Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer's dementia

• Type of Material: Article
• Authors: Shizhen Tang, Emory University; Aron S. Buchman, Rush University; Philip L. De Jager, Columbia University; David A. Bennett, Rush University; Michael Epstein, Emory University; Jingjing Yang, Emory University
• Language: English
• Date: 2021-04-01
• Publisher: PUBLIC LIBRARY SCIENCE
• Publication Version: Final Published Version
• Copyright Statement: © 2021 Tang et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pgen.1009482
• Title of Journal or Parent Work: PLOS GENETICS
• Volume: 17
• Issue: 4
• Start Page: e1009482
• End Page: e1009482
• Grant/Funding Information: ST and JY are supported by National Institutes of Health (NIH/NIGMS) grant award R35GM138313. MPE was supported by NIH/NIGMS grant award R01GM117946 and NIH/NIA grant award RF1AG071170. Data collection was supported through funding by NIH/NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, R01AG56352, U01AG32984, U01AG46152, U01AG61356, the Illinois Department of Public Health, and the Translational Genomics Research Institute. These grants support the generation of the ROS/MAP data, which is led by ASB, PLDJ and DAB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: SUSCEPTIBILITY; RARE; Science & Technology; Genetics & Heredity; GENOME-WIDE ASSOCIATION; GWAS; METAANALYSIS; TRANSCRIPTOME; IDENTIFIES MULTIPLE; GENETIC PATHWAY; TRAITS; Life Sciences & Biomedicine; RUSH MEMORY
• Research Categories: Psychology, Cognitive; Biology, Neuroscience; Biology, Genetics

Abstract

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL’s estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer’s dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs; these findings also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.