Assessing efficiency of fine-mapping obesity associated variants through leveraging ancestry architecture and functional annotation using PAGE and UKBB Cohorts

Abstract

Abstract Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In 10 of the investigated regions with genome wide significant associations for obesity related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Results also suggested three novel candidates for functional effect on waist-to-hip ratio adjusted for BMI (WHRBMI-adj) (rs5781117 near gene RP11-392O17.1, rs10187501 in gene COBLL1, and rs1964599 near gene CCDC92), all within the 99% CS. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggest generalizability of genetic mechanisms underpinning obesity related traits across populations.