and Mj (a.sub.j/b.sub.j), fitting the
Hardy-Weinberg equilibrium, one can estimate the four possible
haplotype frequencies in a given population according to the
approach described above.
[0121] The estimation of gametic disequilibrium between ai and aj
is simply:
D.sub.aiaj=pr(haplotype(a.sub.i,a.sub.j))-pr(a.sub.i).pr(a.sub.j).
[0122] Where pr(ai) is the probability of allele ai and aj is the
probability of allele aj. and where pr(haplotype (ai, aj)) is
estimated as in eq3 above.
[0123] For a couple of biallelic marker only one measure of
disequilibrium is necessary to describe the association between Mi
and Mj.
[0124] Then a normalized value of the above is calculated as
follows:
D'aiaj=Daiaj/max(-pr(ai).pr(aj),-pr(bi).(bj)) with Daiaj<0
D'aiaj=Daiaj/min(pr(bi).pr(aj),pr(ai).(bj)) with Daiaj>0
[0125] The skilled person will readily appreciate that other LD
calculation methods can be used without undue experimentation.
[0126] As depicted in FIG. 2c, the above method was utilized on 54
random BACs covering 8100 kb. The average intermarker distances and
linkage disequilibrium between markers were determined. At an
average intermarker distance of 38 kb the average linkage
disequilibrium estimate was 0.63. In contrast, for 19 unlinked
markers the average linkage disequilibrium estimate was 0.12.
[0127] Example 10 illustrates the measurement of LD between a
publicly known biallelic marker, the "ApoE Site A", located within
the Alzheimer's related ApoE gene, and other biallelic markers
randomly derived from the genomic region containing the ApoE
gene.
[0128] Genome-wide LD mapping aims at identifying, for any TCA
being searched, at least one biallelic marker in LD with said TCA.
Preferably, in order to enhance the power of LD maps, in some
embodiments, the biallelic markers therein have average
inter-marker distances of 150 kb or less, 75 kb or less, or 50 kb
or less, 30 kb or less, or 25 kb or less to accommodate the fact
that, in some regions of the genome, the detection of LD requires
lower inter-marker distances.
[0129] The methods described herein allow the generation of
biallelic marker maps with average inter-marker distances of 150 kb
or less. In some embodiments, the mean distance between biallelic
markers constituting the high density map will be less than 75 kb,
preferably less than 50 kb. Further preferred maps according to the
present invention contain markers that are less than 37.5 kb apart.
In highly preferred embodiments, the average inter-marker spacing
for the biallelic markers constituting very high density maps is
less than 30 kb, most preferably less than 25 kb.
[0130] Genetic maps containing biallelic markers may be used to
identify and isolate genes associated with detectable traits. The
use of the genetic maps of the present invention is described in
more detail below.
Use of the High Density Biallelic Marker Mat to Identify Genes
Associated with a Detectable Trait
[0131] The biallelic marker maps described above may be used in
methods for identifying and isolating genes associated with a
detectable trait.
[0132] In the past, the identification of genes linked with
detectable traits has relied on a statistical approach called
linkage analysis. Linkage analysis is based upon establishing a
correlation between the transmission of genetic markers and that of
a specific trait throughout generations within a family. In this
approach, all members of a series of affected families are
genotyped with a few hundred markers, typically microsatellite
markers, which are distributed at an average density of one every
10 Mb. By comparing genotypes in all family members, one can
attribute sets of alleles to parental haploid genomes (haplotyping
or phase determination). The origin of recombined fragments is then
determined in the offspring of all families. Those that
co-segregate with the trait are tracked. After pooling data from
all families, statistical methods are used to determine the
likelihood that the marker and the trait are segregating
independently in all families. As a result of the statistical
analysis, one or several regions having a high probability of
harboring a gene linked to the trait are selected as candidates for
further analysis. The result of linkage analysis is considered as
significant (i.e. there is a high probability that the region
contains a gene involved in a detectable trait) when the chance of
independent segregation of the marker and the trait is lower than 1
in 1000 (expressed as a LOD score >3). Generally, the length of
the candidate region identified using linkage analysis is between 2
and 20 Mb.
[0133] Once a candidate region is identified as described above,
analysis of recombinant individuals using additional markers allows
further delineation of the candidate linked region.
[0134] Linkage analysis studies have generally relied on the use of
a maximum of 5,000 microsatellite markers, thus limiting the
maximum theoretical attainable resolution of linkage analysis to
ca. 600 kb on average.
[0135] Linkage analysis has been successfully applied to map simple
genetic traits that show clear Mendelian inheritance patterns and
which have a high penetrance (penetrance is the ratio between the
number of trait positive carriers of allele a and the total number
of a carriers in the population). About 100 pathological
trait-causing genes were discovered using linkage analysis over the
last 10 years. In most of these cases, the majority of affected
individuals had affected relatives and the detectable trait was
rare in the general population (frequencies less than 0.1%). In
about 10 cases, such as Alzheimer's Disease, breast cancer, and
Type II diabetes, the detectable trait was more common but the
allele associated with the detectable trait was rare in the
affected population. Thus, the alleles associated with these traits
were not responsible for the trait in all sporadic cases.
[0136] Linkage analysis suffers from a variety of drawbacks. First,
linkage analysis is limited by its reliance on the choice of a
genetic model suitable for each studied trait. Furthermore, as
already mentioned, the resolution attainable using linkage analysis
is limited, and complementary studies are required to refine the
analysis of the typical 2 Mb to 20 Mb regions initially identified
through linkage analysis.
[0137] In addition, linkage analysis approaches have proven
difficult when applied to complex genetic traits, such as those due
to the combined action of multiple genes and/or environmental
factors. In such cases, too large an effort and cost are needed to
recruit the adequate number of affected families required for
applying linkage analysis to these situations, as recently
discussed by Risch, N. and Merikangas, K. (Science 273: 1516-1517
(1996), the disclosure of which is incorporated herein by
reference).
[0138] Finally, linkage analysis cannot be applied to the study of
traits for which no large informative families are available.
Typically, this will be the case in any attempt to identify
trait-causing alleles involved in sporadic cases, such as alleles
associated with positive or negative responses to drug
treatment.
[0139] The maps and biallelic markers obtained as described herein
may be used to identify and isolate genes associated with
detectable traits using association studies, an approach which does
not require the use of affected families and which permits the
identification of genes associated with sporadic traits.
[0140] Association studies are described in more detail below.
Association Studies
[0141] As already mentioned, any gene responsible or partly
responsible for a given trait will be in LD with some flanking
markers. To map such a gene, specific alleles of these flanking
markers which are associated with the gene or genes responsible for
the trait are identified. Although the following discussion of
techniques for finding the gene or genes associated with a
particular trait using linkage disequilibrium mapping, refers to
locating a single gene which is responsible for the trait, it will
be appreciated that the same techniques may also be used to
identify genes which are partially responsible for the trait.
[0142] Association studies may be conducted within the general
population (as opposed to the linkage analysis techniques discussed
above which are limited to studies performed on related individuals
in one or several affected families).
[0143] Association between a biallelic marker A and a trait T may
primarily occur as a result of three possible relationships between
the biallelic marker and the trait.
[0144] First, allele a of biallelic marker A may be directly
responsible for trait T (e.g., Apo E .epsilon.4 site A and
Alzheimer's disease). However, since the majority of the biallelic
markers used in genetic mapping studies are selected randomly, they
mainly map outside of genes. Thus, the likelihood of allele a being
a functional mutation directly related to trait T is very low.
[0145] Second, an association between a biallelic marker A and a
trait T may also occur when the biallelic marker is very closely
linked to the trait locus. In other words, an association occurs
when allele a is in linkage disequilibrium with the trait-causing
allele. When the biallelic marker is in close proximity to a gene
responsible for the trait, more extensive genetic mapping will
ultimately allow a gene to be discovered near the marker locus
which carries mutations in people with trait T (i.e. the gene
responsible for the trait or one of the genes responsible for the
trait). As will be further exemplified below, using a group of
biallelic markers which are in close proximity to the gene
responsible for the trait the location of the causal gene can be
deduced from the profile of the association curve between the
biallelic markers and the trait. The causal gene will usually be
found in the vicinity of the marker showing the highest association
with the trait.
[0146] Finally, an association between a biallelic marker and a
trait may occur when people with the trait and people without the
trait correspond to genetically different subsets of the population
who, coincidentally, also differ in the frequency of allele a
(population stratification). This phenomenon may be avoided by
using large ethnically matched samples.
[0147] Association studies are particularly suited to the efficient
identification of genes that present common polymorphisms, and are
involved in multifactorial traits whose frequency is relatively
higher than that of diseases with monofactorial inheritance.
[0148] Association studies mainly consist of four steps:
recruitment of trait-positive (T+) and trait-negative (T-)
populations with well-defined phenotypes, identification of a
candidate region suspected of harboring a trait causing gene,
identification of said gene among candidate genes in the region,
and finally validation of mutation(s) responsible for the trait in
said trait causing gene.
[0149] In a first step, trait+ and trait- phenotypes have to be
well-defined. In order to perform efficient and significant
association studies such as those described herein, the trait under
study should preferably follow a bimodal distribution in the
population under study, presenting two clear non-overlapping
phenotypes, trait+ and trait-.
[0150] Nevertheless, in the absence of such a bimodal distribution
(as may in fact be the case for complex genetic traits), any
genetic trait may still be analyzed using the association method
proposed herein by carefully selecting the individuals to be
included in the trait+ and trait- phenotypic groups. The selection
procedure involves selecting individuals at opposite ends of the
non-bimodal phenotype spectrum of the trait under study, so as to
include in these trait+ and trait- populations individuals who
clearly represent non-overlapping, preferably extreme
phenotypes.
[0151] The definition of the inclusion criteria for the trait+ and
trait- populations is an important aspect of the present invention.
The selection of those drastically different but relatively uniform
phenotypes enables efficient comparisons in association studies and
the possible detection of marked differences at the genetic level,
provided that the sample sizes of the populations under study are
significant enough.
[0152] Generally, trait+ and trait- populations to be included in
association studies such as those proposed in the present invention
consist of phenotypically homogeneous populations of individuals
each representing 100% of the corresponding phenotype if the trait
distribution is bimodal. If the trait distribution is non-bimodal,
trait+ and trait- populations consist of phenotypically uniform
populations of individuals representing each between 1 and 98%,
preferably between 1 and 80%, more preferably between 1 and 50%,
and more preferably between 1 and 30%, most preferably between 1
and 20% of the total population under study, and selected among
individuals exhibiting non-overlapping phenotypes. In some
embodiments, the T.sup.+ and T.sup.- groups consist of individuals
exhibiting the extreme phenotypes within the studied population.
The clearer the difference between the two trait phenotypes, the
greater the probability of detecting an association with biallelic
markers.
[0153] In preferred embodiments, a first group of between 50 and
300 trait+ individuals, preferably about 100 individuals, are
recruited according to their phenotypes. In each case, a similar
number of trait negative individuals are included in such studies
who are preferably both ethnically- and age-matched to the trait
positive cases. Both trait+ and trait- individuals should
correspond to unrelated cases.
[0154] FIG. 3 shows, for a series of hypothetical sample sizes, the
p-value significance obtained in association studies performed
using individual markers from the high-density biallelic map,
according to various hypotheses regarding the difference of allelic
frequencies between the T+ and T- samples. It indicates that, in
all cases, samples ranging from 150 to 500 individuals are numerous
enough to achieve statistical significance. It will be appreciated
that bigger or smaller groups can be used to perform association
studies according to the methods of the present invention.
[0155] In a second step, a marker/trait association study is
performed that compares the genotype frequency of each biallelic
marker in the above described T+ and T- populations by means of a
chi square statistical test (one degree of freedom). In addition to
this single marker association analysis, a haplotype association
analysis is performed to define the frequency and the type of the
ancestral carrier haplotype. Haplotype analysis, by combining the
informativeness of a set of biallelic markers increases the power
of the association analysis, allowing false positive and/or
negative data that may result from the single marker studies to be
eliminated.
[0156] Genotyping can be performed using the microsequencing
procedure described in Example 13, or any other genotyping
procedure suitable for this intended purpose.
[0157] If a positive association with a trait is identified using
an array of biallelic markers having a high enough density, the
causal gene will be physically located in the vicinity of the
associated markers, since the markers showing positive association
with the trait are in linkage disequilibrium with the trait locus.
Regions harboring a gene responsible for a particular trait which
are identified through association studies using high density sets
of biallelic markers will, on average, be 20-40 times shorter in
length than those identified by linkage analysis.
[0158] Once a positive association is confirmed as described above,
a third step consists of completely sequencing the BAC inserts
harboring the markers identified in the association analyzes. These
BACs are obtained through screening human genomic libraries with
the markers probes and/or primers, as described herein. Once a
candidate region has been sequenced and analyzed, the functional
sequences within the candidate region (e.g. exons, splice sites,
promoters, and other potential regulatory regions) are scanned for
mutations which are responsible for the trait by comparing the
sequences of the functional regions in a selected number of T+ and
T- individuals using appropriate software. Tools for sequence
analysis are further described in Example 14.
[0159] Finally, candidate mutations are then validated by screening
a larger population of T+ and T- individuals using genotyping
techniques described below. Polymorphisms are confirmed as
candidate mutations when the validation population shows
association results compatible with those found between the
mutation and the trait in the test population.
[0160] In practice, in order to define a region bearing a candidate
gene, the trait+ and trait- populations are genotyped using an
appropriate number of biallelic markers. The markers used to define
a region bearing a candidate gene may be distributed at an average
density of 1 marker per 10-200 kb. Preferably, the markers used to
define a region bearing a candidate gene are distributed at an
average density of 1 marker every 15-150 kb. In further preferred
methods, the markers used to define a region bearing a candidate
gene are distributed at an average density of 1 marker every 20-100
kb. In yet another preferred method, the markers used to define a
region bearing a candidate gene are distributed at an average
density of 1 marker every 100 to 150 kb. In a further highly
preferred method, the markers used to define a region bearing a
candidate gene are distributed at an average density of 1 marker
every 50 to 100 kb. In yet another method, the biallelic markers
used to define a region bearing a candidate gene are distributed at
an average density of 1 marker every 25-50 kilobases. As mentioned
above, in order to enhance the power of linkage disequilibrium
based maps, in a preferred embodiment, the marker density of the
map will be adapted to take the linkage disequilibrium distribution
in the genomic region of interest into account.
[0161] In some methods, the initial identification of a candidate
genomic region harboring a gene associated with a detectable
phenotype may be conducted using a preliminary map containing a few
thousand biallelic markers. Thereafter, the genomic region
harboring the gene responsible for the detectable trait may be
better delineated using a map containing a larger number of
biallelic markers. Furthermore, the genomic region harboring the
gene responsible for the detectable trait may be further delineated
using a high density map of biallelic markers. Finally, the gene
associated with the detectable trait may be identified and isolated
using a very high density biallelic marker map.
[0162] Example 11 describes a hypothetical procedure for
identifying a candidate region harboring a gene associated with a
detectable trait. It will be appreciated that although Example 11
compares the results of analyzes using markers derived from maps
having 3,000, 20,000, and 60,000 markers, the number of markers
contained in the map is not restricted to these exemplary figures.
Rather, Example 11 exemplifies the increasing refinement of the
candidate region with increasing marker density. As increasing
numbers of markers are used in the analysis, points in the
association analysis become broad peaks. The gene associated with
the detectable trait under investigation will lie within or near
the region under the peak.
[0163] The statistical power of LD mapping using a high density
marker map is also reinforced by complementing the single point
association analysis described in Example 11 with a multi-marker
association analysis, called haplotype analysis.
[0164] When a chromosome carrying a disease allele is first
introduced into a population as a result of either mutation or
migration, the mutant allele necessarily resides on a chromosome
having a unique set of linked markers: the ancestral haplotype. As
already mentioned, a haplotype association analysis allows the
frequency and the type of the ancestral carrier haplotype to be
defined.
[0165] A haplotype analysis is performed by estimating the
frequencies of all possible haplotypes for a given set of biallelic
markers in the T+ and T- populations, and comparing these
frequencies by means of a chi square statistical test (one degree
of freedom).
[0166] In a diploid population of unrelated individuals, the
estimation of multi-locus haplotype frequencies based on observed
genotypes is problematic because the gametic phase of genotype
(i.e. the sets of alleles of the different markers transmitted
together by the parents) cannot be unambiguously inferred, as
simply shown in the following example:
[0167] Suppose two biallelic markers Mi and Mj with alleles ai/bi
and aj/bj. Suppose an individual, heterozygote at the two markers.
His genotype is thus (ai,bi;aj,bj). Without any additional
information, the possible phases are either: 1
[0168] This example for two loci can be easily generalized for an
arbitrary number of biallelic loci. For a given set of markers,
ambiguous phase occur for each individual being heterozyguous at
two or more sites. To overcome this difficulty, an algorithm was
described and implemented (Excoffier L, Slatkin M (1995)
Maximum-likelihood estimation of molecular haplotype frequencies in
a diploid population. Mol. Biol. Evol. 12: 921-927, the disclosure
of which is incorporated herein by reference) which allows maximum
likelihood estimation of haplotypes frequencies using the general
framework of E-M algorithms (Dempster A. P. (1977) Maximum
likelihood from incomplete data via the EM algorithm. J. Roy. Stat.
Soc. 39: 1-38, the disclosure of which is incorporated herein by
reference).
[0169] This type of algorithm is used for handling data where
categories of interest (here the haplotypes) cannot be directly
distinguished from the observed data (unknown-phase multi-locus
genotypes).
[0170] The present approach relies on the hypothesis that all
markers fit the Hardy-Weinberg equilibrium.
[0171] In the present invention, the estimations may be performed
by applying the Expectation-Maximization (EM) algorithm (Excoffier
L and Slatkin M, Mol. Biol. Evol. 12: 921-927 (1995), the
disclosure of which is incorporated herein by reference), using the
EM-HAPLO program (Hawley M E, Pakstis A J & Kidd K K, Am. J.
