Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.
Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.
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DOI：
10.1371/journal.pgen.1009575
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发表时间：
2021-06
期刊：
影响因子：
4.5
通讯作者：
Zhang NR
中科院分区：
文献类型：
--
作者：
Wang J;Zhao Q;Bowden J;Hemani G;Davey Smith G;Small DS;Zhang NR
经过十多年的全基因组关联研究，人们发现了复杂性状的极端多基因现象。“所有基因影响每一个复杂特征”的现象使孟德尔随机化(MR)研究复杂化，在MR研究中，自然遗传变异被用作推断可遗传风险因素的因果效应的工具。我们重新检查了现有MR方法的假设，并展示了它们如何被澄清以允许普遍的水平多效性和不同的效果大小。我们提出了一个综合的框架GRAPLE，用不同的遗传工具分析目标风险因素的因果效应，并从数据中识别可能的多效性模式。通过使用GWAS汇总统计，GRAPPLE可以有效地使用强弱遗传工具，检测多个多效性途径的存在，确定因果方向，并进行多变量MR以调整混杂的危险因素。利用GRAPPLE，我们分析了血脂、体重指数和收缩压对25种疾病结局的影响，获得了关于它们之间的因果关系和潜在的多效性途径的新信息。孟德尔随机化使用与可改变的风险因素相关的遗传变异，从观察性研究中获得关于其对疾病的因果影响的证据。然而，复杂性状的高度多基因性质，几乎所有基因都决定了每一个复杂性状，这对从这些遗传变异中进行因果推断的可靠性提出了挑战。在这篇文章中，我们对孟德尔随机化的合理假设进行了彻底的重新检验，并提出了一个框架GRAPPLE，通过使用强关联和弱关联SNP来获得权力，并从隐藏的风险因素中识别混杂的多效性途径。利用GRAPPLE软件，我们分析了血脂、体重指数和收缩压对25种疾病的影响，从而加深了对这些危险因素的理解。
Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that “all genes affect every complex trait” complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved. Mendelian randomization uses genetic variants related to a modifiable risk factor to obtain evidence regarding its causal influence on disease from observational studies. However, the highly polygenic nature of complex traits where almost all genes contribute to every complex trait challenges the reliability of the causal inference from these genetic variants. In this paper, we give a thorough reexamination of the assumptions that can be reasonably made for Mendelian randomization and propose a framework, GRAPPLE, to gain power by using both strongly and weakly associated SNPs and to identify confounding pleiotropic pathways from hidden risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 diseases, gaining an improved understanding of these risk factors.
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影响因子：
11
作者：
International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS)
通讯作者：
International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS)
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通讯作者：
Chasman DI
影响因子：
2.1
作者：
Bowden J;Davey Smith G;Haycock PC;Burgess S
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Burgess S
影响因子：
30.8
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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.