Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.
Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.
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DOI：
10.1038/s41588-018-0099-7
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发表时间：
2018-05
期刊：
影响因子：
30.8
通讯作者：
Do R
中科院分区：
文献类型：
--
作者：
Verbanck M;Chen CY;Neale B;Do R
在孟德尔随机化(MR)中，当变异对疾病有影响而不是对暴露的影响时，就会发生水平多效性。我们开发了孟德尔随机化平方差残差和异常值(MR-PRESSO)检验来识别多仪器汇总级MR测试中的水平多向异常值。我们用模拟表明，当水平多效性出现在50%的仪器中时，MR-PRESO最适合。接下来，我们将MR-PRESSO与其他几个MR测试一起应用于复杂的特征和疾病，发现水平多效性：(I)在MR中超过48%的显著因果关系中可以检测到；(Ii)在MR中引入的因果估计扭曲，平均从−131%到201%；(Iii)在高达10%的关系中诱导假阳性因果关系；以及(Iv)在一些但不是所有情况下可以纠正。
Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR. We developed the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that MR-PRESSO is best suited when horizontal pleiotropy occurs in <50% of instruments. Next, we applied MR-PRESSO, along with several other MR tests to complex traits and diseases, and found that horizontal pleiotropy: (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from −131% to 201%; (iii) induced false positive causal relationships in up to 10% of relationships; and (iv) can be corrected in some but not all instances.
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影响因子：
30.8
作者：
Liu C;Kraja AT;Smith JA;Brody JA;Franceschini N;Bis JC;Rice K;Morrison AC;Lu Y;Weiss S;Guo X;Palmas W;Martin LW;Chen YD;Surendran P;Drenos F;Cook JP;Auer PL;Chu AY;Giri A;Zhao W;Jakobsdottir J;Lin LA;Stafford JM;Amin N;Mei H;Yao J;Voorman A;CHD Exome+ Consortium;ExomeBP Consortium;GoT2DGenes Consortium;T2D-GENES Consortium;Larson MG;Grove ML;Smith AV;Hwang SJ;Chen H;Huan T;Kosova G;Stitziel NO;Kathiresan S;Samani N;Schunkert H;Deloukas P;Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia;Li M;Fuchsberger C;Pattaro C;Gorski M;CKDGen Consortium;Kooperberg C;Papanicolaou GJ;Rossouw JE;Faul JD;Kardia SL;Bouchard C;Raffel LJ;Uitterlinden AG;Franco OH;Vasan RS;O'Donnell CJ;Taylor KD;Liu K;Bottinger EP;Gottesman O;Daw EW;Giulianini F;Ganesh S;Salfati E;Harris TB;Launer LJ;Dörr M;Felix SB;Rettig R;Völzke H;Kim E;Lee WJ;Lee IT;Sheu WH;Tsosie KS;Edwards DR;Liu Y;Correa A;Weir DR;Völker U;Ridker PM;Boerwinkle E;Gudnason V;Reiner AP;van Duijn CM;Borecki IB;Edwards TL;Chakravarti A;Rotter JI;Psaty BM;Loos RJ;Fornage M;Ehret GB;Newton-Cheh C;Levy D;Chasman DI
通讯作者：
Chasman DI
影响因子：
2
作者：
Bowden J;Del Greco M F;Minelli C;Davey Smith G;Sheehan N;Thompson J
通讯作者：
Thompson J
影响因子：
7.7
作者：
Bowden J;Del Greco M F;Minelli C;Davey Smith G;Sheehan NA;Thompson JR
通讯作者：
Thompson JR
影响因子：
2.1
作者：
Bowden J;Davey Smith G;Haycock PC;Burgess S
通讯作者：
Burgess S
DOI：
10.1097/ede.0000000000000559
发表时间：
2017-01
期刊：
Epidemiology (Cambridge, Mass.)
影响因子：
--
作者：
Burgess S;Bowden J;Fall T;Ingelsson E;Thompson SG
通讯作者：
Thompson SG