Department of Mathematical Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA.
Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA.
Stat Med. 2022 Aug 30;41(19):3643-3660. doi: 10.1002/sim.9440. Epub 2022 May 18.
Correlated phenotypes often share common genetic determinants. Thus, a multi-trait analysis can potentially increase association power and help in understanding pleiotropic effect. When multiple traits are jointly measured over time, the correlation information between multivariate longitudinal responses can help to gain power in association analysis, and the longitudinal traits can provide insights on the dynamic gene effect over time. In this work, we propose a multivariate partially linear varying coefficients model to identify genetic variants with their effects potentially modified by environmental factors. We derive a testing framework to jointly test the association of genetic factors and illustrated with a bivariate phenotypic trait, while taking the time varying genetic effects into account. We extend the quadratic inference functions to deal with the longitudinal correlations and used penalized splines for the approximation of nonparametric coefficient functions. Theoretical results such as consistency and asymptotic normality of the estimates are established. The performance of the testing procedure is evaluated through Monte Carlo simulation studies. The utility of the method is demonstrated with a real data set from the Twin Study of Hormones and Behavior across the menstrual cycle project, in which single nucleotide polymorphisms associated with emotional eating behavior are identified.
相关表型通常具有共同的遗传决定因素。因此，多性状分析有可能增加关联能力，并有助于理解多效性效应。当多个性状随时间被联合测量时，多元纵向响应之间的相关信息可以帮助关联分析获得更多的信息，而纵向性状可以提供随时间变化的基因效应的见解。在这项工作中，我们提出了一个多元部分线性变系数模型，以识别可能受环境因素影响的遗传变异及其效应。我们推导了一个联合检验框架，用于检验遗传因素的相关性，并以双变量表型性状为例，同时考虑了随时间变化的遗传效应。我们将二次推断函数扩展到处理纵向相关性，并使用惩罚样条进行非参数系数函数的逼近。建立了估计的一致性和渐近正态性等理论结果。通过蒙特卡罗模拟研究评估了检验程序的性能。该方法的实用性通过来自激素和行为跨月经周期双胞胎研究项目的真实数据集得到了证明，其中鉴定了与情绪化饮食行为相关的单核苷酸多态性。
