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Tools For Association Studies: Quantitative Trait Analysis
Mark J. Rieder, PhDMark J. Rieder, PhDDepartment of Genome SciencesDepartment of Genome Sciences
CWRU, April 11, 2008CWRU, April 11, 2008
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)
*
††
**
All patients 2C9 WT patients 2C9 VAR patientsAA AB BBAA AB BB AA AB BB
Univ. of Washingtonn = 185
All patients 2C9 WT patients 2C9 VAR patientsAA AB BBAA AB BB AA AB BB
†
†
*
†
*
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)
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)
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)
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