ULRIKE PETERS, FRED HUTCHINSON CANCER RESEARCH CENTER, UNIVERSITY OF WASHINGTON Fine-mapping of obesity GWAS loci using the Metabochip in PAGE (Population Architecture using Genetics and Epidemiology)

Design of Metabochip for anthropometric related traits  Anthropometric related MetaboChip content  Replication 13k SNPs for BMI, WHR, WC, height, % fat mass  Fine-mapping 41 regions, 26k SNPs

Current Study Population in PAGE and Collaborative Studies Studyn ARIC3.300 MEC3,900-5,300 WHI imputed6,300 WHI genotyped5,300 GenNet500 HyperGEN1,200 Total20,500-22,000

SUBMITTED TO PLOS GENETICS q12.2/FTO Strongest GWAS finding for obesity-related traits

16q12.2/FTO Association with BMI r 2 based on AAr 2 based on EA 1,529 SNPs across 650kb

Bioinformatic Characterization by Praveen Sethupathy, UNC  Candidate intronic regulatory elements: rs , rs , rs , rs , rs , and rs  Highly sequence-conserved elements among vertebrates: rs and rs  Predicted to have allele-specific binding affinities for different transcription factors: rs >Paired box protein 5 (PAX5) rs >Cut-like homeobox 1 (CUX1), previously implicated in the transcriptional regulation of FTO (Stratigopoulos, J Biol Chem 2011)

Definition of Significance Levels  Different alpha-levels for different aims: A.Fine-mapping regions: 1.Fine-mapping of GWAS index SNPs Adjust only for SNPs that are correlated with GWAS index SNP at r 2 >0.2, >0.5, >0.8 in population that identified GWAS index SNP (mostly EA or Asian) Accounting for correlation among SNPs, e.g. by permutation or estimate # of bins 2.Search for second independent signals Adjust for all other SNPs in the fine-mapping region (excluding those included in #1) while accounting for correlation B.Replication/generalization C.Pleiotropy– or analysis across the Metabochip

FTO region with correlation in EA In total 88 SNPs are correlated at r 2 >0.2 with 9 GWAS index SNPs in EA (all dotes that are red, yellow, green or light blue) GWAS hit 1 GWAS hit 2 GWAS hit 3

Example FTO Region SNPCAF% change in BMI per coding alleleNominal pAdjusted P Beta estimate95%CI Fine-mapping of GWAS index SNP (# of independent tests = 30) rs (0.51,1.74)2.4E-047.2E-03 rs (0.47,1.7)4.9E rs (0.5,1.73)2.8E-048.4E-03 rs (0.47,1.7)5.3E rs (0.49,1.72)3.0E-049.1E-03 Search for second independent signals (# of independent tests = 1,109) rs E rs E rs E

Based on ~21,000 subjects (ARIC, HyperGEN, GenNet, MEC, WHI)

Summary for primary signals region# SNPsSNPMAFEffectP.valueadjusted Pr 2 in AAr 2 in EA 11p31.1 Toprs E GWASrs rs p31.1 Toprs E GWASrs q25.286Top/GWASrs E E-07 42p25.3 Toprs E E GWASrs E rs rs E p12.1 Toprs E GWASrs q27.2 Top/GWASrs E GWASrs E p12 Toprs E E-05 * 49GWASrs E q13.3 Toprs E E GWASrs p12.3 Toprs E E GWASrs p21.1 Toprs E E GWASrs E p15.4 Toprs E E GWASrs

region# SNPsSNPMAFEffectP.valueadjusted Pr 2 in AAr 2 in EA 1211p14.1toprs E E GWASrs toprs E E-05 (r2<0.1 with rs925946) 110GWASrs E-03 ** rs E p11.2 Toprs E E GWASrs rs q13.12 Toprs E+00 63GWASrs q12 Toprs E+00 20GWASrs q23 Toprs E E GWASrs p12.3 Toprs E E GWASrs p11.2 Toprs E E GWASrs rs q21.32 Toprs E E-03 * 192GWASrs E rs E rs E rs q13.11 Toprs E E-02 26GWASrs rs q24 Toprs E-01 24GWASrs

