Genome-Wide Association Study and Genetic Correlation Scan Provide Insights into Its Genetic Architecture of Sleep Health Score in the UK Biobank Cohort

Abstract

Most previous genetic studies of sleep behaviors were conducted individually, without comprehensive consideration of the complexity of various sleep behaviors. Our aim is to identify the genetic architecture and potential biomarker of the sleep health score, which more powerfully represents overall sleep traits. Patients and Methods: We conducted a genome-wide association study (GWAS) of sleep health score (overall assessment of sleep duration, snoring, insomnia, chronotype, and daytime dozing) using 336,463 participants from the UK Biobank. Proteome-wide association study (PWAS) and transcriptome-wide association study (TWAS) were then performed to identify candidate genes at the protein and mRNA level, respectively. We finally used linkage disequilibrium score regression (LDSC) to estimate the genetic correlations between sleep health score and other functionally relevance traits. Results: GWAS identified multiple variants near known candidate genes associated with sleep health score, such as MEIS1, FBXL13, MED20 and SMAD5. HDHD2 (P PWAS = 0.0146) and GFAP (P PWAS = 0.0236) were identified associated with sleep health score by PWAS. TWAS identified ORC4 (P TWAS = 0.0212) and ZNF732 (P TWAS = 0.0349) considering mRNA expression level. LDSC found significant genetic correlations of sleep health score with 3 sleep behaviors (including insomnia, snoring, dozing), 4 psychiatry disorders (major depressive disorder, attention deficit/hyperactivity disorder, schizophrenia, autism spectrum disorder), and 9 plasma protein (such as Stabilin-1, Stromelysin-2, Cytochrome c) (all LDSC P LDSC < 0.05). Conclusion: Our results advance the comprehensive understanding of the aetiology and genetic architecture of the sleep health score, refine the understanding of the relationship of sleep health score with other traits and diseases, and may serve as potential targets for future mechanistic studies of sleep phenotype.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

“…In the UK Biobank, significant genetic correlations were noted between a sleep health score composed of measures of sleep duration, snoring, insomnia, chronotype, and daytime dozing with 4 psychiatric disorders (major depressive disorder, attention deficit/hyperactivity disorder, schizophrenia, and autism spectrum disorder) and 9 plasma proteins, including cytochrome c oxidase. 32 Elevated cytochrome c oxidase levels were associated with long-term sleep deprivation in rats. 33…”

Genome-wide association analysis of composite sleep health scores in 413,904 individuals

“…FUMA was used to annotate the genome-wide significant (GWS) risk loci (p<5e-8), lead independent SNPs (r 2 <0.1), and all SNPs in LD with independent SNPs (r 2 >=0.6) within a genomic region (including ANNOVAR functional consequence, CADD score, RegulomeDB score, as well as 15 chromatin states, and GWAS Catalog associations). We compared the GWS loci to loci reported by biobank-based GWAS of sleep traits published by June 2022, including insomnia 8,9 , sleep duration 7 , daytime sleepiness 10 , chronotype 12 , snoring 11 , daytime napping 60 , obstructive sleep apnea (OSA) 61 , restless legs syndrome (RLS) 62 , and prior SHS-ADD in UKB unrelated individuals 13 . GWS loci at least 500kb from any of the reported loci were noted as unreported.…”

“…For the UK Biobank additive sleep health score (SHS-ADD), consistent with previous studies 13,14 we assigned one point to each of five positive sleep traits, as follows: Chronotype: More a 'morning' than an 'evening' person or Definitely a 'morning' person; Sleep Duration: from 7 to 8 hours (inclusive); Insomnia Symptoms: Never/rarely; Snoring: No; Subjective daytime sleepiness: 0. Never/rarely.…”

Associations of a multidimensional polygenic sleep health score and a sleep lifestyle index on health outcomes and their interaction in a clinical biobank

“…Sleep is a complex behavior regulated in part by environmental and genetic factors [10,11]. Recent genome-wide association studies (GWAS) have identified genetic variants robustly associated with composite sleep [12][13][14], enabling the computation of Polygenic Scores (PRS) (predictors of genetic susceptibility to traits or diseases) of multidimensional sleep [15]. Combining PRS with phenotypic risk factors could help elucidate the interplay between genetic and environmental influences on disease risk [16,17].…”