Using Item Response Theory to Model Multiple Phenotypes and Their Joint Heritability in Family Data
Using Item Response Theory to Model Multiple Phenotypes and Their Joint Heritability in Family Data
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DOI：
10.1002/gepi.21784
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发表时间：
2014-02-01
影响因子：
2.1
通讯作者：
Soler, Julia M. P.
中科院分区：
文献类型：
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作者：
Fragoso, Tiago M.;Giolo, Suely R.;Soler, Julia M. P.
许多重要的复杂疾病是由一系列表型组成的，这给疾病的诊断和基因解剖带来了困难。确定此类复杂疾病遗传性的标准程序可用于单一表型分析或比较表型或多维还原程序（如使用所有表型的主成分分析）的结果。然而，每种方法都有自己的问题，对于大家族数据和分类表型来说，挑战甚至更加复杂。在本文中，我们提出了一种方法来确定一个规模的复杂的结果，涉及多个分类表型在扩展的谱系使用项目反应理论（IRT）模型，考虑到所有的分类表型，允许个人之间的信息比较。IRT框架的一个优点是，一个简单的联合遗传参数可以估计分类表型。此外，我们的方法允许许多可能的扩展，如包含协变量和多个方差分量。我们使用马尔可夫链蒙特卡罗算法的参数估计和验证我们的方法通过模拟数据。作为应用，我们认为代谢综合征是多表型疾病，使用的数据来自Baependi心脏研究，包括95个家庭的1，696人。我们调整IRT模型没有协变量，包括年龄和年龄平方作为协变量。结果表明，调整协变量后的联合遗传力（h（2）= 0.53）高于无协变量时的联合遗传力（h（2）= 0.21），表明协变量吸收了部分误差方差。
Many important complex diseases are composed of a series of phenotypes, which makes the disease diagnosis and its genetic dissection difficult. The standard procedures to determine heritability in such complex diseases are either applied for single phenotype analyses or to compare findings across phenotypes or multidimensional reduction procedures, such as principal components analysis using all phenotypes. However each method has its own problems and the challenges are even more complex for extended family data and categorical phenotypes. In this paper, we propose a methodology to determine a scale for complex outcomes involving multiple categorical phenotypes in extended pedigrees using item response theory (IRT) models that take all categorical phenotypes into account, allowing informative comparison among individuals. An advantage of the IRT framework is that a straightforward joint heritability parameter can be estimated for categorical phenotypes. Furthermore, our methodology allows many possible extensions such as the inclusion of covariates and multiple variance components. We use Markov Chain Monte Carlo algorithm for the parameter estimation and validate our method through simulated data. As an application we consider the metabolic syndrome as the multiple phenotype disease using data from the Baependi Heart Study consisting of 1,696 individuals in 95 families. We adjust IRT models without covariates and include age and age squared as covariates. The results showed that adjusting for covariates yields a higher joint heritability (h(2)=0.53) than without co variates (h(2)=0.21) indicating that the covariates absorbed some of the error variance.