Logistic Regression Models for Polymorphic and Antagonistic Pleiotropic Gene Action on Human Aging and Longevity

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Abstract
In this paper, we apply logistic regression models to measure genetic association with human survival for highly polymorphic and pleiotropic genes. By modelling genotype frequency as a function of age, we introduce a logistic regression model with polytomous responses to handle the polymorphic situation. Genotype and allele‐based parameterization can be used to investigate the modes of gene action and to reduce the number of parameters, so that the power is increased while the amount of multiple testing minimized. A binomial logistic regression model with fractional polynomials is used to capture the age‐dependent or antagonistic pleiotropic effects. The models are applied to HFE genotype data to assess the effects on human longevity by different alleles and to detect if an age‐dependent effect exists. Application has shown that these methods can serve as useful tools in searching for important gene variations that contribute to human aging and longevity.