Communications Biology (Oct 2024)

Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior

  • Kyle A. Sullivan,
  • Matthew Lane,
  • Mikaela Cashman,
  • J. Izaak Miller,
  • Mirko Pavicic,
  • Angelica M. Walker,
  • Ashley Cliff,
  • Jonathon Romero,
  • Xuejun Qin,
  • Niamh Mullins,
  • Anna Docherty,
  • Hilary Coon,
  • Douglas M. Ruderfer,
  • International Suicide Genetics Consortium,
  • VA Million Veteran Program,
  • MVP Suicide Exemplar Workgroup,
  • Michael R. Garvin,
  • John P. Pestian,
  • Allison E. Ashley-Koch,
  • Jean C. Beckham,
  • Benjamin McMahon,
  • David W. Oslin,
  • Nathan A. Kimbrel,
  • Daniel A. Jacobson,
  • David Kainer

DOI
https://doi.org/10.1038/s42003-024-06943-7
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 16

Abstract

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Abstract Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. We present GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks when GWAS thresholds are relaxed. GRIN was validated on both simulated interrelated gene sets as well as multiple GWAS traits. From multiple GWAS summary statistics of suicide attempt, a complex phenotype, GRIN identified additional genes that replicated across independent cohorts and retained biologically interrelated genes despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways that may influence suicidal behavior, and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate GRIN’s utility in boosting GWAS results by increasing the number of true positive genes identified from GWAS results.