Tools For Association Studies: Quantitative Trait Analysis Mark J. Rieder, PhD Department of Genome Sciences [email protected] CWRU, April 11, 2008

Tools For Association Studies: Quantitative Trait Analysis

Mark J. Rieder, PhDMark J. Rieder, PhDDepartment of Genome SciencesDepartment of Genome Sciences

[email protected]

CWRU, April 11, 2008CWRU, April 11, 2008

mailto:[email protected]

Cases Controls

40% T, 60% C 15% T, 85% C

Strategies for Genetic Analysis

Populations Association Studies

C/C C/T

C/C C/T C/C

C/C

C/TC/C C/C

C/T C/CC/TC/TC/C

Single Gene

Rare Variants

~600 Short Tandem Repeat Markers

FamiliesLinkage Studies

Simple Inheritance

Phenotype Measure

Fre

qu

enc

yContinuousContinuous

Multiple Genes

Common Variants

300,000 -1,000,000 SNPs

Complex Inheritance

Cases Controls

40% T, 60% C 15% T, 85% C

Approaches to Association Studies

Phenotype Measure

Fre

qu

enc

y

ContinuousContinuousQuantitativeQuantitative

Directed - Candidate Gene StudiesDirected - Candidate Gene StudiesResequencing SNP dataResequencing SNP data

Whole Genome Association Studies (WGAS) - tagSNPsWhole Genome Association Studies (WGAS) - tagSNPs

Candidate gene association studies

• Choose gene based on previous knowledge– Gene function– Biological pathway– Previous linkage or association study

• Choose DNA variations for genotyping• SNPs from HapMap, resequencing

- Direct association approach- Indirect association approach

Advantages– Detect common variation with small genetic contributions

Multiple independent genes can be involved

– Association (indirect) defines a relatively small region (based on linkage disequilibrium)

– Does not require a priori knowledge of what genes or regions are involved

Caveats–Typically, requires thousands of samples to find a significant association

–Statistical issues related to multiple testing (Bonferroni correction)

–Extremely large datasets are generated (e.g., 2000 samples X

500,000 loci or more than 1 billion genotypes)

–Analysis and replication strategies are important

The Hope The identified targets will lead to new biological and medical insights (hypothesis generating)

Whole Genome Association Studies (WGAS)

Strategies for Genetic Analysis (WGAS)

Regression Analysis

y = 1x1 + 2x2 + … + ixi +

UNIVARIATE:

dose = 1 genotype1

MULTIVARIATE

dose = 1 genotype1 + 2 age + 3 sex

Warfarin Pharmacogenetics

1. Background• Vitamin K cycle• Pharmacokinetics/Pharmacodynamics

2. Candidate Gene Approach - VKORC1 • SNP discovery, tagSNP selection

3. Clinical Association Study• VKORC1 and warfarin dose• SNP/Haplotype approach• Replication, Function

4. Whole Genome Association Study• New candidates for warfarin dosing?• Power, Significance, Replication

Warfarin Dosing - Background

• Commonly prescribed oral anti-coagulant and an inhibitor of the vitamin K cycle

• Prescribed following MI, atrial fibrillation, stroke, venous thrombosis, prosthetic heart valve replacement, and following major surgery

• Warfarin (Coumadin) >20 million US prescriptions (2007)

(++) Prevents 20 strokes for each bleeding event

(-) Major bleeding episodes in 1-2% of all patients

11% of all adverse events (Gurwitz et al. JAMA 2003)

(-) Difficult to determine effective dosage- Narrow therapeutic range (INR 2-3)- Large inter-individual variation

Goal: Use genetics to understand dose requirements --> fewer complications

0

10

20

30

40

50

0 2 4 6 8 10 12 14 16

Warfarin Dose (mg/d)

No. of patients

Ave: 5.2 mg/dn = 186European-American

Stabilized warfarin dose (3 consecutive visits within INR range)

UWMC warfarin patient population

Vitamin K-dependent clotting factorsVitamin K-dependent clotting factors(FII, FVII, FIX, FX, Protein C/S/Z)(FII, FVII, FIX, FX, Protein C/S/Z)

