Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America.
Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, United States of America.
PLoS Genet. 2023 May 22;19(5):e1010693. doi: 10.1371/journal.pgen.1010693. eCollection 2023 May.
It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. We discover (in the UK Biobank) and replicate (in independent cohorts) several interaction associations, and find several hub genes with numerous interactions. We also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. Epistasis is may be widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets.
基因-基因相互作用在多大程度上影响复杂性状尚不清楚。在这里，我们引入了一种新的方法，使用预测的基因表达来对多个组织类型中表达的所有基因对之间的多个性状进行全转录组互作研究（TWISs）。通过推断的转录组，我们同时降低了计算挑战，提高了可解释性和统计功效。我们在 UK Biobank 中发现了（并在独立队列中复制了）一些互作关联，并找到了多个具有许多互作的枢纽基因。我们还证明 TWIS 可以识别新的关联基因，因为具有许多或强相互作用的基因的单基因模型效应大小较小。最后，我们开发了一种检验 TWIS 关联的基因集富集的方法（E-TWIS），发现了许多在互作关联中富集的途径和网络。上位性可能很普遍，我们的方法为开始探索基因相互作用和识别新的基因组靶标提供了一个可行的框架。