Phys. Anthropol. 18: 104 (1994), the disclosure of which is
incorporated herein by reference). The EM algorithm is used to
estimate haplotype frequencies in the case when only genotype data
from unrelated individuals are available. The EM algorithm is a
generalized iterative maximum likelihood approach to estimation
that is useful when data are ambiguous and/or incomplete.
[0172] In the E-M algorithm, the assumption is made that the
Hardy-Weinberg equilibrium holds for the markers in the markers
involved in the haplotype whose frequencies are estimated in the
population at study.
[0173] Hardy-Weinberg equilibrium is a hypothesis relative to one
marker and one population. It supposes that the population is
sufficiently large and that the mating is random at that locus.
Hence, if, at that polymorphic locus, there are no perturbing
forces such as migration, selection, or mutation, the genotype
frequencies will be the products of allelic frequencies of each of
the two alleles involved in the genotype, i.e. alleles are
statistically independent in a genotype.
[0174] Consider one biallelic marker M with allele A and B, and
p.sub.A and p.sub.B the allelic frequencies and p.sub.AA, p.sub.AB
and p.sub.BB the genotypes frequencies.
[0175] One parameter, D.sub.A, can measure the departure from
Hardy-Weinberg equilibrium, which is:
D.sub.A=p.sub.AA-(p.sub.A).sup.2.
[0176] It should be noted that D.sub.A is also: 1 D A = p BB - ( p
B ) 2 - 2 D A = p AB - 2 * ( p A p B )
[0177] In a sample of N individuals, one can test the
Hardy-Weinberg hypothesis using the statistical test: 2 X 2 = N D ^
A p ^ A 2 ( 1 - p ^ A 2 )
[0178] , where {circumflex over (p)}.sub.A and {circumflex over
(D)}.sub.A are the estimation of allelic frequency and the
departure from Hardy-Weinberg equilibrium estimations in the sample
of N individuals.
[0179] For a large sample, as described in Weir (supra), the
statistics follow a chi-square with one degree of freedom. For
large estimation of departure from Hardy-Weinberg equilibrium, the
statistic will have large values leading to the rejection of the
hypothesis of equilibrium for the considered marker in the
population. For testing Hardy-Weinberg equilibrium one can also use
exact tests (Weir 1996, supra).
[0180] In the following part of this text, phenotypes will refer to
multi-locus genotypes with unknown phase. Genotypes will refer to
known-phase multi-locus genotypes.
[0181] Suppose a sample of N unrelated individuals typed for K
markers. The data observed are the unknown-phase K-locus phenotypes
that can categorized in F different phenotypes. Suppose that we
have H underlying possible haplotypes (in case of K biallelic
markers, H=2.sup.K).
[0182] For phenotype j, suppose that cj genotypes are possible. We
thus have the following equation: 3 P j = i = 1 c j pr ( genotype i
) = i = 1 c j pr ( h k , h l ) eq . 1
[0183] where Pj is the probability of the phenotype j, hk and hl
are the two haplotypes constituent the genotype i. Under the
Hardy-Weinberg equilibrium, pr(hk,hl) becomes:
pr(k.sub.k,h.sub.l)=pr(h.sub.k).sup.2 if h.sub.k=h.sub.l,
pr(h.sub.k,h.sub.l)=2pr(h.sub.k).pr(h.sub.l) if
h.sub.k.noteq.h.sub.l. eq.2
[0184] The successive steps of the E-M algorithm can be described
as follows: Starting with initial values of the of haplotypes
frequencies, noted, p.sub.1.sup.(0), p.sub.2.sup.(0), . . .
p.sub.T.sup.(0). these initial values serves to estimate the
genotypes frequencies (Expectation step) and then estimate another
set of haplotype frequencies (Maximisation step): p.sub.1.sup.(1),
p.sub.2.sup.(0), . . . p.sub.T.sup.(1). these two steps are
iterated until change in the sets of haplotypes frequency are very
small.
[0185] A stop criterion can be that the maximum difference between
haplotype frequencies between wo iterations is less than 10.sup.-7.
This values can be adjusted according to the desired precision of
estimations.
[0186] In details, at a given iteration s, the Expectation step
consists in calculating the genotypes frequencies by the following
equation: 4 pr ( genotype i ) ( s ) = pr ( phenotype j ) pr (
genotype i phenotype j ) ( s ) = n j N pr ( h k , h l ) ( s ) P j (
s ) eq . 3
[0187] where genotype i occurs in phenotype j, and where hk and hl
constitute genotype i. Each probability are derived according to
eq.1, and eq.2 above.
[0188] Then the Maximisation step simply estimates another set of
haplotype frequencies given the genotypes frequencies. This
approach is also known as gene-counting method (Smith C A B (1957)
Counting methods in genetical statistics, Ann. Hum. Genet. 21:
254-276, the disclosure of which is incorporated herein by
reference). 5 p t ( s + 1 ) = 1 2 j = 1 F i = 1 c j it pr (
genotype i ) ( s ) eq . 4
[0189] where .epsilon..sub.it is an indicator variable which count
the number of time haplotype t in genotype i. It takes the values
of 0, 1 or 2.
[0190] To ensure that the estimation finally obtained are the
maximum-likelihood estimations several values of departures are
required. The estimations obtained are compared and if they differ
the estimations leading to the best likelihood are kept.
[0191] To improve the statistical power of the individual marker
association analyses using maps of increasing marker densities,
haplotype studies can be performed using groups of markers located
in proximity to one another within regions of the genome. For
example, using the methods in which the association of an
individual marker with a detectable phenotype was analyzed using
maps of 3,000 markers, 20,000 markers, and 60,000 markers, a series
of haplotype studies can be performed using groups of contiguous
markers from such maps or from maps having higher marker
densities.
[0192] In a preferred embodiment, a series of successive haplotype
studies including groups of markers spanning regions of more than 1
Mb may be performed. In some embodiments, the biallelic markers
included in each of these groups may be located within a genomic
region spanning less than 1 kb, from 1 to 5 kb, from 5 to 10 kb,
from 10 to 25 kb, from 25 to 50 kb, from 50 to 150 kb, from 150 to
250 kb, from 250 to 500 kb, from 500 kb to 1 Mb, or more than 1 Mb.
Preferably, the genomic regions containing the groups of biallelic
markers used in the successive haplotype analyses are overlapping.
It will be appreciated that the groups of biallelic markers need
not completely cover the genomic regions of the above-specified
lengths but may instead be obtained from incomplete contigs having
one or more gaps therein. As discussed in further detail below,
biallelic markers may be used in single point and haplotype
association analyses regardless of the completeness of the
corresponding physical contig harboring them.
[0193] It will be appreciated that the above approaches may be
conducted on any scale (i.e. over the whole genome, a set of
chromosomes, a single chromosome, a particular subchromosomal
region, or any other desired portion of the genome). As mentioned
above, once significance thresholds have been assessed, population
sample sizes may be adapted as exemplified in FIG. 3.
[0194] The methods described in Examples 20-23 below allow the
determination of whether a candidate genomic region suspected of
harboring one or more genes associated with a detectable trait
warrants further evaluation. The candidate genomic region may be
identified as described above or, alternatively, the candidate
genomic region may be selected on the basis of an already suspected
association with the detectable trait as described in Examples
12-19 below.
[0195] The methods of the present invention involve performing
haplotype analyses on groups of biallelic markers. Example 12 below
illustrates the increase in statistical power brought to an
association study by a haplotype analysis.
[0196] Once a given polymorphic site has been found and
characterized as a biallelic marker according to the methods of the
present invention, several methods can be used in order to
determine the specific allele carried by an individual at the given
polymorphic base.
[0197] Most genotyping methods require the previous amplification
of a DNA region carrying the polymorphic site of interest.
[0198] The identification of biallelic markers described
previously, allows the design of appropriate oligonucleotides,
which can be used as primers to amplify a DNA fragment containing
the polymorphic site of interest and for the detection of such
polymorphisms.
[0199] For example, in the examples below, pairs of primers of SEQ
ID Nos: 13-18 and 19-23 may be used to generate amplicons harboring
the markers of SEQ D Nos: 1-6 and 7-12 or the sequences
complementary thereto.
[0200] It will be appreciated that amplification primers may be
designed having any length suitable for their intended purpose, in
particular any length allowing their hybridization with a region of
the DNA fragment to be amplified.
[0201] It will be further appreciated that the hybridization site
of said amplification primers may be located at any distance from
the polymorphic base to be genotyped, provided said amplification
primers allow the proper amplification of a DNA fragment carrying
said polymorphic site. The amplification primers may be
oligonucleotides of 10, 15, 20 or more bases in length which enable
the amplification of the polymorphic site in the markers. In some
embodiments, the amplification product produced using these primers
may be at least 100 bases in length (i.e. on average 50 nucleotides
on each side of the polymorphic base). In other embodiments, the
amplification product produced using these primers may be at least
500 bases in length (i.e. on average 250 nucleotides on each side
of the polymorphic base). In still further embodiments, the
amplification product produced using these primers may be at least
1000 bases in length (i.e. on average 500 nucleotides on each side
of the polymorphic base).
[0202] The amplification of polymorphic fragments can be performed
as described in Example 6 on DNA samples extracted as described in
Example 5.
[0203] As already mentioned, allele frequencies of biallelic
markers tested in association studies (individual or haplotype) may
be determined using microsequencing procedures.
[0204] A first step in microsequencing procedures consists in
designing microsequencing primers adapted to each biallelic marker
to be genotyped. Microsequencing primers hybridize upstream of the
polymorphic base to be genotyped, either with the coding or with
the non-coding strand. Microsequencing primers may be
oligonucleotides of 8, 10, 15, 20 or more bases in length.
Preferably, the 3' end of the microsequencing primer is immediately
upstream of the polymorphic base of the biallelic marker being
genotyped, such that upon extension of the primer, the polymorphic
base is the first-base incorporated.
[0205] It will be appreciated that the biallelic markers of the
present invention may be genotyped using microsequencing primers
having any desirable length, and hybridizing to any of the strands
of the marker to be tested, provided their design is suitable for
their intended purpose. In some embodiments, the amplification
primers or microsequencing primers may be labeled. For example, in
some embodiments, the amplification primers or microsequencing
primers may be biotinylated.
[0206] Typical microsequencing procedures that can be used in the
context of the present invention are described in Example 13
below.
[0207] As another alternative, solid phase microsequencing
reactions have been developed, for which either the oligonucleotide
microsequencing primers or the PCR-amplified products derived from
the DNA fragment of interest are immobilized. For example,
immobilization can be carried out via an interaction between
biotinylated DNA and streptavidin-coated microtitration wells or
avidin-coated polystyrene particles.
[0208] As a further alternative, the PCR reaction generating the
amplicons to be genotyped can be performed directly in solid phase
conditions, following procedures such as those described in WO
96/13609, the disclosure of which is incorporated herein by
reference.
[0209] In such solid phase microsequencing reactions, incorporated
ddNTPs can either be radiolabeled (see Syvnen, Clin. Chim. Acta.
226: 225-236 (1994), the disclosure of which is incorporated herein
by reference) or linked to fluorescein (see Livak and Hainer, Hum.
Metat. 3: 379-385 (1994), the disclosure of which is incorporated
herein by reference). The detection of radiolabeled ddNTPs can be
achieved through scintillation-based techniques. The detection of
fluorescein-linked ddNTPs can be based on the binding of
antifluorescein antibody conjugated with alkaline phosphatase,
followed by incubation with a chromogenic substrate (such as
p-nitrophenyl phosphate).
[0210] Other possible reporter-detection couples for use in the
above microsequencing procedures include:
[0211] ddNTP linked to dinitrophenyl (DNP) and anti-DNP alkaline
phosphatase conjugate (see Harju et al., Clin Chem: 39(11Pt 1):
2282-2287 (1993), incorporated herein by reference)
[0212] biotinylated ddNTP and horseradish peroxidase-conjugated
streptavidin with o-phenylenediamine as a substrate (see WO
92/15712, incorporated herein by reference).
[0213] A diagnosis kit based on fluorescein-linked ddNTP with
antifluorescein antibody conjugated with alkaline phosphatase has
been commercialized under the name PRONTO by GamidaGen Ltd.
[0214] As yet another alternative microsequencing procedure, Nyren
et al. (Anal. Biochem. 208: 171-175 (1993), the disclosure of which
is incorporated herein by reference) have described a solid-phase
DNA sequencing procedure that relies on the detection of DNA
polymerase activity by an enzymatic luminometric inorganic
pyrophosphate detection assay (ELIDA). In this procedure, the
PCR-amplified products are biotinylated and immobilized on beads.
The microsequencing primer is annealed and four aliquots of this
mixture are separately incubated with DNA polymerase and one of the
four different ddNTPs. After the reaction, the resulting fragments
are washed and used as substrates in a primer extension reaction
with all four dNTPs present. The progress of the DNA-directed
polymerization reactions is monitored with the ELIDA. Incorporation
of a ddNTP in the first reaction prevents the formation of
pyrophosphate during the subsequent dNTP reaction. In contrast, no
ddNTP incorporation in the first reaction gives extensive
pyrophosphate release during the dNTP reaction and this leads to
generation of light throughout the ELIDA reactions. From the ELIDA
results, the identity of the first base after the primer is easily
deduced.
[0215] It will be appreciated that several parameters of the
above-described microsequencing procedures may be successfully
modified by those skilled in the art without undue experimentation.
In particular, high throughput improvements to these procedures may
be elaborated, following principles such as those described further
below.
[0216] It will be further appreciated that any other genotyping
procedure may be applied to the genotyping of biallelic
markers.
[0217] Examples 14-19 below illustrate the application of methods
using biallelic markers to identify a gene associated with a
complex disease, prostate cancer, within a ca. 450 kb candidate
region. Additional details of the identification of the gene
associated with prostate cancer are provided in the U.S. patent
application entitled "Prostate Cancer Gene" (GENSET.018A, Ser. No.
08/996,306), the disclosure of which is incorporated herein by
reference.
[0218] Once a candidate genomic region, such as a BAC insert, which
is suspected of harboring a gene associated with a detectable trait
has been identified, it is evaluated using the methods of Examples
20-23 in order to determine whether it is in fact likely to harbor
a gene associated with the detectable trait.
[0219] If it appears likely that the candidate genomic region
harbors a gene associated with the trait, the existence of one or
more genes associated with the detectable trait within the
candidate region is confirmed by identifying more biallelic markers
lying in the candidate region using the techniques described above.
Preferably, the biallelic markers in the candidate genomic region
have an average intermarker spacing of less than 1 kb, 1-3 kb, 3
kb-5 kb, about 5 kb, about 10 kb, about 20 kb or about 30 kb. In a
highly preferred embodiment, the biallelic markers span the entire
candidate genomic region. In particular embodiments, all the
biallelic markers located in the candidate gene or in the vicinity
of the candidate gene may be used in the analysis. In some
embodiments, biallelic markers which lie within coding regions may
be used. In other embodiments, the biallelic markers used in the
analyses may be biallelic markers in which the frequency of the
least common allele in the population is at least 30%, at least
20%, or at least 10%. FIG. 14 illustrates that rare biallelic
markers may be in linkage disequilibrium with more frequent markers
or with other rare markers. Alternatively, biallelic markers inside
noncoding exons or inside introns may be used. FIG. 15 illustrates
that non-exonic markers may be in linkage disequilibrium with
exonic markers or other non-exonic markers. In FIG. 15, Nb pairs
are the number of marker pairs for which linkage disequilibrium was
calculated.
[0220] A first haplotype analysis is performed for each possible
combination of groups of biallelic markers within the genomic
region suspected of harboring a trait-associated gene. The number
of biallelic markers in each group is preferably at least three,
but may be two, 4, 5, 6 or groups comprising any number of markers
which are compatible with the computer system being used for the
analysis. It will be appreciated that the greater the number of
markers per group, the greater the number of markers required to
perform the analysis and the greater the number of haplotype
results which are generated. Thus, with increasing numbers of
markers per group, the sample size of the populations needed for
the analysis also increases. It will also be appreciated that the
relationship between the number of haplotypes generated in the
analysis and the number of individuals in the control population
and the population expressing the trait which are needed to run the
analysis may be influenced by the penetrance of the
trait-associated gene, the degree of risk attributable to the gene,
and the linkage disequilibrium pattern between the markers around
the candidate gene which are used in the analysis. Alternatively,
rather than performing haplotype analyses with groups of markers,
the association of individual markers with the detectable trait may
be measured.
[0221] For purposes of exemplifying the present methods, groups of
three biallelic markers will be used in the examples below, such
that a total of eight combinations of marker alleles are possible
for each group. However, it will be appreciated that the methods
may be performed with groups of two markers, groups of 3 markers,
groups of 4 markers, groups of 5 markers, groups of 6 markers or
groups comprising any number of markers which are compatible with
the computer system being used for the analysis The frequency of
each combination (i.e. each haplotype, or, if individual markers
are used, of each allele of the individual markers) is estimated in
individuals expressing the trait and individuals who do not express
the trait. For example, the frequency of each haplotype (or each
allele of the individual markers) in each of the populations of
individuals may be estimated using the Expectation-Maximization
method of Excoffier L and Slatkin M, Mol. Biol. Evol. 12: 921-927
(1995), the disclosure of which is incorporated herein by reference
and which was described above, using the EM-HAPLO program (Hawley M
E, Pakstis A J & Kidd K K, Am. J Phys. Anthropol. 18: 104
(1994), the disclosure of which is incorporated herein by
reference). Alternatively, the analysis may be performed using
single markers.
[0222] The frequencies of each of the possible haplotypes (or each
allele of the individual markers) in individuals expressing the
trait and individuals who do not express the trait are compared.
