PolarMorphism enables discovery of shared genetic variants across multiple traits from GWAS summary statistics.
PolarMorphism enables discovery of shared genetic variants across multiple traits from GWAS summary statistics.
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DOI：
10.1093/bioinformatics/btac228
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发表时间：
2022-06-24
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多效性SNP与多个性状相关。这样的SNP可以帮助确定对多个性状有影响的生物过程，或者指出性状之间的共同病因。我们提出了PolarMorphism，一种新的方法，用于从全基因组关联研究（GWAS）汇总统计中识别多效性SNP。PolarMorphism可以很容易地应用于两个以上的性状或整个性状域。PolarMorphism利用了这样一个事实，即性状特异性SNP效应大小可以被视为笛卡尔坐标，因此可以转换为极坐标r（距原点的距离）和θ（在两个性状的情况下，与笛卡尔x轴的角度）。r描述了SNP的总体效果，而theta描述了SNP共享的程度。r和θ用于确定SNP共享性的显著性，从而产生可用于进一步分析的每个SNP的P值。我们将PolarMorphism应用于大量公开的GWAS汇总统计数据，从而构建了一个多效性网络，该网络显示了性状共享SNP的程度。我们展示了如何PolarMorphism可以用来深入了解性状和性状域之间的关系，并将其与遗传相关性进行对比。此外，新发现的多效性SNPs的通路分析表明，分析两个以上的性状同时产生更多的生物相关的结果比相同性状的成对分析的组合结果。最后，我们证明了PolarMorphism比以前发表的方法更有效，更强大。代码：https：//github.com/UMCUGenetics/PolarMorphism，结果：10.5281/zenodo.5844193。 补充数据可在Bioinformatics在线获得。
Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from genome-wide association studies (GWAS) summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis, in the case of two traits). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a P-value per SNP that can be used for further analysis. We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. We show how PolarMorphism can be used to gain insight into relationships between traits and trait domains and contrast it with genetic correlation. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods. code: https://github.com/UMCUGenetics/PolarMorphism, results: 10.5281/zenodo.5844193. Supplementary data are available at Bioinformatics online.
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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.
影响因子：
9.8
作者：
Maier R;Moser G;Chen GB;Ripke S;Cross-Disorder Working Group of the Psychiatric Genomics Consortium;Coryell W;Potash JB;Scheftner WA;Shi J;Weissman MM;Hultman CM;Landén M;Levinson DF;Kendler KS;Smoller JW;Wray NR;Lee SH
通讯作者：
Lee SH
影响因子：
30.8
作者：
Malik R;Chauhan G;Traylor M;Sargurupremraj M;Okada Y;Mishra A;Rutten-Jacobs L;Giese AK;van der Laan SW;Gretarsdottir S;Anderson CD;Chong M;Adams HHH;Ago T;Almgren P;Amouyel P;Ay H;Bartz TM;Benavente OR;Bevan S;Boncoraglio GB;Brown RD Jr;Butterworth AS;Carrera C;Carty CL;Chasman DI;Chen WM;Cole JW;Correa A;Cotlarciuc I;Cruchaga C;Danesh J;de Bakker PIW;DeStefano AL;den Hoed M;Duan Q;Engelter ST;Falcone GJ;Gottesman RF;Grewal RP;Gudnason V;Gustafsson S;Haessler J;Harris TB;Hassan A;Havulinna AS;Heckbert SR;Holliday EG;Howard G;Hsu FC;Hyacinth HI;Ikram MA;Ingelsson E;Irvin MR;Jian X;Jiménez-Conde J;Johnson JA;Jukema JW;Kanai M;Keene KL;Kissela BM;Kleindorfer DO;Kooperberg C;Kubo M;Lange LA;Langefeld CD;Langenberg C;Launer LJ;Lee JM;Lemmens R;Leys D;Lewis CM;Lin WY;Lindgren AG;Lorentzen E;Magnusson PK;Maguire J;Manichaikul A;McArdle PF;Meschia JF;Mitchell BD;Mosley TH;Nalls MA;Ninomiya T;O'Donnell MJ;Psaty BM;Pulit SL;Rannikmäe K;Reiner AP;Rexrode KM;Rice K;Rich SS;Ridker PM;Rost NS;Rothwell PM;Rotter JI;Rundek T;Sacco RL;Sakaue S;Sale MM;Salomaa V;Sapkota BR;Schmidt R;Schmidt CO;Schminke U;Sharma P;Slowik A;Sudlow CLM;Tanislav C;Tatlisumak T;Taylor KD;Thijs VNS;Thorleifsson G;Thorsteinsdottir U;Tiedt S;Trompet S;Tzourio C;van Duijn CM;Walters M;Wareham NJ;Wassertheil-Smoller S;Wilson JG;Wiggins KL;Yang Q;Yusuf S;AFGen Consortium;Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium;International Genomics of Blood Pressure (iGEN-BP) Consortium;INVENT Consortium;STARNET;Bis JC;Pastinen T;Ruusalepp A;Schadt EE;Koplev S;Björkegren JLM;Codoni V;Civelek M;Smith NL;Trégouët DA;Christophersen IE;Roselli C;Lubitz SA;Ellinor PT;Tai ES;Kooner JS;Kato N;He J;van der Harst P;Elliott P;Chambers JC;Takeuchi F;Johnson AD;BioBank Japan Cooperative Hospital Group;COMPASS Consortium;EPIC-CVD Consortium;EPIC-InterAct Consortium;International Stroke Genetics Consortium (ISGC);METASTROKE Consortium;Neurology Working Group of the CHARGE Consortium;NINDS Stroke Genetics Network (SiGN);UK Young Lacunar DNA Study;MEGASTROKE Consortium;Sanghera DK;Melander O;Jern C;Strbian D;Fernandez-Cadenas I;Longstreth WT Jr;Rolfs A;Hata J;Woo D;Rosand J;Pare G;Hopewell JC;Saleheen D;Stefansson K;Worrall BB;Kittner SJ;Seshadri S;Fornage M;Markus HS;Howson JMM;Kamatani Y;Debette S;Dichgans M
通讯作者：
Dichgans M
影响因子：
30.8
作者：
Loh, Po-Ru;Tucker, George;Bulik-Sullivan, Brendan K.;Vilhjalmsson, Bjarni J.;Finucane, Hilary K.;Salem, Rany M.;Chasman, Daniel I.;Ridker, Paul M.;Neale, Benjamin M.;Berger, Bonnie;Patterson, Nick;Price, Alkes L.
通讯作者：
Price, Alkes L.
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen