Distribution of 54 polygenic risk scores for common diseases in long lived individuals and their offspring

Gunn, Sophia; Wainberg, Michael; Song, Ze–Zhou; Andersen, Stacy L.; Boudreau, Robert M.; Tan, Qihua; Montasser, May E.; O’Connell, Jeffrey R.; Stitziel, Nathan O.; Price, Nathan D.; Perls, Thomas T.; Schork, Nicholas J.; Sebastiani, Paola

doi:10.1007/s11357-022-00518-2

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…Furthermore, genetic correlation analyses by Deelen et al, indicated that two of the components of MetS, HDL-cholesterol and waist circumference, were significantly correlated with EL [38], providing evidence that there are common genetic variants underlying these measures and longevity. A recent analysis found that extremely long-lived individuals have a similar burden of genetic disease risk as the general population except for significantly lower polygenic risk scores for coronary artery disease (CAD) and Alzheimer's disease (AD) and higher scores for cognitive function and parental longevity [63]. However, this study did not examine MetS.…”

Section: Discussionmentioning

confidence: 68%

High polygenic risk score for exceptional longevity is associated with a healthy metabolic profile

et al. 2022

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Healthy metabolic measures in humans are associated with longevity. Dysregulation leads to metabolic syndrome (MetS) and negative health outcomes. Recent exceptional longevity (EL) genome wide association studies have facilitated estimation of an individual’s polygenic risk score (PRS) for EL. We tested the hypothesis that individuals with high ELPRS have a low prevalence of MetS. Participants were from five cohorts of middle-aged to older adults. The primary analyses were performed in the UK Biobank (UKBB) (n = 407,800, 40–69 years). Replication analyses were undertaken using three Australian studies: Hunter Community Study (n = 2122, 55–85 years), Older Australian Twins Study (n = 539, 65–90 years) and Sydney Memory and Ageing Study (n = 925, 70–90 years), as well as the Swedish Gothenburg H70 Birth Cohort Studies (n = 2273, 70–93 years). MetS was defined using established criteria. Regressions and meta-analyses were performed with the ELPRS and MetS and its components. Generally, MetS prevalence (22–30%) was higher in the older cohorts. In the UKBB, high EL polygenic risk was associated with lower MetS prevalence (OR = 0.94, p = 1.84 × 10–42) and its components (p < 2.30 × 10–8). Meta-analyses of the replication cohorts showed nominal associations with MetS (p = 0.028) and 3 MetS components (p < 0.05). This work suggests individuals with a high polygenic risk for EL have a healthy metabolic profile promoting longevity.

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Section: Discussionmentioning

confidence: 68%

High polygenic risk score for exceptional longevity is associated with a healthy metabolic profile

et al. 2022

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“…Similarly, recent whole-exome sequencing studies that compared the number of disease-associated gene variants between individuals with longevity and those without familial longevity did not find significant differences (Gutman et al 2020a;Lin et al 2021). Although some studies did find that individuals with exceptional longevity had lower polygenic risk scores (PRSs) for several age-related diseases compared to younger population controls, their genomes were not devoid of risk-associated variants (Gunn et al 2022;Torres et al 2022). These observations led to the conclusion that there are longevity-associated protective genotypes in centenarians that delay aging or specifically protect against the manifestations of age-related diseases.…”

Section: Genetics Of Exceptional Longevitymentioning

confidence: 99%

Discovering Biological Mechanisms of Exceptional Human Health Span and Life Span

Milman

Barzilai

2023

Cold Spring Harb Perspect Med

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Humans age at different rates and families with exceptional longevity provide an opportunity to understand why some people age slower than others. Unique features exhibited by centenarians include a family history of extended life span, compression of morbidity with resultant extension of health span, and longevity-associated biomarker profiles. These biomarkers, including low-circulating insulin-like growth factor 1 (IGF-1) and elevated highdensity lipoprotein (HDL) cholesterol levels, are associated with functional genotypes that are enriched in centenarians, suggesting that they may be causative for longevity. While not all genetic discoveries from centenarians have been validated, in part due to exceptional life span being a rare phenotype in the general population, the APOE2 and FOXO3a genotypes have been confirmed in a number of populations with exceptional longevity. However, life span is now recognized as a complex trait and genetic research methods to study longevity are rapidly extending beyond classical Mendelian genetics to polygenic inheritance methodologies. Moreover, newer approaches are suggesting that pathways that have been recognized for decades to control life span in animals may also regulate life span in humans. These discoveries led to strategic development of therapeutics that may delay aging and prolong health span.

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“…Learning Polygenic Risk Score Network Polygenic Risk Scores (PRS) for 54 health outcomes using genetic data of 8,190 samples were calculated as reported by (Gunn et al, 2022). We further cleaned the PRS data by removing two PRS with very skewed distribution [Supplemental Figure 2] and additional five PRS that had several potential outliers that lie 4 standard deviations away from the means [Supplemental Figure 3].…”

Section: Datamentioning

confidence: 99%

Learning Gaussian Graphical Models from Correlated Data

Song,

Gunn,

Monti

et al. 2024

Preprint

Self Cite

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Gaussian Graphical Models (GGM) have been widely used in biomedical research to explore complex relationships between many variables. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a Bootstrap algorithm to infer GGM from correlated data. We use extensive simulations of correlated data from family-based studies to show that the Bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions. We apply our method to learn the GGM that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from a family-based study known as the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well in this real example.

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Distribution of 54 polygenic risk scores for common diseases in long lived individuals and their offspring

Cited by 8 publications

References 36 publications

High polygenic risk score for exceptional longevity is associated with a healthy metabolic profile

High polygenic risk score for exceptional longevity is associated with a healthy metabolic profile

Discovering Biological Mechanisms of Exceptional Human Health Span and Life Span

Learning Gaussian Graphical Models from Correlated Data

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