EpoxideReductase

-Carboxylase(GGCX)

Warfarin inhibits the vitamin K cycle

Warfarin

Inactivation

CYP2C9

Pharmacokinetic

Warfarin pharmaokinetics (metabolism) Warfarin pharmaokinetics (metabolism) • Major pathway for termination of pharmacologic effect is through

metabolism of S-warfarin in the liver by CYP2C9

• CYP2C9 SNPs alter warfarin metabolism:CYP2C9*1 (WT) - normalCYP2C9*2 (Arg144Cys) - intermediate metabolizerCYP2C9*3 (Ile359Leu) - poor metabolizer

McClain, Genet Med, 2008McClain, Genet Med, 2008

Vitamin K-dependent clotting factors(FII, FVII, FIX, FX, Protein C/S/Z)

EpoxideReductase

-Carboxylase(GGCX)

Warfarin acts as a vitamin K antagonist

Warfarin

Inactivation

CYP2C9

Pharmacodynamic

New Target Protein for Warfarin

EpoxideReductase

-Carboxylase(GGCX)

Clotting Factors(FII, FVII, FIX, FX, Protein C/S/Z)

Rost et al. & Li, et al., Nature (2004)

(VKORC1)

5 kb - Chromosome 16

Warfarin Resistance VKORC1 Polymorphisms

• Rare non-synonymous mutations in VKORC1 causative for warfarin resistance (15-35 mg/d)• NONO non-synonymous mutations found in ‘control’ chromosomes (n = ~400)

Rost, et. al. Nature (2004)

Warfarin maintenance dose (mg/day)

Inter-Individual Variability in Warfarin Dose: Genetic Liabilities

SENSITIVITYSENSITIVITY

CYP2C9 coding

SNPs - *3/*3

RESISTANCERESISTANCEVKORC1

nonsynonymous coding SNPs

0.5 5 15

Fre

qu

ency

Common Common VKORC1VKORC1

non-coding non-coding SNPs?SNPs?

SNP Discovery: Resequencing VKORC1

• PCR amplicons --> Resequencing of the complete genomic region

• 5 Kb upstream and each of the 3 exons and intronic segments; ~11 Kb

• Warfarin treated clinical patients (UWMC): 186 European

Rieder et al, NEJM 352, 2285-2293, 2005 Rieder et al, NEJM 352, 2285-2293, 2005

VKORC1 - PGA samples (European, n = 23)Total: 13 SNPs identified

10 common/3 rare (<5% MAF)

VKORC1 - Clinical Samples (European patients n = 186)Total: 28 SNPs identified 10 common/18 rare (<5% MAF)

1 - nonsynonymous - single heterozygous indiv. - highest warfarin dose = 15.5 mg/d

Do common SNPs associate with warfarin dose?Do common SNPs associate with warfarin dose?

Candidate Gene Association StudyCandidate Gene Association StudyDirect ApproachDirect Approach

SNP Discovery: Resequencing Results

SNP Selection: VKORC1 tagSNPs

Five Bins to Test1. 381, 3673, 6484, 6853, 75662. 2653, 60093. 8614. 58085. 9041

SNP Testing: VKORC1 tagSNPs

Regression Analysis

y = 1x1 + 2x2 + … + ixi +

Additive = 0,1,2 (e.g. AA = 0, AG = 1, GG = 2)

Recessive, Dominant = 0,1 (e.g. AA, AG = 0, GG = 1)

UNIVARIATE:

dose = 1 genotype1

MULTIVARIATE:

dose = 1 genotype1 + 2 age + 3 sex

Five Bins to TestFive Bins to Test1.1. 381, 3673, 6484, 6853, 7566381, 3673, 6484, 6853, 75662.2. 2653, 60092653, 60093.3. 8618614.4. 580858085.5. 90419041

Bin 1 - p < 0.001Bin 2 - p < 0.02 Bin 3 - p < 0.01 Bin 4 - p < 0.001 Bin 5 - p < 0.001

