Nature GeneticsGenomicsPeer-reviewed journal article
Biological and clinical insights from genetics of insomnia symptoms
HUNT All In Sleep, Jacqueline M. LaneiD, Samuel E. JonesiD, Hassan S. DashtiiD, Andrew R. WoodiD, Krishna G. AragamiD, Vincent T. van HeesiD, Linn Beate StrandiD, Bendik Slagsvold WinsvoldiD, Heming WangiD, Jack BowdeniD, Yanwei SongiD, Krunal Patel, Simon George AndersoniD, Robin N. BeaumontiD, David A. BechtoldiD, Brian E. CadeiD, Mary E. HaasiD, Sekar KathiresaniD, Max A. LittleiD, Annemarie Ida LuikiD, Andrew S. I. LoudoniD, Shaun PurcelliD, Rebecca Claire RichmondiD, Frank A. J. L. ScheeriD, , Richa SaxenaiD
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Abstract
Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes, but underlying pathophysiological processes and causal relationships with disease are poorly understood. Here we identify 57 loci for self-reported insomnia symptoms in the UK Biobank (n=453,379) and confirm their impact on self-reported insomnia symptoms in the HUNT study (n=14,923 cases, 47,610 controls), physician diagnosed insomnia in Partners Biobank (n=2,217 cases, 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n=83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis, phototransduction and muscle development pathways and of genes expressed in multiple brain regions, skeletal muscle and adrenal gland. Evidence of shared genetic factors is found between frequent insomnia symptoms and restless legs syndrome, aging, cardio-metabolic, behavioral, psychiatric and reproductive traits. Evidence is found for a possible causal link between insomnia symptoms and coronary heart disease, depressive symptoms and subjective well-being.\n\nOne Sentence SummaryWe identify 57 genomic regions associated with insomnia pointing to the involvement of phototransduction and ubiquitination and potential causal links to CAD and depression.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
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