Friedman, J. M. The function of leptin in nutrition, weight, and physiology. Nutr. Rev. 60, S1–S14; discussion S68–S84, S85–S87 (2002).
Cone, R. D. Anatomy and regulation of the central melanocortin system. Nat. Neurosci. 8, 571–578 (2005).
Cowley, M. A. et al. Leptin activates anorexigenic POMC neurons through a neural network in the arcuate nucleus. Nature 411, 480–484 (2001).
Hill, J. W. et al. Direct insulin and leptin action on pro-opiomelanocortin neurons is required for normal glucose homeostasis and fertility. Cell Metab. 11, 286–297 (2010).
Varela, L. & Horvath, T. L. Leptin and insulin pathways in POMC and AgRP neurons that modulate energy balance and glucose homeostasis. EMBO Rep. 13, 1079–1086 (2012).
Chen, A. S. et al. Role of the melanocortin-4 receptor in metabolic rate and food intake in mice. Transgenic Res. 9, 145–154 (2000).
Fan, W., Boston, B. A., Kesterson, R. A., Hruby, V. J. & Cone, R. D. Role of melanocortinergic neurons in feeding and the agouti obesity syndrome. Nature 385, 165–168 (1997).
Vaisse, C., Clement, K., Guy-Grand, B. & Froguel, P. A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nat. Genet. 20, 113–114 (1998).
Yeo, G. S. et al. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat. Genet. 20, 111–112 (1998).
Huszar, D. et al. Targeted disruption of the melanocortin-4 receptor results in obesity in mice. Cell 88, 131–141 (1997).
Farooqi, I. S. et al. Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. N. Engl. J. Med. 348, 1085–1095 (2003).
Krakoff, J. et al. Lower metabolic rate in individuals heterozygous for either a frameshift or a functional missense MC4R variant. Diabetes 57, 3267–3272 (2008).
Brown, P. I. & Brasel, J. in The Malnourished Child Nestlé Nutrition Workshop Series (eds Lewinter-Suskind, L. & Suskind, R. M.) 213–228 (Nestlé Nutrition Institute and Vevey/Raven Press, 1990).
Clement, K. et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 392, 398–401 (1998).
Strobel, A., Issad, T., Camoin, L., Ozata, M. & Strosberg, A. D. A leptin missense mutation associated with hypogonadism and morbid obesity. Nat. Genet. 18, 213–215 (1998).
Roselli-Rehfuss, L. et al. Identification of a receptor for gamma melanotropin and other proopiomelanocortin peptides in the hypothalamus and limbic system. Proc. Natl Acad. Sci. USA 90, 8856–8860 (1993).
Gantz, I. et al. Molecular cloning of a novel melanocortin receptor. J. Biol. Chem. 268, 8246–8250 (1993).
Butler, A. A. et al. A unique metabolic syndrome causes obesity in the melanocortin-3 receptor-deficient mouse. Endocrinology 141, 3518–3521 (2000).
Chen, A. S. et al. Inactivation of the mouse melanocortin-3 receptor results in increased fat mass and reduced lean body mass. Nat. Genet. 26, 97–102 (2000).
Renquist, B. J. et al. Melanocortin-3 receptor regulates the normal fasting response. Proc. Natl Acad. Sci. USA 109, E1489–E1498 (2012).
Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).
Day, F. R. et al. Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk. Nat. Genet. 49, 834–841 (2017).
Demidowich, A. P., Jun, J. Y. & Yanovski, J. A. Polymorphisms and mutations in the melanocortin-3 receptor and their relation to human obesity. Biochim. Biophys. Acta Mol. Basis Dis. 1863, 2468–2476 (2017).
Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).
Mencarelli, M. et al. Rare melanocortin-3 receptor mutations with in vitro functional consequences are associated with human obesity. Hum. Mol. Genet. 20, 392–399 (2011).
Zegers, D. et al. Identification of three novel genetic variants in the melanocortin-3 receptor of obese children. Obesity (Silver Spring) 19, 152–159 (2011).
