Yang Yihe, Lorincz-Comi Noah, Zhu Xiaofeng
Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Nat Commun. 2025 Jul 2;16(1):6098. doi: 10.1038/s41467-025-61423-8.
Transcriptome-wide association studies (TWAS) are commonly used to prioritize causal genes underlying associations found in genome-wide association studies (GWAS) and have been extended to identify causal genes through multivariate TWAS methods. However, recent studies have shown that widespread infinitesimal effects due to polygenicity can impair the performance of these methods. In this report, we introduce a multivariate TWAS method named tissue-gene pairs, direct causal variants, and infinitesimal effects selector (TGVIS) to identify tissue-specific causal genes and direct causal variants while accounting for infinitesimal effects. In simulations, TGVIS maintains an accurate prioritization of causal gene-tissue pairs and variants and demonstrates comparable or superior power to existing approaches, regardless of the presence of infinitesimal effects. In the real data analysis of GWAS summary data of 45 cardiometabolic traits and expression/splicing quantitative trait loci from 31 tissues, TGVIS is able to improve causal gene prioritization and identifies novel genes that were missed by conventional TWAS.
全转录组关联研究（TWAS）通常用于在全基因组关联研究（GWAS）中发现的关联背后确定因果基因，并已扩展到通过多变量TWAS方法识别因果基因。然而，最近的研究表明，由于多基因性导致的广泛微小效应会损害这些方法的性能。在本报告中，我们引入了一种名为组织-基因对、直接因果变异和微小效应选择器（TGVIS）的多变量TWAS方法，以识别组织特异性因果基因和直接因果变异，同时考虑微小效应。在模拟中，TGVIS保持了因果基因-组织对和变异的准确排序，并且无论是否存在微小效应，都表现出与现有方法相当或更优的效能。在对45种心脏代谢性状的GWAS汇总数据以及来自31个组织的表达/剪接定量性状位点的真实数据分析中，TGVIS能够改善因果基因的排序，并识别出传统TWAS遗漏的新基因。
