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Association Mapping of Multivariate Phenotypes in the Presence of Missing Data
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Association Mapping of Multivariate Phenotypes in the Presence of Missing Data
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Journal Article
Association Mapping of Multivariate Phenotypes in the Presence of Missing Data
2018
Overview
Clinical end-point traits are often characterized by quantitative and/or qualitative precursors and it has been argued that it may be statistically a more powerful strategy to analyze a multivariate phenotype comprising these precursor traits to decipher the genetic architecture of the underlying complex end-point trait. We (Majumdar et al., 2015) recently developed a Binomial Regression framework that models the conditional distribution of the allelic count at a SNP given a vector of phenotypes. The model does not require a priori assumptions on the probability distributions of the phenotypes. Moreover, it provides the flexibility of incorporating both quantitative and qualitative phenotypes simultaneously. However, it may often arise in practice that data may not be available on all phenotypes for a particular individual. In this study, we explore methodologies to estimate missing phenotypes conditioned on the available ones and carry out the Binomial Regression based test for association on the “complete” data. We partition the vector of phenotypes into three subsets: continuous, count and categorical phenotypes. For each missing continuous phenotype, the trait value is estimated using a conditional normal model. For each missing count phenotype, the trait value is estimated using a conditional Poisson model. For each missing categorical phenotype, the risk of the phenotype status is estimated using a conditional logistic model. We carry out simulations under a wide spectrum of multivariate phenotype models and assess the effect of the proposed imputation strategy on the power of the association test vis-a-vis the ideal situation with no missing data as well as analyses based only on individuals with complete data. We illustrate an application of our method using data on Coronary Artery Disease.
Publisher
S. Karger AG