C/C C/T T/T

e.g. Bin 1 - SNP 381

SNP Testing: VKORC1 tagSNPs

r2 = 21%r2 = 3%r2 = 4.5%r2 = 12%r2 = 11%

MULTIVARIATE:MULTIVARIATE:adjusted for other confoundersadjusted for other confounderse.g., age, sex, medication, CYP2C9 e.g., age, sex, medication, CYP2C9

rr22 = variance explained in dose = variance explained in dose

CCGATCTCTG-H1 CCGAGCTCTG-H2

TAGGTCCGCA-H8 TACGTTCGCG-H9

(381, 3673, 6484, 6853, 7566) 5808

9041

861

B

A

VKORC1 haplotypes cluster into divergent clades

Patients can be assigned a clade diplotype:e.g. Patient 1 - H1/H2 = A/A

Patient 2 - H1/H7 = A/BPatient 3 - H7/H9 = B/B

Explore the evolutionary relationship across haplotypes

TCGGTCCGCA-H7

Multi-SNP testing: Haplotypes

VKORC1 haplotypes show a strong association with warfarin dose

Low

High

A/AA/BB/B

*

††

**

All patients 2C9 WT patients 2C9 VAR patientsAA AB BBAA AB BB AA AB BB

(n = 181) (n = 124) (n = 57)

*

††

**


Univ. of Washingtonn = 185


†

†

*

†

*

21% variance in dose explained

Washington Universityn = 386

Brian GageHoward McCleodCharles Eby

SNP Function: VKORC1 Expression

Expression in human liver tissue (n = 53) shows a graded change in expression.

GWAS approach for association studies

1. Establish baseline genetic diversity in a discovery population (HapMap) (e.g. find all common SNPs, >5% frequency)

2. Calculate correlation between SNPs to find informative SNPs (tagSNPs)

3. Genotype tagSNPs in populationUse commercially available whole genome chips (e.g. Illumina, Affy)QC genotype data

4. Perform statistical test for association

Association Results: Multivariate RegressionEstablish p-value cutoff (1E-7) - Bonferroni corrected ~p<0.05

5. Replicate in similar populations

Clinical Adoption of Dosing Algorithms

NHLBI Clinical Warfarin Trial:Randomized trial of prospective genotype-guided dosing Multi-center, double-blind randomized trial (n=2,000)

"standard of care" vs. clinical alg. vs. clinical + genetic alg.

Total warfarin variance (r2):

Outcomes:% time with INR range, time to stable dose

Design - WGA Warfarin Dose550 K Illumina (561,278 SNPs) 181 samples tested (Higashi, et al., Rieder, et al.) - (~100 million genotypes)

Call rates 99%100% concordance - VKORC1-3673 (rs9923231) with rs10871454 (LD)100% concordance - CYP2C9*3 (rs1057910)

Univariate - Individual SNP effects: ln(dose) = SNP

Additive = 0,1,2 (e.g. AA = 0, AG = 1, GG = 2) **

Recessive, Dominant = 0,1 (e.g. AA, AG = 0, GG = 1)

Multivariate - Full Model (Genetic + Clinical):ln(dose) = Age + Sex + Amiodarone + Losartan

+ CYP2C9 (*2 or *3) + VKORC1-3673

Warfarin Dose - Detection Power

0.012

0.18

0.56

0.86

0.97

0

0.2

0.4

0.6

0.8

1

1.2

0.05 0.10 0.15 0.20 0.25

Dose Variance (R2)

Power (n = 184)

Ave = 5.2 ± 2.5 mg/dAdditive effect, quantitative traitP = 1x10-7 (Bonferroni, p=0.05)

CYP2C9

VKORC1

Univariate Results - SNP Associations

Bonferroni correction (p = 0.05 corrected for 500,000 SNPs)

CYP2C9 *2/*3 = rs4917639 composite CYP2C9 allele (Wadelius, et al)

