World Health Organization. Fact sheet: obesity and overweight. WHO http://www.who.int/mediacentre/factsheets/fs311/en/ (2016).
Bray, G. A., Barry, W. S. & Mothon, S. Lipogenesis in adipose tissue from genetically obese rats. Metabolism 19, 839–848 (1970).
Miller, D. S. & Mumford, P. Gluttony. 1. An experimental study of overeating low- or high-protein diets. Am. J. Clin. Nutr. 20, 1212–1222 (1967).
Sims, E. A., Horton, E. S. & Salans, L. B. Inducible metabolic abnormalities during development of obesity. Annu. Rev. Med. 22, 235–250 (1971).
Vague, J. La differenciation sexuelle; facteur determinant des formes de l'obesite [French]. Presse Med. 55, 339 (1947).
Ogden, C. L., Carroll, M. D., Fryar, C. D. & Flegal, K. M. Prevalence of obesity among adults and youth: United States, 2011–2014. Centers for Disease Control and Prevention https://www.cdc.gov/nchs/data/databriefs/db219.pdf (2015).
Ogden, C. L., Carroll, M. D., Kit, B. K. & Flegal, K. M. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA 311, 806–814 (2014).
Audrain-McGovern, J. & Benowitz, N. L. Cigarette smoking, nicotine, and body weight. Clin. Pharmacol. Ther. 90, 164–168 (2011).
Bouchard, C. et al. The response to long-term overfeeding in identical twins. N. Engl. J. Med. 322, 1477–1482 (1990).
Bouchard, C. et al. The response to exercise with constant energy intake in identical twins. Obes. Res. 2, 400–410 (1994).
Bray, G. A., Redman, L. M., de Jonge, L., Rood, J. & Smith, S. R. Effect of three levels of dietary protein on metabolic phenotype of healthy individuals with 8 weeks of overfeeding. J. Clin. Endocrinol. Metab. 101, 2836–2843 (2016).
Chiolero, A., Faeh, D., Paccaud, F. & Cornuz, J. Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am. J. Clin. Nutr. 87, 801–809 (2008).
Hankinson, A. L. et al. Maintaining a high physical activity level over 20 years and weight gain. JAMA 304, 2603–2610 (2010).
Hu, F. B., Li, T. Y., Colditz, G. A., Willett, W. C. & Manson, J. E. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA 289, 1785–1791 (2003).
Katzmarzyk, P. T. et al. Physical activity, sedentary time, and obesity in an international sample of children. Med. Sci. Sports Exerc. 47, 2062–2069 (2015).
Sacks, F. M. et al. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N. Engl. J. Med. 360, 859–873 (2009).
Wareham, N. J., van Sluijs, E. M. & Ekelund, U. Physical activity and obesity prevention: a review of the current evidence. Proc. Nutr. Soc. 64, 229–247 (2005).
Bouchard, C., Tchernof, A. & Tremblay, A. Predictors of body composition and body energy changes in response to chronic overfeeding. Int. J. Obes. (Lond.) 38, 236–242 (2014).
de Jonge, L. & Bray, G. A. The thermic effect of food and obesity: a critical review. Obes. Res. 5, 622–631 (1997).
Gebauer, J., Schuster, S., de Figueiredo, L. F. & Kaleta, C. Detecting and investigating substrate cycles in a genome-scale human metabolic network. FEBS J. 279, 3192–3202 (2012).
Harper, M. E., Green, K. & Brand, M. D. The efficiency of cellular energy transduction and its implications for obesity. Annu. Rev. Nutr. 28, 13–33 (2008).
This paper discusses the coupling efficiency of mitochondrial oxidative phosphorylation and its potential as a target for anti-obesity interventions.
Katzmarzyk, P. T., Perusse, L., Tremblay, A. & Bouchard, C. No association between resting metabolic rate or respiratory exchange ratio and subsequent changes in body mass and fatness: 5-1/2 year follow-up of the Quebec family study. Eur. J. Clin. Nutr. 54, 610–614 (2000).
Newsholme, E. A. Sounding Board. A possible metabolic basis for the control of body weight. N. Engl. J. Med. 302, 400–405 (1980).
Ravussin, E. et al. Reduced rate of energy expenditure as a risk factor for body-weight gain. N. Engl. J. Med. 318, 467–472 (1988).