Preferably, the frequency of each of the possible haplotypes in
individuals expressing the trait and individuals who do not express
the trait are compared by performing a chi-squared analysis. Within
each group of markers, the haplotype (or allele of the individual
markers) having the best value (i.e. the greatest association with
the trait) is selected for inclusion in a distribution of
association values which will be referred to herein as the
"candidate region" distribution. For example, if the haplotype or
allele frequencies are compared using a chi-squared analysis, the
chi-squared value for the combination of markers in each group
which has the strongest association with the trait is included in
the "candidate region" distribution.
[0223] A second haplotype analysis is performed for each possible
combination of groups of biallelic markers or individual markers
within random genomic regions. For purposes of exemplifying the
present methods, groups of three biallelic markers will be used in
the examples below, such that a total of eight combinations of
marker alleles are possible for each group. However, it will be
appreciated that the methods may be performed with groups of two
markers, groups of 3 markers, groups of 4 markers, groups of 5
markers, groups of 6 markers or groups comprising any number of
markers which are compatible with the computer system being used
for the analysis. Preferably, the markers in the random genomic
regions have an average intermarker spacing of one marker every 3
kb, one marker every 5 kb, one marker every 10 kb, one marker every
20 kb, or one marker every 30 kb. Alternatively, the markers in the
random genomic regions may comprise markers which are not in total
linkage disequilibrium with one another. In an alternative
embodiment, rather than performing haplotype analyses with groups
of markers, the association of individual markers in the random
genomic regions with the detectable trait may be measured.
[0224] The frequency of each combination (i.e. each haplotype, or,
if an individual marker is used, of each allele of the individual
marker) is estimated in individuals expressing the trait and
individuals who do not express the trait. For example, the
frequency of each haplotype (or each allele of an individual
marker) in each of the populations of individuals may be estimated
using the Expectation-Maximization method of Excoffier and Slatkin
and the EM-HAPLO program as described above.
[0225] The frequencies of each of the possible haplotypes (or each
allele of an individual marker) in individuals expressing the trait
and individuals who do not express the trait are compared.
Preferably, the frequency of each of the possible haplotypes (or
each allele of an individual marker) in individuals expressing the
trait and individuals who do not express the trait are compared by
performing a chi-squared analysis. Within each group of markers,
the chi squared value from the haplotype (or allele of an
individual marker) having the the greatest association with the
trait is selected for inclusion in a distribution of test values
which will be referred to herein as the "random region"
distribution.
[0226] In some embodiments, the haplotype frequencies (or allele
frequencies of individual markers) of biallelic markers in the
random genomic regions being considered for inclusion in the
construction of the random region distribution are compared to
those obtained with markers located in other random genomic regions
to ensure that the random genomic regions being considered for
inclusion in the random region distribution do not in fact include
markers having a significant association with the trait.
[0227] Alternatively, to confirm that the markers included in the
random genomic regions are suitable for use in the random region
distribution, the biallelic markers from the random genomic regions
can be randomly split into two halves. A distribution can then be
established on each half. It can be assessed whether these two
distributions are different. If the difference between the two
distributions is not significant, the random marker set is proper.
In this manner, all the biallelic markers within the random genomic
regions may be included within the random region distribution. This
approach is described below.
[0228] The candidate distribution of association values and the
random region distribution of association values are then compared
to one another to determine whether there are significant
differences between the two distributions. If there are significant
differences between the two distributions, the candidate genomic
region is likely to harbor a gene associated with the detectable
trait. In contrast if there are not significant differences between
the two distributions, the candidate genomic region is unlikely to
harbor a gene associated with the detectable trait.
[0229] The two distributions may compared to one another using any
means familiar to those skilled in the art including, but not
limited to, the chi-squared test, tests based on empirical
distribution, likelihood ratio test, permutation test, sign test,
median test, Wilcoxon rank test and Komogorov-Smirnov test.
Preferably, the two distributions are compared to one another using
tests which do not assume that the two distributions have a normal
distribution. In some preferred embodiments, the two distributions
are compared to one another using either the Wilcoxon rank test
(Noether, G. E. (1991) Introduction to statistics: "The
nonparametric way", Springer-Verlag, New York, Berlin, the
disclosure of which is incorporated herein by reference) or the
Kolmogorov-Smirnov test (Saporta, G. (1990) "Probabilits, analyse
des donnees et statistiques" Technip editions, Paris, the
disclosure of which is incorporated herein by reference) or both
the Wilcoxon rank test and the Kolmogorov-Smirnov test.
[0230] In the Wilcoxon rank test, one compares two samples of
respectively n.sub.1 and n.sub.2 values of a continuous variable,
here the chi-square values based on haplotypes frequency
differences between cases and controls. All n.sub.1 and n.sub.2
values are pooled and then ordered. Each value gets assigned its
rank in such ordered set. Let:
[0231] W.sub.1=the sum of the rank assigned to the first sample of
n.sub.1 values,
[0232] W.sub.2=the sum of the rank assigned to the second sample of
n.sub.2 values.
[0233] If N=n.sub.1+n.sub.2, the sum of ranks W, is fixed and
equals to:
W=W.sub.1+W.sub.2=N(N+1)/2.
[0234] Under the null hypothesis, i.e. the two distributions are
equivalent, the expectation and variance of W.sub.1 are
respectively:
E(W.sub.1)=n.sub.1(N+1)/2 and
V(W.sub.1)=n.sub.1.times.n.sub.2(N+1)/12
[0235] It is worth noting that the above equations allow the
calculation of expectation and variances of W.sub.1 provided that
no test values have the same rank. In such a situation, expectation
and variance should be calculated by assigning an average rank to
each of such test values. Such adjustments to the variance
calculation are described by Hajek (Hajek (1969) A course in non
parametric statistics, 2.sup.nd edition, New York, John Wiley &
sons, Inc.).
[0236] Accordingly, the statistic Z can be defined as follows: 6 Z
= W 1 - E ( W 1 ) V ( W 1 )
[0237] Under the null hypothesis, i.e. the two distributions are
equivalent, for an overall sample size greater than 8 (N greater
than or equal to 8) Z will have a normal distribution with an
expectation of 0 and a variance of 1.
[0238] For an observed value z of Z, a p-value can be derived which
defines the probability that Z is greater than the observed value.
A probability of less than 1%, corresponding to an observed value
greater than 2.32 or less than -2.32 indicates that there is a
significant difference between the random region distribution and
the candidate region distribution (i.e. that the candidate genomic
region is likely to contain a gene associated with the detectable
trait and should be investigated further).
[0239] Alternatively, the random region distribution and the
candidate region distribution may be compared to one another using
the Kolmogorov-Smirnov test as follows. As described above, n.sub.1
and n.sub.2 are observations of a continuous variable. If n.sub.1
and n.sub.2 are random sets of quantities distributed according to
two random variables X.sub.1 and X.sub.2 then the cumulative
distribution functions F.sub.1(x) of X.sub.1 is defined (and
respectively F.sub.2(x), the cumulative function of X.sub.2 is
defined) as follows:
F.sub.1(x)=pr(X.sub.1<x) and F.sub.2(x)=pr(X.sub.2<x)
[0240] where x is a value in the definition domain of X.sub.1 or
X.sub.2 respectively.
[0241] The estimates of the two cumulative functions F.sub.1*(x)
and F.sub.2*(x) can be calculated. For each observed x the
following difference may be calculated based on the n, and n.sub.2
observation sets:
D(x)=.vertline.F.sub.1*(x)-F.sub.2*(x).vertline.
[0242] Over the N(N=n.sub.1+n.sub.2) observed values, Dmax denotes
the maximum value of D(x). Based on the foregoing the following
statistic was derived: 7 T = n 1 n 2 ( n 1 + n 2 ) D max
[0243] Under the null hypothesis of equivalence between the two
distributions, it is known that the probability of observing a
value t superior to the observed value of T follows a distribution
known as the Kolmogorov function (Ka(t)). Important deviations,
corresponding to a probability inferior to 0.01 are considered
significant (i.e. the candidate genomic region is likely to harbor
a gene associated with the detectable trait). The p-value
associated with the observed value of T is an indication of how the
distributions are different.
[0244] Given a sample size, the Dmax value corresponding to the
p-value threshold of 0.01 can easily be found as in Kim and Jenrich
(Selected tables in mathematical statistics, Harter & Owenn
eds., Chicago, Markham publishing Co., 1990), incorporated herein
by reference.
[0245] Alternatively, the random region distribution and the
candidate region distribution may be compared to one another using
both the Wilcoxon test and the Smirnov test.
[0246] An alternative method of confirming that a genomic region
harbors a gene associated with a detectable trait comprises the
steps of:
[0247] constructing a candidate region distribution of test values
using a plurality of biallelic markers in a candidate genomic
region suspected of harboring said gene associated with said
detectable trait, said candidate region distribution of test values
being indicative of the difference in the frequencies of said
plurality of biallelic markers in said candidate region in
individuals who possess said detectable trait and control
individuals who do not possess said detectable trait;
[0248] constructing a simulated distribution of test values using a
plurality of biallelic markers randomly selected from biallelic
markers located in random genomic regions and biallelic markers
located in a candidate genomic region suspected of harboring said
gene associated with said detectable trait, said simulated
distribution of test values being indicative of the difference in
the frequencies of said plurality of biallelic markers in said
random genomic regions in individuals who possess said detectable
trait and control individuals who do not possess said detectable
trait; and
[0249] determining whether said candidate region distribution of
test values and said simulated distribution of test values are
significantly different from one another.
[0250] Preferably said step of constructing a candidate region
distribution of test values comprises:
[0251] performing a haplotype analysis on each possible combination
of biallelic markers in each group in a series of groups of
biallelic markers in said candidate region;
[0252] calculating test values for each possible combination;
and
[0253] including the test value for the haplotype which has the
greatest association with said trait in said candidate region
distribution of test values for each group in said series of groups
of biallelic markers in said candidate genomic region, and wherein
said step of constructing a simulated distribution of test values
comprises:
[0254] assigning each of said biallelic markers in said candidate
genomic region and each of said biallelic markers in said random
genomic regions an identification number;
[0255] defining groups of biallelic markers by randomly selecting
combinations of identification numbers using a random number
generator wherein the markers assigned the selected identification
numbers are included in said groups;
[0256] performing a haplotype analysis on each possible combination
of biallelic markers in each group in a series of groups of
biallelic markers which have been assigned identification
numbers;
[0257] calculating test values for each possible combination;
and
[0258] including the test value for the haplotype which has the
greatest association with said trait in said simulated distribution
of test values for each group in said series of groups of biallelic
markers.
[0259] Examples 20-23 below exemplify the application of the
present method to the candidate genomic region harboring the gene
associated with prostate cancer. All of the analyses below were
performed using the NPAR1WAY procedure of the SAS program (SAS
Institute Inc. (1996) SAS/STAT User's Guide Vol II. Release 6.12,
Ed. Cary, N.C., U.S.A., the disclosure of which is incorporated
herein by reference).
[0260] If the candidate genomic region is found likely to harbor a
gene associated with the detectable trait after the above analysis,
it is evaluated further to isolate the gene which is responsible
for the trait. Those skilled in the art are familiar with
techniques for isolating the trait-associated gene. Essentially,
the sequence of the candidate genomic region is determined and
genes lying therein are identified using software which identifies
open reading frames, introns and exons, homologies to known protein
sequences or known nucleic acid sequences, or homologies to known
protein motifs. For example the potential gene sequences may be
compared to numerous databases to identify potential exons using a
set of scoring algorithms such as trained Hidden Markov Models,
statistical analysis models (including promoter prediction tools)
and the GRAIL neural network.
[0261] In fact, the preceding techniques were utilized to identify
the protein coding sequences lying within the candidate region of
example 20 and 21 suspected of harboring the gene associated with
prostate cancer used in the above analysis and a single protein
coding region designated the PG1 gene was identified.
[0262] Preferably, the above methods are implemented using a
computer program stored on a computer.
[0263] The procedures for determining whether a particular
biallelic marker, or group of biallelic markers (haplotype) are
associated with a particular genetic trait are preferably
automated, as described below. The automated system would comprise
a combination of hardware and software that can rapidly screen
through thousands, tens of thousands, or millions of potential
haplotypes to determine those haplotypes that are associated with a
particular genetic trait.
[0264] The automated system can be implemented through a variety of
combinations of computer hardware and software. In one
implementation, the computer hardware is a high-speed
multi-processor computer running a well-known operating system,
such as UNIX. The computer should preferably be able to calculate
millions, tens of millions, billions or more possible allelic
variations per second. This amount of speed is advantageous for
determining the statistical significance of the various
distributions of haplotypes within a reasonable period of time.
Such computers are manufactured by companies such as International
Business Machines, Hitachi, DEC, and Cray.
[0265] While it is envisioned that currently available personal
computers using single or multiple microprocessors might also
function within the parameters of the present invention, such a
computer system might be too slow to generate the numbers of
possible haplotype combinations necessary to carry out the methods
of the present invention. However, as the efficiency and speed of
microprocessor-based computer systems increases, the likelihood
that a conventional personal computer would be useful for the
present invention also increases.
[0266] Preferably, the software that runs the calculations for the
present invention is written in a language that is designed run
within the UNIX operating system. The software language can be, for
example, C, C++, Fortran, Perl, Pascal, Cobol or any other
well-known computer language. It should be noted that the nucleic
acid sequence data will be stored in a database and accessed by the
software of the present invention. These programming languages are
commercially available from a variety of companies such as
Microsoft, Digital Equipment Corporation, and Borland
International.
[0267] In addition, the software described herein can be stored on
several different types of media. For example, the software can be
stored on floppy disks, hard disks, CD-ROMs, Electrically Eraseable
Programable Read Only Memory, Random Access Memory or any other
type of programmed storage media.
[0268] The Figures described below provide an overview of the
entire process of determining whether a marker, or set of markers
(haplotype), within a nucleotide sequence is actually associated
with a particular trait in individuals. While most of the processes
can be performed manually, it is particularly advantageous to
perform many of these processes with the assistance of a computer
system, as described above.
[0269] Referring to FIG. 18, a process 10 of determining whether a
candidate clone is associated with a trait is illustrated. The
process 10 begins at a start state 15 and then moves to a process
state 20 wherein a set of random genomic clones are selected. These
genomic clones may be chosen at random. They allow the estimation
of the general frequency difference between the two groups
throughout the genome. The number of genomic clones obtained is
preferably at least about 30, but can be from 10 to 60 or more
genomic clones. The number of clones is chosen so that the
estimation of the distribution of the test statistic is accurate
enough. The process 20 is described more completely with reference
to FIGS. 19 and 22 below.
[0270] Once a set of random genomic clones are identified at the
process state 20, the process 10 moves to a process state 25
wherein the test-value distribution of association to the trait in
the random clones is generated by instructions stored in the
computer. Herein, the test-values are chi-square values based on
haplotype frequency differences between cases and controls. Process
state 25 is described more specifically in FIG. 20 below. The
distribution plot is a set of data points that, when displayed on a
coordinate system, form a diagram indicating the chi-squared values
for each haplotype in each of the random genomic clones. It should
be noted that the distribution does not necessarily need to be
generated from chi-squared values derived from haplotype frequency
differences between the two groups of individuals. Any similar
measurment of difference between control and trait-expressing
individuals based on groups of markers found within the selected
random genomic clones may be used in the present invention.
[0271] The process 10 then moves to a process state 35 wherein the
test values of haplotype frequency differences between the control
and trait-expressing populations within the candidate clone are
determined. The process 10 then moves to a state 40 wherein the
distribution of the test-values in the candidate clone is
generated. Prior to the generation of the distribution of
test-values, it is advantageous to "saturate" the candidate clone
so that as many biallelic markers as possible are known within the
clone. The number of markers in the candidate clone is preferably
twenty-five or more but may be 10, 15 or 20. Once a large number of
biallelic markers are known in the candidate clone, haplotypes
comprising groups of three markers can be chosen at random and
haplotype frequency estimations in cases and in controls can be
compared by means of chi-square statistics. For one group of
markers, one chi-square value (i.e. the chi squared value for the
haplotype having the greatest association with the trait) is stored
to a computer memory for later processing.
[0272] The data plot distribution generated in state 40 is derived
from all chi-square values and the chi-squares are stored as
described above. Of course, it should be understood that any other
statistical mechanism for generating a distribution of test values
based on haplotype frequencies or any measured observation of
haplotypes is useful in the present invention. Once the
distribution plot is calculated in the computer at the state 40,
the process 10 moves to a state 45 wherein the distribution plots
from the test values in the random clones and the test values in
the candidate clone are compared. The process 45 of comparing
random region distributions and candidate region distribuations is
described in FIG. 24 below.
[0273] Once the distributions are compared, the process 10 moves to
a decision state 50 to determine whether the distributions are
different. If the random region and the candidate region
distributions are determined to be different at decision state 50,
the process moves to a decision state 55 wherein a determination is
made whether more trait associated clones are available. If more
trait associated clones are available, the process 10 returns to
the state 35. However, if no more trait associated clones are
available, the process 10 terminates at an end state 65.
[0274] If a determination is made at the state 50 that the
distributions are different, the process 10 moves to a state 60
wherein the computer system indicates that the candidate clone was
found to be effectively associated to the studied trait. This
indication can be through computer's display, printer or any other
well-known mechanism for notifying a computer user of the results
of a particular process. The process then terminates at the end
state 65.
[0275] As one alternative, the process 10 of FIG. 18 can be altered
as shown in FIG. 25 below. Referring to FIG. 25, a process 700 of
determining whether an individual biallelic marker or set of
biallelic markers (haplotype) is linked to a particular trait is
described. The process 700 begins at a start state 702 and then
moves to a process state 704 wherein, using a random number
generator, the simulated haplotypes that have no relation to the
trait are assigned to each individual.
