# Integrated Retrospective and Post‐GWAS Analyses Reveal Mechanisms Linking Nightly Short Sleep to Asthma

## TL;DR

This study shows that short nightly sleep increases asthma risk, using both population data and genetic analysis to confirm a causal link.

## Contribution

The study combines retrospective cohort and genetic analyses to establish a causal relationship between short sleep and asthma.

## Key findings

- Short nightly sleep significantly increases asthma risk (OR = 0.83 for increased sleep protection).
- Genetic analysis identified three shared causal loci and genes (TBX6, ABT1, YPEL3) linking sleep and asthma.
- Multiple MR models confirm a causal effect of short sleep on asthma (e.g., MR-cML β = 0.927, p = 0.0009).

## Abstract

This study employs a dual‐pronged approach, integrating retrospective cohort analysis with genetic methodologies, to elucidate the causal role of nightly short sleep in the pathogenesis of asthma and to unravel its underpinning biological mechanisms.

This study employed a dual‐evidence framework for a comprehensive analysis. The retrospective cohort component utilized data from 6770 participants in the China Health and Retirement Longitudinal Study (CHARLS) to assess the dose‐response relationship and risk thresholds for asthma incidence via multivariable logistic regression, restricted cubic splines, and segmented regression models. The genetic analysis component integrated large‐scale Genome‐Wide Association Studies (GWAS) data for nightly short sleep from the UK Biobank and for asthma from the FinnGen biobank. A suite of methodologies, including Linkage Disequilibrium Score Regression (LDSC), High‐Definition Likelihood (HDL), Pleiotropic Analysis under Composite Null Hypothesis, (PLACO), Colocalization (COLOC), and Summary‐data‐based Mendelian Randomization (SMR), was employed to evaluate genetic correlations and identify shared loci. To infer causality, this study applied a battery of advanced, robust MR models—including Mendelian Randomization‐Clustering (MR‐Clust), Maximum Likelihood Mendelian Randomization (MRcML), Contamination Mixture (ConMix), and CAUSE—to systematically correct for and evaluate the influence of horizontal pleiotropy.

The cohort regression analysis revealed that increased sleep duration confers a significant protective effect against asthma (Model 3: OR = 0.83, 95% CI 0.75–0.92). A pronounced dose‐response trend was observed (P for trend < 0.0004), wherein longer sleep corresponded to lower risk. Further analysis with restricted cubic splines confirmed a U‐shaped, nonlinear relationship, identifying a risk inflection point at 7.5 h of sleep. Subgroup analyses indicated that this protective effect was robust across diverse age, gender, and lifestyle strata, with no significant interaction effects detected (all P‐interaction > 0.05). At the genetic level, a significant positive genetic correlation was established between nightly short sleep and asthma (LDSC rg = 0.257; HDL rg = 0.247, with p < 0.001 for both). Colocalization analysis identified three shared causal loci (rs6939576, rs13107325, and rs205024) with distinct protein‐altering and regulatory functions. Subsequent SMR analysis identified three shared causal genes—TBX6, ABT1, and YPEL3—with directionally consistent effects. Consistent evidence from multiple analytical models—including MR‐cML (β = 0.927, p = 0.0009), ConMix (β = 1.585, p = 0.0006), and CAUSE (favoring the causal model, ΔELPD = −3.3; causal effect γ = 0.54)—supports the conclusion that genetically predicted nightly short sleep is a causal factor for an increased risk of asthma.

This research provides compelling evidence substantiating nightly short sleep as a causal risk factor for asthma, with genetically predicted nightly short sleep significantly elevating disease risk.

This study investigates the potential link between short sleep and asthma through two approaches.

Retrospective Cohort Study: Utilizing data from the China Health & Retirement Study, this study explored the association between short nighttime sleep and incident asthma by adjusting for multiple baseline factors.

Post‐Genome‐Wide Association Study: Using GWAS summary data from large biobanks, this study first explored the genetic link through genetic correlation and identification of pleiotropic loci. Subsequently, a Mendelian Randomization analysis was performed to evaluate the causal effect. The reliability of the results was verified through multiple sensitivity analyses and advanced correction methods.