Schutz, Y. & Dulloo, A. G. in Handbook of Obesity: Epidemiology, Etiology, and Pathophysiology Vol. 1 (eds Bray, G. A. & Bouchard, C.) 267–280 (CRC Press, 2014).
Seidell, J. C., Muller, D. C., Sorkin, J. D. & Andres, R. Fasting respiratory exchange ratio and resting metabolic rate as predictors of weight gain: the Baltimore Longitudinal Study on Aging. Int. J. Obes. Relat. Metab. Disord. 16, 667–674 (1992).
Ahima, R. S. Revisiting leptin's role in obesity and weight loss. J. Clin. Invest. 118, 2380–2383 (2008).
Biondi, B. Thyroid and obesity: an intriguing relationship. J. Clin. Endocrinol. Metab. 95, 3614–3617 (2010).
Eckel, R. H. Insulin resistance: an adaptation for weight maintenance. Lancet 340, 1452–1453 (1992).
Farooqi, I. S. et al. Partial leptin deficiency and human adiposity. Nature 414, 34–35 (2001).
Farshchi, H. R. & Macdonald, I. A. in Handbook of obesity: epidemiology, etiology, and physiopathology Vol. 1 (eds Bray, G. A. Bouchard, C.) 193–201 (CRC Press, 2014).
Lambert, E. A., Straznicky, N. E., Dixon, J. B. & Lambert, G. W. Should the sympathetic nervous system be a target to improve cardiometabolic risk in obesity? Am. J. Physiol. Heart Circ. Physiol. 309, H244–H258 (2015).
Mantzoros, C. S. The role of leptin in human obesity and disease: a review of current evidence. Ann. Intern. Med. 130, 671–680 (1999).
Montague, C. T. et al. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature 387, 903–908 (1997).
Pasquali, R. Obesity and androgens: facts and perspectives. Fertil. Steril. 85, 1319–1340 (2006).
Scacchi, M., Pincelli, A. I. & Cavagnini, F. Growth hormone in obesity. Int. J. Obes. Relat. Metab. Disord. 23, 260–271 (1999).
Swinburn, B. A. & Ravussin, E. Energy and macronutrient metabolism. Baillieres Clin. Endocrinol. Metab. 8, 527–548 (1994).
Aja, S., Sahandy, S., Ladenheim, E. E., Schwartz, G. J. & Moran, T. H. Intracerebroventricular CART peptide reduces food intake and alters motor behavior at a hindbrain site. Am. J. Physiol. Regul. Integr. Comp. Physiol. 281, R1862–R1867 (2001).
Bullo-Bonet, M., Garcia-Lorda, P., Lopez-Soriano, F. J., Argiles, J. M. & Salas-Salvado, J. Tumour necrosis factor, a key role in obesity? FEBS Lett. 451, 215–219 (1999).
Clement, K. et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 392, 398–401 (1998).
Coll, A. P., Farooqi, I. S. & O'Rahilly, S. The hormonal control of food intake. Cell 129, 251–262 (2007).
Davis, C. A. et al. Dopamine for “wanting” and opioids for “liking”: a comparison of obese adults with and without binge eating. Obesity (Silver Spring) 17, 1220–1225 (2009).
Flint, A., Raben, A., Astrup, A. & Holst, J. J. Glucagon-like peptide 1 promotes satiety and suppresses energy intake in humans. J. Clin. Invest. 101, 515–520 (1998).
Gerald, C. et al. A receptor subtype involved in neuropeptide-Y-induced food intake. Nature 382, 168–171 (1996).
Gibbs, J., Young, R. C. & Smith, G. P. Cholecystokinin decreases food intake in rats. J. Comp. Physiol. Psychol. 84, 488–495 (1973).
Gropp, E. et al. Agouti-related peptide-expressing neurons are mandatory for feeding. Nat. Neurosci. 8, 1289–1291 (2005).
Gutzwiller, J. P. et al. Glucagon-like peptide-1: a potent regulator of food intake in humans. Gut 44, 81–86 (1999).
Ilnytska, O. & Argyropoulos, G. The role of the Agouti-related protein in energy balance regulation. Cell. Mol. Life Sci. 65, 2721–2731 (2008).