[0276] The process 700 then moves to a state 706 wherein the
test-value distribution of each of the simulated haplotypes is
generated by instructions stored in the computer. Herein, the
test-values are chi-square values based on haplotype frequency
differences between cases and controls. The distribution plot is a
set of data points that, when displayed on a coordinate system,
form a diagram indicating the chi-squared values for each haplotype
in each of the random genomic clones. It should be noted that the
distribution plot does not necessarily need to be generated from
frequencies derived from chi-squared values. Any similar
measurement of a statistical difference between control and
trait-associated individuals having the haplotypes found within the
selected random genomic clones may be used within the present
invention.
[0277] The process 700 then moves to a state 708 wherein the
maximum test values of haplotype differences between the control
and trait-associated populations within the trait-associated clone
is determined. The process 700 then moves to a state 710 wherein
the distribution of the test-value in the trait-associated clone is
generated. Prior to the generation of the distribution of
test-value, it is advantageous to "saturate" the trait-associated
clone so that as many biallelic markers as possible are known
within the clone.
[0278] The number of markers in the trait-associated clone is
preferably twenty-five or more but may be 10, 15 or 20. Once a
large number of biallelic markers are known in the trait-associated
clone, haplotypes comprising groups of three markers can be chosen
at random and haplotype frequency estimations in cases and in
controls can be compared by means of chi-square statistics. For one
group of markers, one chi-square value (i.e. the chi squared value
for the haplotype having the greatest association with the trait)
is stored to a computer memory for later processing.
[0279] The data plot distribution generated in state 710 is derived
from all chi-square values and the chi-squares are stored as
described above. Of course, it should be understood that any other
statistical mechanism for generating a distribution of test values
based on haplotype frequencies or any measured observation of
haplotypes is useful in the present invention. Once the
distribution plot is calculated in the computer at the state 710,
the process 700 moves to a state 714 wherein the distribution plots
from the haplotypes in the random clones and the haplotypes in the
trait-associated clone are compared.
[0280] Once the distributions are compared, the process 700 moves
to a decision state 716 to determine whether the distributions are
different. If the random and the trait-associated distributions are
not determined to be different at decision state 716, the process
moves to a state 720 wherein a determination is made whether more
trait-associated clones are available. If more trait-associated
clones are available, the process 700 returns to the state 708.
However, if no more trait-associated clones are available, the
process 700 terminates at an end state 730.
[0281] If a determination is made at the state 716 that the
distributions are different, the process 700 moves to a state 724
wherein the computer system indicates that the suspected
trait-associated clone was found to be effectively associated to
the studied trait. This indication can be through computer's
display, printer or any other well-known mechanism for notifying a
computer user of the results of a particular process. The process
then terminates at the end state 730.
[0282] Referring now to FIG. 19, the process 20 of identifying
suitable random genomic clones is illustrated. The process 20
begins at a start state 100 and then moves to a state 110 wherein
data representing a DNA sequence corresponding to the first random
clone to be analyzed is selected. Normally, this data is stored on
the hard disk of the computer. However, it should be noted that
this data can be stored in any conventional memory within the
computer system or outside the computer on a server or other data
storage computer.
[0283] The data representing the DNA sequence is preferably derived
from nucleotide sequencing of a bacterial artificial chromosome
(BAC). However, the data can be derived from the nucleotide
sequence of any type of clone that carries DNA sequences.
[0284] Once data representing the first random clone is selected at
the state 110, the process 20 moves to a decision state 115 wherein
a determination is made whether there are more than three biallelic
markers within the clone. Prior to performing this process, the
data representing the DNA sequence is matched against several
databases of genes to determine whether any biallelic markers exist
within the sequence. If any biallelic markers do exist, that data
is held in a marker table on the computer. The marker table holds
the name of each file corresponding to nucleic acid sequence data
from a random clone and the description of any biallelic markers
within the DNA sequence. Through the marker table, one can access
the number of biallelic markers in the data corresponding to each
random and candidate clone.
[0285] At the decision state 115, a determination is made by
reference to the marker table whether more than three biallelic
markers are found in the data from the selected clone. If more than
three markers are not found in the clone, the process 20 moves to a
state 120 wherein the next random clone is selected since this
clone does not have enough biallelic markers for an efficient
analysis. Following the state 120, the process 20 then returns to
the decision state 115 to determine if more than three biallelic
markers are available in the nucleic acid sequence data from newly
selected clone.
[0286] If more than three markers are found in the clone, the
process 20 moves to a process state 125 where markers that are in
Hardy-Weinberg equilibrium in case and control populations are
identified. Process 125 is described in FIG. 22 below. Process 20
then moves to a decision state 127 to determine if there are at
least three markers in Hardy-Weinberg equilibrium in both
populations. If there are not at least three markers in H-W
equilibrium, the process returns to state 120 to select another
random clone. If there are at least three markers in H-W
equilibrium, process 20 moves to a state 135 wherein the selected
random clone is stored to a random clone table on the computer's
hard disk.
[0287] The process 20 then moves to a decision state 140 to
determine whether more random clones exist that need to be
analyzed. As described above, it is advantageous to have at least
25 random clones with biallelic markers to be used as chi-squared
data points in the distribution plot. If more random clones do
exist, the process 20 returns to the state 120 to select the data
from the nucleotide sequence of the next random clone. If no more
data is available for nucleotide sequences of random clones at the
decision state 140, the process 20 terminates at an end state
150.
[0288] Now referring to FIG. 22, the process 125 of identifying
markers in Hardy-Weinberg equilibrium in case and control
populations (FIG. 19) is described in more detail. The process 125
begins at start state 400 and moves to a state 410 where the
markers in the random clone are selected from the table described
above. The process 125 then moves to a state 420 where the first
marker is selected. The process 125 then moves to state 430 wherein
the Hardy-Weinberg equilibrium calculations are performed in cases
and in control populations as described above.
[0289] Once the test calculations in cases and in control
populations are performed at state 430, the process 125 moves to a
decision state 435 to determine whether the selected marker is in
Hardy-Weinberg equilibrium in both populations. If a determination
is made at decision state 435 that the marker is in Hardy-Weinberg
equilibrium in both populations, the process 125 moves to state 440
where the marker is stored in a table. The process then moves to a
decision state 445 to determine whether there is another marker in
the clone.
[0290] If a determination is made at decision state 435 that the
marker is not in Hardy-Weinberg equilibrium in one or the other
population, the process 125 moves directly to state 445 to
determine whether more markers are available in the clone.
[0291] If a determination is made at decision state 445 that other
markers are available for testing in the clone, the process 125
moves to state 450 where another marker is selected. The process
125 then returns to state 430. If a determination is made at
decision state 445 that all markers were tested for Hardy-Weinberg
equilibrium, the process 125 ends at an end state 460.
[0292] Referring now to FIG. 20, the process 25 of generating the
distribution of test-values in selected random clones begins at a
start state 200 and moves to state 202 where the first clone is
selected. The process moves to state 205 where the total number of
markers in Hardy-Weinberg equilibrium in both case and control
populations is counted. Once the total number of available markers
is counted in state 205, the process 25 moves to state 210 where
the first group of N markers is selected.
[0293] In one embodiment, N=3 so that each group of markers is
analyzed as a triplet. In this embodiment, each haplotype comprises
a group of three biallelic markers. However, it should be noted
that each group could consist of either more or less markers. In
one embodiment, a haplotype comprising only two markers is selected
instead of a group of three or more associated markers. In another
embodiment, a group of eight markers is selected for further
analysis.
[0294] The process 25 then moves to a state 215 wherein the total
number of possible haplotypes based on the total number N of
markers within the first group is determined. The formula 2.sup.N
can be used to determine all of the possible haplotypes in a group
of N markers. This formula is correct since, given any set of N
biallelic markers, there are 2.sup.N possible rearrangements of
those markers on a nucleic acid sequence.
[0295] Once the total number of haplotypes is calculated in state
215, the process 25 moves to state 220 wherein haplotype
frequencies in the cases group are estimated using the E-M
algorithm as described above. When the 2.sup.N haplotype
frequencies are estimated in the cases group in state 220, the
process 25 moves to state 225 wherein the 2.sup.N haplotype
frequencies are estimated in the control group using the same
algorithm.
[0296] Once the haplotype frequencies are estimated in both groups,
the process 25 moves to a state 230 wherein the first haplotype is
selected. The process 25 then moves to state 232, wherein the
chi-square test value based on haplotype frequency difference
between the cases and control groups is calculated.
[0297] Once the chi-square statistic is calculated, the process 25
then moves to a decision state 235 to determine whether more
haplotypes exist for the selected random clone. If a determination
is made that more haplotypes do exist at the decision state 235,
the process 25 moves to a state 240 to select the next haplotype.
It should be noted that in every group of three biallelic markers
there are 2,.sup.3 or eight, possible haplotypes. Thus, this
process will be repeated eight times for every group of three
markers until each of the eight possible haplotypes is aligned with
nucleic acid sequences from each of the control and
trait-associated clones. If there are more haplotypes left to
analyze in the selected group, the process 25 returns to state 232
to calculate the chi-square based on a difference in haplotype
frequencies.
[0298] If a determination is made at the decision state 235 that
the frequencies of all of the possible haplotypes in the selected
group have been determined in the control and trait expressing
populations, the process 25 moves to a state 245 to select the test
value for the haplotype in the group with the greatest association
with the selected trait. This analysis is described above, but is
preferably carried out using a chi-squared test to compare the
frequency of each haplotype in the control and trait expressing
groups. The chi-squared test gives a value reflective of how
tightly associated the individual haplotype is with the trait. The
chi squared value from the haplotype in the group that has the
greatest association with the trait is then stored at a state 255
to a test value table on the computer's hard disk. Thus, for each
group of biallelic markers, one chi squared value from the
haplotype having the greatest association with the trait is chosen
for inclusion in the test value table. This procedure is done in
order to follow the procedures done with the trait-associated
clone.
[0299] Once the selected chi squared value is stored to the test
value table at the state 255, the process moves to a decision state
260 to determine whether more groups of, for example, sets of three
biallelic markers exist in the selected clone to be analyzed. If
more groups do exist in the nucleotide sequence of the selected
clone, the process 25 moves to a state 265 and selects the next
group of three markers. The process 25 then returns to the state
215 to determine the total number of haplotypes within the newly
selected group. If a determination is made at decision state 260
that all groups of markers have been analyzed in the random clone,
the process 25 moves to a decision state 266 to determine whether
there are more clones available in the marker table stored in the
computer. If more clones do exist, the process 25 moves to state
267 in order to select the next clone. The process 25 then returns
to state 205 where the total number of markers in Hardy-Weinberg
equilibrium in the selected clone is counted. If a determination is
made at decision state 266 that no more clones are available in the
marker table, the process 25 terminates at an end state 270.
[0300] Referring now to FIG. 21, the process 35 (FIG. 18) of
calculating the test values in the candidate clone is described in
more detail. The process 35 begins a start state 300 and moves to a
process state 305 wherein the total number of biallelic markers in
Hardy-Weinberg equilibrium in case and control groups in the
candidate clone is determined. The process 305 is described in more
detail in FIG. 23. The process 35 then counts the total number of
markers in Hardy-Weinberg equilibrium at a state 310. It should be
noted that determining the number of markers in Hardy-Weinberg
equilibrium is advantageous because the method used to infer
haplotype frequencies in the two populations studied (cases and
controls) rely on this assumption, i.e. that the markers involved
in the haplotype fit the Hardy-Weinberg equilibrium, as described
above. The number of markers is preferably retrieved from a table
that has been previously created to store the location of each
marker within the trait-associated sequence.
[0301] The process 35 then moves to a state 320 wherein the first
group of N markers is selected. In one embodiment, N=3 so that each
group of markers that is analyzed as a triplet. In this embodiment,
each haplotype comprises a group of three biallelic markers.
However, it should be noted that each group could consist of either
more or less markers.
[0302] The process 35 then moves to a state 325 wherein the total
number of possible haplotypes based on the total number N of
markers within the first group is determined. The formula 2.sup.N
can be used to determine all of the possible haplotypes in a group
of N markers. This formula is correct since, given any set of N
biallelic markers, there are 2.sup.N possible combinations of those
markers on a nucleic acid sequence.
[0303] Once the number of markers in Hardy-Weinberg equilibrium is
determined in both populations, the process 35 moves to state 330
wherein the first possible haplotype is selected. The process 35
then moves to a state 335 wherein the haplotype frequencies are
estimated in the control group using the E-M algorithm as described
above. Once the haplotype frequencies are estimated in the control
group, the process 35 moves to state 340, where the haplotype
frequencies are estimated in the population of individuals with the
selected trait.
[0304] Once the haplotype frequencies are estimated in both
populations at study in states 335 and 340, the process 35 moves to
state 342, wherein a chi-square statistic based on the differences
in haplotype frequencies is computed.
[0305] Once this calculation is made, the process 35 then moves to
a decision state 345 to determine whether more haplotypes exist for
the candidate clone. If a determination is made that more
haplotypes do exist at the decision state 345, the process 35 moves
to a state 350 to select the next haplotype.
[0306] It should be noted that in every group of three biallelic
markers there are 2.sup.3 or eight possible haplotypes. Thus, this
process will be repeated eight times for every group of three
markers until the frequencies of each of the eight possible
haplotypes is determined in the control and case populations. If
there are more haplotypes left to analyze in the selected group,
the process 35 returns to the state 335 to calculate the frequency
of the next haplotype of the group in the population of control
individuals.
[0307] If a determination is made at the decision state 345 that
the frequencies of all of the possible haplotypes in the selected
group have been determined in the control and case populations, the
process 35 moves to a state 355 to select the test value from the
haplotype in the group with the greatest association with the
selected trait. This analysis is described above, but is preferably
carried out using a chi-squared test to determine the frequency
difference of each haplotype in the control and case
populations.
[0308] The chi-squared test gives a value reflective of how tightly
associated the individual haplotype is with the trait. The chi
squared value from the haplotype in the group that has the greatest
association with the trait is then stored at a state 360 to a test
value table on the computer's hard disk. Thus, one chi squared
value from the haplotype having the greatest association with the
trait is chosen.
[0309] Once the selected chi squared value is stored to the test
value table at the state 360, the process moves to a decision state
365 to determine whether more groups of, for example, sets of three
biallelic markers exist in the candidate clone to be analyzed. If
more groups do exist in the candidate clone, the process 35 moves
to a state 370 and selects the next group of three markers. The
process 35 then returns to the state 325 to determine the total
number of haplotypes within the newly selected group. If a
determination is made at the decision state 365 that no more groups
exist, the process 35 terminates at an end state 375.
[0310] Referring now to FIG. 23, the process 305 of determining the
number of markers within the candidate clone that are in
Hardy-Weinberg equilibrium in both case and control populations is
described in more detail. The process 305 begins at a start state
500 and moves to state 505 where all markers in the candidate clone
are counted from a marker table stored in the computer. Once the
number of markers available is determined, the process 305 moves to
state 510 where the first marker is selected. It then moves to
state 515 wherein the Hardy-Weinberg equilibrium is calculated in
case and in control populations for this marker. This test allows
determination of whether the assumption of random mating as
described above fits for this particular marker in the two
populations at study. This step involves a chi-square statistical
computation.
[0311] Once the Hardy-Weinberg equilibrium is computed in both case
and control populations at state 515, the process 305 moves to
decision state 520 to determine whether the marker fits the
hypothesis of Hardy-Weinberg equilibrium in both populations. If a
determination is made that the marker fits this hypothesis, the
process 305 moves to state 530 where the marker is stored to a
table. The process 305 then moves then to a decision state 535 to
determine whether there are other available markers for
Hardy-Weinberg testing.
[0312] If a determination is made at the decision state 520 that
the marker does not fit the hypothesis of Hardy-Weinberg
equilibrium, the process 305 moves to the decision state 535.
[0313] At the decision state 535, if a determination is made that
other markers are available for testing, the process 305 moves to
state 540 to select the next marker. The process 305 then returns
to state 515 to calculate Hardy-Weinberg equilibriums for the
selected marker. If a determination is made at decision state 535
that all markers available in the clone have been tested for
Hardy-Weinberg equilibrium, the process 305 ends at an end state
550.
[0314] It should be noted that the determination of a
Hardy-Weinberg equilibrium is advantageous because the method of
estimation of haplotype frequencies relies on this hypothesis.
However, if any other haplotype frequency estimation algorithm,
relying on other assumptions, is used other selection processes
based on such assumptions may be used.
[0315] Referring to FIG. 24, the two distributions of test-values
are compared in the random clone and the candidate clone. The
process 45 begins at a start state 600 and moves to state 610 where
the two distributions are selected from the two test-values tables
mentioned above. The process 45 then moves to a state 620 wherein a
non parametric analysis is performed to compare these two
distributions.
[0316] The two distributions can be compared using any method that
is familiar to one of ordinary skill in the art. For example, a
computer program can apply either the Wilcoxon rank test or the
Kolmogorov-Smirnov test, which are discussed above. These software
programs would simply apply either of the formulas to the data
derived above relating to the statistical difference between
particular haplotypes found in control and trait-associated
individuals.
[0317] The process 45 then moves to state 630 where the results of
the analysis are stored in a result table. The results can then be
printed through a computer display, printer or any other well-known
mechanism for notifying a result of a particular process. The
process 630 then ends at an end state 640.
[0318] Several of the aspects of the present invention are
described in the following examples, which are offered by way of
illustration and not by way of limitation. Many other modifications
and variations of the invention as herein set forth can be made
without departing from the spirit and scope thereof and therefore
only such limitations should be imposed as are indicated by the
appended claims.
EXAMPLE 1
Construction of a BAC Library
[0319] Three different human genomic DNA libraries were produced by
cloning partially digested DNA from a human lymphoblastoid cell
line (derived from individual No. 8445, CEPH families) into the
pBeloBAC11 vector (Kim et al., Genomics 34: 213-218 (1996), the
disclosure of which is incorporated herein by reference). One
library was produced using a BamHI partial digestion of the genomic
DNA from the lymphoblastoid cell line and contains 110,000 clones
having an average insert size of 150 kb (corresponding to 5 human
haploid genome equivalents). Another library was prepared from a
HindIII partial digest and corresponds to 3 human genome
equivalents with an average insert size of 150 kb. A third library
was prepared from a NdeI partial digest and corresponds to 4 human
genome equivalents with an average insert size of 150 kb.
