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arxiv: 2605.23521 · v1 · pith:NQ4YUON5new · submitted 2026-05-22 · 🧬 q-bio.GN

Population-Specific Genetic and Non-Genetic Influences on Sleep Traits and Health Outcomes

Jiheum Park , Stephanie Y. Shue , Rocio Barragan , Jeong Yun Yang , Tian Gu , Chin Hur , Marie-Pierre St-Onge This is my paper

Pith reviewed 2026-05-25 02:42 UTC · model grok-4.3

classification 🧬 q-bio.GN
keywords sleep durationpolygenic risk scoresancestry differencesobesitydiabetesgenetic predispositionhealth outcomesmeasured sleep
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The pith

Genetic predisposition to shorter sleep duration associates with higher obesity and diabetes risk across ancestries, but measured sleep duration accounts for most of the link.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how genetic predispositions to sleep traits such as chronotype and sleep duration relate to health outcomes like obesity and diabetes, using data from multiple ancestry groups. It finds ancestry-specific patterns in these associations and tests whether actual measured sleep duration mediates the genetic links. If the observed attenuation holds, it suggests that behavioral factors like sleep duration play a larger role than genetic predisposition alone in driving these health risks. The work highlights differences between ancestry-specific and meta-analyses, pointing to the value of population-specific approaches in genetic studies of sleep and health.

Core claim

Across ancestry groups, higher polygenic risk for shorter sleep duration links to elevated risks of obesity and diabetes, with the strongest SNP associations appearing for chronotype variants in obesity and metabolic conditions. Measured sleep duration from wearable data attenuates the PRS-health outcome associations substantially in cross-sectional analyses (85.6%-99.9%) and to a lesser degree longitudinally (7.1%-44.0%), indicating that actual sleep duration captures much of the pathway from genetic predisposition to these outcomes.

What carries the argument

Polygenic risk scores (PRS) constructed from sleep-trait-associated SNPs, combined with measured sleep duration as a mediator in cross-sectional and longitudinal models.

If this is right

  • Interventions targeting actual sleep duration could reduce obesity and diabetes risk even in individuals with high genetic predisposition.
  • Ancestry-specific PRS effects imply that uniform genetic risk models may miss or misestimate risks in non-European groups.
  • Longitudinal designs show smaller mediation by measured sleep, suggesting other factors accumulate over time.
  • SNP-level analysis identifies specific variants like rs1421085 that drive multiple metabolic and cardiovascular links.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If measured sleep duration mediates most cross-sectional links, wearable-based monitoring could serve as an early indicator for genetically at-risk individuals.
  • The ancestry variation suggests that future studies should prioritize diverse cohorts to avoid biased estimates of genetic contributions to sleep-related disease.
  • The gap between cross-sectional and longitudinal mediation percentages points to the need for longer follow-up periods to clarify causal direction.

Load-bearing premise

The polygenic risk scores from prior studies capture true causal effects on sleep without major confounding from population structure or other genetic effects, and the reduction in association when adding measured sleep duration reflects mediation rather than other explanations like reverse causation.

What would settle it

Finding no meaningful attenuation of the PRS-obesity or PRS-diabetes associations after including measured sleep duration in the models, or detecting strong evidence of pleiotropy or stratification bias in the sleep SNPs.

