Genome wide association study (GWAS) is becoming fundamental in the arduous task of deciphering the etiology of complex diseases. The majority of the statistical models used to address the genes-disease association consider a single response variable. However, it is common for certain diseases to have correlated phenotypes such as in cardiovascular diseases. Usually, GWAS typically sample unrelated individuals from a population and the shared familial risk factors are not investigated. In this paper, we propose to apply a bivariate model using family data that associates two phenotypes with a genetic region. Using generalized estimation equations (GEE), we model two phenotypes, either discrete, continuous or a mixture of them, as a function of genetic variables and other important covariates. We incorporate the kinship relationships into the working matrix extended to a bivariate analysis. The estimation method and the joint gene-set effect in both phenotypes are developed in this work. We also evaluate the proposed methodology with a simulation study and an application to real data.

中文翻译：

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使用家庭数据中的广义估计方程进行双变量性状关联分析。

全基因组关联研究（GWAS）在解读复杂疾病的病因这一艰巨的任务中正变得至关重要。用于解决基因-疾病关联的大多数统计模型都考虑单个响应变量。但是，某些疾病通常具有相关的表型，例如在心血管疾病中。通常，GWAS通常从人群中抽样无关的个体，并且不调查共有的家族性危险因素。在本文中，我们建议使用家庭数据应用双变量模型，该数据将两个表型与一个遗传区域相关联。使用广义估计方程（GEE），我们根据遗传变量和其他重要协变量对两个表型（离散，连续或混合）进行建模。我们将亲属关系纳入工作矩阵，以进行双变量分析。在这项工作中开发了两种表型的估计方法和联合基因组效应。我们还将通过仿真研究和对实际数据的应用来评估所提出的方法。