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Psychiatric genetics

Machine learning drives genetic discovery for binge eating disorder

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Identifying genetic risk factors for binge-eating disorder (BED) is vital to understand its etiology and develop effective prevention and intervention strategies. To overcome under-reporting of clinical BED diagnosis, a new study uses machine learning to identify genetic variants associated with quantitative BED risk scores and finds evidence for a pathological role of heme metabolism.

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Fig. 1: Genome-wide association analyses provide insights into BED.

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Author notes

  1. These authors contributed equally: Jackson G. Thorp, Zachary F. Gerring.

Authors and Affiliations

  1. Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Jackson G. Thorp, Zachary F. Gerring & Eske M. Derks

Authors
  1. Jackson G. Thorp
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  2. Zachary F. Gerring
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  3. Eske M. Derks
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Corresponding author

Correspondence to Eske M. Derks.

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The authors report no competing interests.

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Thorp, J.G., Gerring, Z.F. & Derks, E.M. Machine learning drives genetic discovery for binge eating disorder. Nat Genet 55, 1424–1425 (2023). https://doi.org/10.1038/s41588-023-01473-0

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