A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease

• Type of Material: Article
• Authors: Junyu Chen, Emory University; Lei Wang, Emory University; Philip L. De Jager, Columbia University Irving Medical Center; David A. Bennett, Rush Alzheimer’s Disease Center; Aron S. Buchman, Rush Alzheimer’s Disease Center; Jingjing Yang, Emory University
• Language: English
• Date: 2022-10-13
• Publisher: Elsevier
• Publication Version: Final Published Version
• Copyright Statement: © 2022 The Author(s)
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.xhgg.2022.100143
• Title of Journal or Parent Work: Human Genetics and Genomics Advances
• Volume: 3
• Issue: 4
• Start Page: 100143
• End Page: 100143
• Grant/Funding Information: J.Y. is supported by NIH/NIGMS grant R35GM138313 and NIH/NIA grant R21AG070659. ROS/MAP study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL. Data collection was supported through funding by NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152, and U01AG61356; the Illinois Department of Public Health; and the Translational Genomics Research Institute. The MCADGC, led by Dr. Nilüfer Ertekin-Taner and Dr. Steven G. Younkin, Mayo Clinic, Jacksonville, FL, uses samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer’s Disease Research Center, and the Mayo Clinic Brain Bank. MCADGC data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, and R01 AG003949; NINDS grant R01 NS080820; the CurePSP Foundation, and support from the Mayo Foundation.
• Keywords: Alzheimer disease; molecular quantitative trait loci; fine-mapping; quantitative functional annotation; genome-wide association study; polygenic risk score; Bayesian hierarchical variable selection regression
• Research Categories: Psychology, Cognitive; Biology, Neuroscience; Biology, Genetics

Abstract

Existing methods for integrating functional annotations in genome-wide association studies (GWASs) to fine-map and prioritize potential causal variants are 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 Alzheimer disease (AD)-related phenotypes using individual-level GWAS data (n = ∼1,000), we found that histone modification annotations have higher enrichment than expression quantitative trait locus (eQTL) annotations for all considered phenotypes, with the highest enrichment in H3K27me3 (polycomb regression). We also found that cis-eQTLs in microglia had higher enrichment than eQTLs of bulk brain frontal cortex tissue for all considered phenotypes. A similar enrichment pattern was also identified using the International Genomics of Alzheimer's Project (IGAP) summary-level GWAS data of AD (n = ∼54,000). 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 1,073 genome-wide significant variants in the IGAP data. We also demonstrated that the polygenic risk scores (PRSs) using effect size estimates by BFGWAS_QUANT had a similar prediction accuracy as other methods assuming a sparse causal model. Overall, BFGWAS_QUANT is a useful GWAS tool for quantifying annotation enrichment and prioritizing potential causal variants.