Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain
作者： Bernard Ng , William Casazza , Ellis Patrick , Shinya Tasaki , Gherman Novakovsky
DOI: 10.1016/J.AJHG.2019.07.016
关键词: Expression quantitative trait loci 、 Replicate 、 Genotype 、 Context specific 、 Transcriptome 、 Biology 、 Computational biology 、 Genome-wide association study 、 Hidden variable theory 、 Gene 、 Genetics(clinical) 、 Genetics
摘要: Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a large number factors high granularity might not always be feasible. Instead, here we propose extracting cellular embedding from gene expression data by using latent variable (LV) analysis taking these LVs as proxies detecting gene-by-environment (GxE) interaction on expression, i.e., GxE quantitative trait loci (eQTLs). Applying this approach to two largest brain eQTL datasets (n = 1,100), show that eQTLs one dataset replicate well other dataset. Combining samples via meta-analysis, 895 identified. On average, effect explains an additional ∼4% variation each displays effect. Ten 52 genes associated with cell-type-specific eQTLs, remaining multi-functional. Furthermore, after substituting transcription (TF), found 91 TF-specific demonstrates important our eQTLs.
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