Machine learning drives genetic discovery for binge eating disorder

Psychiatric genetics

Nature Genetics (2023)Cite this article

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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.

Binge-eating disorder (BED) is the most common eating disorder, characterized by regular loss of control over eating, excessive food consumption, psychological distress, and the absence of compensatory behaviors1. It is a new diagnosis that was added in the DSM-5. Consequently, BED is underdiagnosed, no large case–control cohorts have been established, and few cases can be identified from biobanks — the prevalence of BED from electronic health records in the Million Veterans Program (MVP) was 0.1%2, which is well below the estimated lifetime prevalence of between 1 and 3%3,4. Until now, no large-scale genome-wide association studies (GWAS) of BED have been conducted, and the genetic architecture of the disorder is largely unknown. In this issue of Nature Genetics, Burstein et al.2 tackle the underdiagnosis of BED by using machine learning to derive a proxy BED phenotype in the MVP in order to increase sample size and conduct a well-powered GWAS.

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American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 Vol. 5 (2013).

Burstein, D. et al. Nat. Genet. https://doi.org/10.1038/s41588-023-01464-1 (2023).

Article Google Scholar

Brownley, K. A. et al. Ann. Intern. Med. 165, 409–420 (2016).

Article PubMed PubMed Central Google Scholar

Keski-Rahkonen, A. Curr. Opin. Psychiatry 34, 525–531 (2021).

Article PubMed Google Scholar

Hübel, C. et al. Int. J. Eat. Disord. 54, 785–793 (2021).

Article PubMed PubMed Central Google Scholar

Grotzinger, A. D. et al. Nat. Hum. Behav. 3, 513–525 (2019).

Article PubMed PubMed Central Google Scholar

Qiu, J., Lian, F. & Fang, X. Front. Nutr. 9, 1084860 (2022).

Article PubMed PubMed Central Google Scholar

Heidari, M. et al. Mol. Psychiatry 21, 1599–1607 (2016).

Article CAS PubMed PubMed Central Google Scholar

Lotan, A. et al. Mol. Psychiatry https://doi.org/10.1038/s41380-023-01979-3 (2023).

Article PubMed Google Scholar

McAllum, E. J. et al. Prog. Neurobiol. 186, 101744 (2020).

Article CAS PubMed Google Scholar

Connor, J. R. & Menzies, S. L. Glia 17, 83–93 (1996).

3.0.CO;2-7" data-track-action="article reference" href="https://doi.org/10.1002%2F%28SICI%291098-1136%28199606%2917%3A2%3C83%3A%3AAID-GLIA1%3E3.0.CO%3B2-7" aria-label="Article reference 11" data-doi="10.1002/(SICI)1098-1136(199606)17:23.0.CO;2-7">Article CAS PubMed Google Scholar

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

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

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

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Correspondence to Eske M. Derks.

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 (2023). https://doi.org/10.1038/s41588-023-01473-0

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Published: 07 August 2023

DOI: https://doi.org/10.1038/s41588-023-01473-0

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