Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations.
Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations.
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遗传敏感性分析：调整流行病学关联中的遗传混杂。
DOI：
10.1371/journal.pgen.1009590
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发表时间：
2021-06
期刊：
影响因子：
4.5
通讯作者：
Dudbridge F
中科院分区：
文献类型：
--
作者：
Pingault JB;Rijsdijk F;Schoeler T;Choi SW;Selzam S;Krapohl E;O'Reilly PF;Dudbridge F
在流行病学研究中报告的暴露和结果之间的关联通常未校正遗传混杂。我们提出了一种两阶段方法来估计这种观察到的关联在多大程度上可以通过遗传混杂来解释。首先，我们评估衰减的暴露效应的回归控制越来越强大的多基因分数。其次，我们使用结构方程模型来估计遗传混杂使用遗传力估计来自基于SNP和基于双胞胎的研究。我们研究了母亲教育和三个发展成果之间的关联-儿童教育成就，身体质量指数和注意缺陷多动障碍。多基因得分解释了14.3%至23.0%的原始关联，而基于SNP和双胞胎遗传力的分析表明，观察到的关联几乎完全可以由遗传混杂解释。因此，在解释来自非遗传信息流行病学研究的关联时需要谨慎。我们的方法，类似于基因知情的敏感性分析，可以广泛应用。生命科学、行为科学和社会科学的一个共同目标是确定增加特定疾病或特征风险的因素。然而，确定真正的风险因素是具有挑战性的。通常，风险因素与疾病在统计上相关，即使它并不真正相关，这意味着即使成功地改善风险因素也不会影响疾病。存在这种误导性关联的一个原因是遗传混杂。这是当遗传因素直接影响风险因素和疾病时，即使在没有风险因素的真实影响的情况下，也会产生统计关联。在这里，我们提出了一种方法来估计遗传混杂和量化其对观察到的关联的影响。我们发现，很大一部分的母亲教育和三个孩子的教育成就，体重指数和注意力缺陷多动障碍之间的关联是由遗传混杂解释。我们的研究结果可以用于更好地理解遗传学在解释关键风险因素与疾病和特征之间的关联方面的作用。
Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes – child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely. An objective shared across the life, behavioural, and social sciences is to identify factors that increase risk for a particular disease or trait. However, identifying true risk factors is challenging. Often, a risk factor is statistically associated with a disease even if it is not really relevant, meaning that even successfully improving the risk factor will not impact the disease. One reason for the existence of such misleading associations stems from genetic confounding. This is when genetic factors influence directly both the risk factor and the disease, which generates a statistical association even in the absence of a true effect of the risk factor. Here, we propose a method to estimate genetic confounding and quantify its effect on observed associations. We show that a large part of the associations between maternal education and three child outcomes—educational achievement, body mass index and Attention-Deficit Hyperactivity Disorder—is explained by genetic confounding. Our findings can be applied to better understand the role of genetics in explaining associations of key risk factors with diseases and traits.
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基因发现和多基因预测，从基因组全基因组协会的教育程度研究中，有110万个人。
影响因子：
30.8
作者：
Lee JJ;Wedow R;Okbay A;Kong E;Maghzian O;Zacher M;Nguyen-Viet TA;Bowers P;Sidorenko J;Karlsson Linnér R;Fontana MA;Kundu T;Lee C;Li H;Li R;Royer R;Timshel PN;Walters RK;Willoughby EA;Yengo L;23andMe Research Team;COGENT (Cognitive Genomics Consortium);Social Science Genetic Association Consortium;Alver M;Bao Y;Clark DW;Day FR;Furlotte NA;Joshi PK;Kemper KE;Kleinman A;Langenberg C;Mägi R;Trampush JW;Verma SS;Wu Y;Lam M;Zhao JH;Zheng Z;Boardman JD;Campbell H;Freese J;Harris KM;Hayward C;Herd P;Kumari M;Lencz T;Luan J;Malhotra AK;Metspalu A;Milani L;Ong KK;Perry JRB;Porteous DJ;Ritchie MD;Smart MC;Smith BH;Tung JY;Wareham NJ;Wilson JF;Beauchamp JP;Conley DC;Esko T;Lehrer SF;Magnusson PKE;Oskarsson S;Pers TH;Robinson MR;Thom K;Watson C;Chabris CF;Meyer MN;Laibson DI;Yang J;Johannesson M;Koellinger PD;Turley P;Visscher PM;Benjamin DJ;Cesarini D
通讯作者：
Cesarini D
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.
DOI：
10.1093/bioinformatics/btu848
发表时间：
2015-05-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Euesden J;Lewis CM;O'Reilly PF
通讯作者：
O'Reilly PF
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
DOI：
10.1073/pnas.1408777111
发表时间：
2014-10-21
影响因子：
11.1
作者：
Krapohl, Eva;Rimfeld, Kaili;Plomin, Robert
通讯作者：
Plomin, Robert