Abstract
Longitudinal studies are an important tool for analysing traits that change over time, depending on individual characteristics and environmental exposures. Complex quantitative traits, such as lung function, may change over time and appear to depend on genetic and environmental factors, as well as on potential gene-environment interactions. There is a growing interest in modelling both marginal genetic effects and gene-environment interactions. In an admixed population, the use of traditional statistical models may fail to adjust for confounding by ethnicity, leading to bias in the genetic effect estimates. A variety of methods have been developed to account for the genetic substructure of human populations. Family-based designs provide an important resource for avoiding confounding due to admixture. To date, however, most genetic analyses have been applied to cross-sectional designs. In this paper, we propose a methodology which aims to improve the assessment of main genetic effect and gene-environment interaction effects by combining the advantages of both longitudinal studies for continuous phenotypes, and the family-based designs. This approach is based on an extension of ordinary linear mixed models for quantitative phenotypes, which incorporates information from a case-parent design. Our results indicate that use of this method allows both main genetic and gene-environment interaction effects to be estimated without bias, even in the presence of population substructure.
Highlights
In spite of the multiple efforts to find genetic factors conferring susceptibility to complex diseases, the success of genetic association studies is still hampered by the difficulty in replicating findings in different populations
We call the model the ‘adjusted linear mixed model’ (ALMM), and through simulation methods we show that even when population stratification is present, both main genetic and gene–environment interaction effects can be estimated without bias, and that this is more powerful than the two-step modelling approach
In order to set the stage for our methodology, we first provide a brief overview of some existing ordinary linear regression (OLR) models for testing main genetic effects and gene–environment interactions in cross-sectional studies that incorporate information about parental genotype, adjusting for admixture
Summary
In spite of the multiple efforts to find genetic factors conferring susceptibility to complex diseases, the success of genetic association studies is still hampered by the difficulty in replicating findings in different populations. We present what we consider to be a widely applicable method for correctly assessing the main genetic effect and gene–environment interactions for time-dependent quantitative traits in stratified populations For this purpose, we use simulated repeated measurements of forced expiratory flow between 75 per cent and 25 per cent of vital capacity (FEF25 – 75) ie (lung function) on asthmatic children exposed to ozone pollution, based on the observed distributions in a real cohort study conducted in Mexico City.[10]. In order to set the stage for our methodology, we first provide a brief overview of some existing ordinary linear regression (OLR) models for testing main genetic effects and gene–environment interactions in cross-sectional studies that incorporate information about parental genotype (case–parent or trio design), adjusting for admixture. We give details about the simulation procedures and present our results and discussion
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