Mitchell, K. S. et al. Binge eating disorder: a symptom-level investigation of genetic and environmental influences on liability. Psychol. Med. 40, 1899–1906 (2010).
Google Scholar
Reichborn-Kjennerud, T., Bulik, C. M., Tambs, K. & Harris, J. R. Genetic and environmental influences on binge eating in the absence of compensatory behaviors: a population-based twin study. Int. J. Eat. Disord. 36, 307–314 (2004).
Google Scholar
Udo, T. & Grilo, C. M. Prevalence and correlates of DSM-5-defined eating disorders in a nationally representative sample of U.S. adults. Biol. Psychiatry 84, 345–354 (2018).
Google Scholar
Brownley, K. A. et al. Binge-eating disorder in adults: a systematic review and meta-analysis. Ann. Intern. Med. 165, 409–420 (2016).
Google Scholar
Wonderlich, S. A., Gordon, K. H., Mitchell, J. E., Crosby, R. D. & Engel, S. G. The validity and clinical utility of binge eating disorder. Int. J. Eat. Disord. 42, 687–705 (2009).
Google Scholar
Bulik, C. M. et al. The binge eating genetics initiative (BEGIN): study protocol. BMC Psychiatry 20, 307 (2020).
Google Scholar
Javaras, K. N. et al. Co-occurrence of binge eating disorder with psychiatric and medical disorders. J. Clin. Psychiatry 69, 266–273 (2008).
Google Scholar
Javaras, K. N. et al. Familiality and heritability of binge eating disorder: results of a case-control family study and a twin study. Int. J. Eat. Disord. 41, 174–179 (2008).
Google Scholar
Hübel, C. et al. One size does not fit all. Genomics differentiates among anorexia nervosa, bulimia nervosa, and binge-eating disorder. Int. J. Eat. Disord. 54, 785–793 (2021).
Google Scholar
Guss, J. L., Kissileff, H. R., Devlin, M. J., Zimmerli, E. & Walsh, B. T. Binge size increases with body mass index in women with binge-eating disorder. Obes. Res. 10, 1021–1029 (2002).
Google Scholar
Anderson, D. A., Williamson, D. A., Johnson, W. G. & Grieve, C. O. Validity of test meals for determining binge eating. Eat. Behav. 2, 105–112 (2001).
Google Scholar
Kenardy, J. et al. Disordered eating behaviours in women with type 2 diabetes mellitus. Eat. Behav. 2, 183–192 (2001).
Google Scholar
Hudson, J. I. et al. Longitudinal study of the diagnosis of components of the metabolic syndrome in individuals with binge-eating disorder. Am. J. Clin. Nutr. 91, 1568–1573 (2010).
Google Scholar
Hilbert, A. et al. Meta-analysis on the long-term effectiveness of psychological and medical treatments for binge-eating disorder. Int. J. Eat. Disord. 53, 1353–1376 (2020).
Google Scholar
Peat, C. M. et al. Comparative effectiveness of treatments for binge-eating disorder: systematic review and network meta-analysis. Eur. Eat. Disord. Rev. 25, 317–328 (2017).
Google Scholar
Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).
Google Scholar
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Google Scholar
Volkow, N. D. et al. The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. 32, 4–7 (2018).
Google Scholar
Satterthwaite, T. D. et al. The Philadelphia Neurodevelopmental Cohort: a publicly available resource for the study of normal and abnormal brain development in youth. Neuroimage 124, 1115–1119 (2016).
Google Scholar
Ollier, W., Sprosen, T. & Peakman, T. UK Biobank: from concept to reality. Pharmacogenomics 6, 639–646 (2005).
Google Scholar
Diagnostic and Statistical Manual of Mental Disorders (DSM-5) 5th edn (American Psychiatric Association Publishing, 2013).
Kessler, R. C. et al. The prevalence and correlates of binge eating disorder in the World Health Organization World Mental Health Surveys. Biol. Psychiatry 73, 904–914 (2013).
Google Scholar
Sonneville, K. R. & Lipson, S. K. Disparities in eating disorder diagnosis and treatment according to weight status, race/ethnicity, socioeconomic background, and sex among college students. Int. J. Eat. Disord. 51, 518–526 (2018).
Google Scholar
Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
Google Scholar
Polimanti, R. et al. Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. Mol. Psychiatry 25, 1673–1687 (2020).
Google Scholar
Bulik-Sullivan, B. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Google Scholar
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Google Scholar
Turley, P. et al. Multi-ancestry meta-analysis yields novel genetic discoveries and ancestry-specific associations. Preprint at bioRxiv https://doi.org/10.1101/2021.04.23.441003 (2021).
Zou, Y., Carbonetto, P., Wang, G. & Stephens, M. Fine-mapping from summary data with the ‘Sum of Single Effects’ model. PLoS Genet. 18, e1010299 (2022).