[0320] Alternatively, the genomic DNA may be inserted into BAC
vectors which possess both a high copy number origin of
replication, which facilitates the isolation of the vector DNA, and
a low copy number origin of replication. Cloning of a genomic DNA
insert into the high copy number origin of replication inactivates
the origin such that clones containing a genomic insert replicate
at low copy number. The low copy number of clones having a genomic
insert therein permits the inserts to be stably maintained. In
addition, selection procedures may be designed which enable low
copy number plasmids (i.e. vectors having genomic inserts therein)
to be selected. Such vectors and selection procedures are described
in U.S. patent application Ser. No. 09/058,746 entitled "High
Throughput DNA Sequencing Vector", the entire contents of which are
incorporated herein by reference.
[0321] It will be appreciated that the present methods may be
practiced using BAC vectors other than those of Shizuya et al.
(1992, supra), or derived from those, or vectors other than BAC
vectors which possess the above-described characteristics.
EXAMPLE 2
Ordering of a BAC Library: Screening Clones with STSs
[0322] The BAC library is screened with a set of PCR-typeable STSs
to identify clones containing the STSs. To facilitate PCR screening
of several thousand clones, for example 200,000 clones, pools of
clones are prepared.
[0323] Three-dimensional pools of the BAC libraries are prepared as
described in Chumakov et al. and are screened for the ability to
generate an amplification fragment in amplification reactions
conducted using primers derived from the ordered STSs. (Chumakov et
al. (1995), supra). A BAC library typically contains 200,000 BAC
clones. Since the average size of each insert is 100-300 kb, the
overall size of such a library is equivalent to the size of at
least about 7 human genomes. This library is stored as an array of
individual clones in 518 384-well plates. It can be divided into 74
primary pools (7 plates each). Each primary pool can then be
divided into 48 subpools prepared by using a three-dimensional
pooling system based on the plate, row and column address of each
clone (more particularly, 7 subpools consisting of all clones
residing in a given microtiter plate; 16 subpools consisting of all
clones in a given row; 24 subpools consisting of all clones in a
given column).
[0324] Amplification reactions are conducted on the pooled BAC
clones using primers specific for the STSs. For example, the three
dimensional pools may be screened with 45,000 STSs whose positions
relative to one another and locations along the genome are known.
Preferably, the three dimensional pools are screened with about
30,000 STSs whose positions relative to one another and locations
along the genome are known. In a highly preferred embodiment, the
three dimensional pools are screened with about 20,000 STSs whose
positions relative to one another and locations along the genome
are known.
[0325] Amplification products resulting from the amplification
reactions are detected by conventional agarose gel electrophoresis
combined with automatic image capturing and processing. PCR
screening for a STS involves three steps: (1) identifying the
positive primary pools; (2) for each positive primary pool,
identifying the positive plate, row and column `subpools` to obtain
the address of the positive clone; (3) directly confirming the PCR
assay on the identified clone. PCR assays are performed with
primers specifically defining the STS.
[0326] Screening is conducted as follows. First BAC DNA containing
the genomic inserts is prepared as follows. Bacteria containing the
BACs are grown overnight at 37.degree. C. in 120 .mu.l of LB
containing chloramphenicol (12 .mu.g/ml). DNA is extracted by the
following protocol:
[0327] Centrifuge 10 min at 4.degree. C. and 2000 rpm
[0328] Eliminate supernatant and resuspend pellet in 20 .mu.l TE
10-2 (Tris HCl 10 mM, EDTA 2 mM)
[0329] Centrifuge 10 min at 4.degree. C. and 2000 rpm
[0330] Eliminate supernatant and incubate pellet with 20 .mu.l
lyzozyme 1 mg/ml during 15 min at room temperature
[0331] Add 20 .mu.l proteinase K 100 .mu.g/ml and incubate 15 min
at 60.degree. C.
[0332] Add 8 .mu.l DNAse 2 U/.mu.l and incubate 1 hr at room
temperature
[0333] Add 100 .mu.l TE 10-2 and keep at -80.degree. C.
[0334] PCR assays are performed using the following protocol:
1 Final volume 15 .mu.l BAC DNA 1.7 ng/.mu.l MgCl.sub.2 2 mM dNTP
(each) 200 .mu.M primer (each) 2.9 ng/.mu.l Ampli Taq Gold DNA
polymerase 0.05 unit/.mu.l PCR buffer (10x = 0.1 M TrisHCl pH8.3
0.5 M KCl 1x
[0335] The amplification is performed on a Genius II thermocycler.
After heating at 95.degree. C. for 10 min, 40 cycles are performed.
Each cycle comprises: 30 sec at 95.degree. C., 54.degree. C. for 1
min, and 30 sec at 72.degree. C. For final elongation, 10 min at
72.degree. C. end the amplification. PCR products are analyzed on
1% agarose gel with 0.1 mg/ml ethidium bromide.
EXAMPLE 3
Subcloning of BACs
[0336] The cells obtained from three liters overnight culture of
each BAC clone are treated by alkaline lysis using conventional
techniques to obtain the BAC DNA containing the genomic DNA
inserts. After centrifugation of the BAC DNA in a cesium chloride
gradient, ca. 50 .mu.g of BAC DNA are purified. 5-10 .mu.g of BAC
DNA are sonicated using three distinct conditions, to obtain
fragments within a desired size range. The obtained DNA fragments
are end-repaired in a 50 .mu.l volume with two units of Vent
polymerase for 20 min at 70.degree. C., in the presence of the four
deoxytriphosphates (100 .mu.M). The resulting blunt-ended fragments
are separated by electrophoresis on preparative low-melting point
1% agarose gels (60 Volts for 3 hours). The fragments lying within
a desired size range, such as 600 to 6,000 bp, are excised from the
gel and treated with agarase. After chloroform extraction and
dialysis on Microcon 100 columns, DNA in solution is adjusted to a
100 ng/.mu.l concentration. A ligation to a linearised,
dephosphorylated, blunt-ended plasmid cloning vector is performed
overnight by adding 100 ng of BAC fragmented DNA to 20 ng of
pBluescript II Sk (+) vector DNA linearized by enzymatic digestion,
and treating with alkaline phosphatase. The ligation reaction is
performed in a 10 .mu.l final volume in the presence of 40
units/.mu.l T4 DNA ligase (Epicentre). The ligated products are
electroporated into the appropriate cells (ElectroMAX E. coli DH10B
cells). IPTG and X-gal are added to the cell mixture, which is then
spread on the surface of an ampicillin-containing agar plate. After
overnight incubation at 37.degree. C., recombinant (white) colonies
are randomly picked and arrayed in 96 well microplates for storage
and sequencing.
[0337] Alternatively, BAC subcloning may be performed using vectors
which possess both a high copy number origin of replication, which
facilitates the isolation of the vector DNA, and a low copy number
origin of replication. Cloning of a genomic DNA fragment into the
high copy number origin of replication inactivates the origin such
that clones containing a genomic insert replicate at low copy
number. The low copy number of clones having a genomic insert
therein permits the inserts to be stably maintained. In addition,
selection procedures may be designed which enable low copy number
plasmids (i.e. vectors having genomic inserts therein) to be
selected. In a preferred embodiment, BAC subcloning will be
performed in vectors having the above described features and
moreover enabling high throughput sequencing of long fragments of
genomic DNA. Such high throughput high quality sequencing may be
obtained after generating successive deletions within the subcloned
fragments to be sequenced, using transposition-based or enzymatic
systems. Such vectors are described in the U.S. patent application
Ser. No. 09/058,746.
[0338] It will be appreciated that other subcloning methods
familiar to those skilled in the art may also be employed.
[0339] The resulting subclones are then partially sequenced using,
for example, the procedures described below.
EXAMPLE 4
Partial Sequencing of BAC Subclones
[0340] The genomic DNA inserts in the subclones, such as the BAC
subclones prepared above, are amplified by conducting PCR reactions
on the overnight bacterial cultures, using primers complementary to
vector sequences flanking the insertions.
[0341] The sequences of the insert extremities (on average 500
bases at each end, obtained under routine sequencing conditions)
are determined by fluorescent automated sequencing on ABI 377
sequencers, using ABI Prism DNA Sequencing Analysis software.
Following gel image analysis and DNA sequence extraction, sequence
data are automatically processed with adequate software to assess
sequence quality. A proprietary base-caller, automatically flags
suspect peaks, taking into account the shape of the peaks, the
inter-peak resolution, and the noise level. The proprietary
base-caller also performs an automatic trimming. Any stretch of 25
or fewer bases having more than 4 suspect peaks is usually
considered unreliable and is discarded.
[0342] The sequenced regions of the subclones, such as the BAC
subclones prepared above, are then analyzed in order to identify
biallelic markers lying therein. The frequency at which biallelic
markers will be detected in the screening process varies with the
average level of heterozygosity desired. For example, if biallelic
markers having an average heterozygosity rate of greater than 0.42
are desired, they will occur every 2.5 to 3 kb on average.
Therefore, on average, six 500 bp-genomic fragments have to be
screened in order to derive 1 biallelic marker having an adequate
informative content.
[0343] As a preferred alternative to sequencing the ends of an
adequate number of BAC subclones, the above mentioned high
throughput deletion-based sequencing vectors, which allow the
generation of a high quality sequence information covering
fragments of ca. 6 kb, may be used. Having sequence fragments
longer than 2.5 or 3 kb enhances the chances of identifying
biallelic markers therein. Methods of constructing and sequencing a
nested set of deletions are disclosed in the U.S. patent
application Ser. No. 09/058,746.
[0344] Nucleic acids to be evaluated for the presence of biallelic
markers therein may be obtained from groups of individuals, such as
groups of 100 individuals, as described in Example 5.
EXAMPLE 5
Extraction of DNA
[0345] 30 ml of blood are taken from the individuals in the
presence of EDTA. Cells (pellet) are collected after centrifugation
for 10 minutes at 2000 rpm. Red cells are lysed by a lysis solution
(50 ml final volume: 10 mM Tris pH7.6; 5 mM MgCl.sub.2; 10 mM
NaCl). The solution is centrifuged (10 minutes, 2000 rpm) as many
times as necessary to eliminate the residual red cells present in
the supernatant, after resuspension of the pellet in the lysis
solution.
[0346] The pellet of white cells is lysed overnight at 42.degree.
C. with 3.7 ml of lysis solution composed of:
[0347] 3 ml TE 10-2 (Tris-HCl 10 mM, EDTA 2 mM)/NaCl 0.4 M
[0348] 200 .mu.l SDS 10%
[0349] 500 .mu.l K-proteinase (2 mg K-proteinase in TE 10-2/NaCl
0.4 M).
[0350] For the extraction of proteins, 1 ml saturated NaCl (6M)
(1/3.5 v/v) is added. After vigorous agitation, the solution is
centrifuged for 20 minutes at 10000 rpm. For the precipitation of
DNA, 2 to 3 volumes of 100% ethanol are added to the previous
supernatant, and the solution is centrifuged for 30 minutes at 2000
rpm. The DNA solution is rinsed three times with 70% ethanol to
eliminate salts, and centrifuged for 20 minutes at 2000 rpm. The
pellet is dried at 37.degree. C., and resuspended in 1 ml TE 10-1
or 1 ml water. The DNA concentration is evaluated by measuring the
OD at 260 nm (1 unit OD=50 .mu.g/ml DNA).
[0351] To evaluate the presence of proteins in the DNA solution,
the OD 260/OD 280 ratio is determined. Only DNA preparations having
a OD 260/OD 280 ratio between 1.8 and 2 are used in the subsequent
steps described below.
[0352] Once genomic DNA from every individual in the given
population has been extracted, it is preferred that a fraction of
each DNA sample is separated, after which a pool of DNA is
constituted by assembling equivalent DNA amounts of the separated
fractions into a single one. The pooled DNA samples can be used to
identify biallelic markers as described in Example 6.
EXAMPLE 6
Amplification of DNA from Peripheral Blood and Identification of
Biallelic Markers
[0353] The amplification of each sequence is performed on pooled
DNA samples obtained as in Example 5 above, using PCR (Polymerase
Chain Reaction) as follows:
2 final volume 25 .mu.l genomic DNA 2 ng/.mu.l MgCl.sub.2 2 mM dNTP
(each) 200 .mu.M primer (each) 2.9 ng/.mu.l Ampli Taq Gold DNA
polymerase (Perkin) 0.05 unit/.mu.l PCR buffer (10x = 0.1 M Tris
HCl pH 8.3, 0.5 M KCl) 1x.
[0354] The synthesis of primers is performed following the
phosphoramidite method, on a GENSET UFPS 24.1 synthesizer.
[0355] To reduce the expense of preparing amplification primers for
use in the above procedures, short primers may be used. While
primers and probes having between 15 and 20 (or more) nucleotides
are usually highly specific to a given nucleic acid sequence, it
may be inconvenient and expensive to synthesize a relatively long
oligonucleotide for each analysis. In order to at least partially
circumvent this problem, it is often possible to use smaller but
still relatively specific oligonucleotides that are shorter in
length to create a manageable library. For example, a library of
oligonucleotides comprising about 8 to 10 nucleotides is
conceivable and has already been used for sequencing of a 40,000 bp
cosmid DNA (Studier, Proc. Natl. Acad. Sci. USA 86(18): 6917-6921
(1989), the disclosure of which is incorporated herein by
reference).
[0356] Another potential way to obtain specific primers and probes
with a small library of oligonucleotides is to generate longer,
more specific primers and probes from combinations of shorter, less
specific oligonucleotides. Libraries of shorter oligonucleotides,
each one being from about five to eight nucleotides in length, have
already been used (Kieleczawa et al., Science 258: 1787-1791
(1992); Kotler et al., Proc. Natl. Acad. Sci. USA 90: 4241-4245
(1993); Kaczorowski and Szybalski, Anal Biochem. 221: 127-135
(1994), the disclosures of which are incorporated herein by
reference). Suitable probes and primers of appropriate length can
therefore be designed through the association of two or three
shorter oligonucleotides to constitute modular primers. The
association between primers can be either covalent resulting from
the activity of DNA T4 ligase or non-covalent through base-stacking
energy.
[0357] The amplification is performed on a Perkin Elmer 9600
Thermocycler or MJ Research PTC200 with heating lid. After heating
at 95.degree. C. for 10 minutes, 40 cycles are performed. Each
cycle comprises: 30 sec at 95.degree. C., 1 minute at 54.degree.
C., and 30 sec at 72.degree. C. For final elongation, 10 minutes at
72.degree. C. ends the amplification.
[0358] The quantities of the amplification products obtained are
determined on 96-well microtiter plates, using a fluorimeter and
Picogreen as intercalating agent (Molecular Probes).
[0359] The sequences of the amplification products are determined
using automated dideoxy terminator sequencing reactions with a
dye-primer cycle sequencing protocol. The products of the
sequencing reactions are run on sequencing gels and the sequences
are determined using gel image analysis.
[0360] The sequence data are evaluated using software designed to
detect the presence of biallelic sites among the pooled amplified
fragments. The polymorphism search is based on the presence of
superimposed peaks in the electrophoresis pattern resulting from
different bases occurring at the same position. Because each
dideoxy terminator is labeled with a different fluorescent
molecule, the two peaks corresponding to a biallelic site present
distinct colors corresponding to two different nucleotides at the
same position on the sequence. The software evaluates the intensity
ratio between the two peaks and the intensity ratio between a given
peak and surrounding peaks of the same color.
[0361] However, the presence of two peaks can be an artifact due to
background noise. To exclude such an artifact, the two DNA strands
are sequenced and a comparison between the peaks is carried out. In
order to be registered as a polymorphic sequence, the polymorphism
has to be detected on both strands.
[0362] The above procedure permits those amplification products
which contain biallelic markers to be identified.
EXAMPLE 7
Screening BAC Libraries with Biallelic Markers
[0363] Amplification primers enabling the specific amplification of
DNA fragments carrying the biallelic markers may be used to screen
clones in any genomic DNA library, preferably the BAC libraries
described above, for the presence of the biallelic markers.
[0364] Pairs of primers are designed which allow the amplification
of fragments carrying the biallelic markers obtained as described
above. The amplification primers may be used to screen clones in a
genomic DNA library for the presence of the biallelic markers.
[0365] The amplification primers for the biallelic markers may be
any sequences which allow the specific amplification of any DNA
fragment carrying the markers and may be designed using techniques
familiar to those skilled in the art. The amplification primers may
be oligonucleotides of 8, 10, 15, 20 or more bases in length which
enable the amplification of any fragment carrying the polymorphic
site in the markers. The polymorphic base may be in the center of
the amplification product or, alternatively, it may be located
off-center. For example, in some embodiments, the amplification
product produced using these primers may be at least 100 bases in
length (i.e. 50 nucleotides on each side of the polymorphic base in
amplification products in which the polymorphic base is centrally
located). In other embodiments, the amplification product produced
using these primers may be at least 500 bases in length (i.e. 250
nucleotides on each side of the polymorphic base in amplification
products in which the polymorphic base is centrally located). In
still further embodiments, the amplification product produced using
these primers may be at least 1000 bases in length (i.e. 500
nucleotides on each side of the polymorphic base in amplification
products in which the polymorphic base is centrally located).
[0366] The localization of biallelic markers on BAC clones is
performed essentially as described in Example 2.
[0367] The BAC clones to be screened are distributed in three
dimensional pools as described in Example 2.
[0368] Amplification reactions are conducted on the pooled BAC
clones using primers specific for the biallelic markers to identify
BAC clones which contain the biallelic markers, using procedures
essentially similar to those described in Example 2.