11p14.1/BDNF,LIN7C,LGR4 Correlation based on EA with 2 different GWAS index SNPs

11p14.1/BDNF,LIN7C,LGR4 Correlation based on AA with one GWAS index SNP and most significant SNP in the region

r 2 with GWAS hits region # SNPsSNPMAFEffectP.valueAdjusted Pr 2 in EAr 2 in AA 11p rs E *< p rs E < q rs E p rs E < p rs E *< q rs E <0.01< p12240rs E E-03* q rs E p rs E * p rs E < p rs E * p rs E E-06<0.01< p rs E <0.02< q rs *< q12189rs E q23835rs E * p rs E E < p rs E <0.04< q rs E *< q rs <0.02< q24238rs E *<0.01 Summary for secondary signals

Decisions for Next Paper(s)  Study populations Focus on AA, AA and Asian or multiethnic panel? Data freeze  Outcome Two separate papers for BMI and WHR/WC  Metabochip content Focus on fine-mapping regions or entire Metabochip content Note, some of the most significant findings are outside of the BMI regions, but require more complex follow up  Overall timing We need to be fast to avoid being scooped by other groups

Study population for next papers StudynAvailabilityInclude in next papers African Americans ARIC3.300YesX MEC3,900- 5,300 YesX WHI imputed6,300YesX WHI genotyped5,300YesX GenNet500YesX HyperGEN1,200YesX CARDIA~500No CHS800 BioVU~10,000No Hispanic WHI5,500Not cleaned SOL12,000Genotyping ongoing Asian MEC3500Genotyping ongoing WHI3500Genotyping ongoing ThaiChi10,000Yes? CLHNS1,000Yes?

Within HDL region # 3 rs is most significant SNP 1.7 x Correlation between BMI and HDL ~ 0.2

GWAS hit in HDL region #3 is rs BMI HDL lnBMI ~ SNP + HDL + age*sex + PC1 + PC2 HDL ~ SNP + BMI + age*sex + PC1 + PC2 Note results based on 11,792 subjects with HDL and BMI data (~55% of all with BMI in Manhattan plot)!

Extra slides

Example FTO region: Fine-mapping of GWAS index SNPs  1,529 SNP genotyped across 640kb region  Correlation with 9 index SNPs in CEU (EA) 1000 Genome Project pilot: r 2 >0.2 = 88 SNPs on Metabochip (r 2 >0.5 = 72; r 2 >0.8 = 59 SNPs)  Permute random normal distributed phenotype and run analysis of all 97 (88+9) SNPs 10,000 times to compute the # of independent tests =>30 Nominal p-value * number of independent test = multi- comparison adjusted p-value (e.g. 2.4E-04*30=7.2E-03) OR Alpha of 0.05 /# of independent test = multi-comparison adjusted alpha level (e.g. 0.05/30 = 0.002)

 1,529 SNP genotyped across 640kb Exclude 97 SNPs included in fine-mapping of GWAS index SNPs (1, = 1,432) Repeat permutation for all SNPs in entire region => 1109 independent tests Example FTO region: Search for second independent signals There are 1,432 SNPs that are not correlated with GWAS index SNPs in EA (r 2 <0.2, dark blue dots) These result in 1,109 independent tests

Exploration if most significant BMI locus is independent from HDL lnBMI ~ SNP + HDL + age*sex + PC1 + PC2 HDL ~ SNP + BMI + age*sex + PC1 + PC2 BMI HDL Note results based on 11,792 subjects with HDL and BMI data (~55% of all with BMI in Manhattan plot)!

Same as slide before but not mutually adjusted for HDL and BMI lnBMI ~ SNP + age*sex + PC1 + PC2 HDL ~ SNP + age*sex + PC1 + PC2 BMI HDL Note results based on 11,792 subjects with HDL and BMI data (~55% of all with BMI in Manhattan plot)!