GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.
GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.
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DOI：
10.1038/s41467-022-34932-z
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发表时间：
2022-11-23
影响因子：
16.6
通讯作者：
Ionita-Laza, Iuliana
中科院分区：
文献类型：
--
作者：
He, Zihuai;Liu, Linxi;Belloy, Michael E.;Le Guen, Yann;Sossin, Aaron;Liu, Xiaoxia;Qi, Xinran;Ma, Shiyang;Gyawali, Prashnna K.;Wyss-Coray, Tony;Tang, Hua;Sabatti, Chiara;Candes, Emmanuel;Greicius, Michael D.;Ionita-Laza, Iuliana
基因组测序和插补技术的最新进展为全面研究遗传变异对复杂表型的贡献提供了令人兴奋的机会。然而，我们将基因发现转化为机械见解的能力在这一点上仍然有限。在本文中，我们提出了一个有效的敲除为基础的方法，GhostKnockoff，全基因组关联研究（GWAS），导致提高电力和能力，优先考虑假定的因果变异相对于传统的GWAS方法。该方法只需要传统GWAS的Z分数，因此可以很容易地应用于增强现有和未来的研究。该方法还可以应用于多个GWAS的荟萃分析，允许任意样本重叠。我们使用实证模拟和两个应用程序来证明其性能：（1）阿尔茨海默病的荟萃分析，包括9个重叠的大规模GWAS，全外显子组和全基因组测序研究和（2）分析来自英国生物银行数据的1403个二元表型，408，961个欧洲血统样本。我们的研究结果表明，GhostKnockoff可以识别具有较弱统计效应的puppatient功能变体，而这些统计效应是传统关联测试所遗漏的。作者介绍了GhostKnockoff，这是一种全基因组关联研究的方法，可用于增强现有和未来的研究，以识别具有较弱统计效应的功能变体，这些功能变体可能会被传统的关联测试遗漏。
Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer’s disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests. The authors present GhostKnockoff, a method for genome-wide association studies which can be applied to enhance existing and future studies to identify functional variants with weaker statistical effects that might be missed by conventional association tests.
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影响因子：
8.8
作者：
Hosp, Fabian;Vossfeldt, Hannes;Heinig, Matthias;Vasiljevic, Djordje;Arumughan, Anup;Wyler, Emanuel;Landthaler, Markus;Hubner, Norbert;Wanker, Erich E.;Lannfelt, Lars;Ingelsson, Martin;Lalowski, Maciej;Voigt, Aaron;Selbach, Matthias
通讯作者：
Selbach, Matthias
影响因子：
64.5
作者：
Boyle EA;Li YI;Pritchard JK
通讯作者：
Pritchard JK
影响因子：
3.7
作者：
Gussow AB;Copeland BR;Dhindsa RS;Wang Q;Petrovski S;Majoros WH;Allen AS;Goldstein DB
通讯作者：
Goldstein DB
影响因子：
11
作者：
Bis JC;Jian X;Kunkle BW;Chen Y;Hamilton-Nelson KL;Bush WS;Salerno WJ;Lancour D;Ma Y;Renton AE;Marcora E;Farrell JJ;Zhao Y;Qu L;Ahmad S;Amin N;Amouyel P;Beecham GW;Below JE;Campion D;Cantwell L;Charbonnier C;Chung J;Crane PK;Cruchaga C;Cupples LA;Dartigues JF;Debette S;Deleuze JF;Fulton L;Gabriel SB;Genin E;Gibbs RA;Goate A;Grenier-Boley B;Gupta N;Haines JL;Havulinna AS;Helisalmi S;Hiltunen M;Howrigan DP;Ikram MA;Kaprio J;Konrad J;Kuzma A;Lander ES;Lathrop M;Lehtimäki T;Lin H;Mattila K;Mayeux R;Muzny DM;Nasser W;Neale B;Nho K;Nicolas G;Patel D;Pericak-Vance MA;Perola M;Psaty BM;Quenez O;Rajabli F;Redon R;Reitz C;Remes AM;Salomaa V;Sarnowski C;Schmidt H;Schmidt M;Schmidt R;Soininen H;Thornton TA;Tosto G;Tzourio C;van der Lee SJ;van Duijn CM;Valladares O;Vardarajan B;Wang LS;Wang W;Wijsman E;Wilson RK;Witten D;Worley KC;Zhang X;Alzheimer’s Disease Sequencing Project;Bellenguez C;Lambert JC;Kurki MI;Palotie A;Daly M;Boerwinkle E;Lunetta KL;Destefano AL;Dupuis J;Martin ER;Schellenberg GD;Seshadri S;Naj AC;Fornage M;Farrer LA
通讯作者：
Farrer LA
影响因子：
64.8
作者：
Karczewski, Konrad J;Francioli, Laurent C;MacArthur, Daniel G
通讯作者：
MacArthur, Daniel G