Survival genetics methods for detecting sex-dependent genetic effects on Alzheimer’s disease

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Project Summary Alzheimer's disease (AD) is a progressive neurodegenerative disease influenced by both genetic and environmental factors. Although over 50 risk loci with genome-wide significance have been identified to date, a substantial proportion of AD heritability remains unexplained. With the high- throughput technologies, a large amount of genetic data has become available for AD genetic research. While studies utilizing these enriched data resources and considering sex-dependent genetic effects, joint effects of multiple markers, and AD risk information (e.g., time-to-AD phenotype) hold great promise for novel AD gene discovery, rigorous analytical tools for such analysis are still lacking. Most of the statistical tools can't account for genetic heterogeneity. Besides, existing multi-marker survival tests are largely based on the Cox model for covariate adjustment. Mis-specifying the covariate-adjustment model could lead to spurious association findings. Furthermore, time to AD is usually interval censored in cohort studies and subject to the competing risk of death, but no multi-marker survival test is currently available to handle interval censored competing risks data. To address the limitations of existing methods and facilitate genetic association analysis of time-to-AD outcomes considering sex-related genetic heterogeneity, we will develop three multi-marker survival tests based on the additive hazards model, the accelerated failure time model, and interval censored survival traits, respectively. We will further extend these three tests for gene-gene/gene-environment interaction analyses. All the new tests can deal with left truncation and competing risks, two common issues in time-to-AD analyses. The new methods will be programmed into R packages to be disseminated through the Comprehensive R Archive Network. Additionally, we will apply the methods to the UK Biobank and ROSMAP data to search for AD-associated genes and test for gene-sex interactions. The successful completion of this project will address analytic challenges faced by the ongoing AD genetic research, and advance the statistical methodology development for genetic association analysis of survival outcomes in general. The application of the new methods to the UK Biobank and ROSMAP data will provide new insights into the genetic architecture of AD, especially the sex-specific genetic etiology.