Bivariate traits association analysis using generalized estimating equations in family data.

IF 0.9 4区 数学 Q3 Mathematics
Mariza de Andrade, Mauricio A Mazo Lopera, Nubia E Duarte
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引用次数: 0

Abstract

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.

基于广义估计方程的家庭数据双变量性状关联分析。
基因组全关联研究(GWAS)正在成为破译复杂疾病病因的艰巨任务的基础。大多数用于研究基因-疾病关联的统计模型都考虑一个单一的反应变量。然而,某些疾病通常具有相关的表型,例如心血管疾病。通常,GWAS通常从人群中抽样不相关的个体,而不调查共同的家族危险因素。在本文中,我们建议应用一个双变量模型,使用家庭数据,将两种表型与遗传区域联系起来。使用广义估计方程(GEE),我们将两种表型(离散型、连续型或混合型)作为遗传变量和其他重要协变量的函数进行建模。我们将亲属关系纳入工作矩阵扩展到双变量分析。在这项工作中,开发了两种表型的估计方法和联合基因集效应。我们还通过模拟研究和实际数据的应用来评估所提出的方法。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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