全基因组关联研究
遗传关联
可解释性
计算机科学
基因组学
生物信息学
计算生物学
转录组
基因组
生物
单核苷酸多态性
机器学习
基因型
遗传学
基因表达
基因
作者
Patrick Evans,Taylor Nagai,Anuar Konkashbaev,Dan Zhou,Ela W. Knapik,Eric R. Gamazon
摘要
Abstract Transcriptome‐wide association study (TWAS) methodologies aim to identify genetic effects on phenotypes through the mediation of gene transcription. In TWAS, in silico models of gene expression are trained as functions of genetic variants and then applied to genome‐wide association study (GWAS) data. This post‐GWAS analysis identifies gene‐trait associations with high interpretability, enabling follow‐up functional genomics studies and the development of genetics‐anchored resources. We provide an overview of commonly used TWAS approaches, their advantages and limitations, and some widely used applications. © 2024 Wiley Periodicals LLC.
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