• • •

• • •

UW-GWA UW-GWA UF Replication VanderbiltSNP Rank Comment Location Ind. p-value Full Model (n=184) Ind./Full (n=300) Ind./Full (n=96)

1 VKORC1 LD chr16.30955580 5.56E-12 -- -- --2 VKORC1 LD chr16.31031908 8.19E-12 0.47 -- --3 VKORC1 LD chr16.31045213 9.11E-11 0.38 -- --4 VKORC1 LD chr16.31037443 1.61E-09 0.40 -- --5 VKORC1 LD chr16.30730548 3.56E-09 0.04 -- --6 VKORC1 LD chr16.31055049 3.08E-08 0.47 -- --7 FGFBP2/KSP37 chr4.15572771 7.79E-07 3.52E-04 0.13/0.26 0.54/0.938 VKORC1 LD chr16.30811180 1.38E-06 0.05 -- --9 VKORC1 LD chr16.30878242 1.59E-06 0.10 -- --

10 VKORC1 LD chr16.30888403 1.64E-06 0.07 -- --11 FGFBP2 chr4.15559829 3.57E-06 4.06E-04 0.20/0.35 0.86/0.6612 VKORC1 LD chr16.30836471 5.72E-06 0.12 -- --13 VKORC1 LD chr16.30757743 8.09E-06 0.03 -- --14 AK130802 chr1.199713096 9.80E-06 2.56E-04 0.26/0.045 0.80/0.4715 none chr16.1124968 1.03E-05 4.42E-07 0.26/0.03 NA

54 CYP2C9 *2/*3 chr10.96715525 8.03E-05 0.07 -- --

93 CYP2C9*3 chr10.96731043 1.81E-04 0.08 -- --

Warfarin Dose GWA Replication Results

UF Replication (J. Johnson)Vanderbilt Replication (D. Roden)


• • •

• • •




54 CYP2C9 *2/*3 chr10.96715525 8.03E-05 0.07 -- --

93 CYP2C9*3 chr10.96731043 1.81E-04 0.08 -- --




• • •

• • •




54 CYP2C9 *2/*3 chr10.96715525 8.03E-05 0.07 -- --

93 CYP2C9*3 chr10.96731043 1.81E-04 0.08 -- --



1x10-4 replication thresholdCorrect for 384 SNPs (p=0.04)

102

1014

1011

108

105

-log

(p-v

alue

)

chr1 chrX

large real effects (VKORC1)non-replicated SNPs

Range for large effect genes for Range for large effect genes for warfarin dosingwarfarin dosing

102

106

105

104

103

-log

(p-v

alue

)

chr1 chrX

smaller, validated effectCYP2C9

New Candidatessmall, moderate effects

replication, function

Range for new candidate genes with smaller effectRange for new candidate genes with smaller effect

VKORC1 Pharmacogenetics Association Summary

1. Candidate gene associations studies are used for direct hypothesis testing.

2. In the candidate gene association, VKORC1 SNPs are the major contributor to warfarin dose variability (21-25%).

2. VKORC1 SNPs replicate in independent patient populatios and show functional effects on gene expression.

3. Whole genome association studies (WGAS) have power to detect large effect size (20-25% variance) with limited sample size. Power is limited to detect small/moderate effects.

4. WGAS don’t reveal any SNPs that have a large effect on warfarin dose similar to VKORC1.

5. WGAS studies are best suited to studies with large samples sizes and able to detect smaller genetic effects.

Acknowledgments

Greg CooperGreg CooperAllan RettieAllan RettieAlex ReinerAlex ReinerDave VeenstraDave VeenstraDave BloughDave BloughKen ThummelKen ThummelDebbie NickersonDebbie Nickerson

Josh SmithJosh SmithMichelle WongMichelle WongEric JohansonEric Johanson

Washington UniversityBrian GageHoward McLeodCharles Eby

Replication StudiesJulie Johnson, UFDan Roden, VU

Documents

Tools For Association Studies: Quantitative Trait Analysis Mark J. Rieder, PhD Department of Genome Sciences [email protected] CWRU, April 11, 2008