Wu Yang, Zheng Zhili, Thibaut Loic, Goddard Michael E, Wray Naomi R, Visscher Peter M, Zeng Jian
Institute of Rare Diseases, West China Hospital of Sichuan University, Chengdu, China.
Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
Res Sq. 2024 Aug 7:rs.3.rs-4759390. doi: 10.21203/rs.3.rs-4759390/v1.
Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn's disease. Overall, we analyse 600 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.
精细定位可优化基因型-表型关联信号，以识别复杂性状背后的因果变异。然而，当前方法通常聚焦于单个基因组片段，而未考虑整体遗传结构。在此，我们展示了进行全基因组精细定位（GWFM）的优势，并开发了促进GWFM的方法。在模拟和实际数据分析中，GWFM在错误控制、定位能力和精度、复制率以及跨祖先表型预测方面均优于当前方法。对于英国生物银行中48个统计效力充足的性状，我们识别出的因果变异共同解释了基于单核苷酸多态性（SNP）的遗传力的17%，并预测对其中50%进行精细定位平均需要200万个样本。作为原理验证，我们在FTO基因座针对体重指数精确找到了一个已知的因果变异，揭示了一个具有进化保守性的隐藏次要变异，并识别出精神分裂症和克罗恩病的新错义因果变异。总体而言，我们分析了具有1300万个SNP的600个复杂性状，突出了GWFM结合功能注释的有效性。
