Generalized functional linear models for gene-based case-control association studies.
Generalized functional linear models for gene-based case-control association studies.
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DOI：
10.1002/gepi.21840
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发表时间：
2014-11
影响因子：
2.1
通讯作者：
Xiong, Momiao
中科院分区：
文献类型：
--
作者：
Fan, Ruzong;Wang, Yifan;Mills, James L.;Carter, Tonia C.;Lobach, Iryna;Wilson, Alexander F.;Bailey-Wilson, Joan E.;Weeks, Daniel E.;Xiong, Momiao
关键词：
通过使用功能数据分析技术，我们开发了广义功能线性模型，用于在调整协变量的同时检验一个二分性状与一个遗传区域中的多个遗传变量之间的关联。建立了固定效应模型和混合效应模型，并进行了比较。大量的模拟表明，Rao的固定效应模型的有效得分检验是非常保守的，因为它们产生的I型误差比名义水平低，而混合效应模型的全局检验产生准确的I型误差。此外，我们还发现，当因果变量既稀有又常见时，固定效应模型的Rao有效分数检验统计量比序列核关联检验(SKAT)及其最优统一版本(SKAT-O)具有更高的功效。当因果变异都是罕见的(即，次要等位基因频率小于0.03)时，RAO的有效得分检验统计和全局检验具有类似或略低于SKAT和SKAT-O的功率。在实践中，还不知道基因中的罕见变异或常见变异是否与疾病有关。我们所能假设的是，罕见和常见变异的组合会影响疾病易感性。因此，当因果变异既罕见又常见时，我们的模型的性能得到了改善，这表明所提出的模型在解剖复杂特征方面非常有用。我们将我们的方法与SKAT和SKAT-O在真实神经管缺陷和先天性巨结肠症数据集上的性能进行了比较。在实际数据分析中，RAO的有效分数测试统计和全球测试比SKAT和SKAT-O更敏感。我们的方法既可用于基因疾病全基因组/外显子全基因组关联研究，也可用于候选基因分析。
By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene are disease-related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease data sets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses.
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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：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.
影响因子：
2.7
作者：
Goeman, Jelle J.;Van Houwelingen, Hans C.;Finos, Livio
通讯作者：
Finos, Livio
影响因子：
9.8
作者：
Gorlov, Ivan P.;Gorlova, Olga Y.;Amos, Christopher I.
通讯作者：
Amos, Christopher I.