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Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism.


ABSTRACT: Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes and suggest APOE as a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

SUBMITTER: Burstein D 

PROVIDER: S-EPMC10947608 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism.

Burstein David D   Griffen Trevor C TC   Therrien Karen K   Bendl Jaroslav J   Venkatesh Sanan S   Dong Pengfei P   Modabbernia Amirhossein A   Zeng Biao B   Mathur Deepika D   Hoffman Gabriel G   Sysko Robyn R   Hildebrandt Tom T   Voloudakis Georgios G   Roussos Panos P  

Nature genetics 20230807 9


Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from  ...[more]

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