Using Polygenic Risk Scores to Evaluate Definitions of Self-Reported Sleep Phenotypes Across Cohorts
Clicks: 4
ID: 321753
2026
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Emerging Content
0.9
/100
3 views
0 readers
AI Quality Assessment
Not analyzed
Abstract
Abstract Study Objectives Since genome-wide association studies (GWAS) of sleep phenotypes have been conducted in differing populations and definitions of sleep phenotypes vary across studies, we investigated associations between several polygenic risk scores (PRSs) and potential sleep definitions among multiethnic cohorts. Methods Using data from four cohorts (HCHS/SOL, ARIC, MESA, BHS, N = 16 895), we considered multiple definitions of short and long sleep, insomnia, and excessive daytime sleepiness (EDS). PRSs were developed based on summary statistics from GWAS in European ancestry individuals from the UK Biobank (UKB) and from GWAS conducted in a multiethnic population from the Million Veteran Program (MVP). Study-specific analyses estimated associations between sleep PRSs and corresponding sleep measures per 1 standard deviation increase in the PRS. Models were adjusted for age, sex, ancestral principal components, and center and race as appropriate. Results were meta-analyzed across studies. Results PRSs based on European ancestry UKB GWAS had statistically significant associations with multiple definitions of the corresponding sleep phenotypes. Associations that were most consistent across studies included: short sleep PRS with ≤6 hours (OR = 1.23,p = 1.80x10-7,phet = 0.97); long sleep PRS with ≥9 hours (OR = 1.09,p = 6.76x10-4,phet = 0.77); insomnia PRS with the Women’s Health Initiative Insomnia Rating Scale (WHIIRS) ≥10 or a subset of three questions ≥6 in ARIC (OR = 1.17,p = 5.51x10-10,phet = 0.69); and EDS PRS with Epworth Sleepiness Scale (ESS) ≥11 (OR = 1.23,p = 1.83x10-13,phet = 0.83). PRSs based on multi-ancestry MVP GWAS had weaker associations compared to those based on European ancestry only. Conclusions By evaluating several types of sleep PRSs and sleep phenotypes, we were able to highlight which sleep PRS performed well across diverse populations and which sleep definitions better captured genetic underpinnings.
| Reference Key |
openalex_W7169884624
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Annah B. Wyss, M Brown, X Li, Brian Spitzer, Zhijie Huang, Heming Wang, Richa Saxena, Linda C. Gallo, Qibin Qi, Wassim Tarraf, Robert C. Kaplan, Melissa Lamar, Hector M González, Charles DeCarli, Myriam Fornage, Jerome I. Rotter, Stephen S. Rich, Kent D Taylor, Xiuqing Guo, Alexis C. Wood, Peter Y. Liu, Susan R. Heckbert, Chloé Sarnowski, Jan Bressler, A R Morrison, Bing Yu, Pamela L. Lutsey, Carmen R. Isasi, Susan Redline, Tamar Sofer |
| Journal | Sleep & breathing = Schlaf & Atmung |
| Year | 2026 |
| DOI |
10.1093/sleep/zsag200
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.