[0369] Amplification products resulting from the amplification
reactions are detected by conventional agarose gel electrophoresis
combined with automatic image capturing and processing. PCR
screening for a biallelic marker involves three steps: (1)
identifying the positive primary pools; (2) for each positive
primary pools, identifying the positive plate, row and column
`subpools` to obtain the address of the positive clone; (3)
directly confirming the PCR assay on the identified clone. PCR
assays are performed with primers defining the biallelic
marker.
[0370] Screening is conducted as follows. First BAC DNA is isolated
as follows. Bacteria containing the genomic inserts are grown
overnight at 37.degree. C. in 120 .mu.l of LB containing
chloramphenicol (12 .mu.g/ml). DNA is extracted by the following
protocol:
[0371] Centrifuge 10 min at 4.degree. C. and 2000 rpm
[0372] Eliminate supernatant and resuspend pellet in 20 .mu.l TE
10-2 (Tris HCl 10 mM, EDTA 2 mM)
[0373] Centrifuge 10 min at 4.degree. C. and 2000 rpm
[0374] Eliminate supernatant and incubate pellet with 20 .mu.l
lyzozyme 1 mg/ml during 15 min at room temperature
[0375] Add 20 .mu.l proteinase K 100 .mu.g/ml and incubate 15 min
at 60.degree. C.
[0376] Add 8 .mu.l DNAse 2 U/.mu.l and incubate 1 hr at room
temperature
[0377] Add 100 .mu.l TE 10-2 and keep at -80.degree. C.
[0378] PCR assays are performed using the following protocol:
3 Final volume 15 .mu.l BAC DNA 1.7 ng/.mu.l MgCl.sub.2 2 mM dNTP
(each) 200 .mu.M primer (each) 2.9 ng/.mu.l Ampli Taq Gold DNA
polymrase 0.05 unit/.mu.l PCR buffer (10x = 0.1 M TrisHCl pH8.3 0.5
M KCl 1x
[0379] The amplification is performed on a Genius II thermocycler.
After heating at 95.degree. C. for 10 min, 40 cycles are performed.
Each cycle comprises: 30 sec at 95.degree. C., 54.degree. for 1
min, and 30 sec at 72.degree. C. For final elongation, 10 min at
72.degree. C. end the amplification. PCR products are analyzed on
1% agarose gel with 0.1 mg/ml ethidium bromide.
EXAMPLE 8
Assignment of Biallelic Markers to Subchromosomal Regions
[0380] Metaphase chromosomes are prepared from phytohemagglutinin
(PHA)-stimulated blood cell donors. PHA-stimulated lymphocytes from
healthy males are cultured for 72 h in RPMI-1640 medium. For
synchronization, methotrexate (10 mM) is added for 17 h, followed
by addition of 5-bromodeoxyuridine (5-BudR, 0.1 mM) for 6 h.
Colcemid (1 mg/ml) is added for the last 15 min before harvesting
the cells. Cells are collected, washed in RPMI, incubated with a
hypotonic solution of KCl (75 mM) at 37.degree. C. for 15 min and
fixed in three changes of methanol:acetic acid (3:1). The cell
suspension is dropped onto a glass slide and air-dried.
[0381] BAC clones carrying the biallelic markers used to construct
the maps can be isolated as described above. These BACs or portions
thereof, including fragments carrying said biallelic markers,
obtained for example from amplification reactions using pairs of
amplification primers as described above, can be used as probes to
be hybridized with metaphasic chromosomes. It will be appreciated
that the hybridization probes to be used in the contemplated method
may be generated using alternative methods well known to those
skilled in the art. Hybridization probes may have any length
suitable for this intended purpose.
[0382] Probes are then labeled with biotin-16 dUTP by nick
translation according to the manufacturer's instructions (Bethesda
Research Laboratories, Bethesda, Md.), purified using a Sephadex
G-50 column (Pharmacia, Upssala, Sweden) and precipitated. Just
prior to hybridization, the DNA pellet is dissolved in
hybridization buffer (50% formamide, 2.times.SSC, 10% dextran
sulfate, 1 mg/ml sonicated salmon sperm DNA, pH 7) and the probe is
denatured at 70.degree. C. for 5-10 min.
[0383] Slides kept at -20.degree. C. are treated for 1 h at
37.degree. C. with RNase A (100 mg/ml), rinsed three times in
2.times.SSC and dehydrated in an ethanol series. Chromosome
preparations are denatured in 70% formamide, 2.times.SSC for 2 min
at 70.degree. C., then dehydrated at 4.degree. C. The slides are
treated with proteinase K (10 mg/100 ml in 20 mM Tris-HCl, 2 mM
CaCO.sub.2) at 37.degree. C. for 8 min and dehydrated. The
hybridization mixture containing the probe is placed on the slide,
covered with a coverslip, sealed with rubber cement and incubated
overnight in a humid chamber at 37.degree. C. After hybridization
and post-hybridization washes, the biotinylated probe is detected
by avidin-FITC and amplified with additional layers of biotinylated
goat anti-avidin and avidin-FITC. For chromosomal localization,
fluorescent R-bands are obtained as previously described (Cherif et
al., (1990) supra.). The slides are observed under a LEICA
fluorescence microscope (DMRXA). Chromosomes are counterstained
with propidium iodide and the fluorescent signal of the probe
appears as two symmetrical yellow-green spots on both chromatids of
the fluorescent R-band chromosome (red). Thus, a particular
biallelic marker may be localized to a particular cytogenetic
R-band on a given chromosome.
EXAMPLE 9
Assignment of Biallelic Markers to Human Chromosomes
[0384] The biallelic markers used to construct the maps may be
assigned to a human chromosome using monosomal analysis as
described below.
[0385] The chromosomal localization of a biallelic marker can be
performed through the use of somatic cell hybrid panels. For
example 24 panels, each panel containing a different human
chromosome, may be used (Russell et al., Somat Cell Mol. Genet 22:
425-431 (1996); Drwinga et al., Genomics 16: 311-314 (1993), the
disclosures of which are incorporated herein by reference).
[0386] The biallelic markers are localized as follows. The DNA of
each somatic cell hybrid is extracted and purified. Genomic DNA
samples from a somatic cell hybrid panel are prepared as follows.
Cells are lysed overnight at 42.degree. C. with 3.7 ml of lysis
solution composed of:
[0387] 3 ml TE 10-2 (Tris HCl 10 mM, EDTA 2 mM)/NaCl 0.4 M
[0388] 200 .mu.l SDS 10%
[0389] 500 .mu.l K-proteinase (2 mg K-proteinase in TE 10-2/NaCl
0.4 M)
[0390] For the extraction of proteins, 1 ml saturated NaCl (6M)
(1/3.5 v/v) is added. After vigorous agitation, the solution is
centrifuged for 20 min at 10,000 rpm. For the precipitation of DNA,
2 to 3 volumes of 100% ethanol are added to the previous
supernatant, and the solution is centrifuged for 30 min at 2,000
rpm. The DNA solution is rinsed three times with 70% ethanol to
eliminate salts, and centrifuged for 20 min at 2,000 rpm. The
pellet is dried at 37.degree. C., and resuspended in 1 ml TE 10-1
or 1 ml water. The DNA concentration is evaluated by measuring the
OD at 260 nm (1 unit OD=50 .mu.g/ml DNA). To determine the presence
of proteins in the DNA solution, the OD.sub.260/OD.sub.280 ratio is
determined. Only DNA preparations having a OD.sub.260/OD.sub.290
ratio between 1.8 and 2 are used in the PCR assay.
[0391] Then, a PCR assay is performed on genomic DNA with primers
defining the biallelic marker. The PCR assay is performed as
described above for BAC screening. The PCR products are analyzed on
a 1% agarose gel containing 0.2 mg/ml ethidium bromide.
EXAMPLE 10
Measurement of Linkage Disequilibrium
[0392] As originally reported by Strittmatter et al. and by
Saunders et al. in 1993, the Apo E e4 allele is strongly associated
with both late-onset familial and sporadic Alzheimer's disease
(AD). (Saunders, A. M. Lancet 342: 710-711 (1993) and Strittmater,
W. J. et al., Proc. Natl. Acad. Sci. U.S.A. 90: 1977-1981 (1993),
the disclosures of which are incorporated herein by reference). The
3 major isoforms of human Apolipoprotein E (apoE2, -E3, and -E4),
as identified by isoelectric focusing, are coded for by 3 alleles
(e 2, 3, and 4). The e 2, e 3, and e 4 isoforms differ in amino
acid sequence at 2 sites, residue 112 (called site A) and residue
158 (called site B). The ancestral isoform of the protein is Apo
E3, which at sites A/B contains cysteine/arginine, while ApoE2 and
-E4 contain cysteine/cysteine and arginine/arginine, respectively
(Weisgraber, K. H. et al., J. Biol. Chem. 256: 9077-9083 (1981);
Rall, S. C. et al., Proc. Natl. Acad. Sci. U.S.A. 79: 4696-4700
(1982), the disclosures of which are incorporated herein by
reference).
[0393] Apo E e 4 is currently considered as a major susceptibility
risk factor for AD development in individuals of different ethnic
groups (specially in Caucasians and Japanese compared to Hispanics
or African Americans), across all ages between 40 and 90 years, and
in both men and women, as reported recently in a study performed on
5930 AD patients and 8607 controls (Farrer et al., JAMA 278:
1349-1356 (1997), the disclosure of which is incorporated herein by
reference). More specifically, the frequency of a C base coding for
arginine 112 at site A is significantly increased in AD
patients.
[0394] Although the mechanistic link between Apo E e 4 and neuronal
degeneration characteristic of AD remains to be established,
current hypotheses suggest that the Apo E genotype may influence
neuronal vulnerability by increasing the deposition and/or
aggregation of the amyloid beta peptide in the brain or by
indirectly reducing energy availability to neurons by promoting
atherosclerosis.
[0395] Using the methods described above, biallelic markers that
are in the vicinity of the Apo E site A were generated and the
association of one of their alleles with Alzheimer's disease was
analyzed. An Apo E public marker (stSG94) was used to screen a
human genome BAC library as previously described. A BAC, which gave
a unique FISH hybridization signal on chromosomal region 19q13.2.3,
the chromosomal region harboring the Apo E gene, was selected for
finding biallelic markers in linkage disequilibrium with the Apo E
gene as follows.
[0396] This BAC contained an insert of 205 kb that was subcloned as
previously described. Fifty BAC subclones were randomly selected
and sequenced. Twenty five subclone sequences were selected and
used to design twenty five pairs of PCR primers allowing 500
bp-amplicons to be generated. These PCR primers were then used to
amplify the corresponding genomic sequences in a pool of DNA from
100 unrelated individuals (blood donors of French origin) as
already described.
[0397] Amplification products from pooled DNA were sequenced and
analyzed for the presence of biallelic polymorphisms, as already
described. Five amplicons were shown to contain a polymorphic base
in the pool of 100 unrelated individuals, and therefore these
polymorphisms were selected as random biallelic markers in the
vicinity of the Apo E gene. The sequences of both alleles of these
biallelic markers (99-344/439; 99-355/219; 99-359/308; 99-365/344;
99-366/274) correspond to SEQ ID Nos: 1-5 and 7-11 (See the
accompanying Sequence Listing). Corresponding pairs of
amplification primers for generating amplicons containing these
biallelic markers can be chosen from those listed as SEQ ID Nos:
13-17 and 19-23.
[0398] An additional pair of primers (SEQ ID Nos: 18 and 24) was
designed that allows amplification of the genomic fragment carrying
the biallelic polymorphism corresponding to the ApoE marker
(99-2452/54; C/T; The C allele is designated SEQ ID NO: 6 in the
accompanying sequence listing, while the T allele is designated SEQ
ID NO: 12 in the accompanying Sequence Listing; publicly known as
Apo E site A (Weisgraber et al. (1981), supra; Rall et al. (1982),
supra) to be amplified.
[0399] The five random biallelic markers plus the Apo E site A
marker were physically ordered by PCR screening of the
corresponding amplicons using all available BACs originally
selected from the genomic DNA libraries, as previously described,
using the public Apo E marker stSG94. The amplicon's order derived
from this BAC screening is as follows:
[0400] (99-344/99-366)-(99-365/99-2452)-99-359-99-355,
[0401] where brackets indicate that the exact order of the
respective amplicons couldn't be established.
[0402] Linkage disequilibrium among the six biallelic markers (five
random markers plus the Apo E site A) was determined by genotyping
the same 100 unrelated individuals from whom the random biallelic
markers were identified.
[0403] DNA samples and amplification products from genomic PCR were
obtained in similar conditions as those described above for the
generation of biallelic markers, and subjected to automated
microsequencing reactions using fluorescent ddNTPs (specific
fluorescence for each ddNTP) and the appropriate microsequencing
primers having a 3' end immediately upstream of the polymorphic
base in the biallelic markers. The sequence of these
microsequencing primers is indicated within the corresponding
sequence listings of SEQ ID Nos: 25-30. Once specifically extended
at the 3' end by a DNA polymerase using the complementary
fluorescent dideoxynucleotide analog (thermal cycling), the
microsequencing primer was precipitated to remove the
unincorporated fluorescent ddNTPs. The reaction products were
analyzed by electrophoresis on ABI 377 sequencing machines. Results
were automatically analyzed by appropriate software further
described in Example 13.
[0404] Linkage disequilibrium (LD) between all pairs of biallelic
markers (Mi, Mj) was calculated for every allele combination
(Mi1,Mj1; Mi1,Mj2; Mi2,Mj1; Mi2,Mj2) according to the maximum
likelihood estimate (MLE) for delta (the composite linkage
disequilibrium coefficient). The results of the LD analysis between
the Apo E Site A marker and the five new biallelic markers
(99-344/439; 99-355/219; 99-359/308; 99-365/344; 99-366/274) are
summarized in Table 1 below:
4TABLE 1 SEQ ID Nos of the SEQ ID Nos of the Markers d .times. 100
biallelic Markers amplification Primers ApoE SiteA 6 18 99-2452/54
12 24 99-344/439 1 1 13 7 19 99-366/274 1 2 14 8 20 99-365/344 8 5
17 11 23 99-359/308 2 3 15 9 21 99-355/219 1 4 16 10 22
[0405] The above LD results indicate that among the five biallelic
markers randomly selected in a region of about 200 kb containing
the Apo E gene, marker 99-365/344T is in relatively strong linkage
disequilibrium with the Apo E site A allele (99-2452/54C).
[0406] Therefore, since the Apo E site A allele is associated with
Alzheimer's disease, one can predict that the T allele of marker
99-365/344 will probably be found associated with AD. In order to
test this hypothesis, the biallelic markers of SEQ ID Nos: 1-6 and
7-12 were used in association studies as described below.
[0407] Alzheimer's disease patients were recruited according to
clinical inclusion criteria based on the MMSE test. The 248 control
cases included in this study were both ethnically- and age-matched
to the affected cases. Both affected and control individuals
corresponded to unrelated cases. The identities of the polymorphic
bases of each of the biallelic markers was determined in each of
these individuals using the methods described above. Techniques for
conducting association studies are further described below.
[0408] The results of this study are summarized in Table 2
below:
5 TABLE 2 ASSOCIATION DATA Difference in allele frequency between
individuals with Alzheimer's and MARKER control individuals
Corresponding p-value 99-344/439 3.3% 9.54E-02 99-366/274 1.6%
2.09E-01 99-365/344 17.7% 6.9E-10 99-2452/54 (ApoE Site A) 23.8%
3.95E-21 99-359/308 0.4% 9.2E-01 99-355/219 2.5% 2.54E-01
[0409] The frequency of the Apo E site A allele in both AD cases
and controls was found in agreement with that previously reported
(ca. 10% in controls and ca. 34% in AD cases, leading to a 24%
difference in allele frequency), thus validating the Apo E e4
association in the populations used for this study.
[0410] Moreover, as predicted from the LD analysis (Table 1), a
significant association of the T allele of marker 99-365/344 with
AD cases (18% increase in the T allele frequency in AD cases
compared to controls, p value for this difference=6.9 E-10) was
observed.
[0411] The above results indicate that any marker in LD with one
given marker associated with a trait will be associated with the
trait. It will be appreciated that, though in this case the ApoE
Site A marker is the trait-causing allele (TCA) itself, the same
conclusion could be drawn with any other non TCA marker associated
with the studied trait.
[0412] These results further indicate that conducting association
studies with a set of biallelic markers randomly generated within a
candidate region at a sufficient density (here about one biallelic
marker every 40 kb on average), allows the identification of at
least one marker associated with the trait.
[0413] In addition, these results correlate with the physical order
of the six biallelic markers contemplated within the present
example (see above): marker 99-365/344, which had been found to be
the closest in terms of physical distance to the ApoE Site A
marker, also shows the strongest LD with the Apo E site A
marker.
[0414] In order to further refine the relationship between physical
distance and linkage disequilibrium between biallelic markers, a
ca. 450 kb fragment from a genomic region on chromosome 8 was fully
sequenced.
[0415] LD within ca. 230 pairs of biallelic markers derived
therefrom was measured in a random French population and analyzed
as a function of the known physical inter-marker spacing. This
analysis confirmed that, on average, LD between 2 biallelic markers
correlates with the physical distance that separates them. It
further indicated that LD between 2 biallelic markers tends to
decrease when their spacing increases. More particularly, LD
between 2 biallelic markers tends to decrease when their
inter-marker distance is greater than 50 kb, and is further
decreased when the inter-marker distance is greater than 75 kb. It
was further observed that when 2 biallelic markers were further
than 150 kb apart, most often no significant LD between them could
be evidenced. It will be appreciated that the size and history of
the sample population used to measure LD between markers may
influence the distance beyond which LD tends not to be
detectable.
[0416] Assuming that LD can be measured between markers spanning
regions up to an average of 150 kb long, biallelic marker maps will
allow genome-wide LD mapping, provided they have an average
inter-marker distance lower than 150 kb.