Karra, E., Chandarana, K. & Batterham, R. L. The role of peptide YY in appetite regulation and obesity. J. Physiol. 587, 19–25 (2009).
Krude, H. et al. Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nat. Genet. 19, 155–157 (1998).
Kubota, N. et al. Adiponectin stimulates AMP-activated protein kinase in the hypothalamus and increases food intake. Cell Metab. 6, 55–68 (2007).
Lam, D. D., Garfield, A. S., Marston, O. J., Shaw, J. & Heisler, L. K. Brain serotonin system in the coordination of food intake and body weight. Pharmacol. Biochem. Behav. 97, 84–91 (2010).
le Roux, C. W. et al. Attenuated peptide YY release in obese subjects is associated with reduced satiety. Endocrinology 147, 3–8 (2006).
Lenard, N. R. & Berthoud, H. R. Central and peripheral regulation of food intake and physical activity: pathways and genes. Obesity (Silver Spring) 16 (Suppl. 3), S11–S22 (2008).
This article reviews the neural systems that involve thousands of genes that control food intake and energy expenditure. Progress on the role of the hypothalamus and the caudal brainstem in the various hormonal and neural mechanisms by which the brain is informed about ingested and stored nutrients is also reviewed.
Marks, D. L., Hruby, V., Brookhart, G. & Cone, R. D. The regulation of food intake by selective stimulation of the type 3 melanocortin receptor (MC3R). Peptides 27, 259–264 (2006).
Millington, G. W. The role of proopiomelanocortin (POMC) neurones in feeding behaviour. Nutr. Metab. (Lond.) 4, 18 (2007).
Nakazato, M. et al. A role for ghrelin in the central regulation of feeding. Nature 409, 194–198 (2001).
Pelleymounter, M. A., Cullen, M. J. & Wellman, C. L. Characteristics of BDNF-induced weight loss. Exp. Neurol. 131, 229–238 (1995).
Plata-Salaman, C. R., Oomura, Y. & Kai, Y. Tumor necrosis factor and interleukin-1 beta: suppression of food intake by direct action in the central nervous system. Brain Res. 448, 106–114 (1988).
Poritsanos, N. J., Mizuno, T. M., Lautatzis, M. E. & Vrontakis, M. Chronic increase of circulating galanin levels induces obesity and marked alterations in lipid metabolism similar to metabolic syndrome. Int. J. Obes. (Lond.) 33, 1381–1389 (2009).
Reidelberger, R. D. Cholecystokinin and control of food intake. J. Nutr. 124, (Suppl. 8) S1327–S1333 (1994).
Rios, M. BDNF and the central control of feeding: accidental bystander or essential player? Trends Neurosci. 36, 83–90 (2013).
Schick, R. R. et al. Effect of galanin on food intake in rats: involvement of lateral and ventromedial hypothalamic sites. Am. J. Physiol. 264, R355–R361 (1993).
Schwartz, M. W. et al. Central nervous system control of food intake. Nature 404, 661–671 (2000).
Stanley, S., Wynne, K., McGowan, B. & Bloom, S. Hormonal regulation of food intake. Physiol. Rev. 85, 1131–1158 (2005).
Turton, M. D. et al. A role for glucagon-like peptide-1 in the central regulation of feeding. Nature 379, 69–72 (1996).
Volkow, N. D., Wang, G. J., Tomasi, D. & Baler, R. D. The addictive dimensionality of obesity. Biol. Psychiatry 73, 811–818 (2013).
Woods, S. C. & Seeley, R. J. Hap1 and GABA: thinking about food intake. Cell Metab. 3, 388–390 (2006).
Yamauchi, T. & Kadowaki, T. Physiological and pathophysiological roles of adiponectin and adiponectin receptors in the integrated regulation of metabolic and cardiovascular diseases. Int. J. Obes. (Lond.) 32 (Suppl. 7), S13–S18 (2008).
Yeo, G. S. & Heisler, L. K. Unraveling the brain regulation of appetite: lessons from genetics. Nat. Neurosci. 15, 1343–1349 (2012).
This is an insightful review on the role of central pathways related to appetite regulation in the genetics of polygenic obesity.
Cohen, P. & Spiegelman, B. M. Cell biology of fat storage. Mol. Biol. Cell 27, 2523–2527 (2016).
