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Association Studies To Locate Human Disease Genes. Wentian Li, Ph.D The Robert S Boas Center for Genomics and Human Genetics North Shore LIJ Institute for Medical Research. March 08, 2005. GENE PHENOTYPE/DISEASE ENVIRONMENT. Linkage disequilibrium.

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Association studies to locate human disease genes l.jpg

Association Studies To Locate Human Disease Genes

Wentian Li, Ph.D

The Robert S Boas Center for Genomics and Human Genetics

North Shore LIJ Institute for Medical Research

March 08, 2005


Gene phenotype disease environment l.jpg

GENE PHENOTYPE/DISEASEENVIRONMENT


Genetic marker gene phenotype disease environment controlled fixed l.jpg

Linkage disequilibrium

GENETIC MARKERGENEPHENOTYPE/DISEASEENVIRONMENT (controlled, fixed)


Early history of association analysis 1921 l.jpg

Early history of association analysis (1921)

blood type (ABO) and disease association

JA Buchanan, ET Higley (1921) "The relationship of blood groups to disease", British Journal of Experimental Pathology 2:247-255.


Early history of association analysis 1945 l.jpg

Early history of association analysis (1945)

The suggestion to use ABO blood type/secretor polymorphism to detect association with diseases

EB Ford (1945), "Polymorphism", Biological Reviews, 20:73-88.


Early history of association analysis 1953 54 l.jpg

Early history of association analysis (1953-54)

Ian Aird, HH Bentall, JA Fraser-Roberts (1953), "A relationship between cancer of stomach and the ABO blood groups",British Medical Journal, 1:799-801.

I Aird, HH Bentall, JA Mehigan, JAF Roberts (1954), "The blood groups in relation to peptic ulceratiuon and carcinoma of the colon, rectum, breast and bronchus: an association between the ABO groups and peptic ulceration",British Medical Journal, 2:315-321.


Early history of association analysis 1960s l.jpg

Early history of association analysis (1960s)

  • Polymorphism in Human Leukocyte Antigen (HLA) system (also known as Major Histocompatibility (MHC)) and disease association

  • International Histocompatibility Workshop (first one in 1964)


Divergence between linkage and association analysis for human disease gene detection 1970s 1980s l.jpg

Divergence between linkage and association analysis for human disease gene detection (1970s-1980s?)

  • Both are based on the same principle that the genetic polymorphism (itself may not have function) and the disease gene (it has function) lie close to each other on the chromosome.

  • Only the techniques are different

  • Association (and linkage disequilibrium) became mainly a topic in population genetics (with the exception of HLA-disease association analysis)


Differences between linkage analysis and association analysis l.jpg

Differences between linkage analysis and association analysis

  • Linkage analysis is based on pedigree data

  • Association analysis is based on population data

  • Linkage analyses rely on recombination events “in action”

  • Association analyses rely on ancestral recombinations

  • The statistic is linkage analysis is to count the number of recombinants and non-recombinants

  • The statistical method for association analysis is “statistical correlation”


The domination of linkage analysis 1980s l.jpg

The domination of linkage analysis (1980s?)

  • The easy determination for restriction fragment length polymorphism (RFLP) made linkage analysis popular again

  • Linkage analysis helped to locate chromosomal regions for dozens of rare Mendelian diseases (in 1983, the first disease gene, for Huntington disease, was mapped )

  • Even easier for typing and denser genetic marker: microsatellite markers


Association analysis was brought back to disease mapping 1990s i family based association l.jpg

Association analysis was brought back to disease mapping (1990s). I. Family-based association

  • The most often criticized aspect of association analysis, its inability to deal with population stratification, was thought to be solved by the family-based design

    • Genotype-based haplotype relative risk (Falk and Rubinstein, 1987)

    • Haplotype-based haplotype relative risk (Terwilliger and Ott, 1992)

    • McNemar test (Terwilliger and Ott, 1992), Transmission disequilibrium test (TDT) (Spielman, McGinnis, Ewen, 1993)


Association analysis was brought back to disease mapping 1990s ii weaker signal in complex diseases l.jpg

Association analysis was brought back to disease mapping (1990s). II. Weaker signal in complex diseases

  • TDT is shown to be more “powerful” than the affected-sib identical-by-descent sharing method (a nonparametric linkage analysis) for complex diseases (diseases with lower genotypic relative risk)

    • N Risch, K Merikangas (1996), "The future of genetic studies of complex human diseases", Science, 273:1516-1517


Slide14 l.jpg

Unlikely to exist

Effect

Linkage analysis

Association studies

Very difficult

Frequency

Statistical genetic methods for

disease gene identification


Slide15 l.jpg

Association studies

  • Association between risk factor and disease: risk factor is significantly more frequent among affected than among unaffected individuals

  • In genetic epidemiology:

    • Risk factors = alleles/genotypes/haplotypes


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Association studies

  • Candidate genes (functional or positional)

  • Fine mapping in linkage regions

  • Genome wide screen


Slide17 l.jpg

Candidate gene analysis

  • Direct analysis:

    • Association studies between disease and functional SNPs (causative of disease) of candidate gene


Slide18 l.jpg

TagSNP

Candidate gene analysis

  • Indirect analysis:

    • Association studies between disease and “random” SNPs within or near candidate gene

    • Linkage Disequilibrium mapping


Slide19 l.jpg

YesNo

Casesn11n12n1.

