ArXiv · 2026
Population-Specific Genetic and Non-Genetic Influences on Sleep Traits and Health Outcomes.
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Park Jiheum, Shue Stephanie Y, Barragan Rocio, Yang Jeong Yun, Gu Tian, Hur Chin, St-Onge Marie-Pierre
Аннотация
PURPOSE: Sleep traits, shaped by both genetic and environmental factors, influence various physiological conditions. Diverse data representing the U.S. population from the All of Us (AoU) Research Program - including electronic health records (EHR), physical measurements, genomic information, and wearable device data across ancestry groups - offers a unique opportunity to explore the interplay between genetic and non-genetic factors in sleep traits and their associations with health outcomes and disparities. This study aims to examine the associations between genetic predispositions to sleep traits (chronotype, sleep duration, and short sleep) and health outcomes across ancestries, as well as the influence of actual sleep duration.
METHODS: We leveraged AoU genome-wide association study (GWAS) results, including ancestry-specific and meta-analyses for 3,414 phenotypes, to identify phenotypes associated with 455 sleep-related SNPs. Cross-sectional and longitudinal analyses (n = 212,529) evaluated the associations between polygenic risk scores (PRS) for sleep traits and anthropometric/metabolic measures from EHR. A subgroup analysis (n = 7,655) assessed the influence of objectively measured sleep duration using Fitbit data.
RESULTS: SNP analysis across six ancestry groups identified 61 phenotypes linked to 29 sleep-trait-associated SNPs. The chronotype SNP rs1421085 in the fat mass gene showed the strongest associations with anthropometric, obesity, diabetes, and cardiovascular conditions in the meta-analysis. These associations were primarily observed in European, American, and African groups in ancestry-specific analyses. PRS analysis indicated that a higher predisposition to shorter sleep duration was linked to increased risk of both obesity and diabetes, however with ancestry-specific variations. Objectively measured sleep duration acted as a confounder, rendering these associations non-significant, with relative contributions ranging from 85.6%-99.9% (cross-sectional) and 7.1%-44.0% (longitudinal) compared to PRS.
CONCLUSION: This study identified health conditions associated with genetic predispositions to sleep traits, with implications that actual sleep duration may play a more prominent role in sleep-related health outcomes. Differences among meta-, pooled-, and ancestry-specific analyses underscore the importance of population-specific research.
METHODS: We leveraged AoU genome-wide association study (GWAS) results, including ancestry-specific and meta-analyses for 3,414 phenotypes, to identify phenotypes associated with 455 sleep-related SNPs. Cross-sectional and longitudinal analyses (n = 212,529) evaluated the associations between polygenic risk scores (PRS) for sleep traits and anthropometric/metabolic measures from EHR. A subgroup analysis (n = 7,655) assessed the influence of objectively measured sleep duration using Fitbit data.
RESULTS: SNP analysis across six ancestry groups identified 61 phenotypes linked to 29 sleep-trait-associated SNPs. The chronotype SNP rs1421085 in the fat mass gene showed the strongest associations with anthropometric, obesity, diabetes, and cardiovascular conditions in the meta-analysis. These associations were primarily observed in European, American, and African groups in ancestry-specific analyses. PRS analysis indicated that a higher predisposition to shorter sleep duration was linked to increased risk of both obesity and diabetes, however with ancestry-specific variations. Objectively measured sleep duration acted as a confounder, rendering these associations non-significant, with relative contributions ranging from 85.6%-99.9% (cross-sectional) and 7.1%-44.0% (longitudinal) compared to PRS.
CONCLUSION: This study identified health conditions associated with genetic predispositions to sleep traits, with implications that actual sleep duration may play a more prominent role in sleep-related health outcomes. Differences among meta-, pooled-, and ancestry-specific analyses underscore the importance of population-specific research.