Harms, M. & Seale, P. Brown and beige fat: development, function and therapeutic potential. Nat. Med. 19, 1252–1263 (2013).
Hickner, R. C., Racette, S. B., Binder, E. F., Fisher, J. S. & Kohrt, W. M. Suppression of whole body and regional lipolysis by insulin: effects of obesity and exercise. J. Clin. Endocrinol. Metab. 84, 3886–3895 (1999).
Kern, P. A. Potential role of TNFalpha and lipoprotein lipase as candidate genes for obesity. J. Nutr. 127, 1917S–1922S (1997).
Lafontan, M. & Langin, D. Lipolysis and lipid mobilization in human adipose tissue. Prog. Lipid Res. 48, 275–297 (2009).
Langin, D. et al. Adipocyte lipases and defect of lipolysis in human obesity. Diabetes 54, 3190–3197 (2005).
Sidossis, L. & Kajimura, S. Brown and beige fat in humans: thermogenic adipocytes that control energy and glucose homeostasis. J. Clin. Invest. 125, 478–486 (2015).
Wang, H. & Eckel, R. H. Lipoprotein lipase: from gene to obesity. Am. J. Physiol. Endocrinol. Metab. 297, E271–E288 (2009).
Galgani, J. E., Moro, C. & Ravussin, E. Metabolic flexibility and insulin resistance. Am. J. Physiol. Endocrinol. Metab. 295, E1009–E1017 (2008).
Holloway, G. P., Bonen, A. & Spriet, L. L. Regulation of skeletal muscle mitochondrial fatty acid metabolism in lean and obese individuals. Am. J. Clin. Nutr. 89, 455S–462S (2009).
Houmard, J. A., Pories, W. J. & Dohm, G. L. Is there a metabolic program in the skeletal muscle of obese individuals? J. Obes. 2011, 250496 (2011).
Maltin, C. A. Muscle development and obesity: is there a relationship? Organogenesis 4, 158–169 (2008).
Simoneau, J. A. & Bouchard, C. Skeletal muscle metabolism and body fat content in men and women. Obes. Res. 3, 23–29 (1995).
Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The impact of the gut microbiota on human health: an integrative view. Cell 148, 1258–1270 (2012).
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).
Bray, G. A. & Bouchard, C. Handbook of obesity: epidemiology, etiology, and physiopathology 3rd edn (CRC Press, 2014).
This book provides an in-depth discussion of the role of biology, behaviour and the social environment in the aetiology of obesity.
Despres, J. P. Body fat distribution and risk of cardiovascular disease: an update. Circulation 126, 1301–1313 (2012).
World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ. Tech. Rep. Ser. 894, 1–253 (2000).
World Health Organisation Expert Consultation Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363, 157–163 (2004).
Bouchard, C. BMI, fat mass, abdominal adiposity and visceral fat: where is the 'beef'? Int. J. Obes. (Lond.) 31, 1552–1553 (2007).
Katzmarzyk, P. T. & Bouchard, C. Where is the beef? Waist circumference is more highly correlated with BMI and total body fat than with abdominal visceral fat in children. Int. J. Obes. (Lond.) 38, 753–754 (2014).
Jackson, A. S. et al. The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study. Int. J. Obes. Relat. Metab. Disord. 26, 789–796 (2002).
Perusse, L., Rice, T. K. & Bouchard, C. in Handbook of Obesity: epidemiology, etiology, and Physiopathology Vol. 1 (eds Bray, G. A.& Bouchard, C.) 91–104 (Taylor & Francis Group, 2014).
Bouchard, C. Perusse, L., Leblanc, C., Tremblay, A. & Theriault, G. Inheritance of the amount and distribution of human body fat. Int. J. Obes. 12, 205–215 (1988).
Sorensen, T. I. A. & Stunkard, A. J. in The Genetics of Obesity (ed. Bouchard, C.) 49–61 (CRC Press Inc., 1994).
Stunkard, A. J., Foch, T. T. & Hrubec, Z. A twin study of human obesity. JAMA 256, 51–54 (1986).
Stunkard, A. J. et al. An adoption study of human obesity. N. Engl. J. Med. 314, 193–198 (1986).
