The study aims to identify the strongest Gene-Environment Interactions (GxE) associated with cognitive decline in aging populations through advanced statistical methods that allow for simultaneous variable selection and estimation. Dementia is a growing global health crisis, affecting over 55 million people worldwide, with nearly 10 million new cases diagnosed annually. As life expectancy increases, dementia prevalence is projected to rise, placing an even greater burden on healthcare systems. While genetic factors, such as the APOE4 allele, are known to influence dementia risk, they do not fully explain individual differences in cognitive decline. Environmental factors-including lifestyle choices, cardiovascular health, and pollution exposure-also play a critical role. Understanding how genetic susceptibility interacts with environmental exposures is essential for developing targeted prevention and intervention strategies.
Advancements in genomic technology and large-scale cohort studies now provide access to extensive genetic and environmental data. GxE interactions occur when the effect of a genetic variant on cognitive decline depends on environmental exposures, yet detecting these interactions remains statistically challenging due to high-dimensional data complexity. Traditional genome-wide environment interaction studies (GWEIS) analyze one nucleotide polymorphism (SNP) at a time, limiting their ability to capture joint effects. This research addresses these challenges by employing penalized regression techniques, which allow for the simultaneous selection and estimation of important GxE interactions. By integrating genetic and environmental data within a high-dimensional statistical framework, this study aims to uncover key risk factors contributing to cognitive decline. The findings have the potential to improve our understanding of dementia etiology and inform personalized prevention strategies, ultimately reducing the public health burden of this disease
