Modal-based estimation via heterogeneity-penalized weighting: model averaging for consistent and efficient estimation in Mendelian randomization when a plurality of candidate instruments are valid.
Modal-based estimation via heterogeneity-penalized weighting: model averaging for consistent and efficient estimation in Mendelian randomization when a plurality of candidate instruments are valid.
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DOI：
10.1093/ije/dyy080
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发表时间：
2018-08-01
影响因子：
7.7
通讯作者：
Foley CN
中科院分区：
文献类型：
--
作者：
Burgess S;Zuber V;Gkatzionis A;Foley CN
孟德尔随机化的稳健方法不要求所有遗传变异都是有效工具，以给出因果参数的一致估计值。已经开发了几种这样的方法，包括基于模式的估计方法，如果多个遗传变体是有效工具，则该方法给出一致的估计;即，不存在比有效工具的子集更大的估计相同因果参数的无效工具的子集。我们在这里开发了一个模型平均的方法，在相同的“多个有效工具”的假设下，给出一致的估计。该方法考虑了来自每个遗传变异子集的估计值的混合分布。对估计值进行加权，使得具有更多遗传变体的子集获得更多权重，除非子集中的变体具有异质因果估计值，在这种情况下，该子集被严重向下加权。该混合分布的众数是因果估计。这种非均匀性惩罚模型平均方法与先前提出的基于模式的估计方法相比具有几个技术优势。在广泛的仿真分析中，异质性惩罚模型平均方法在效率方面优于基于模式的估计，并且在类型1错误率方面优于其他稳健方法。所提出的方法表明炎症影响冠心病风险的两种不同机制，变体子集表明积极和消极的因果关系。异质性惩罚模型平均法是孟德尔随机化的一种额外的稳健方法，具有优异的理论和实践性质，并且可以揭示数据中的特征，例如多个因果机制的存在。
A robust method for Mendelian randomization does not require all genetic variants to be valid instruments to give consistent estimates of a causal parameter. Several such methods have been developed, including a mode-based estimation method giving consistent estimates if a plurality of genetic variants are valid instruments; i.e. there is no larger subset of invalid instruments estimating the same causal parameter than the subset of valid instruments. We here develop a model-averaging method that gives consistent estimates under the same ‘plurality of valid instruments’ assumption. The method considers a mixture distribution of estimates derived from each subset of genetic variants. The estimates are weighted such that subsets with more genetic variants receive more weight, unless variants in the subset have heterogeneous causal estimates, in which case that subset is severely down-weighted. The mode of this mixture distribution is the causal estimate. This heterogeneity-penalized model-averaging method has several technical advantages over the previously proposed mode-based estimation method. The heterogeneity-penalized model-averaging method outperformed the mode-based estimation in terms of efficiency and outperformed other robust methods in terms of Type 1 error rate in an extensive simulation analysis. The proposed method suggests two distinct mechanisms by which inflammation affects coronary heart disease risk, with subsets of variants suggesting both positive and negative causal effects. The heterogeneity-penalized model-averaging method is an additional robust method for Mendelian randomization with excellent theoretical and practical properties, and can reveal features in the data such as the presence of multiple causal mechanisms.
