全基因组关联研究
特质
遗传关联
背景(考古学)
优先次序
联想(心理学)
数量性状位点
生物
可解释性
幸运
计算生物学
多重比较问题
表达数量性状基因座
基因
统计能力
多效性
遗传学
维加维斯
遗传建筑学
心理学
汇总统计
关联测试
口译(哲学)
计算机科学
作者
Jeffrey P. Spence,Hakhamanesh Mostafavi,Mineto Ota,Nikhil Milind,Tamara Gjorgjieva,Courtney J. Smith,Yuval B. Simons,Guy Sella,Jonathan K. Pritchard
出处
期刊:Nature
[Nature Portfolio]
日期:2025-11-05
卷期号:649 (8098): 918-925
被引量:15
标识
DOI:10.1038/s41586-025-09703-7
摘要
Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes1. Although these methods are conceptually similar, by analysing association studies of 209 quantitative traits in the UK Biobank2-4, we show that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: (1) trait importance - how much a gene quantitatively affects a trait; and (2) trait specificity - the importance of a gene for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, whereas burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, whereas burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.
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