read the original abstract

Sleep traits are shaped by genetic and environmental factors and may influence many health conditions. The All of Us Research Program, which includes EHR, physical measurements, genomic data, and wearable data across ancestry groups, provides an opportunity to study genetic and non-genetic contributors to sleep-related health outcomes. We examined associations between genetic predispositions to chronotype, sleep duration, and short sleep and health outcomes across ancestries, as well as the role of measured sleep duration. We used All of Us genome-wide association study 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 associations between polygenic risk scores (PRS) and anthropometric and metabolic measures from EHR. A subgroup analysis (n = 7,655) assessed sleep duration using Fitbit data. Across six ancestry groups, SNP analysis identified 61 phenotypes linked to 29 sleep-trait-associated SNPs. The chronotype SNP rs1421085 in FTO showed the strongest associations with obesity, diabetes, and cardiovascular conditions, mainly in European, American, and African groups. PRS analysis showed that higher predisposition to shorter sleep duration was associated with increased risk of obesity and diabetes, with ancestry-specific variation. Measured sleep duration attenuated these associations, with relative contributions of 85.6%-99.9% in cross-sectional analyses and 7.1%-44.0% in longitudinal analyses compared with PRS. This study identified health conditions associated with genetic predispositions to sleep traits and suggests that actual sleep duration may play a prominent role in sleep-related health outcomes. Differences among meta-, pooled-, and ancestry-specific analyses highlight the importance of population-specific research.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper uses All of Us data (n=212,529 for main analyses; n=7,655 with Fitbit) to examine associations of 455 sleep-related SNPs and derived PRS for chronotype, sleep duration, and short sleep with EHR-derived anthropometric and metabolic outcomes across six ancestry groups. It reports that higher PRS for shorter sleep duration associates with increased obesity and diabetes risk (ancestry-specific patterns), identifies 61 phenotypes linked to 29 SNPs (strongest for FTO rs1421085), and finds that measured sleep duration attenuates PRS-outcome associations by 85.6-99.9% cross-sectionally but only 7.1-44.0% longitudinally, concluding that actual sleep duration plays a prominent role in these health outcomes.

Significance. If the core associations and mediation interpretation hold after addressing confounding and temporality, the work would provide population-specific evidence on genetic versus behavioral contributions to sleep-related metabolic disease in a large, ancestrally diverse cohort with wearable validation. The combination of SNP-level, PRS, cross-sectional, and longitudinal designs plus Fitbit subgroup data is a strength for generalizability beyond European-ancestry studies.

major comments (3)
  1. [Abstract] Abstract and PRS/subgroup analysis sections: the central mediation claim (measured sleep duration as prominent contributor) is undermined by the large discrepancy in attenuation percentages (85.6%-99.9% cross-sectional vs. 7.1%-44.0% longitudinal). Longitudinal models better respect temporality and reduce reverse causation, indicating that any true mediating role of sleep duration is modest at best; the near-complete cross-sectional attenuation is more consistent with unmeasured confounding or health status influencing reported sleep.
  2. [Abstract] Abstract and SNP analysis section: the claim that rs1421085 (FTO) associations with obesity/diabetes reflect sleep-trait effects is load-bearing for the genetic predisposition narrative, yet FTO is a well-documented pleiotropic locus with direct effects on BMI independent of sleep; no formal test for pleiotropy or mediation through sleep (e.g., via MR or sensitivity analyses) is described to rule this out.
  3. [Methods] Methods (PRS construction, ancestry correction, multiple testing): the abstract and described analyses provide no details on multiple-testing correction across 3,414 phenotypes or 455 SNPs, exact PRS construction (weights, clumping, external vs. All of Us GWAS), or handling of ancestry principal components and missing Fitbit data. These omissions directly affect validity of the 61 identified phenotypes and the ancestry-specific PRS associations that underpin the main claims.
minor comments (2)
  1. [Abstract] Abstract: the sample sizes for cross-sectional (n=212,529) versus Fitbit subgroup (n=7,655) analyses should be stated with clearer distinction between the two designs.
  2. [Results] The paper would benefit from explicit comparison of meta- versus ancestry-specific PRS results in a dedicated table to quantify the reported differences.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments. We address each major point below and indicate where revisions will be made to improve clarity, transparency, and interpretation.

read point-by-point responses

  1. Referee: [Abstract] Abstract and PRS/subgroup analysis sections: the central mediation claim (measured sleep duration as prominent contributor) is undermined by the large discrepancy in attenuation percentages (85.6%-99.9% cross-sectional vs. 7.1%-44.0% longitudinal). Longitudinal models better respect temporality and reduce reverse causation, indicating that any true mediating role of sleep duration is modest at best; the near-complete cross-sectional attenuation is more consistent with unmeasured confounding or health status influencing reported sleep.