Lee, Y. S., Poh, L. K. & Loke, K. Y. A novel melanocortin 3 receptor gene (MC3R) mutation associated with severe obesity. J. Clin. Endocrinol. Metab. 87, 1423–1426 (2002).
Studenski, S. A. et al. The FNIH Sarcopenia Project: rationale, study description, conference recommendations, and final estimates. J. Gerontol. A Biol. Sci. Med. Sci. 69, 547–558 (2014).
Kim, T. N. et al. Comparisons of three different methods for defining sarcopenia: an aspect of cardiometabolic risk. Sci. Rep. 7, 6491 (2017).
Boyd, A. et al. Cohort profile: the ‘children of the 90s’-the index offspring of the Avon Longitudinal Study of Parents and Children. Int. J. Epidemiol. 42, 111–127 (2013).
Wade, K. H. et al. Loss-of-function mutations in the melanocortin 4 receptor in a UK birth cohort. Nat. Med. 27, 1088–1096 (2021).
Lotta, L. A. et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nat. Genet. 53, 54–64 (2021).
Khaw, K. T. et al. Combined impact of health behaviours and mortality in men and women: the EPIC-Norfolk prospective population study. PLoS Med. 5, e12 (2008).
Pietzner, M. et al. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat. Med. 27, 471–479 (2021).
Tapanainen, J. et al. Short and long term effects of growth hormone on circulating levels of insulin-like growth factor-I (IGF-I), IGF-binding protein-1, and insulin: a placebo-controlled study. J. Clin. Endocrinol. Metab. 73, 71–74 (1991).
Finer, S. et al. Cohort profile: East London Genes & Health (ELGH), a community-based population genomics and health study in British Bangladeshi and British Pakistani people. Int. J. Epidemiol. 49, 20–21i (2020).
de Onis, M. et al. Development of a WHO growth reference for school-aged children and adolescents. Bull. World Health Organ. 85, 660–667 (2007).
Campbell, J. N. et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20, 484–496 (2017).
Sweeney, P. et al. The melanocortin-3 receptor is a pharmacological target for the regulation of anorexia. Sci. Transl. Med. 13, eabd6434 (2021).
Lam, B. Y. H. et al. Heterogeneity of hypothalamic pro-opiomelanocortin-expressing neurons revealed by single-cell RNA sequencing. Mol. Metab. 6, 383–392 (2017).
Romanov, R. A. et al. Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes. Nat. Neurosci. 20, 176–188 (2017).
Chen, R., Wu, X., Jiang, L. & Zhang, Y. Single-cell RNA-seq reveals hypothalamic cell diversity. Cell Rep. 18, 3227–3241 (2017).
Backholer, K. et al. Kisspeptin cells in the ewe brain respond to leptin and communicate with neuropeptide Y and proopiomelanocortin cells. Endocrinology 151, 2233–2243 (2010).
Cocchi, D., De Gennaro Colonna, V., Bagnasco, M., Bonacci, D. & Muller, E. E. Leptin regulates GH secretion in the rat by acting on GHRH and somatostatinergic functions. J. Endocrinol. 162, 95–99 (1999).
Tannenbaum, G. S., Gurd, W. & Lapointe, M. Leptin is a potent stimulator of spontaneous pulsatile growth hormone (GH) secretion and the GH response to GH-releasing hormone. Endocrinology 139, 3871–3875 (1998).
Wang, L. & Moenter, S. M. Differential roles of hypothalamic AVPV and arcuate kisspeptin neurons in estradiol feedback regulation of female reproduction. Neuroendocrinology 110, 172–184 (2020).
Dunger, D. B., Ahmed, M. L. & Ong, K. K. Effects of obesity on growth and puberty. Best Pract. Res. Clin. Endocrinol. Metab. 19, 375–390 (2005).
Hauspie, R. C., Vercauteren, M. & Susanne, C. Secular changes in growth and maturation: an update. Acta Paediatr. Suppl. 423, 20–27 (1997).
