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.
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BED GWAS summary statistics from the MVP data are available on dbGaP (accession no. phs001672). For the external validation sets for the partitioned heritability analysis, we used open chromatin regions from a murine erythroid cell beta-estradiol stimulation model (GEO accession no. GSE114996), open chromatin atlas of adult human brains (GEO accession no. GSE147672) and open chromatin atlas of developing human organs (https://descartes.brotmanbaty.org/bbi/human-chromatin-during-development). For GWAS summary statistics for genetic correlation analyses, see the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk).
Software used in this study included the following programs: EIGENSOFT v.6 (https://github.com/dreichlab/eig); FUMA v.1.3.7 (https://fuma.ctglab.nl); GCTA v.1.93.2 (https://yanglab.westlake.edu.cn/software/gcta/#Overview); KING v.2.0 (https://www.chen.kingrelatedness.com); LD score regression v.1.0.1 (https://github.com/bulik/ldsc); liftOver v.1.2.0 (https://genome.ucsc.edu/cgi-bin/hgLiftOver); Minimac v.3 (https://genome.sph.umich.edu/wiki/Minimac3); Multi-ancestry meta-analysis (https://github.com/JonJala/mama); PheMED (https://github.com/DiseaseNeuroGenomics/PheMED); PRS-CS (https://github.com/getian107/PRScs); SuSiE as implemented in echolocatoR72 (https://github.com/RajLabMSSM/echolocatoR).
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This research is based on data from the MVP, Office of Research and Development, Veterans Health Administration, and was supported by award no. MVP006. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. This study was also supported by the National Institutes of Health (NIH), Bethesda, Maryland, USA, under award numbers T32MH087004 (K.T.), T32MH096679 (T.C.G.), T32MH122394 (A.M.), K08MH122911 (G.V.), R01MH125246, R01AG067025, U01MH116442 and R01MH109677 (P.R.), and by the Veterans Affairs Merit grants BX002395 and BX004189 (P.R.). This study was also funded in part by the Brain & Behavior Research Foundation through the 2020 NARSAD Young Investigator Grant no. 29350 (G.V.). We thank S. W. Choi and P. F. O’Reilly for their guidance and expertise in using data from the UKBB. We thank the participants in the UKBB and the scientists involved in the construction of this resource. This research was conducted using the UKBB resource under application 18177 (P. F. O’Reilly). This work was supported in part by the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai, New York, New York, USA. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the National Institute of Mental Health Data Archive (NDA), Bethesda, Maryland, USA. This is a multi-site, longitudinal study designed to recruit more than 10,000 children aged 9–10 years and follow them over 10 years into early adulthood. The ABCD Study is supported by the NIH and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093 and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/scientists/workgroups. ABCD consortium investigators designed and implemented the study and/or provided data but did not participate in the analysis or writing of this report. The manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/1527728. DOIs can be found at https://nda.nih.gov/study.html?id=1661. Support for data collection for the PNC, acquired through dbGaP (accession no. phs000607.v3.p2), was provided by grant RC2MH089983 awarded to R. Gur and RC2MH089924 awarded to H. Hakonarson. Participants were recruited and genotyped through the Center for Applied Genomics (CAG) at the Children’s Hospital in Philadelphia (CHOP), Pennsylvania, USA. Phenotypic data collection occurred at the CAG and CHOP and at the Brain Behavior Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
T.H. is a scientific advisory board member of Noom. T.H. and R.S. receive funding from and have equity in Noom (a non-publicly traded company). R.S. receives royalties from Wolters Kluwer Health. The remaining authors declare no competing interests.
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Burstein, D., Griffen, T.C., Therrien, K. et al. Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism. Nat Genet 55, 1462–1470 (2023). https://doi.org/10.1038/s41588-023-01464-1
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