Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.
Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.
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DOI：
10.1002/gepi.21895
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发表时间：
2015-05
影响因子：
2.1
通讯作者：
Fan, Ruzong
中科院分区：
文献类型：
--
作者：
Wang, Yifan;Liu, Aiyi;Mills, James L.;Boehnke, Michael;Wilson, Alexander F.;Bailey-Wilson, Joan E.;Xiong, Momiao;Wu, Colin O.;Fan, Ruzong
关键词：
在遗传学中，多效性描述了单个基因对多个表型性状的遗传效应。常用的方法是采用单变量分析分别分析表型性状，通过多重比较将检验结果结合起来。这种方法可能导致低功耗。建立了多元泛函线性模型，将遗传变异数据与调整协变量的多个数量性状联系起来，进行统一分析。介绍了基于Pillai-Bartlett轨迹、Hotelling-Lawley轨迹和Wilks’s Lambda的三种近似f分布检验，用于检验一个遗传区域内多个数量性状与多个遗传变异之间的关联。近似f分布检验比单变量分析的f检验和最优序列核关联检验（SKAT-O）提供了更显著的结果。进行了大量的仿真，以评估所提出的模型和测试的假阳性率和功率性能。我们证明近似f分布检验很好地控制了I型错误率。总的来说，与单个测试每个特征相比，同时分析多个特征可以提高动力性能。应用所提出的方法分析了(1)8个欧洲队列中的4个脂质性状，以及(2)三位一体学生研究中的3个生化性状。近似f分布检验的结果比单变量分析的f检验和SKAT-O检验的结果更显著。所提出的功能线性模型的近似f分布检验比传统的多元线性模型更敏感，而传统的多元线性模型在单变量情况下又比SKAT-O更敏感。在单变量情况下，四种脂质性状和三种生化性状的分析发现比SKAT-O有更多的相关性。
In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case.
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影响因子：
2.1
作者：
Fan, Ruzong;Wang, Yifan;Mills, James L.;Wilson, Alexander F.;Bailey-Wilson, Joan E.;Xiong, Momiao
通讯作者：
Xiong, Momiao
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：
3
作者：
GREENHOUSE, SW;GEISSER, S
通讯作者：
GEISSER, S
影响因子：
2.1
作者：
Jung, Jeesun;Zhong, Ming;Fan, Ruzong
通讯作者：
Fan, Ruzong
影响因子：
2.1
作者：
Klei, Lambertus;Luca, Diana;Roeder, Kathryn
通讯作者：
Roeder, Kathryn