Analyze multivariate phenotypes in genetic association studies by combining univariate association tests.
Analyze multivariate phenotypes in genetic association studies by combining univariate association tests.
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DOI：
10.1002/gepi.20497
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发表时间：
2010-07
影响因子：
2.1
通讯作者：
Fox, Caroline S.
中科院分区：
文献类型：
--
作者：
Yang, Qiong;Wu, Hongsheng;Guo, Chao-Yu;Fox, Caroline S.
关键词：
多变量表型在全基因组关联研究（GWAS）中经常遇到。这些表型比单变量表型包含更多的信息，但如何最好地利用这些信息来增加检测多效性遗传变异的机会并不总是很清楚。此外，当多变量表型包含定量和定性措施的混合物，有限的方法是适用的。在本文中，我们首先评估了最初由O 'Brien、Wei和Johnson提出的方法，该方法结合了单变量检验统计量，然后我们对该方法提出了两个扩展。原始和提出的方法适用于包含任何类型成分的多变量表型，包括连续型，分类型和生存型，并适用于由家族或不相关样本组成的样本。仿真结果表明，所有方法均具有有效的I类错误率。在单变量检验统计量中，我们的扩展比O 'Brien方法具有更好的能力，但在单个检验统计量中，我们的扩展比O 'Brien方法具有更强的能力。在某些情况下，与单独测试多变量表型的每个组成部分相比，所有方法都显示出相当大的功率增加。我们将所有方法应用于Framingham心脏研究中含有55万个snp的血清尿酸水平和痛风的GWAS。
Multivariate phenotypes are frequently encountered in genome-wide association studies(GWAS). Such phenotypes contain more information than univariate phenotypes, but how to best exploit the information to increase the chance of detecting genetic variant of pleiotropic effect is not always clear. Moreover, when multivariate phenotypes contain a mixture of quantitative and qualitative measures, limited methods are applicable. In this paper, we first evaluated the approach originally proposed by O’Brien and by Wei and Johnson that combines the univariate test statistics and then we proposed two extensions to that approach. The original and proposed approaches are applicable to a multivariate phenotype containing any type of components including continuous, categorical and survival phenotypes, and applicable to samples consisting of families or unrelated samples. Simulation results suggested that all methods had valid type I error rates. Our extensions had a better power than O’Brien’s method with heterogeneous means among univariate test statistics, but were less powerful than O’Brien’s with homogeneous means among individual test statistics. All approaches have shown considerable increase in power compared to testing each component of a multivariate phenotype individually in some cases. We apply all the methods to GWAS of serum uric acid levels and gout with 550,000 SNPs in the Framingham Heart Study.
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影响因子：
64.8
作者：
