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Abstract:
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For many complex diseases, multiple phenotypic measurements can be used to quantify the disease status. Such correlated phenotypes often share common genetic determinants. For multivariate longitudinal data, when multiple response variables are jointly measured over time, the correlation information between multivariate longitudinal responses can be taken into account to identify pleotropic effects. In this work, we proposed a multivariate partially linear varying coefficients model to identify genetic variants with their effects nonlinearly modified by environmental factors over time. We derived a testing framework to jointly test the association of genetic factors with a bivariate phenotypic trait. We extended the quadratic inference functions to deal with the longitudinal correlations and used penalized splines for the approximation of nonparametric coefficients, and further showed the consistency and asymptotic normality of the estimates. The performance of the testing procedure was evaluated through Monte Carlo simulation studies. The utility of the method was further demonstrated through a real data analysis.
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