全基因组关联研究
共域化
数量性状位点
多效性
生物
特质
遗传学
计算生物学
表达数量性状基因座
遗传关联
遗传建筑学
基因
进化生物学
表型
单核苷酸多态性
计算机科学
基因型
神经科学
程序设计语言
作者
Christopher N. Foley,James R Staley,Philip G. Breen,Benjamin B. Sun,Paul Kirk,Stephen Burgess,Joanna M. M. Howson
标识
DOI:10.1038/s41467-020-20885-8
摘要
Abstract Genome-wide association studies (GWAS) have identified thousands of genomic regions affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms involved. One approach is to use statistical colocalization to assess shared genetic aetiology across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose HyPrColoc (Hypothesis Prioritisation for multi-trait Colocalization), an efficient deterministic Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 s). We perform a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits, identifying 43 regions in which CHD colocalized with ≥1 trait, including 5 previously unknown CHD loci. Across the 43 loci, we further integrate gene and protein expression quantitative trait loci to identify candidate causal genes.
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