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Identification of common genetic risk variants for autism spectrum disorder

Research output: Contribution to journalArticle

  • Jakob Grove
  • Stephan Ripke
  • Thomas D. Als
  • Manuel Mattheisen
  • Raymond K. Walters
  • Hyejung Won
  • Jonatan Pallesen
  • Esben Agerbo
  • Ole A. Andreassen
  • Richard Anney
  • Swapnil Awashti
  • Rich Belliveau
  • Francesco Bettella
  • Joseph D. Buxbaum
  • Jonas Bybjerg-Grauholm
  • Marie Bækvad-Hansen
  • Felecia Cerrato
  • Kimberly Chambert
  • Jane H. Christensen
  • Claire Churchhouse
  • Karin Dellenvall
  • Ditte Demontis
  • Silvia De Rubeis
  • Bernie Devlin
  • Srdjan Djurovic
  • Ashley L. Dumont
  • Jacqueline I. Goldstein
  • Christine S. Hansen
  • Mads Engel Hauberg
  • Mads V. Hollegaard
  • Sigrun Hope
  • Daniel P. Howrigan
  • Hailiang Huang
  • Christina M. Hultman
  • Lambertus Klei
  • Julian Maller
  • Joanna Martin
  • Carsten Bøcker Pedersen
  • George Davey Smith
  • Beate St Pourcain
  • Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium
  • Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
  • the 23 and Me Research Team
Original languageEnglish
Pages (from-to)431-444
Number of pages14
JournalNature Genetics
Issue number3
Early online date25 Feb 2019
DateAccepted/In press - 12 Dec 2018
DateE-pub ahead of print - 25 Feb 2019
DatePublished (current) - Mar 2019


Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.

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