Logistic regression models for polymorphic and antagonistic pleiotropic gene action on human aging and longevity

Area of Science:

• Genetics
• Biostatistics
• Gerontology

Background:

• Human survival is influenced by complex genetic factors.
• Highly polymorphic and pleiotropic genes present challenges in genetic association studies.
• Understanding gene-environment interactions, particularly with age, is crucial for longevity research.

Purpose of the Study:

• To develop and apply novel logistic regression models for measuring genetic association with human survival.
• To investigate gene action modes and age-dependent effects of genetic variations.
• To identify specific gene variations contributing to human aging and longevity.

Main Methods:

• Application of logistic regression models, including polytomous and binomial variants with fractional polynomials.
• Modeling genotype frequency as a function of age to handle polymorphic genes.
• Genotype and allele-based parameterization to investigate gene action and reduce statistical complexity.

Main Results:

• The developed models effectively measure genetic association with human survival.
• Age-dependent effects and antagonistic pleiotropy were investigated using binomial logistic regression.
• Application to HFE genotype data demonstrated the utility in assessing allele effects on longevity.

Conclusions:

• The proposed logistic regression models are valuable tools for genetic association studies of human survival.
• These methods can identify important gene variations influencing human aging and longevity.
• The approach aids in understanding the genetic architecture of lifespan and age-related diseases.