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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：
37.8
作者：
Dehghan A;Dupuis J;Barbalic M;Bis JC;Eiriksdottir G;Lu C;Pellikka N;Wallaschofski H;Kettunen J;Henneman P;Baumert J;Strachan DP;Fuchsberger C;Vitart V;Wilson JF;Paré G;Naitza S;Rudock ME;Surakka I;de Geus EJ;Alizadeh BZ;Guralnik J;Shuldiner A;Tanaka T;Zee RY;Schnabel RB;Nambi V;Kavousi M;Ripatti S;Nauck M;Smith NL;Smith AV;Sundvall J;Scheet P;Liu Y;Ruokonen A;Rose LM;Larson MG;Hoogeveen RC;Freimer NB;Teumer A;Tracy RP;Launer LJ;Buring JE;Yamamoto JF;Folsom AR;Sijbrands EJ;Pankow J;Elliott P;Keaney JF;Sun W;Sarin AP;Fontes JD;Badola S;Astor BC;Hofman A;Pouta A;Werdan K;Greiser KH;Kuss O;Meyer zu Schwabedissen HE;Thiery J;Jamshidi Y;Nolte IM;Soranzo N;Spector TD;Völzke H;Parker AN;Aspelund T;Bates D;Young L;Tsui K;Siscovick DS;Guo X;Rotter JI;Uda M;Schlessinger D;Rudan I;Hicks AA;Penninx BW;Thorand B;Gieger C;Coresh J;Willemsen G;Harris TB;Uitterlinden AG;Järvelin MR;Rice K;Radke D;Salomaa V;Willems van Dijk K;Boerwinkle E;Vasan RS;Ferrucci L;Gibson QD;Bandinelli S;Snieder H;Boomsma DI;Xiao X;Campbell H;Hayward C;Pramstaller PP;van Duijn CM;Peltonen L;Psaty BM;Gudnason V;Ridker PM;Homuth G;Koenig W;Ballantyne CM;Witteman JC;Benjamin EJ;Perola M;Chasman DI
通讯作者：
Chasman DI
影响因子：
2.1
作者：
Bowden J;Davey Smith G;Haycock PC;Burgess S
通讯作者：
Burgess S
影响因子：
7.7
作者：
Burgess, Stephen;Thompson, Simon G.
通讯作者：
Thompson, Simon G.
对冠状动脉疾病进行基于 1,000 个基因组的全面全基因组关联荟萃分析。
影响因子：
30.8
作者：
Nikpay M;Goel A;Won HH;Hall LM;Willenborg C;Kanoni S;Saleheen D;Kyriakou T;Nelson CP;Hopewell JC;Webb TR;Zeng L;Dehghan A;Alver M;Armasu SM;Auro K;Bjonnes A;Chasman DI;Chen S;Ford I;Franceschini N;Gieger C;Grace C;Gustafsson S;Huang J;Hwang SJ;Kim YK;Kleber ME;Lau KW;Lu X;Lu Y;Lyytikäinen LP;Mihailov E;Morrison AC;Pervjakova N;Qu L;Rose LM;Salfati E;Saxena R;Scholz M;Smith AV;Tikkanen E;Uitterlinden A;Yang X;Zhang W;Zhao W;de Andrade M;de Vries PS;van Zuydam NR;Anand SS;Bertram L;Beutner F;Dedoussis G;Frossard P;Gauguier D;Goodall AH;Gottesman O;Haber M;Han BG;Huang J;Jalilzadeh S;Kessler T;König IR;Lannfelt L;Lieb W;Lind L;Lindgren CM;Lokki ML;Magnusson PK;Mallick NH;Mehra N;Meitinger T;Memon FU;Morris AP;Nieminen MS;Pedersen NL;Peters A;Rallidis LS;Rasheed A;Samuel M;Shah SH;Sinisalo J;Stirrups KE;Trompet S;Wang L;Zaman KS;Ardissino D;Boerwinkle E;Borecki IB;Bottinger EP;Buring JE;Chambers JC;Collins R;Cupples LA;Danesh J;Demuth I;Elosua R;Epstein SE;Esko T;Feitosa MF;Franco OH;Franzosi MG;Granger CB;Gu D;Gudnason V;Hall AS;Hamsten A;Harris TB;Hazen SL;Hengstenberg C;Hofman A;Ingelsson E;Iribarren C;Jukema JW;Karhunen PJ;Kim BJ;Kooner JS;Kullo IJ;Lehtimäki T;Loos RJF;Melander O;Metspalu A;März W;Palmer CN;Perola M;Quertermous T;Rader DJ;Ridker PM;Ripatti S;Roberts R;Salomaa V;Sanghera DK;Schwartz SM;Seedorf U;Stewart AF;Stott DJ;Thiery J;Zalloua PA;O'Donnell CJ;Reilly MP;Assimes TL;Thompson JR;Erdmann J;Clarke R;Watkins H;Kathiresan S;McPherson R;Deloukas P;Schunkert H;Samani NJ;Farrall M
通讯作者：
Farrall M