0016 Genetic Basis of Daytime Napping and Consequence on Cardiometabolic Health

Abstract

Abstract Introduction Although daytime napping is a common, evolutionarily conserved behavior, its genetic basis is unknown. Elucidating its genetic basis may clarify relevant underlying biological pathways and determine causal links with cardiometabolic health. Methods We performed a genome-wide association study of self-reported daytime napping using linear regression in adults of European ancestry in the UK Biobank (n=452,633) and assessed robustness of signals with accelerometer-derived daytime inactivity duration (n=84,671). Next, we conducted a phenome-wide association study in a hospital-based clinical biobank (n=30,683) using napping genome-wide polygenic score (GPS), and Mendelian randomization (MR) with cardiometabolic traits. To deconstruct the napping genetic variants, we applied a novel “soft clustering” Bayesian nonnegative matrix factorization method and generated partitioned cluster-specific polygenic risk scores (PRS). Results We identified 121 distinct genome-wide significant loci for daytime napping, with lead signals at or near genes KSR2 (kinase-suppressor of ras 2), HCRTR1/HCRTR2 (hypocretin-receptor 1/2), SKOR2 (SKI family transcriptional-corepressor 2), and MAPT (microtubule-associated protein tau), among others. The loci associated with accelerometer-derived daytime inactivity duration. Gene enrichment analyses pointed to pathways involved in neurogenesis and others including nervous system development and opioid signaling. Genetic overlaps were evident in a clinical biobank where highest, compared to lowest, decile of napping GPS associated with 30%, 40%, and 50% higher odds for essential hypertension, obesity, and nonalcoholic liver disease, respectively (P<0.0001). In MR, potential causal links were identified with higher diastolic blood pressure (2.67 mmHg per napping category-increase, 95% CI 1.62–3.23, P=6.80e-07), systolic blood pressure (3.65mmHg, 1.86–5.44, P=6.40e-05), and waist circumference (0.28 SD-units, 0.11–0.45, P=0.0015). The clustering of variants identified 3 robust clusters (cluster-1: “higher sleep propensity”; cluster-2: “more fragmented/inefficient night sleep”; cluster-3: “early sleep timing”). Only clusters 2 and 3 PRSs were associated with worse cardiometabolic health outcomes, including higher BMI, waist circumference, CRP, and triglycerides (all P<0.05). Conclusion These findings expand our understanding of the genetic architecture of napping implicating multiple biological pathways, indicating possible genetic overlap and causal links to cardiometabolic traits, and suggesting distinct nap-promoting mechanisms with differential associations with health outcomes. Support This work is supported by grants NIH-F32DK102323, NIH-4T32HL007901, NIH-R01DK107859, NIH-R35HL135818, and MGH Research Scholar Fund.