Pigeyre, M., Yazdi, F. T., Kaur, Y. & Meyre, D. Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin. Sci. (Lond.) 130, 943–986 (2016).
van der Klaauw, A. A. & Farooqi, I. S. The hunger genes: pathways to obesity. Cell 161, 119–132 (2015).
Dai, H. J., Wu, J. C., Tsai, R. T., Pan, W. H. & Hsu, W. L. T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes. Database (Oxford) 2013, bas061 (2013).
Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).
Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).
This study highlights the role of common and rare variants in the variance of BMI. It proposes that the heritability of BMI is lower than has been predicted based on epidemiological approaches.
Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).
This large meta-analysis of GWAS of BMI in adults encompasses more than 339,000 individuals. It provides the first exploration of the GWAS findings underlying BMI biology.
Felix, J. F. et al. Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum. Mol. Genet. 25, 389–403 (2016).
This large meta-analysis of GWAS of BMI in children encompasses more than 46,000 children and provides useful comparisons with prior GWAS results.
Lu, Y. et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat. Commun. 7, 10495 (2016).
This is the largest meta-analysis to date of GWAS of body fat percentage in adults, encompassing more than 100,000 individuals. The findings are compared to those from GWAS on BMI.
Randall, J. C. et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet. 9, e1003500 (2013).
Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).
Sung, Y. J. et al. Genome-wide association studies suggest sex-specific loci associated with abdominal and visceral fat. Int. J. Obes. (Lond.) 40, 662–674 (2016).
Kilpelainen, T. O. et al. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels. Nat. Commun. 7, 10494 (2016).
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
This study was the first report to describe the DEPICT software for use in advanced bioinformatic analysis of GWAS findings, including candidate gene and tissue prioritization and gene-set enrichment analysis.
Winkler, T. W. et al. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. PLoS Genet. 11, e1005378 (2015).
Fernandez-Rhodes, L. et al. Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci. Hum. Genet. 136, 771–800 (2017).
Monda, K. L. et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat. Genet. 45, 690–696 (2013).
Wen, W. et al. Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index. Hum. Mol. Genet. 23, 5492–5504 (2014).
Rakyan, V. K., Down, T. A., Balding, D. J. & Beck, S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529–541 (2011).
Clifton, E. A. et al. Associations between body mass index-related genetic variants and adult body composition: The Fenland cohort study. Int. J. Obes. (Lond.) 41, 613–619 (2017).
GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 348, 648–660 (2015).
Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).
This recent paper reports the generation of tissue-specific gene networks and their application in gaining biological insights and gene prioritization from GWAS data.
Frayling, T. M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894 (2007).
Scuteri, A. et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 3, e115 (2007).
de Leeuw, C. A., Neale, B. M., Heskes, T. & Posthuma, D. The statistical properties of gene-set analysis. Nat. Rev. Genet. 17, 353–364 (2016).
Wang, K., Li, M. & Hakonarson, H. Analysing biological pathways in genome-wide association studies. Nat. Rev. Genet. 11, 843–854 (2010).
Pathway-based approaches are proposed to provide a more powerful analysis of GWAS data sets. These methods are reviewed, and their practical use and caveats are discussed.
Schork, A. J. et al. All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet. 9, e1003449 (2013).
Zhong, H., Yang, X., Kaplan, L. M., Molony, C. & Schadt, E. E. Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am. J. Hum. Genet. 86, 581–591 (2010).
Menche, J. et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).
Mostafavi, S. & Morris, Q. Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics 26, 1759–1765 (2010).
Akula, N. et al. A network-based approach to prioritize results from genome-wide association studies. PLoS ONE 6, e24220 (2011).
Jia, P. & Zhao, Z. Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives. Hum. Genet. 133, 125–138 (2014).
Leiserson, M. D., Eldridge, J. V., Ramachandran, S. & Raphael, B. J. Network analysis of GWAS data. Curr. Opin. Genet. Dev. 23, 602–610 (2013).
Tasan, M. et al. Selecting causal genes from genome-wide association studies via functionally coherent subnetworks. Nat. Methods 12, 154–159 (2015).
This study reports a strategy for the use of software using genome-scale shared-function networks to identify sets of mutually and functionally related genes spanning multiple GWAS-identified loci.