Google Scholar
Burstein, D. et al. Detecting and adjusting for hidden biases due to phenotype misclassification in genome-wide association studies. Preprint at medRxiv https://doi.org/10.1101/2023.01.17.23284670 (2023).
Genovese, C. R., Roeder, K. & Wasserman, L. False discovery control with p-value weighting. Biometrika 93, 509–524 (2006).
Google Scholar
Karlsson Linnér, R. et al. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat. Neurosci. 24, 1367–1376 (2021).
Google Scholar
Williams, C. et al. Guidelines for evaluating the comparability of down-sampled GWAS summary statistics. Preprint at bioRxiv https://doi.org/10.1101/2023.03.21.533641 (2023).
Elsworth, B. et al. The MRC IEU OpenGWAS data infrastructure. Preprint at bioRxiv https://doi.org/10.1101/2020.08.10.244293 (2020).
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
Google Scholar
Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).
Google Scholar
Bell, S. et al. A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis. Commun. Biol. 4, 156 (2021).
Google Scholar
Tanimura, N. et al. GATA/heme multi-omics reveals a trace metal-dependent cellular differentiation mechanism. Dev. Cell 46, 581–594.e4 (2018).
Google Scholar
Domcke, S. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).
Google Scholar
Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).
Google Scholar
An, S. J., Kim, T. J. & Yoon, B.-W. Epidemiology, risk factors, and clinical features of intracerebral hemorrhage: an update. J. Stroke 19, 3–10 (2017).
Google Scholar
Stunkard, A. J. & Allison, K. C. Binge eating disorder: disorder or marker? Int. J. Eat. Disord. 34 (Suppl.), S107–S116 (2003).
Google Scholar
Hinckley, J. D. et al. Quantitative trait locus linkage analysis in a large Amish pedigree identifies novel candidate loci for erythrocyte traits. Mol. Genet. Genom. Med. 1, 131–141 (2013).
Google Scholar
Galmozzi, A. et al. PGRMC2 is an intracellular haem chaperone critical for adipocyte function. Nature 576, 138–142 (2019).
Google Scholar
Borgna-Pignatti, C. & Zanella, S. Pica as a manifestation of iron deficiency. Expert Rev. Hematol. 9, 1075–1080 (2016).
Google Scholar
Ersche, K. D. et al. Disrupted iron regulation in the brain and periphery in cocaine addiction. Transl. Psychiatry 7, e1040 (2017).
Google Scholar
Barnea, R. et al. Trait and state binge eating predispose towards cocaine craving. Addict. Biol. 22, 163–171 (2017).
Google Scholar
Succurro, E. et al. Obese patients with a binge eating disorder have an unfavorable metabolic and inflammatory profile. Medicine 94, e2098 (2015).
Google Scholar
Al-Massadi, O. et al. Multifaceted actions of melanin-concentrating hormone on mammalian energy homeostasis. Nat. Rev. Endocrinol. 17, 745–755 (2021).
Google Scholar
Noble, E. E. et al. Hypothalamus–hippocampus circuitry regulates impulsivity via melanin-concentrating hormone. Nat. Commun. 10, 4923 (2019).
Google Scholar
Harrington, K. M. et al. Gender differences in demographic and health characteristics of the Million Veteran Program cohort. Women’s Health Issues 29 (Suppl. 1), S56–S66 (2019).
Google Scholar
Gelernter, J. et al. Genome-wide association study of post-traumatic stress disorder reexperiencing symptoms in >165,000 US veterans. Nat. Neurosci. 22, 1394–1401 (2019).
Google Scholar
Fang, H. et al. Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet. 105, 763–772 (2019).
Google Scholar
1000 Genomes Project Consortium An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Google Scholar
Karcher, N. R. & Barch, D. M. The ABCD study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology 46, 131–142 (2021).
Google Scholar
Wu, P. et al. Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation. JMIR Med. Inform. 7, e14325 (2019).
Google Scholar
Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016).
Google Scholar
1000 Genomes Project Consortium A global reference for human genetic variation. Nature 526, 68–74 (2015).
Google Scholar
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
Google Scholar
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Google Scholar
Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).
Google Scholar
Bigdeli, T. B. et al. A simple yet accurate correction for winner’s curse can predict signals discovered in much larger genome scans. Bioinformatics 32, 2598–2603 (2016).
Google Scholar
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
Google Scholar
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Google Scholar
Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. B 82, 1273–1300 (2020).
Google Scholar
Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).
Google Scholar
Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).
Google Scholar
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Google Scholar
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Google Scholar
Churchhouse, C. & Neale, B. Rapid GWAS of Thousands of Phenotypes for 337,000 Samples in the UK Biobank http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-the-uk-biobank (Biobank, 2017).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Google Scholar
Schilder, B. M., Humphrey, J. & Raj, T. echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline. Bioinformatics 38, 536–539 (2021).
Google Scholar