Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.
Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.
复制标题
使用线性混合模型和Fisher组合函数从相关受试者进行全基因组关联研究中的多效基因。
DOI：
10.1186/s12859-017-1791-9
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发表时间：
2017-08-24
影响因子：
3
通讯作者：
Buu A
中科院分区：
文献类型：
--
作者：
Yang JJ;Williams LK;Buu A
A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.
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DOI：
10.1038/ejhg.2009.221
发表时间：
2010-06
期刊：
European journal of human genetics : EJHG
影响因子：
--
作者：
通讯作者：
--
影响因子：
2
作者：
Buu, Anne;Li, Runze;Tan, Xianming;Zucker, Robert A.
通讯作者：
Zucker, Robert A.
影响因子：
3.7
作者：
O'Reilly PF;Hoggart CJ;Pomyen Y;Calboli FC;Elliott P;Jarvelin MR;Coin LJ
通讯作者：
Coin LJ
影响因子：
9.8
作者：
Allison, DB;Thiel, B;Schork, NJ
通讯作者：
Schork, NJ
影响因子：
5.7
作者：
Laird, Nan M.;Lange, Christoph
通讯作者：
Lange, Christoph