Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution.
Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution.
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DOI：
10.1093/hmg/ddx280
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发表时间：
2017-09-15
影响因子：
3.5
通讯作者：
Morris AP
中科院分区：
文献类型：
--
作者：
Mägi R;Horikoshi M;Sofer T;Mahajan A;Kitajima H;Franceschini N;McCarthy MI;COGENT-Kidney Consortium, T2D-GENES Consortium;Morris AP
当潜在的因果变异在祖先群体之间共享时，不同人群的全基因组关联研究（GWAS）的跨种族荟萃分析可以提高检测复杂性状位点的能力。然而，这些位点的 GWAS 之间的等位基因效应可能会出现与祖先相关的异质性。在此，提出了一种新方法来检测 SNP 关联并量化与祖先相关的等位基因效应的异质性程度。我们采用跨种族元回归将等位基因效应建模为遗传变异轴的函数，该函数源自 GWAS 之间的平均成对等位基因频率差异矩阵，并在 MR-MEGA 软件中实现。通过详细的模拟，我们证明了在种族群体之间等位基因效应异质性的一系列场景中，与固定效应和随机效应荟萃分析相比，检测 MR-MEGA 关联的能力有所增强。与这些荟萃分析方法和 PAINTOR 相比，我们还证明了在包含单个因果变异的基因座中精细作图分辨率得到了提高，并且在降低计算成本的情况下具有与 MANTRA 相当的性能。当等位基因效应的异质性与血统相关时，将 MR-MEGA 应用到 71,461 名个体肾功能的跨种族 GWAS 中，表明比固定效应荟萃分析更强的关联信号。应用 MR-MEGA 对 22,086 个病例和 42,539 个对照中的四个 2 型糖尿病易感基因座进行精细定位，突出显示：(i) 等位基因效应异质性的有力证据，仅在 CDKAL1 基因座关联信号的索引 SNP 处与祖先相关； (ii) 5 个不同关联信号具有 6 个或更少变体的 99% 可信集。
Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals.
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DOI：
10.1038/nrg2760
发表时间：
2010-05
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
通讯作者：
--
影响因子：
30.8
作者：
DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium;Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium;South Asian Type 2 Diabetes (SAT2D) Consortium;Mexican American Type 2 Diabetes (MAT2D) Consortium;Type 2 Diabetes Genetic Exploration by Nex-generation sequencing in muylti-Ethnic Samples (T2D-GENES) Consortium;Mahajan A;Go MJ;Zhang W;Below JE;Gaulton KJ;Ferreira T;Horikoshi M;Johnson AD;Ng MC;Prokopenko I;Saleheen D;Wang X;Zeggini E;Abecasis GR;Adair LS;Almgren P;Atalay M;Aung T;Baldassarre D;Balkau B;Bao Y;Barnett AH;Barroso I;Basit A;Been LF;Beilby J;Bell GI;Benediktsson R;Bergman RN;Boehm BO;Boerwinkle E;Bonnycastle LL;Burtt N;Cai Q;Campbell H;Carey J;Cauchi S;Caulfield M;Chan JC;Chang LC;Chang TJ;Chang YC;Charpentier G;Chen CH;Chen H;Chen YT;Chia KS;Chidambaram M;Chines PS;Cho NH;Cho YM;Chuang LM;Collins FS;Cornelis MC;Couper DJ;Crenshaw AT;van Dam RM;Danesh J;Das D;de Faire U;Dedoussis G;Deloukas P;Dimas AS;Dina C;Doney AS;Donnelly PJ;Dorkhan M;van Duijn C;Dupuis J;Edkins S;Elliott P;Emilsson V;Erbel R;Eriksson JG;Escobedo J;Esko T;Eury E;Florez JC;Fontanillas P;Forouhi NG;Forsen T;Fox C;Fraser RM;Frayling TM;Froguel P;Frossard P;Gao Y;Gertow K;Gieger C;Gigante B;Grallert H;Grant GB;Grrop LC;Groves CJ;Grundberg E;Guiducci C;Hamsten A;Han BG;Hara K;Hassanali N;Hattersley AT;Hayward C;Hedman AK;Herder C;Hofman A;Holmen OL;Hovingh K;Hreidarsson AB;Hu C;Hu FB;Hui J;Humphries SE;Hunt SE;Hunter DJ;Hveem K;Hydrie ZI;Ikegami