Polygenic risk score identifies associations between sleep duration and diseases determined from an electronic medical record biobank.

Sleep
Authors
Keywords
Abstract

STUDY OBJECTIVES: We aimed to detect cross-sectional phenotype and polygenic risk score (PRS) associations between sleep duration and prevalent diseases using the Partners Biobank, a hospital-based cohort study linking electronic medical records (EMR) with genetic information.

METHODS: Disease prevalence was determined from EMR, and sleep duration was self-reported. A PRS for sleep duration was derived using 78 previously associated SNPs from genome-wide association studies (GWAS) for self-reported sleep duration. We tested for associations between (1) self-reported sleep duration and 22 prevalent diseases (n = 30 251), (2) the PRS and self-reported sleep duration (n = 6903), and (3) the PRS and the 22 prevalent diseases (n = 16 033). For observed PRS-disease associations, we tested causality using two-sample Mendelian randomization (MR).

RESULTS: In the age-, sex-, and race-adjusted model, U-shaped associations were observed for sleep duration and asthma, depression, hypertension, insomnia, obesity, obstructive sleep apnea, and type 2 diabetes, where both short and long sleepers had higher odds for these diseases than normal sleepers (p

CONCLUSIONS: By validating the PRS for sleep duration and identifying cross-phenotype associations, we lay the groundwork for future investigations on the intersection between sleep, genetics, clinical measures, and diseases using large EMR datasets.

Year of Publication
2019
Journal
Sleep
Volume
42
Issue
3
Date Published
2019 Mar 01
ISSN
1550-9109
DOI
10.1093/sleep/zsy247
PubMed ID
30521049
PubMed Central ID
PMC6424085
Links
Grant list
R01 DK105072 / DK / NIDDK NIH HHS / United States
R01 DK107859 / DK / NIDDK NIH HHS / United States
R35 HL135818 / HL / NHLBI NIH HHS / United States
R01 HL113338 / HL / NHLBI NIH HHS / United States