Kuhnen, P. et al. Proopiomelanocortin deficiency treated with a melanocortin-4 receptor agonist. N. Engl. J. Med. 375, 240–246 (2016).
Roa, J. & Herbison, A. E. Direct regulation of GnRH neuron excitability by arcuate nucleus POMC and NPY neuron neuropeptides in female mice. Endocrinology 153, 5587–5599 (2012).
Manfredi-Lozano, M. et al. Defining a novel leptin–melanocortin–kisspeptin pathway involved in the metabolic control of puberty. Mol. Metab. 5, 844–857 (2016).
Salomon, F., Cuneo, R. C., Hesp, R. & Sonksen, P. H. The effects of treatment with recombinant human growth hormone on body composition and metabolism in adults with growth hormone deficiency. N. Engl. J. Med. 321, 1797–1803 (1989).
Doherty, T. J. Invited review: aging and sarcopenia. J. Appl. Physiol. 95, 1717–1727 (2003).
McCance, R. A. & Widdowson, E. M. The determinants of growth and form. Proc. R. Soc. Lond. B Biol. Sci. 185, 1–17 (1974).
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).
Eastwood, S. V. et al. Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLoS ONE 11, e0162388 (2016).
Powell, R. M. et al. Development and validation of total and regional body composition prediction equations from anthropometry and single frequency segmental bioelectrical impedance with DEXA. Preprint at medRxiv https://doi.org/10.1101/2020.12.16.20248330 (2020).
Zhao, Y. et al. GIGYF1 loss of function is associated with clonal mosaicism and adverse metabolic health. Nat. Commun. 12, 4178 (2021).
McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).
Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Van Hout, C. V. et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
Millard, L. A. C., Davies, N. M., Gaunt, T. R., Davey Smith, G. & Tilling, K. Software application profile: PHESANT: a tool for performing automated phenome scans in UK Biobank. Int. J. Epidemiol. 47, 29–35 (2018).
Ghoussaini, M. et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311–D1320 (2021).
Elsworth, B. et al. The MRC IEU OpenGWAS data infrastructure. Preprint at bioRxiv https://doi.org/10.1101/2020.08.10.244293 (2020).
McInnes, G. et al. Global Biobank Engine: enabling genotype–phenotype browsing for biobank summary statistics. Bioinformatics 35, 2495–2497 (2019).
Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics 35, 4851–4853 (2019).
Howe, L. D. et al. Changes in ponderal index and body mass index across childhood and their associations with fat mass and cardiovascular risk factors at age 15. PLoS ONE 5, e15186 (2010).
Frysz, M., Howe, L. D., Tobias, J. H. & Paternoster, L. Using SITAR (superimposition by translation and rotation) to estimate age at peak height velocity in Avon Longitudinal Study of Parents and Children. Wellcome Open Res. 3, 90 (2018).
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv https://export.arxiv.org/abs/1303.3997 (2013).
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
Wade, K. H. et al. Loss-of-function mutations in the melanocortin 4 receptor in a UK birth cohort. Nat. Med. 27, 1088–1096 (2021).
Lindsay, T. et al. Descriptive epidemiology of physical activity energy expenditure in UK adults (the Fenland study). Int. J. Behav. Nutr. Phys. Act. 16, 126 (2019).
Williams, S. A. et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857 (2019).
Pietzner, M. et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat. Commun. 11, 6397 (2020).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Day, N. et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br. J. Cancer 80 95–103 (1999).
Narasimhan, V. M. et al. Health and population effects of rare gene knockouts in adult humans with related parents. Science 352, 474–477 (2016).
Bayraktar, O. A. et al. Astrocyte layers in the mammalian cerebral cortex revealed by a single-cell in situ transcriptomic map. Nat. Neurosci. 23, 500–509 (2020).
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell Detection with Star-Convex Polygons in MICCAI 2018265–273 (Springer Nature Switzerland, 2018)
Widmann, J. et al. RNASTAR: an RNA structural alignment repository that provides insight into the evolution of natural and artificial RNAs. RNA 18, 1319–1327 (2012).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).