Wu Baolin, Pankow James S
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Ann Hum Genet. 2015 Jul;79(4):282-93. doi: 10.1111/ahg.12110. Epub 2015 Apr 7.
Multiple correlated traits are often collected in genetic studies. The joint analysis of multiple traits could have increased power by aggregating multiple weak effects and offer additional insights into the aetiology of complex human diseases by revealing pleiotropic variants. We propose to study multivariate test statistics to detect single nucleotide polymorphism (SNP) association with multiple correlated traits. Most existing methods have been based on the generalized estimating equation (GEE) approach without explicitly modelling the trait correlations. In this article, we explore an alternative likelihood-based framework to test the multiple trait associations. It is based on the familiar multinomial logistic regression modelling of genotypes, which can be readily implemented using widely available software, and offers very competitive performance. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) Study.
在基因研究中，常常会收集多个相关性状。对多个性状进行联合分析，通过汇总多个微弱效应可提高检验效能，并通过揭示多效性变异，为复杂人类疾病的病因学提供更多见解。我们建议研究多变量检验统计量，以检测单核苷酸多态性（SNP）与多个相关性状之间的关联。大多数现有方法基于广义估计方程（GEE）方法，未明确对性状相关性进行建模。在本文中，我们探索了一种基于似然的替代框架来检验多个性状的关联。它基于大家熟悉的基因型多项逻辑回归模型，可使用广泛可用的软件轻松实现，并且具有非常有竞争力的性能。我们通过大量数值研究表明，所提出的方法具有竞争力。在社区动脉粥样硬化风险（ARIC）研究中对糖尿病相关性状进行关联分析的应用进一步说明了其有用性。
