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Updated: May 27, 2026

Association Between Sleep Quality and Cognitive Symptoms in Patients with Major Depressive Disorder
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Published on: April 26, 2024

Phenotype Refinement Influences GWAS-Implicated Variant Effect Sizes for Insomnia.

Amber J Zimmerman1,2,3,4, Patrick Z Liu1, Brendan T Keenan1

  • 1Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Journal of Sleep Research
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) for sleep traits like insomnia are hindered by subjective reporting. This study refines phenotypes using UK Biobank data, revealing that common genetic signals for insomnia may actually be linked to restless legs syndrome (RLS).

Keywords:
BTBD9GWASMEIS1RLSUK biobankinsomnia

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Area of Science:

  • Genetics
  • Sleep Medicine
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for understanding complex traits but are limited by phenotype measurement, especially for sleep disorders.
  • Subjective reports often conflate distinct sleep conditions like insomnia and restless legs syndrome (RLS), complicating genetic analyses.
  • Improving phenotype classification is essential for advancing our understanding of the genetic architecture of sleep traits.

Purpose of the Study:

  • To refine phenotypes for sleep traits using detailed questionnaire data from the UK Biobank.
  • To differentiate between insomnia and RLS based on subjective reports in a large cohort.
  • To re-evaluate significant genetic loci previously associated with insomnia.

Main Methods:

  • Utilized expanded questionnaire data from approximately 180,000 UK Biobank participants.
  • Employed statistical models to analyze phenotype data and genetic associations.
  • Examined the influence of RLS on previously identified GWAS signals for insomnia at the MEIS1 and BTBD9 loci.

Main Results:

  • Demonstrated that prior GWAS for insomnia likely mischaracterized participants due to a simplified, single-question approach.
  • Found that two major GWAS signals for insomnia are predominantly or entirely driven by their association with RLS.
  • Highlighted significant overlap and potential misattribution of genetic effects between insomnia and RLS.

Conclusions:

  • Accurate phenotype classification is critical for the success of GWAS in sleep research.
  • UK Biobank data offers valuable resources for detailed sleep phenotype refinement.
  • Genetic associations previously attributed to insomnia may need re-evaluation due to confounding with RLS.