    Authors: We agree that longitudinal models provide a stronger basis for causal inference regarding temporality and that the marked difference in attenuation percentages indicates the cross-sectional results are likely influenced by confounding or reverse causation. In the revised manuscript we will update the abstract and discussion to give greater prominence to the longitudinal attenuation estimates (7.1-44.0%) and to state explicitly that any mediating role of measured sleep duration appears modest. We will also add text noting that the near-complete cross-sectional attenuation may reflect unmeasured confounding or health status affecting reported sleep. revision: partial

  2. Referee: [Abstract] Abstract and SNP analysis section: the claim that rs1421085 (FTO) associations with obesity/diabetes reflect sleep-trait effects is load-bearing for the genetic predisposition narrative, yet FTO is a well-documented pleiotropic locus with direct effects on BMI independent of sleep; no formal test for pleiotropy or mediation through sleep (e.g., via MR or sensitivity analyses) is described to rule this out.

    Authors: We acknowledge that rs1421085 in FTO is a well-established pleiotropic locus with direct effects on BMI and obesity risk that are independent of sleep. Although the SNP was retained because it met the sleep-trait association criterion in the source GWAS, we did not perform formal pleiotropy or mediation tests. In the revised manuscript we will revise the abstract and SNP-analysis sections to note this pleiotropy explicitly and to clarify that the observed associations cannot be attributed solely to sleep-trait effects. We will also list this as a limitation. revision: yes

  3. Referee: [Methods] Methods (PRS construction, ancestry correction, multiple testing): the abstract and described analyses provide no details on multiple-testing correction across 3,414 phenotypes or 455 SNPs, exact PRS construction (weights, clumping, external vs. All of Us GWAS), or handling of ancestry principal components and missing Fitbit data. These omissions directly affect validity of the 61 identified phenotypes and the ancestry-specific PRS associations that underpin the main claims.

    Authors: We apologize for these omissions. The revised methods section will specify: (i) the multiple-testing correction applied (Bonferroni threshold across 3,414 phenotypes and 455 SNPs); (ii) PRS construction details, including source of weights (external GWAS), clumping parameters, and whether All of Us summary statistics were used; (iii) inclusion of the top 10 ancestry principal components in all models; and (iv) handling of missing Fitbit data (complete-case analysis with sensitivity checks using multiple imputation). These additions will allow readers to evaluate the validity of the reported associations. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper constructs PRS for sleep traits from external All of Us GWAS summary statistics (ancestry-specific and meta-analyses) and applies them to regression models for associations with health outcomes in the cohort (n=212,529). Attenuation percentages by measured sleep duration are computed directly from changes in regression coefficients across cross-sectional and longitudinal models. These are empirical observations, not reductions by construction, self-definitions, or fitted inputs renamed as predictions. No load-bearing self-citation chains or uniqueness theorems are invoked; the central claims rest on independent external GWAS inputs and within-cohort associations.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on standard GWAS transferability and PRS validity assumptions drawn from prior literature; no new free parameters, invented entities, or ad-hoc axioms are introduced in the abstract.

axioms (2)
  • domain assumption Genetic associations identified in prior GWAS transfer to the All of Us multi-ancestry sample without major population-specific effect size differences
    Invoked when applying the 455 sleep SNPs and constructing PRS across six ancestry groups.
  • domain assumption Measured Fitbit sleep duration is an accurate proxy for habitual sleep and can be treated as a mediator in regression models
    Used when calculating the relative contribution percentages in cross-sectional and longitudinal analyses.

pith-pipeline@v0.9.0 · 5874 in / 1520 out tokens · 24881 ms · 2026-05-25T02:42:14.214760+00:00 · methodology

discussion (0)

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Reference graph

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