Controls n21n22n2.

n.1n.2n..

Case-control studies: 2test

Risk factor

contingency table

Test of independence:

2=  (O-E)2 / E with 1 df


Slide20 l.jpg

Case-control studies: 2test

2x3 contingency table

Genotypes

AAAaaa

CasesnAAnAanaaN

Controls mAAmAamaaM

tAAtAataaN+M

Test of independence:

2=  (O-E)2 / E with 2 df


Slide21 l.jpg

Case-control studies: 2test

2x2 contingency table

Alleles

Aa

CasesnAna2N

Controls mAma2M

tAta2(N+M)

Test of independence:

2=  (O-E)2 / E with 1 df


Slide22 l.jpg

Hardy-Weinberg Equilibrium

Biallelic locus: A, a genotypes AA, Aa, aa

Allele frequencies: AP(A) = p

aP(a) = q

Genotype frequencies are in HWE if:

AAP(AA) = p2

AaP(Aa) = 2pq

aaP(aa) = q2


Slide23 l.jpg

1

3

2

1

6

HAPLOTYPES

1

5

9

1

7

4

9

1

6

2

9

1

7

1

2

2

7

1

2

6

1

4

7

1

1

8

1

8

1

4

1

0

1

0

Haplotypes

GENOTYPES

Locus 1

2

1

3

Locus 2

1

6

1

5

9

4

1

7

9

1

Identification of phase

6

2

9

1

7

2

1

2

1

2

7

6

1

4

1

7

1

8

1

8

4

1

Locus N

1

0

1

0


Statistical significance of a correlation versus correlation strength l.jpg

Statistical significance of a correlation versus correlation strength

  • Statistical significance is usually measured by “p-value”: the probability for observing the same amount of correlation or more if the true correlation is zero.

  • Correlation strength can be measured by many many quantities: D, D’, r2…

  • Correlation strength between a marker and the disease status is usually measured by odd-ratio (OR)

  • The 95% confidence interval (CI) of OR contains both information on “strength” and “significance”

  • When the sample size is increased, typically the p-value can become even more significant, whereas OR usually stays the same (but 95% CI of OR becomes more narrow).


Slide25 l.jpg

Graphic representation of LD

r2

D’

GOLD


Main issues in association analysis l.jpg

Main Issues in Association Analysis

  • The association is typically detected between a non-function marker and the disease, instead of the disease gene itself and the disease status. (“non-direct” role of the disease gene in association analysis)

  • When the disease (case) group and the normal (control) group both are a mixture of subpopulations with a different proportion of mixing, even markers not associated with the disease will exhibit spurious association (heterogeneity)


Slide27 l.jpg

Zondervan & Cardon, 2004


Solution to the first issue l.jpg

Solution to the first issue

  • Choose the marker, haplotype,… to have a matching (allele, haplotype,… ) frequency as the disease gene.

  • Whenever possible, typing a marker that is also functional (e.g. “coding SNP”, “functional SNP”, “regulatory SNP”)


Slide29 l.jpg

Association due to population stratification

Marchini et al, 2004


Slide30 l.jpg

Well-known problem when case/control groups consist of two different subpopulations with different mixing proportion

  • Example: comparing people’s height between two places: 1. prison, and 2. nurse school

  • In prison, maybe 80% are men

  • In nursing school, maybe 80% are women

  • Men are on average taller than women

  • People in prison are taller than people in nurse school

    But the cause of this difference is due to the different mixing proportions, not due to “staying in prison makes people taller”


Solution to the second issue l.jpg

Solution to the second issue

  • Try to use people from the same population in both case and control group.

  • Use neutral marker to test whether subpopulations exist

  • If possible use an isolated population (the extra benefit is to reduce the heterogeneity in the case group)

  • Use family-based association design (the disadvantage is that it is more costly, and parents of late-onset patients are hard to find)


Lee et al gene and immunity 2005 l.jpg

Lee et al. Gene and Immunity (2005)


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dis.e.qui.lib.ri.um, n. Loss or lack of stability or equilibrium

link.age, n. (genetics) An association between two or more genes such that the traits they control tend to be inherited together.

as.so.ci.a.tion, n. 1. The act of associating or the state of being associated.

cor.re.la.tion, n. (statistics) the simultaneous change in value of two numerically valued random variables:

ASSOCITION IS THE LEAST RIGOROUSLY DEFINED WORD!


Criswell et al am j hum genetics 2005 l.jpg

Criswell et al. Am J Hum Genetics (2005)


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