EXAMPLE 11
Identification of a Candidate Region Harboring a Gene Associated
with a Detectable Trait
[0417] The initial identification of a candidate genomic region
harboring a gene associated with a detectable trait may be
conducted using a genome-wide map comprising about 20,000 biallelic
markers. The candidate genomic region may be further defined using
a map having a higher marker density, such as a map comprising
about 40,000 markers, about 60,000 markers, about 80,000 markers,
about 100,000 markers, or about 120,000 markers.
[0418] The use of high density maps such as those described above
allows the identification of genes which are truly associated with
detectable traits, since the coincidental associations will be
randomly distributed along the genome while the true associations
will map within one or more discrete genomic regions. Accordingly,
biallelic markers located in the vicinity of a gene associated with
a detectable trait will give rise to broad peaks in graphs plotting
the frequencies of the biallelic markers in T+ individuals versus
T- individuals. In contrast, biallelic markers which are not in the
vicinity of the gene associated with the detectable trait will
produce unique points in such a plot. By determining the
association of several markers within the region containing the
gene associated with the detectable trait, the gene associated with
the detectable trait can be identified using an association curve
which reflects the difference between the allele frequencies within
the T+ and T- populations for each studied marker. The gene
associated with the detectable trait will be found in the vicinity
of the marker showing the highest association with the trait.
[0419] FIGS. 4, 5, and 6 illustrate the above principles. As
illustrated in FIG. 4, an association analysis conducted with a map
comprising about 3,000 biallelic markers yields a group of points.
However, when an association analysis is performed using a denser
map which includes additional biallelic markers, the points become
broad peaks indicative of the location of a gene associated with a
detectable trait. For example, the biallelic markers used in the
initial association analysis may be obtained from a map comprising
about 20,000 biallelic markers, as illustrated in FIG. 5.
[0420] In the hypothetical example of FIG. 4, the association
analysis with 3,000 markers suggests peaks near markers 9 and
17.
[0421] Next, a second analysis is performed using additional
markers in the vicinity of markers 9 and 17, as illustrated in the
hypothetical example of FIG. 5, using a map of about 20,000
markers. This step again indicates an association in the close
vicinity of marker 17, since more markers in this region show an
association with the trait. However, none of the additional markers
around marker 9 shows a significant association with the trait,
which makes marker 9 a potential false positive. In order to
further test the validity of these two suspected associations, a
third analysis may be obtained with a map comprising about 60,000
biallelic markers. In the hypothetical example of FIG. 6, more
markers lying around marker 17 exhibit a high degree of association
with the detectable trait. Conversely, no association is confirmed
in the vicinity of marker 9. The genomic region surrounding marker
17 can thus be considered a candidate region for the hypothetical
trait of this simulation.
EXAMPLE 12
Haplotype Analysis: Identification of Biallelic Markers Delineating
a Genomic Region Associated with Alzheimer's Disease (AD)
[0422] As shown in Table 2 within Example 10, at an average map
density of one marker per 40 kb only one marker (99-365/344) out of
five random biallelic markers from a ca. 200 kb genomic region
around the Apo E gene showed a clear association to AD (delta
allelic frequency in cases and controls=18%; p value=6.9 E-10). The
allelic frequencies of the other four random markers were not
significantly different between AD cases and controls
(p-values.gtoreq.E-01). However, since linkage disequilibrium can
usually be detected between markers located further apart than an
average 40 kb as previously discussed, one should expect that,
performing an association study with a local excerpt of a biallelic
marker map covering ca. 200 kb with an average inter-marker
distance of ca. 40 kb should allow the identification of more than
one biallelic marker associated with AD.
[0423] A haplotype analysis was thus performed using the biallelic
markers 99-344/439; 99-355/219; 99-359/308; 99-365/344; and
99-366/274 (of SEQ ID Nos: 1-5 and 7-11).
[0424] In a first step, marker 99-365/344 that was already found
associated with AD was not included in the haplotype study. Only
biallelic markers 99-344/439; 99-355/219; 99-359/308 and
99-366/274, which did not show any significant association with AD
when taken individually, were used. This first haplotype analysis
measured frequencies of all possible two-, three-, or four-marker
haplotypes in the AD case and control populations. As shown in FIG.
7, there was one haplotype among all the potential different
haplotypes based on the four individually non-significant markers
("haplotype 8", TAGG comprising SEQ ID No. 2 which is the T allele
of marker 99-366/274, SEQ ID No. 1 which is the A allele of marker
99-344/439, SEQ ID No. 3 which is the G allele of marker 99-359/308
and SEQ ID No. 4 which is the G allele of marker 99-355/219), that
was present at statistically significant different frequencies in
the AD case and control populations (D=12%; p value=2.05 E-06).
Moreover, a significant difference was already observed for a
three-marker haplotype included in the above mentioned "haplotype
8" ("haplotype 7", TGG, D=10%; p value=4.76 E-05). Haplotype 7
comprises SEQ ID No. 2 which is the T allele of marker 99-366/274,
SEQ ID No. 3 which is the G allele of marker 99-359/308 and SEQ ID
No. 4 which is the G allele of marker 99-355/219). The haplotype
association analysis thus clearly increased the statistical power
of the individual marker association studies by more than four
orders of magnitude when compared to single-marker analysis (from p
values.gtoreq.E-01 for the individual markers--see Table 2--top
value.ltoreq.2 E-06 for the four-marker "haplotype 8").
[0425] The significance of the values obtained for this haplotype
association analysis was evaluated by the following computer
simulation. The genotype data from the AD cases and the unaffected
controls were pooled and randomly allocated to two groups which
contained the same number of individuals as the case/control groups
used to produce the data summarized in FIG. 7. A four-marker
haplotype analysis (99-344/439; 99-355/219; 99-359/308; and
99-366/274) was run on these artificial groups. This experiment was
reiterated 100 times and the results are shown in FIG. 8. No
haplotype among those generated was found for which the p-value of
the frequency difference between both populations was more
significant than 1 E-05. In addition, only 4% of the generated
haplotypes showed p-values lower than 1 E-04. Since both these
p-value thresholds are less significant than the 2 E-06 p-value
showed by "haplotype 8", this haplotype can be considered
significantly associated with AD.
[0426] In a second step, marker 99-365/344 was included in the
haplotype analyzes. The frequency differences between the affected
and non affected populations was calculated for all two-, three-,
four- or five-marker haplotypes involving markers: 99-344/439;
99-355/219; 99-359/308; 99-366/274; and 99-365/344. The most
significant p-values obtained in each category of haplotype
(involving two, three, four or five markers) were examined
depending on which markers were involved or not within the
haplotype. This showed that all haplotypes which included marker
99-365/344 showed a significant association with AD (p-values in
the range of E-04 to E-11).
[0427] An additional way of evaluating the significance of the
values obtained in the haplotype association analysis was to
perform a similar AD case-control study on biallelic markers
generated from BACs containing inserts corresponding to genomic
regions derived from chromosomes 13 or 21 and not known to be
involved in Alzheimer's disease. Performing similar haplotype and
individual association analyzes as those described above and in
Example 10 did not generate any significant association results
(all p-values for haplotype analyzes were less significant than
E-03; all p-values for single marker association studies were less
significant than E-O.sub.2).
[0428] In a preferred embodiment, the candidate genomic region may
be evaluated using the methods described in Examples 20-23 below to
determine whether it is likely to harbor a gene associated with
Alzheimer's Disease.
[0429] The results described in Examples 10 and 12, generated from
individual and haplotype studies using a biallelic marker set of an
average density equal to ca. 40 kb in the region of an Alzheimer's
disease trait causing gene, indicate that all biallelic markers of
sufficient informative content located within a ca. 200 kb genomic
region around a TCA can potentially be successfully used to
localize a trait causing gene with the methods provided by the
present invention. This conclusion is further supported by the
results obtained through measuring the linkage disequilibrium
between markers 99-365/344 or 99-359/308 and ApoE 4 Site A marker
within Alzheimer's patients: as one could predict since LD is the
supporting basis for association studies, LD between these pairs of
markers was enhanced in the diseased population vs. the control
population. In a similar way, the haplotype analysis enhanced the
significance of the corresponding association studies.
EXAMPLE 13
Genotyping of Biallelic Markers Using Microsequencing
Procedures
[0430] Several microsequencing protocols conducted in liquid phase
are well known to those skilled in the art. A first possible
detection analysis allowing the allele characterization of the
microsequencing reaction products relies on detecting fluorescent
ddNTP-extended microsequencing primers after gel electrophoresis. A
first alternative to this approach consists in performing a liquid
phase microsequencing reaction, the analysis of which may be
carried out in solid phase.
[0431] For example, the microsequencing reaction may be performed
using 5'-biotinylated oligonucleotide primers and
fluorescein-dideoxynucleotide- s. The biotinylated oligonucleotide
is annealed to the target nucleic acid sequence immediately
adjacent to the polymorphic nucleotide position of interest. It is
then specifically extended at its 3'-end following a PCR cycle,
wherein the labeled dideoxynucleotide analog complementary to the
polymorphic base is incorporated. The biotinylated primer is then
captured on a microtiter plate coated with streptavidin. The
analysis is thus entirely carried out in a microtiter plate format.
The incorporated ddNTP is detected by a fluorescein
antibody-alkaline phosphatase conjugate.
[0432] In practice this microsequencing analysis is performed as
follows. 201 .mu.l of the microsequencing reaction is added to 80
.mu.l of capture buffer (SSC 2.times., 2.5% PEG 8000, 0.25 M Tris
pH7.5, 1.8% BSA, 0.05% Tween 20) and incubated for 20 minutes on a
microtiter plate coated with streptavidin (Boehringer). The plate
is rinsed once with washing buffer (0.1 M Tris pH 7.5, 0.1 M NaCl,
0.1% Tween 20). 100 .mu.l of anti-fluorescein antibody conjugated
with phosphatase alkaline, diluted 1/5000 in washing buffer
containing 1.8% BSA is added to the microtiter plate. The antibody
is incubated on the microtiter plate for 20 minutes. After washing
the microtiter plate four times, 100 .mu.l of 4-methylumbelliferyl
phosphate (Sigma) diluted to 0.4 mg/ml in 0.1 M diethanolamine pH
9.6, 10 mM MgCl.sub.2 are added. The detection of the
microsequencing reaction is carried out on a fluorimeter (Dynatech)
after 20 minutes of incubation.
EXAMPLE 14
YAC Contig Construction in the Candidate Genomic Region
[0433] Substantial amounts of LOH data supported the hypothesis
that genes associated with distinct cancer types are located within
a particular region of the human genome. More specifically, this
region was likely to harbor a gene associated with prostate
cancer.
[0434] Association studies were performed as described below in
order to identify this prostate cancer gene. A YAC contig
containing the genomic region suspected of harboring a gene
associated with prostate cancer was constructed as follows.
[0435] First, a YAC contig which contains the candidate genomic
region was constructed as follows. The CEPH-Genethon YAC map for
the entire human genome (Chumakov et al. (1995), supra) was used
for detailed contig building in the genomic region containing
genetic markers known to map in the candidate genomic region.
Screening data available for several publicly available genetic
markers were used to select a set of CEPH YACs localized within the
candidate region. This set of YACs was tested by PCR with the above
mentioned genetic markers as well as with other publicly available
markers supposedly located within the candidate region. As a result
of these studies, a YAC STS contig map was generated around genetic
markers known to map in this genomic region. Two CEPH YACs were
found to constitute a minimal tiling path in this region, with an
estimated size of ca. 2 Megabases.
[0436] During this mapping effort, several publicly known STS
markers were precisely located within the contig.
[0437] Example 15 below describes the identification of sets of
biallelic markers within the candidate genomic region.
EXAMPLE 15
BAC Contig Construction and Biallelic Markers Isolation within the
Candidate Chromosomal Region
[0438] Next, a BAC contig covering the candidate genomic region
suspected of harboring a gene associated with prostate cancer was
constructed as follows. BAC libraries were obtained as described in
Woo et al., Nucleic Acids Res. 22: 4922-4931 (1994), the disclosure
of which is incorporated herein by reference. Briefly, the two
whole human genome BamHI and HindIII libraries already described in
Example 1 were constructed using the pBeloBAC11 vector (Kim et al.
(1996), supra).
[0439] The BAC libraries were then screened with all of the above
mentioned STSs, following the procedure described in Example 2
above.
[0440] The ordered BACs selected by STS screening and verified by
FISH, were assembled into contigs and new markers were generated by
partial sequencing of insert ends from some of them. These markers
were used to fill the gaps in the contig of BAC clones covering the
candidate chromosomal region having an estimated size of 2
megabases.
[0441] FIG. 9 illustrates a minimal array of overlapping clones
which was chosen for further studies, and the positions of the
publicly known STS markers along said contig.
[0442] Selected BAC clones from the contig were subcloned and
sequenced, essentially following the procedures described in
Examples 3 and 4.
[0443] Biallelic markers lying along the contig were identified
following the processes described in Examples 5 and 6.
[0444] FIG. 9 shows the locations of the biallelic markers along
the BAC contig. This first set of markers corresponds to a medium
density map of the candidate locus, with an inter-marker distance
averaging 50 kb-150 kb.
[0445] A second set of biallelic markers was then generated as
described above in order to provide a very high-density map of the
region identified using the first set of markers which can be used
to conduct association studies, as explained below. This very high
density map has markers spaced on average every 2-50 kb.
[0446] The biallelic markers were then used in association studies.
DNA samples were obtained from individuals suffering from prostate
cancer and unaffected individuals as described in Example 16.
EXAMPLE 16
Collection of DNA Samples from Affected and Non-Affected
Individuals
[0447] Prostate cancer patients were recruited according to
clinical inclusion criteria based on pathological or radical
prostatectomy records. Control cases included in this study were
both ethnically- and age-matched to the affected cases; they were
checked for both the absence of all clinical and biological
criteria defining the presence or the risk of prostate cancer, and
for the absence of related familial prostate cancer cases. Both
affected and control individuals were all unrelated.
[0448] The two following groups of independent individuals were
used in the association studies. The first group, comprising
individuals suffering from prostate cancer, contained 185
individuals. Of these 185 cases of prostate cancer, 47 cases were
sporadic and 138 cases were familial. The control group contained
104 non-diseased individuals.
[0449] Haplotype analysis was conducted using additional diseased
(total samples: 281) and control samples (total samples: 130), from
individuals recruited according to similar criteria.
[0450] DNA was extracted from peripheral venous blood of all
individuals as described in Example 5.
[0451] The frequencies of the biallelic markers in each population
were determined as described in Example 17.
EXAMPLE 17
Genotyping Affected and Control Individuals
[0452] Genotyping was performed using the following microsequencing
procedure. Amplification was performed on each DNA sample using
primers designed as previously explained. The pairs of primers were
used to generate amplicons harboring the biallelic markers 99-123,
4-26, 4-14, 4-77, 99-217, 4-67, 99-213, 99-221, 99-135, 99-1482,
4-73, and 4-65 using the protocols described in Example 6
above.
[0453] Microsequencing primers were designed for each of the
biallelic markers, as previously described. After purification of
the amplification products, the microsequencing reaction mixture
was prepared by adding, in a 2011 final volume: 10 pmol
microsequencing oligonucleotide, 1 U Thermosequenase (Amersham
E79000G), 1.25 .mu.l Thermosequenase buffer (260 mM Tris HCl pH
9.5, 65 mM MgCl.sub.2), and the two appropriate fluorescent ddNTPs
(Perkin Elmer, Dye Terminator Set 401095) complementary to the
nucleotides at the polymorphic site of each biallelic marker
tested, following the manufacturer's recommendations. After 4
minutes at 94.degree. C., 20 PCR cycles of 15 sec at 55.degree. C.,
5 sec at 72.degree. C., and 10 sec at 94.degree. C. were carried
out in a Tetrad PTC-225 thermocycler (MJ Research). The
unincorporated dye terminators were then removed by ethanol
precipitation. Samples were finally resuspended in formamide-EDTA
loading buffer and heated for 2 min at 95.degree. C. before being
loaded on a polyacrylamide sequencing gel. The data were collected
by an ABI PRISM 377 DNA sequencer and processed using the GENESCAN
software (Perkin Elmer).
[0454] Following gel analysis, data were automatically processed
with software that allows the determination of the alleles of
biallelic markers present in each amplified fragment.
[0455] The software evaluates such factors as whether the
intensities of the signals resulting from the above microsequencing
procedures are weak, normal, or saturated, or whether the signals
are ambiguous. In addition, the software identifies significant
peaks (according to shape and height criteria). Among the
significant peaks, peaks corresponding to the targeted site are
identified based on their position. When two significant peaks are
detected for the same position, each sample is categorized as
homozygous or heterozygous based on the height ratio.
[0456] Association analyzes were then performed using the biallelic
markers as described below.
EXAMPLE 18
Association Analysis
[0457] Association studies were run in two successive steps. In a
first step, a rough localization of the candidate gene was achieved
by determining the frequencies of the biallelic markers of FIG. 9
in the affected and unaffected populations. The results of this
rough localization are shown in FIG. 10. This analysis indicated
that a gene responsible for prostate cancer was located near the
biallelic marker designated 4-67.
[0458] In a second phase of the analysis, the position of the gene
responsible for prostate cancer was further refined using the very
high density set of markers including the 99-123, 4-26, 4-14, 4-77,
99-217, 4-67, 99-213, 99-221, 99-135, 99-1482, 4-73, and 4-65
markers.
[0459] As shown in FIG. 11, the second phase of the analysis
confirmed that the gene responsible for prostate cancer was near
the biallelic marker designated 4-67, most probably within a ca.
150 kb region comprising the marker.
[0460] A haplotype analysis was also performed as described in
Example 19.
EXAMPLE 19
Haplotype Analysis
[0461] The allelic frequencies of each of the alleles of biallelic
markers 99-123, 4-26, 4-14, 4-77, 99-217, 4-67, 99-213, 99-221, and
99-135 were determined in the affected and unaffected populations.
