Chu Wanghuan, Li Runze, Liu Jingyuan, Reimherr Matthew
Google Inc.
Department of Statistics and the Methodology Center, Pennsylvania State University.
Ann Appl Stat. 2020 Mar;14(1):276-298. doi: 10.1214/19-aoas1310. Epub 2020 Apr 16.
Motivated by an empirical analysis of data from a genome-wide association study on obesity, measured by the body mass index (BMI), we propose a two-step gene-detection procedure for generalized varying coefficient mixed-effects models with ultrahigh dimensional covariates. The proposed procedure selects significant single nucleotide polymorphisms (SNPs) impacting the mean BMI trend, some of which have already been biologically proven to be "fat genes." The method also discovers SNPs that significantly influence the age-dependent variability of BMI. The proposed procedure takes into account individual variations of genetic effects and can also be directly applied to longitudinal data with continuous, binary or count responses. We employ Monte Carlo simulation studies to assess the performance of the proposed method and further carry out causal inference for the selected SNPs.
受一项关于肥胖的全基因组关联研究（通过体重指数（BMI）衡量）数据的实证分析启发，我们针对具有超高维协变量的广义变系数混合效应模型提出了一种两步基因检测程序。所提出的程序选择影响平均BMI趋势的显著单核苷酸多态性（SNP），其中一些已在生物学上被证明是“肥胖基因”。该方法还发现了显著影响BMI年龄依赖性变异性的SNP。所提出的程序考虑了遗传效应的个体差异，并且还可以直接应用于具有连续、二元或计数响应的纵向数据。我们采用蒙特卡罗模拟研究来评估所提出方法的性能，并进一步对所选SNP进行因果推断。
