Scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications to studying Alzheimer's disease

Existing methods for integrating functional annotations in GWAS to fine-map and prioritize potential causal variants are either limited to using non-overlapped categorical annotations, or limited by the computation burden of modeling genome-wide variants. To overcome these limitations, we propose a scalable Bayesian functional GWAS method to account for multivariate quantitative functional annotations (BFGWAS_QUANT), accompanied by a scalable computation algorithm enabling joint modeling of genome-wide variants. Simulation studies validated the performance of BFGWAS_QUANT for accurately quantifying annotation enrichment and improving GWAS power. Applying BFGWAS_QUANT to study five Alzheimers disease (AD) related phenotypes using individual-level GWAS data (n=[~]1K), we found that histone modification annotations have higher enrichment than eQTL annotations for all considered phenotypes, with the highest enrichment in H3K27me3 (polycomb regression). We also found that cis-eQTL in microglia had higher enrichment than eQTL of bulk brain frontal cortex tissue for all considered phenotypes. A similar enrichment pattern was also identified using the IGAP summary-level GWAS data of AD (n=[~]54K). The strongest known APOE E4 risk allele was identified for all five phenotypes and the APOE locus was validated using the IGAP data. BFGWAS_QUANT fine-mapped 32 significant variants from 1073 genome-wide significant variants in the IGAP data. We further demonstrated that the polygenic risk scores (PRS) using effect size estimates by BFGWAS_QUANT had similar prediction accuracy as other methods assuming a sparse causal model. Overall, BFGWAS_QUANT provides a useful GWAS tool for quantifying annotation enrichment and prioritizing potential causal variants.