Multitrait transcriptome‐wide association study (TWAS) tests

Abstract

Multitrait tests can improve power to detect associations between individual single‐nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi‐SNP transcriptome‐wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes. We show that the correlation in TWAS test statistics for multiple phenotypes has the same form as multitrait statistics for the single‐SNP setting. Thus, established methods for combining single‐SNP test statistics across multiple traits can be extended directly to the TWAS setting. We performed an extensive evaluation across eight multitrait methods in simulations that varied gene‐phenotype effect sizes in addition to the underlying covariance structure among the phenotypes. We found that all multitrait TWAS tests have well‐calibrated Type I error (except ASSET, which can have a slightly elevated or depressed Type I error rate). Our results show that multitrait TWAS can improve statistical power compared with multiple single‐trait TWAS followed by Bonferroni correction. To illustrate our approach to real data, we conducted a multitrait TWAS of four circulating lipid traits from the Global Lipids Genetics Consortium. We found that our multitrait Wald TWAS approach identified 506 genes associated with lipid levels compared with 87 identified through Bonferroni‐corrected single‐trait TWAS. Overall, we find that our proposed multitrait TWAS framework outperforms single‐trait approaches to identify new genetic associations, especially for functionally correlated phenotypes and phenotypes with overlapping genome‐wide association studies samples, leading to insights into the genetic architecture of multiple phenotypes.

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

“…There are several limitations in our study. First, we focused on single-gene, single-trait analyses of splicing data, and there are exciting opportunities for methods development and gene discovery in multi-tissue, multi-trait, multi-gene, and cis and trans effects analyses [25,[75][76][77]. There are some important challenges for multi-tissue, multiple splicing events analysis.…”

Adjustment for the uncertainty of predicted expression in transcriptome-wide association study: a fusion of measurement error theory and bootstrapping

“…We adopt the standard approach to compute the trait-specific TWAS Z statistic for y j = β j x + ϵ j , for each j at a time, 1 ≤ j ≤ k , while adjusting for the uncertainty of predicted expression. The covariance matrix of the adjusted Z statistics across the traits remains the same as in the standard TWAS [13] because the trait-specific adjusted Z statistic is obtained by scaling the Z statistic obtained from the standard TWAS. To demonstrate our approach’s usefulness in multi-trait TWAS, we implement the subset-based meta-analysis method ASSET [6], which requires the vector of Z statistics and its standard error (unity vector for Z statistics) and correlation matrix as the input.…”

“…Finally, we compute the adjusted TWAS statistic combining the whole sample and bootstrap estimates of λ and the in-sample GWAS LD matrix. For multi-trait TWAS, we calculate both the standard and adjusted TWAS statistics for each trait and run ASSET using the GWAS trait correlation matrix as an estimate of the correlation matrix of Z statistics [13].…”

Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses

“…We calculated the number and percentage (success rate) of putative cancer related genes that overlapped with those extracted from the MGB database among the identified genes in this study. Previous TWAS or eQTL studies for breast cancer 19,28,32,38,48 , prostate cancer 31,38,[51][52][53] and lung cancer 38,53,54 reported genes related with these cancers. Genetic variants related with risk of breast cancer 49,50 , prostate cancer 97 and lung cancer 83,98 were reported in previous GWAS.…”