Frazer, Kelly A.;Ballinger, Dennis G.;Cox, David R.;Hinds, David A.;Stuve, Laura L.;Gibbs, Richard A.;Belmont, John W.;Boudreau, Andrew;Hardenbol, Paul;Leal, Suzanne M.;Pasternak, Shiran;Wheeler, David A.;Willis, Thomas D.;Yu, Fuli;Yang, Huanming;Zeng, Changqing;Gao, Yang;Hu, Haoran;Hu, Weitao;Li, Chaohua;Lin, Wei;Liu, Siqi;Pan, Hao;Tang, Xiaoli;Wang, Jian;Wang, Wei;Yu, Jun;Zhang, Bo;Zhang, Qingrun;Zhao, Hongbin;Zhao, Hui;Zhou, Jun;Gabriel, Stacey B.;Barry, Rachel;Blumenstiel, Brendan;Camargo, Amy;Defelice, Matthew;Faggart, Maura;Goyette, Mary;Gupta, Supriya;Moore, Jamie;Nguyen, Huy;Onofrio, Robert C.;Parkin, Melissa;Roy, Jessica;Stahl, Erich;Winchester, Ellen;Ziaugra, Liuda;Altshuler, David;Shen, Yan;Yao, Zhijian;Huang, Wei;Chu, Xun;He, Yungang;Jin, Li;Liu, Yangfan;Shen, Yayun;Sun, Weiwei;Wang, Haifeng;Wang, Yi;Wang, Ying;Xiong, Xiaoyan;Xu, Liang;Waye, Mary M. Y.;Tsui, Stephen K. W.;Wong, J. Tze-Fei;Galver, Luana M.;Fan, Jian-Bing;Gunderson, Kevin;Murray, Sarah S.;Oliphant, Arnold R.;Chee, Mark S.;Montpetit, Alexandre;Chagnon, Fanny;Ferretti, Vincent;Leboeuf, Martin;Olivier, Jean-Franccois;Phillips, Michael S.;Roumy, Stephanie;Sallee, Clementine;Verner, Andrei;Hudson, Thomas J.;Kwok, Pui-Yan;Cai, Dongmei;Koboldt, Daniel C.;Miller, Raymond D.;Pawlikowska, Ludmila;Taillon-Miller, Patricia;Xiao, Ming;Tsui, Lap-Chee;Mak, William;Song, You Qiang;Tam, Paul K. H.;Nakamura, Yusuke;Kawaguchi, Takahisa;Kitamoto, Takuya;Morizono, Takashi;Nagashima, Atsushi;Ohnishi, Yozo;Sekine, Akihiro;Tanaka, Toshihiro;Tsunoda, Tatsuhiko;Deloukas, Panos;Bird, Christine P.;Delgado, Marcos;Dermitzakis, Emmanouil T.;Gwilliam, Rhian;Hunt, Sarah;Morrison, Jonathan;Powell, Don;Stranger, Barbara E.;Whittaker, Pamela;Bentley, David R.;Daly, Mark J.;de Bakker, Paul I. W.;Barrett, Jeff;Chretien, Yves R.;Maller, Julian;McCarroll, Steve;Patterson, Nick;Pe'er, Itsik;Price, Alkes;Purcell, Shaun;Richter, Daniel J.;Sabeti, Pardis;Saxena, Richa;Schaffner, Stephen F.;Sham, Pak C.;Varilly, Patrick;Altshuler, David;Stein, Lincoln D.;Krishnan, Lalitha;Smith, Albert Vernon;Tello-Ruiz, Marcela K.;Thorisson, Gudmundur A.;Chakravarti, Aravinda;Chen, Peter E.;Cutler, David J.;Kashuk, Carl S.;Lin, Shin;Abecasis, Goncalo R.;Guan, Weihua;Li, Yun;Munro, Heather M.;Qin, Zhaohui Steve;Thomas, Daryl J.;McVean, Gilean;Auton, Adam;Bottolo, Leonardo;Cardin, Niall;Eyheramendy, Susana;Freeman, Colin;Marchini, Jonathan;Myers, Simon;Spencer, Chris;Stephens, Matthew;Donnelly, Peter;Cardon, Lon R.;Clarke, Geraldine;Evans, David M.;Morris, Andrew P.;Weir, Bruce S.;Tsunoda, Tatsuhiko;Johnson, Todd A.;Mullikin, James C.;Sherry, Stephen T.;Feolo, Michael;Skol, Andrew
通讯作者：
Skol, Andrew
影响因子：
--
作者：
Lawrence, Reva C.;Felson, David T.;Wolfe, Frederick
通讯作者：
Wolfe, Frederick
影响因子：
2.1
作者：
Klei, Lambertus;Luca, Diana;Roeder, Kathryn
通讯作者：
Roeder, Kathryn
影响因子：
168.9
作者：
Dehghan, Abbas;Kottgen, Anna;Yang, Qiong;Hwang, Shih Jen;Kao, W. H. Linda;Rivadeneira, Fernando;Boerwinkle, Eric;Levy, Daniel;Hofman, Albert;Astor, Brad C.;Benjamin, Emelia J.;van Duijn, Cornelia M.;Witteman, Jacqueline C.;Coresh, Josef;Fox, Caroline S.
通讯作者：
Fox, Caroline S.
影响因子：
9.8
作者：
Almasy, L;Blangero, J
通讯作者：
Blangero, J