Lamparter, D., Marbach, D., Rueedi, R., Kutalik, Z. & Bergmann, S. Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput. Biol. 12, e1004714 (2016).
Lanciego, J. L., Luquin, N. & Obeso, J. A. Functional neuroanatomy of the basal ganglia. Cold Spring Harb. Perspect. Med. 2, a009621 (2012).
Albert, F. W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).
Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).
GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
RNA sequencing data from the GTEx project are presented. Gene expression across tissues and tissue-specific and shared regulatory expression eQTL variants from GWAS are discussed.
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).
Bernstein, B. E. et al. The NIH roadmap epigenomics mapping consortium. Nat. Biotechnol. 28, 1045–1048 (2010).
Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).
Ward, L. D. & Kellis, M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 44, D877–D881 (2016).
Oellrich, A., Sanger Mouse Genetics Project & Smedley, D. Linking tissues to phenotypes using gene expression profiles. Database (Oxford) 2014, bau017 (2014).
Koeijvoets, K. C. et al. Complement factor H Y402H decreases cardiovascular disease risk in patients with familial hypercholesterolaemia. Eur. Heart J. 30, 618–623 (2009).
Nadeau, J. H. Modifier genes in mice and humans. Nat. Rev. Genet. 2, 165–174 (2001).
Benesch, R. E., Kwong, S., Edalji, R. & Benesch, R. alpha Chain mutations with opposite effects on the gelation of hemoglobin S. J. Biol. Chem. 254, 8169–8172 (1979).
Boehm, J., Ehrlich, I., Hsieh, H. & Malinow, R. Two mutations preventing PDZ-protein interactions of GluR1 have opposite effects on synaptic plasticity. Learn. Mem. 13, 562–565 (2006).
Carter, A. J. & Nguyen, A. Q. Antagonistic pleiotropy as a widespread mechanism for the maintenance of polymorphic disease alleles. BMC Med. Genet. 12, 160 (2011).
von Gernet, S., Golla, A., Ehrenfels, Y., Schuffenhauer, S. & Fairley, J. D. Genotype-phenotype analysis in Apert syndrome suggests opposite effects of the two recurrent mutations on syndactyly and outcome of craniofacial surgery. Clin. Genet. 57, 137–139 (2000).
Fischer, J. et al. Inactivation of the Fto gene protects from obesity. Nature 458, 894–898 (2009).
Berulava, T., Rahmann, S., Rademacher, K., Klein-Hitpass, L. & Horsthemke, B. N6-adenosine methylation in MiRNAs. PLoS ONE 10, e0118438 (2015).
Jowett, J. B. et al. Genetic variation at the FTO locus influences RBL2 gene expression. Diabetes 59, 726–732 (2010).
Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).
Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).
Stratigopoulos, G. et al. Hypomorphism of Fto and Rpgrip1l causes obesity in mice. J. Clin. Invest. 126, 1897–1910 (2016).
Carbon, S. et al. AmiGO: online access to ontology and annotation data. Bioinformatics 25, 288–289 (2009).
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).
Brodie, A., Azaria, J. R. & Ofran, Y. How far from the SNP may the causative genes be? Nucleic Acids Res. 44, 6046–6054 (2016).
Zhang, H. et al. MicroRNA-455 regulates brown adipogenesis via a novel HIF1an-AMPK-PGC1alpha signaling network. EMBO Rep. 16, 1378–1393 (2015).
Farh, K. K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
Greene, C. S. & Himmelstein, D. S. Genetic association-guided analysis of gene networks for the study of complex traits. Circ. Cardiovasc. Genet. 9, 179–184 (2016).
This article reviews network-based approaches and new techniques that use nominally significant, as opposed to genome-wide significant, associations to guide bioinformatic analyses. The example of a network-wide association study (NetWAS) is discussed.
Volkow, N. D., Wang, G. J. & Baler, R. D. Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn. Sci. 15, 37–46 (2011).
Bio-Text Mining Group in Text-mined Hypertension, Obesity, and Diabetes Candidate Gene Database (Intelligent Agent Systems Lab, 2011).
Walters, R. G. et al. Rare genomic structural variants in complex disease: lessons from the replication of associations with obesity. PLoS ONE 8, e58048 (2013).
Wheeler, E. et al. Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity. Nat. Genet. 45, 513–517 (2013).