Browsing by Author "Zhang, Jingyi"

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    Generalized functional varying-index coefficient model for dynamic synergistic gene-environment interactions with binary longitudinal traits
    (Public Library of Science, 2025-01-27) Zhang, Jingyi; Wang, Honglang; Cui, Yuehua; Mathematical Sciences, School of Science
    The genetic basis of complex traits involves the function of many genes with small effects as well as complex gene-gene and gene-environment interactions. As one of the major players in complex diseases, the role of gene-environment interactions has been increasingly recognized. Motivated by epidemiology studies to evaluate the joint effect of environmental mixtures, we developed a functional varying-index coefficient model (FVICM) to assess the combined effect of environmental mixtures and their interactions with genes, under a longitudinal design with quantitative traits. Built upon the previous work, we extend the FVICM model to accommodate binary longitudinal traits through the development of a generalized functional varying-index coefficient model (gFVICM). This model examines how the genetic effects on a disease trait are nonlinearly influenced by a combination of environmental factors. We derive an estimation procedure for the varying-index coefficient functions using quadratic inference functions combined with penalized splines. A hypothesis testing procedure is proposed to evaluate the significance of the nonparametric index functions. Extensive Monte Carlo simulations are conducted to evaluate the performance of the method under finite samples. The utility of the method is further demonstrated through a case study with a pain sensitivity dataset. SNPs were found to have their effects on blood pressure nonlinearly influenced by a combination of environmental factors.
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    Multivariate partial linear varying coefficients model for gene‐environment interactions with multiple longitudinal traits
    (Wiley, 2022) Wang, Honglang; Zhang, Jingyi; Klump, Kelly L.; Burt, Sybil Alexandra; Cui, Yuehua; Mathematical Sciences, School of Science
    Correlated phenotypes often share common genetic determinants. Thus, a multi‐trait analysis can potentially increase association power and help in understanding pleiotropic effect. When multiple traits are jointly measured over time, the correlation information between multivariate longitudinal responses can help to gain power in association analysis, and the longitudinal traits can provide insights on the dynamic gene effect over time. In this work, we propose a multivariate partially linear varying coefficients model to identify genetic variants with their effects potentially modified by environmental factors. We derive a testing framework to jointly test the association of genetic factors and illustrated with a bivariate phenotypic trait, while taking the time varying genetic effects into account. We extend the quadratic inference functions to deal with the longitudinal correlations and used penalized splines for the approximation of nonparametric coefficient functions. Theoretical results such as consistency and asymptotic normality of the estimates are established. The performance of the testing procedure is evaluated through Monte Carlo simulation studies. The utility of the method is demonstrated with a real data set from the Twin Study of Hormones and Behavior across the menstrual cycle project, in which single nucleotide polymorphisms associated with emotional eating behavior are identified.