Nature and Science of Sleep (Jan 2022)

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

  • Yao Y,
  • Jia Y,
  • Wen Y,
  • Cheng B,
  • Cheng S,
  • Liu L,
  • Yang X,
  • Meng P,
  • Chen Y,
  • Li C,
  • Zhang J,
  • Zhang Z,
  • Pan C,
  • Zhang H,
  • Wu C,
  • Wang X,
  • Ning Y,
  • Wang S,
  • Zhang F

Journal volume & issue
Vol. Volume 14
pp. 1 – 12

Abstract

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Yao Yao,* Yumeng Jia,* Yan Wen, Bolun Cheng, Shiqiang Cheng, Li Liu, Xuena Yang, Peilin Meng, Yujing Chen, Chun’e Li, Jingxi Zhang, Zhen Zhang, Chuyu Pan, Huijie Zhang, Cuiyan Wu, Xi Wang, Yujie Ning, Sen Wang, Feng Zhang Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, People’s Republic of China*These authors contributed equally to this workCorrespondence: Feng ZhangKey Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of ChinaEmail [email protected]: 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 (PPWAS = 0.0146) and GFAP (PPWAS = 0.0236) were identified associated with sleep health score by PWAS. TWAS identified ORC4 (PTWAS = 0.0212) and ZNF732 (PTWAS = 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 PLDSC < 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.Keywords: sleep, sleep health score, genome-wide association study, genetics, complex-traits

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