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

> **NIH NIH R56** · MICHIGAN STATE UNIVERSITY · 2022 · $384,456

## Abstract

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.

## Key facts

- **NIH application ID:** 10670493
- **Project number:** 1R56AG075803-01A1
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Chenxi Li
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $384,456
- **Award type:** 1
- **Project period:** 2022-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10670493

## Citation

> US National Institutes of Health, RePORTER application 10670493, Survival genetics methods for detecting sex-dependent genetic effects on Alzheimer’s disease (1R56AG075803-01A1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10670493. Licensed CC0.

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