Bayesian multivariate genetic analysis improves translational insights.
Bayesian multivariate genetic analysis improves translational insights.
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DOI：
10.1016/j.isci.2023.107854
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发表时间：
2023-10-20
期刊：
影响因子：
5.8
通讯作者：
Natarajan, Pradeep
中科院分区：
文献类型：
--
作者：
Urbut, Sarah M.;Koyama, Satoshi;Hornsby, Whitney;Bhukar, Rohan;Kheterpal, Sumeet;Truong, Buu;Selvaraj, Margaret S.;Neale, Benjamin;O'Donnell, Christopher J.;Peloso, Gina M.;Natarajan, Pradeep
虽然脂质性状是已知的心血管疾病的重要介质，但很少有方法利用其共同的遗传效应。我们应用贝叶斯多变量大小估计，捣碎，GWAS的四个脂质性状的百万退伍军人计划（MVP），并提供后验均值和局部的错误信号率的所有影响。这些估计借用了跨性状的信息，以提高效应量的准确性。我们表明，控制当地的假信号率准确和有力地识别可复制的遗传关联和多变量控制进一步解释复杂疾病的能力。我们的应用程序在独立数据集之间产生了高度一致性，更准确地优先考虑因果基因，并显着提高了脂质性状的多基因预测，超过了最先进的方法高达59%。贝叶斯多变量遗传收缩的使用尚未应用于人类数量性状GWAS的结果，我们提出了一个多基因规模上的分阶段预测方法。贝叶斯收缩工具mash提高了对血脂的基因组理解控制局部错误发现识别与血脂相关的非零效应将多变量方法与现有多基因评分相结合提高了预测能力提高了对血脂因果基因的注释和优先级的富集人类遗传学;生物计算方法;计算生物信息学;基因组分析;关联分析
While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale. Bayesian shrinkage tool, mash, improves genomic understanding of plasma lipids Controlling local false discovery identifies non-null effects associated with lipids Coupling a multivariate approach with existing polygenic scoring improves prediction Improved enrichment for annotations and prioritization of causal genes for lipids Human genetics; Biocomputational method; Computational bioinformatics; Genomic analysis; Association analysis
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
DOI：
10.1161/circoutcomes.118.005375
发表时间：
2019-06-01
影响因子：
6.9
作者：
Nowbar, Alexandra N.;Gitto, Mauro;Al-Lamee, Rasha
通讯作者：
Al-Lamee, Rasha
影响因子：
30.8
作者：
Bulik-Sullivan, Brendan K.;Loh, Po-Ru;Finucane, Hilary K.;Ripke, Stephan;Yang, Jian;Patterson, Nick;Daly, Mark J.;Price, Alkes L.;Neale, Benjamin M.
通讯作者：
Neale, Benjamin M.
影响因子：
30.8
作者：
Finucane HK;Bulik-Sullivan B;Gusev A;Trynka G;Reshef Y;Loh PR;Anttila V;Xu H;Zang C;Farh K;Ripke S;Day FR;ReproGen Consortium;Schizophrenia Working Group of the Psychiatric Genomics Consortium;RACI Consortium;Purcell S;Stahl E;Lindstrom S;Perry JR;Okada Y;Raychaudhuri S;Daly MJ;Patterson N;Neale BM;Price AL
通讯作者：
Price AL
影响因子：
64.5
作者：
Boyle EA;Li YI;Pritchard JK
通讯作者：
Pritchard JK