Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits

作者：Siming Zhao, Wesley L. Crouse, Sheng Qian, Kaixuan Luo, Matthew Stephens, Xin He · 发表于：Nature Genetics · 年份：2024 · DOI：10.1038/s41588-023-01648-9 · 被引用次数：83 · 研究领域：Genetic Associations and Epidemiology、Genetic and phenotypic traits in livestock、Genetic Mapping and Diversity in Plants and Animals

Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem-when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes' expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these 'genetic confounders', resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.