Generalized functional varying-index coefficient model for dynamic synergistic gene-environment interactions with binary longitudinal traits

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 epidemi...

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Bibliographic Details
Published in:PloS one Vol. 20; no. 1; p. e0318103
Main Authors: Zhang, Jingyi, Wang, Honglang, Cui, Yuehua
Format: Journal Article
Language:English
Published: United States Public Library of Science 27.01.2025
Public Library of Science (PLoS)
Subjects:
Approximation
Biology and Life Sciences
Blood pressure
Computer Simulation
Data analysis
Disease
Engineering and Technology
Environmental effects
Environmental factors
Epidemiology
Functionals
Gene-Environment Interaction
Genes
Genetic effects
Genetic research
Genetic susceptibility
Genotype-environment interactions
Humans
Hypotheses
Hypothesis testing
Longitudinal method
Longitudinal Studies
Medicine and Health Sciences
Methods
Mixtures
Models, Genetic
Monte Carlo Method
Monte Carlo simulation
Pain sensitivity
Performance evaluation
Physical sciences
Pressure effects
Research and analysis methods
Sensitivity analysis
Single-nucleotide polymorphism
Spline functions
Statistical models
Variables
United States
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary: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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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0318103