GWAS and Systems Biology Analyses for Aging-Related Conditions: Longevity and Disease
- PI
- Crimmins, Eileen
- DAR ID
- 10622
- Initial Approval Date
- August 31, 2022
- Current DAR Status
- Active
- PI Institution
- University of Southern California
- PI Institution Country
- United States
- PI Institution Type
- Non-Profit
Requested Datasets (3)
Research Use Statement
Research Use Statement:
Our project will rely on phenotype and genotype data from the Health and Retirement Study (HRS), a nationally representative longitudinal study of the older adult population in the U.S. This is an on-going study. Data we have been using beginning in 2016 are from an approved application through dbGaP, from 15,507 HRS participants and include single nucleotide polymorphism (SNP) data on just under 2.5 million markers, imputed data on approximately 21 million DNA variants, and phenotype data on disease incidence and prevalence, functioning, biomarkers, mortality, and environmental and behavioral covariates. Our request from NIAGADS would provide us with an additional genetic sample to what we have been using, for the additional data on 3,409 participations (yielding N=18,916 total with harmonized genetic data through NIAGADS). Data usage will not create additional risk to participants. Aims of the project are to (1) Identify genetic networks and pathways that influence human aging, disease, functioning, and longevity; (2) Develop predictive models of aging-related health outcomes using information from gene networks; and (3) Examine how social and environmental conditions interact with genes within these aging-related gene networks. We will implement statistical models to test for associations between genetic variants and the same phenotype data. In moving forward with the additional samples, we will use the HRS genome-wide data to examine genetic signatures of healthspan, lifespan, and cognitive aging. Using these genetic signatures, we plan to (i) run pathway enrichment analysis to identify influential biological pathways, (ii) use them for predictive modeling of morbidity/mortality risk and cognitive aging, and (iii) incorporate information from social and behavioral data to examine GxE interactions. The overall goal of the project is to identify mechanistic gene and environment networks that contribute to aging acceleration or deceleration.
Our project will rely on phenotype and genotype data from the Health and Retirement Study (HRS), a nationally representative longitudinal study of the older adult population in the U.S. This is an on-going study. Data we have been using beginning in 2016 are from an approved application through dbGaP, from 15,507 HRS participants and include single nucleotide polymorphism (SNP) data on just under 2.5 million markers, imputed data on approximately 21 million DNA variants, and phenotype data on disease incidence and prevalence, functioning, biomarkers, mortality, and environmental and behavioral covariates. Our request from NIAGADS would provide us with an additional genetic sample to what we have been using, for the additional data on 3,409 participations (yielding N=18,916 total with harmonized genetic data through NIAGADS). Data usage will not create additional risk to participants. Aims of the project are to (1) Identify genetic networks and pathways that influence human aging, disease, functioning, and longevity; (2) Develop predictive models of aging-related health outcomes using information from gene networks; and (3) Examine how social and environmental conditions interact with genes within these aging-related gene networks. We will implement statistical models to test for associations between genetic variants and the same phenotype data. In moving forward with the additional samples, we will use the HRS genome-wide data to examine genetic signatures of healthspan, lifespan, and cognitive aging. Using these genetic signatures, we plan to (i) run pathway enrichment analysis to identify influential biological pathways, (ii) use them for predictive modeling of morbidity/mortality risk and cognitive aging, and (iii) incorporate information from social and behavioral data to examine GxE interactions. The overall goal of the project is to identify mechanistic gene and environment networks that contribute to aging acceleration or deceleration.
Non-Technical Summary
Non-Technical Summary:
Aging is the largest risk factor for morbidity and mortality. Previous research using animal models or case-control studies of centenarians have suggested that variations in the pace of aging may be partially explained by genetic and genomic differences. However, few genetic regulators of human lifespan and healthspan have been identified. Furthermore, there is reason to suggest that the pace of aging may be a polygenic trait, for which multiple genes form complex networks that collectively influence aging and longevity phenotypes. These complex genetic networks may further interact with exogenous factors causing variation to arise in health outcomes under diverse environments. The goal of this project is to use advantaged statistical modeling techniques to understand how gene-gene and gene-environment interactions influence longevity and aging-related conditions.
Aging is the largest risk factor for morbidity and mortality. Previous research using animal models or case-control studies of centenarians have suggested that variations in the pace of aging may be partially explained by genetic and genomic differences. However, few genetic regulators of human lifespan and healthspan have been identified. Furthermore, there is reason to suggest that the pace of aging may be a polygenic trait, for which multiple genes form complex networks that collectively influence aging and longevity phenotypes. These complex genetic networks may further interact with exogenous factors causing variation to arise in health outcomes under diverse environments. The goal of this project is to use advantaged statistical modeling techniques to understand how gene-gene and gene-environment interactions influence longevity and aging-related conditions.
Reported Publications (10)
Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.
- Assessing Metabolic Ageing via DNA Methylation Surrogate Markers: A Multicohort Study in Britain, Ireland and the USA. Xu K, et al. Aging cell. 2025. PMID: 39829316
- Does the southern health disadvantage extend to accelerated biological aging: evidence from the health and retirement study. Reynolds A, et al. The journals of gerontology. Series B, Psychological sciences and social sciences. 2025. PMID: 40580468
- Early menopause, hysterectomy, and biological aging: Health and Retirement Study. Kim JK, et al. Menopause (New York, N.Y.). 2025. PMID: 40460368
- Epigenetic Clocks Relate to 4 Age-related Health Outcomes Similarly Across 3 Countries. Crimmins EM, et al. The journals of gerontology. Series A, Biological sciences and medical sciences. 2025. PMID: 40091605
- Remarkable concordance in associations between epigenetic clocks and health behaviors across three countries. Klopack ET, et al. Social science & medicine (1982). 2025. PMID: 40532495
- The Joint Effects of Exposure to Ambient Long-term Air Pollution and Short-term Heat on Epigenetic Aging in the Health and Retirement Study. Van Dang K, et al. The journals of gerontology. Series A, Biological sciences and medical sciences. 2025. PMID: 40326421
- Epigenetic Aging Helps Explain Differential Resilience in Older Adults. Klopack ET, et al. Demography. 2024. PMID: 39012228
- Parental Divorce in Childhood and the Accelerated Epigenetic Aging for Earlier and Later Cohorts: Role of Mediators of Chronic Depressive Symptoms, Education, Smoking, Obesity, and Own Marital Disruption. Kim JK, et al. Journal of population ageing. 2024. PMID: 39131698
- Short Sleep and Insomnia Are Associated With Accelerated Epigenetic Age. Kusters CDJ, et al. Psychosomatic medicine. 2024. PMID: 37594243
- The embodiment of parental death in early life through accelerated epigenetic aging: Implications for understanding how parental death before 18 shapes age-related health risk among older adults. Farina MP, et al. SSM - population health. 2024. PMID: 38596364