Table 3 lists the internal identification numbers of the markers
used in the haplotype analysis, the alleles of each marker, the
most frequent allele in both unaffected individuals and individuals
suffering from prostate cancer, the least frequent allele in both
unaffected individuals and individuals suffering from prostate
cancer, and the frequencies of the least frequent alleles in each
population.
6 TABLE 3 Frequency of least frequent allele** Markers Polymorphic
base* Cases Controls 99-123 C/T 0.35 0.3 4-26 A/G 0.39 0.45 4-14
C/T 0.35 0.41 4-77 C/G 0.33 0.24 99-217 C/T 0.31 0.23 4-67 C/T 0.26
0.16 99-213 T/C 0.45 0.38 99-221 C/A 0.43 0.43 99-135 A/G 0.25 0.3
*most frequent allele/least frequent allele **standard deviations -
0.023 to 0.031 for controls - 0.018 to 0.021 for cases
[0462] Among all the theoretical potential different haplotypes
based on 2 to 9 markers, 11 haplotypes showing a strong association
with prostate cancer were selected. The results of these haplotype
analyzes are shown in FIG. 12.
[0463] FIGS. 11, and 12 aggregate association analysis results with
sequencing results--generated following the procedures further
described in Example 21--which permitted the physical order and/or
the distance between markers to be estimated.
[0464] Thus, using the data of FIG. 13 and evaluating the
associations for single marker alleles or for haplotypes will
permit estimation of the risk a corresponding carrier has to
develop prostate cancer. It will be appreciated that significance
thresholds of relative risks will be more finely assessed according
to the population tested.
EXAMPLE 20
Construction of the Random Region Distribution and the Candidate
Region Distribution for a First Genomic Region Suspected of
Harboring a Gene Associated with Prostate Cancer
[0465] In a BAC insert suspected of harboring a gene associated
with prostate cancer 35 biallelic markers were identified over a
genomic region of 161 kb (i.e. an average intermarker distance of
approximately 4.5 kb). The 35 markers were divided into groups of 3
markers each and the frequencies of each of the eight possible
haplotypes of markers (a total of 6,545 combinations of 3 markers)
in the groups were estimated in individuals suffering from prostate
cancer and control individuals who did not have prostate cancer
using the Expectation-Maximization algorithm of Excoffier and
Slatkin. For each group of 3 markers, the frequency of each of the
eight possible haplotypes in individuals having prostate cancer and
control individuals were compared using a chi-squared analysis,
which measures the difference between the two frequencies weighted
by the sample sizes and haplotype frequencies. The chi squared
value for the haplotype having the greatest association with
prostate cancer was selected for inclusion in the candidate region
distribution. Every combination of 3 markers was used in the
analysis. Thus, there were 6,545 chi-squared values included in the
candidate region distribution.
[0466] The random region distribution was obtained as follows. A
total of thirty biallelic markers from BAC inserts were used to
generate the random region distribution. The number of markers per
BAC in the random BACs ranged from 3 to 9, with a median at 3. All
the markers fit the Hardy-Weinberg equilibrium.
[0467] For each BAC insert, the markers on that insert were divided
into groups of 3 markers. The frequencies of each of the eight
possible haplotypes of markers (a total of 240 combinations of 3
markers) in the groups were estimated in individuals suffering from
prostate cancer and control individuals who did not have prostate
cancer using the Expectation-Maximization algorithm of Excoffier L
and Slatkin. For each group of 3 markers, the frequency of each
haplotype in individuals having prostate cancer and control
individuals were compared using a chi-squared analysis and the chi
squared value for the haplotype having the greatest association
with prostate cancer was selected for inclusion in the random
region distribution. Every combination of 3 markers was used in the
analysis. Thus, there were 240 chi-squared values included in the
random region distribution.
[0468] Table 4 below shows the number of BACs, the number of
markers, the number of 3 marker combinations, and the sample sizes
used to construct the random region distribution and the candidate
region distribution.
7TABLE 4 description of markers and population used in the analysis
Sample size # of # of # of 3 mks- (# of Cases vs # of Region bacs
markers combinations controls) Bac 1 35 6545 [180; 350] vs [100;
130]* containing the Gene Random 30 116 240 [180; 350] vs [100;
130] bacs *for cases samples size varied from 180 to 350. For
controls sample sizes vary from 100 to 130.
[0469] FIG. 16A depicts the estimated cumulative distribution
function in the random BACs and the candidate BAC. FIG. 16B depicts
the corresponding estimated density functions in random and
candidate bacs (Saporta 1990, supra).
EXAMPLE 21
Comparison of the Random Region Distribution and the Candidate
Region Distribution for a First Genomic Region Suspected of
Harboring a Gene Associated with Prostate Cancer
[0470] The validity of the random region distribution was assessed
as follows. The group of markers being considered for inclusion in
the random region distribution were randomly split into two equal
halves. This led to two sets of random markers which will be
referred to as BAC(1) and BAC(2). The distributions obtained from
the markers in the candidate genomic region and from the entire set
of random markers (i.e. BAC(1)+BAC(2)) were compared with the
results indicated on the first line of Table 5 below. The
distributions obtained from the markers in the candidate genomic
region and the BAC(1) group of random markers were compared with
the results indicated on the second line of Table 5 below. The
distributions from the markers in the candidate genomic region and
the BAC(2) group of random markers were compared with the results
indicated on the third line of Table 5 below. The distributions
from the BAC(1) group of random markers and the BAC(2) group of
random markers were compared with the results indicated on fourth
line of Table 5 below. As shown in lines 1-3 of Table 5, the
distributions of the markers in the candidate genomic region and
the various groups of random markers were significantly different,
indicating that the candidate genomic region does in fact harbor a
gene associated with prostate cancer. In contrast, the
distributions of the markers in the BAC(1) and BAC(2) random
genomic regions were not significantly different, indicating that
these markers were in fact appropriate for inclusion in the random
region distribution.
8 TABLE 5 WILCOXON TEST SMIRNOV TEST TEST SR* SRE** IzI chi-S(Z)
1df Prob > z Dmax chi-S(Dmax) t Prob > t BAG vs GENE1 250055
814330 18.93 358 7.68E-80 0.56 2.75 8.45 <0.0001 BAC(1) vs GENE1
122423 2E+07 13.28 176 2.62E-40 0.54 3.53 5.91 <0.0001 BAC(2) vs
GENE1 113232 2E+07 13.72 188 8.69E-43 0.59 2.75 6.39 <0.0001
BAC(1) vs BAC(2) 14729 14460 0.5 0.25 6.17E-01 0.09 2.94 0.71
6.95E-01 *SR: Sum of the ranks of Chi-S values **SRE: Sum of the
ranks of Chi-S values expected if the candidate region did not
harbor a gene associated with trait
[0471] Using the Wilcoxon method outlined above, the sum of the
ranks of the chi-squared values was 250055. Under the null
hypothesis, the sum of the ranks of the chi-squared values would be
expected to be 814430. Accordingly, the observed z value was -19.
This z-value is associated with a p-value less than 10.sup.-4.
Thus, the candidate region distribution and the random region
distribution are significantly different. Accordingly, there is a
very high probability that the candidate genomic region harbors a
gene associated with prostate cancer.
[0472] A similar result was observed using the Kolmogorov-Smirnov
method. The Dmax obtained was 0.56 for a chi-square value of 2.75.
This result is again highly significant (probability less than
10.sup.-4).
[0473] The F.sub.1*(x) and the F.sub.2*(x) cumulative distribution
functions were calculated for the random region distribution and
the candidate region distribution as described above. The results
are shown in FIG. 16A. As shown in FIG. 16A, the candidate region
distribution was significantly different from the random region
distribution. As shown in FIG. 16A, the curve from the candidate
BAC is always inferior to the curve from the random BACs. This type
of difference is expected if a gene associated with the trait is
present in the candidate BAC such that the chi-squared values in
the candidate BAC are greater. On FIG. 16B, the curve for the trait
associated BAC is shifted to the right.
EXAMPLE 22
Construction of the Random Region Distribution and the Candidate
Region Distribution for a Second Genomic Region Suspected of
Harboring a Gene Associated with Prostate Cancer
[0474] An analysis similar to that performed in Examples 20 and 21
was performed for a second genomic region suspected of harboring a
gene associated with prostate cancer. However, in this case two
different groups of markers in the candidate genomic region were
used in the analyses. The first group included all the markers
available in the candidate region (Table 6, line 1). The second
group included only markers that were not in complete linkage
disequilibrium with one another. (Table 6, line 2)
9TABLE 6 # of 3 mks sample size REGION # of bacs # of markers
combinations (# of cases vs of controls) BAC containing the Gene 21
1 9 84 [90.250] vs [100.130] (all mks not in complete linkage
disequilibrium) BAC containing the Gene 22 1 26 2600 [90.250] vs
[100.130] (all mks in the Bac) Random BACs 30 116 240 [180.350] vs
[100.130]
[0475] FIG. 17A depicts the estimated cumulative distribution
function in the random BACs and the candidate BAC.
EXAMPLE 23
Comparison of the Random Region Distribution and the Candidate
Region Distribution for a Second Genomic Region Suspected of
Harboring a Gene Associated with Prostate Cancer
[0476] The following distributions were compared to one another.
The distribution obtained with all markers from the candidate
region (gene 2.1) was compared to the distribution from the random
genomic regions (Table 7, line 1). The same distribution from the
candidate region was compared with the distribution from a first
random half, BAC(1) of the markers from the random genomic regions
(Table 7, line 2). The same distribution of markers from candidate
region was compared with the distribution from a second random half
BAC(2) of the markers from the random genomic regions (Table 7,
line 3). Each of these approaches indicated that the candidate
genomic region harbored a gene associated with prostate cancer.
[0477] The distribution obtained from the second group of markers
(see Example 22) from the candidate region (gene 2.2) was compared
to the distribution from the random markers (Table 7, line 4). The
distribution obtained from the second group of markers from the
candidate region was compared to the distribution from a first
random half of markers (BAC(1)) from the random genomic regions
(Table 7, line 5). The distribution obtained from the second group
of markers from the candidate region was compared to the
distribution from a second random half of markers (BAC(2)) from the
random genomic regions (Table 7, line 6). All three approaches
indicated that the candidate genomic region is very likely to
harbor a gene associated with prostate cancer.
[0478] In contrast, the distributions of the markers in the BAC(1)
and BAC(2) random genomic regions were not significantly different,
indicating that these markers were in fact appropriate for
inclusion in the random distribution.
10 TABLE 7 WILCOXON TEST SMIRNOV TEST TEST SR* SRE** IzI chi-S(Z)
1df Prob > z Dmax chi-S(Dmax) t Prob > t BAC vs GENE2.1
260653 340920 6.6 43 5.47E-11 0.21 4.44 3.09 2.00E-04 BAC(1) vs
GENE2.1 125400 353700 4.5 20 7.74E-06 0.20 4.44 2.15 2.00E-04
BAC(2) vs GENE2.1 121053 4E+08 5.02 25 5.73E-07 0.21 4.41 2.34
1.00E-04 BAC vs GENE2.2 18667 39000 6.8 46 1.18E-11 0.41 4.44 3.23
1.00E-04 BAC(1) vs GENE2.2 81083 8610 5.96 35 3.30E-09 0.40 4.44
2.8 1.00E-04 BAC(2) vs GENE2.2 11154 8610 6.12 38 7.07E-10 0.20
4.41 2.93 1.00E-04 BAC(1) vs BAC(2) 14729 14460 0.5 0.25 6.17E-01
0.09 2.84 0.71 6.95E-01 *SR: Sum of the ranks of Chi-S values
**SRE: Sum of the ranks of Chi-S values expected if the candidate
region did not harbor a gene associated with trait
[0479] It is worth noting that the p-values obtained using the
second group of markers in the candidate region tend to be more
significant than the ones obtained using all markers in the
candidate region, which encompasses some markers which are strongly
linked to one another. It is also worth noting that these results
were obtained with 9 markers having an average intermarker spacing
of 40 kb. This is also shown in FIGS. 17A and 17B, which show a
greater difference between the distribution from markers in the
candidate region and the random region distribution when the
distribution of markers in the candidate region is generated using
only markers that are not in complete linkage disequilibrium.
[0480] FIG. 17B shows a comparison of these distributions.
[0481] Although this invention has been described in terms of
certain preferred embodiments, other embodiments which will be
apparent to those of ordinary skill in the art in view of the
disclosure herein are also within the scope of this invention.
Accordingly, the scope of the invention is intended to be defined
only by reference to the appended claims. All references cited in
this application are incorporated herein by reference in their
entirety.
Sequence Listing Free Text
[0482] The following free text appears in the accompanying sequence
listing:
[0483] microsequencing oligo
[0484] potential microsequencing oligo
[0485] polymorphic base
[0486] allele
[0487] upstream amplification primer
[0488] downstream amplification primer
[0489] extracted from sequence
Sequence CWU 1
1
30 1 47 DNA Homo sapiens allele 24 polymorphic base A 1 tgctgccaag
gatccatgtc agcatgctcc tctctgagcc ctggtct 47 2 47 DNA Homo sapiens
allele 24 polymorphic base T 2 agggcctggc ttcagggaca gcttaggaaa
tgtttgttga gttagtg 47 3 47 DNA Homo sapiens allele 24 polymorphic
base G 3 ctacagagtc atcgcctcca tccggtctca acaaatcctg gcagctc 47 4
47 DNA Homo sapiens allele 24 polymorphic base G 4 ggagtttcgg
ggagtttcgg gagggttcct gggaagaagc tcctccc 47 5 47 DNA Homo sapiens
allele 24 polymorphic base C 5 cctaccaagc aagcagcccc agcctagggt
cagacagggt gagcctc 47 6 47 DNA Homo sapiens allele 24 polymorphic
base C 6 tgggcgcgga catggaggac gtgcgcggcc gcctggtgca gtaccgc 47 7
47 DNA Homo sapiens allele 24 polymorphic base G, A in SEQID1 7
tgctgccaag gatccatgtc agcgtgctcc tctctgagcc ctggtct 47 8 47 DNA
Homo sapiens allele 24 polymorphic base C, T in SEQID2 8 agggcctggc
ttcagggaca gctcaggaaa tgtttgttga gttagtg 47 9 47 DNA Homo sapiens
allele 24 polymorphic base A, G in SEQID3 9 ctacagagtc atcgcctcca
tccagtctca acaaatcctg gcagctc 47 10 47 DNA Homo sapiens allele 24
polymorphic base A, G in SEQID4 10 ggagtttcgg ggagtttcgg gagagttcct
gggaagaagc tcctccc 47 11 47 DNA Homo sapiens allele 24 polymorphic
base T, C in SEQID5 11 cctaccaagc aagcagcccc agcttagggt cagacagggt
gagcctc 47 12 47 DNA Homo sapiens allele 24 polymorphic base T, C
in SEQID6 12 tgggcgcgga catggaggac gtgtgcggcc gcctggtgca gtaccgc 47
13 20 DNA Homo sapiens primer_bind 1..20 upstream amplification
primer for SEQID 1 and SEQID 7 13 gctctcatat tcattgggtg 20 14 18
DNA Homo sapiens primer_bind 1..18 upstream amplification primer
for SEQID 2 and SEQID 8 14 tctctcccgt gttaaatg 18 15 18 DNA Homo
sapiens primer_bind 1..18 upstream amplification primer for SEQID 3
and SEQID 9 15 aatcttcttg ctcctgtc 18 16 18 DNA Homo sapiens
primer_bind 1..18 upstream amplification primer for SEQID 4 and
SEQID 10 16 aggttagggg tgtatttc 18 17 18 DNA Homo sapiens
primer_bind 1..18 upstream amplification primer for SEQID 5 and
SEQID 11 17 agactgtgac cttagacc 18 18 18 DNA Homo sapiens
primer_bind 1..18 upstream amplification primer for SEQID 6 and
SEQID 12 18 gacgagacca tgaaggag 18 19 19 DNA Homo sapiens
primer_bind 1..19 downstream amplification primer for SEQID 1 and
SEQID 7 19 tggctgcggt tagatgctc 19 20 18 DNA Homo sapiens
primer_bind 1..18 downstream amplification primer for SEQID 2 and
SEQID 8 20 aggggtaact cttgattg 18 21 18 DNA Homo sapiens
primer_bind 1..18 downstream amplification primer for SEQID 3 and
SEQID 9 21 accaaggcat agcttctc 18 22 18 DNA Homo sapiens
primer_bind 1..18 downstream amplification primer for SEQID 4 and
SEQID 10 22 atacagccag ggagatag 18 23 18 DNA Homo sapiens
primer_bind 1..18 downstream amplification primer for SEQID 5 and
SEQID 11 23 aattgctacc cccaattc 18 24 18 DNA Homo sapiens
primer_bind 1..18 downstream amplification primer for SEQID 6 and
SEQID 12 24 tcgaaccagc tcttgagg 18 25 23 DNA Homo sapiens
misc_binding 1..23 potential microsequencing oligo 99-344.mis1 25
tgctgccaag gatccatgtc agc 23 26 19 DNA Homo sapiens misc_binding
1..19 microsequencing oligo 99-366.mis1 26 cctggcttca gggacagct 19
27 23 DNA Homo sapiens misc_binding 1..23 potential microsequencing
oligo 99-359.mis1 27 ctacagagtc atcgcctcca tcc 23 28 23 DNA Homo
sapiens misc_binding 1..23 potential microsequencing oligo
99-355.mis 28 ggagtttcgg ggagtttcgg gag 23 29 19 DNA Homo sapiens
misc_binding 1..19 microsequencing oligo 99-365.mis 29 ccaagcaagc
agccccagc 19 30 19 DNA Homo sapiens misc_binding 1..19
microsequencing oligo 99-2452.mis 30 cgcggacatg gaggacgtg 19
* * * * *