H;Illig T;Ingelsson E;Islam M;Isomaa B;Jackson AU;Jafar T;James A;Jia W;Jöckel KH;Jonsson A;Jowett JB;Kadowaki T;Kang HM;Kanoni S;Kao WH;Kathiresan S;Kato N;Katulanda P;Keinanen-Kiukaanniemi KM;Kelly AM;Khan H;Khaw KT;Khor CC;Kim HL;Kim S;Kim YJ;Kinnunen L;Klopp N;Kong A;Korpi-Hyövälti E;Kowlessur S;Kraft P;Kravic J;Kristensen MM;Krithika S;Kumar A;Kumate J;Kuusisto J;Kwak SH;Laakso M;Lagou V;Lakka TA;Langenberg C;Langford C;Lawrence R;Leander K;Lee JM;Lee NR;Li M;Li X;Li Y;Liang J;Liju S;Lim WY;Lind L;Lindgren CM;Lindholm E;Liu CT;Liu JJ;Lobbens S;Long J;Loos RJ;Lu W;Luan J;Lyssenko V;Ma RC;Maeda S;Mägi R;Männisto S;Matthews DR;Meigs JB;Melander O;Metspalu A;Meyer J;Mirza G;Mihailov E;Moebus S;Mohan V;Mohlke KL;Morris AD;Mühleisen TW;Müller-Nurasyid M;Musk B;Nakamura J;Nakashima E;Navarro P;Ng PK;Nica AC;Nilsson PM;Njølstad I;Nöthen MM;Ohnaka K;Ong TH;Owen KR;Palmer CN;Pankow JS;Park KS;Parkin M;Pechlivanis S;Pedersen NL;Peltonen L;Perry JR;Peters A;Pinidiyapathirage JM;Platou CG;Potter S;Price JF;Qi L;Radha V;Rallidis L;Rasheed A;Rathman W;Rauramaa R;Raychaudhuri S;Rayner NW;Rees SD;Rehnberg E;Ripatti S;Robertson N;Roden M;Rossin EJ;Rudan I;Rybin D;Saaristo TE;Salomaa V;Saltevo J;Samuel M;Sanghera DK;Saramies J;Scott J;Scott LJ;Scott RA;Segrè AV;Sehmi J;Sennblad B;Shah N;Shah S;Shera AS;Shu XO;Shuldiner AR;Sigurđsson G;Sijbrands E;Silveira A;Sim X;Sivapalaratnam S;Small KS;So WY;Stančáková A;Stefansson K;Steinbach G;Steinthorsdottir V;Stirrups K;Strawbridge RJ;Stringham HM;Sun Q;Suo C;Syvänen AC;Takayanagi R;Takeuchi F;Tay WT;Teslovich TM;Thorand B;Thorleifsson G;Thorsteinsdottir U;Tikkanen E;Trakalo J;Tremoli E;Trip MD;Tsai FJ;Tuomi T;Tuomilehto J;Uitterlinden AG;Valladares-Salgado A;Vedantam S;Veglia F;Voight BF;Wang C;Wareham NJ;Wennauer R;Wickremasinghe AR;Wilsgaard T;Wilson JF;Wiltshire S;Winckler W;Wong TY;Wood AR;Wu JY;Wu Y;Yamamoto K;Yamauchi T;Yang M;Yengo L;Yokota M;Young R;Zabaneh D;Zhang F;Zhang R;Zheng W;Zimmet PZ;Altshuler D;Bowden DW;Cho YS;Cox NJ;Cruz M;Hanis CL;Kooner J;Lee JY;Seielstad M;Teo YY;Boehnke M;Parra EJ;Chambers JC;Tai ES;McCarthy MI;Morris AP
通讯作者：
Morris AP
影响因子：
30.8
作者：
Onengut-Gumuscu, Suna;Chen, Wei-Min;Burren, Oliver;Cooper, Nick J.;Quinlan, Aaron R.;Mychaleckyj, Josyf C.;Farber, Emily;Bonnie, Jessica K.;Szpak, Michal;Schofield, Ellen;Achuthan, Premanand;Guo, Hui;Fortune, Mary D.;Stevens, Helen;Walker, Neil M.;Ward, Lucas D.;Kundaje, Anshul;Kellis, Manolis;Daly, Mark J.;Barrett, Jeffrey C.;Cooper, Jason D.;Deloukas, Panos;Todd, John A.;Wallace, Chris;Concannon, Patrick;Rich, Stephen S.
通讯作者：
Rich, Stephen S.
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
5.3
作者：
Fernandez-Rhodes, Lindsay;Gong, Jian;North, Kari E.
通讯作者：
North, Kari E.