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Convergence between biological, behavioural and genetic determinants of obesity

Nature Reviews Genetics volume 18, pages 731748 (2017) | Download Citation

Abstract

Multiple biological, behavioural and genetic determinants or correlates of obesity have been identified to date. Genome-wide association studies (GWAS) have contributed to the identification of more than 100 obesity-associated genetic variants, but their roles in causal processes leading to obesity remain largely unknown. Most variants are likely to have tissue-specific regulatory roles through joint contributions to biological pathways and networks, through changes in gene expression that influence quantitative traits, or through the regulation of the epigenome. The recent availability of large-scale functional genomics resources provides an opportunity to re-examine obesity GWAS data to begin elucidating the function of genetic variants. Interrogation of knockout mouse phenotype resources provides a further avenue to test for evidence of convergence between genetic variation and biological or behavioural determinants of obesity.

Key points

  • Common genomic variants associated with obesity are interrogated for their potential implications for biological and behavioural mechanisms and their concordance with established risk factors for obesity.

  • An integrative analysis, taking advantage of the recently available large data repositories on tissue-specific gene networks, expression quantitative trait loci (eQTLs) and genome-wide promoter and enhancer location was undertaken, along with a review of evidence for phenotypic relevance through knockout mouse databases.

  • Exploring panels of SNPs (n = 118 SNPs) from three large genome-wide association studies on adult and childhood adiposity confirms that central nervous system (CNS)-related processes dominate human variation in BMI, whereas peripheral signalling pathways are more evident in variability in percentage body fat.

  • Several obesity-associated SNPs function as cis-eQTLs by altering the expression of nearby genes. Conditional analysis of the most significantly associated SNPs suggests that the majority of the obesity-associated SNPs tag other variants that may causally regulate nearby gene expression.

  • A large fraction of obesity-associated SNPs (46 of 118 GWAS variants) are located primarily in non-coding, regulatory domains of the human genome and overlap with at least one promoter- or enhancer-associated histone modification mark, particularly across multiple brain regions.

  • Knocked out genes proximal to the GWAS significant loci were interrogated in mouse databases for their potential convergence with obesity traits. The analysis identified 49 genes that displayed a relationship with more than one obesity trait when knocked out in mice.

  • Overall, common genomic variants tend to occur in genes, pathways and networks influencing brain regulation of energy balance, an observation consistent with the current consensus on the aetiology of obesity. However, at present, these types of variants do not seem to strongly implicate other established determinants of obesity such as hormonal regulation, skeletal muscle metabolism and energy expenditure traits.

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References

  1. 1.

    World Health Organization. Fact sheet: obesity and overweight. WHO (2016).

  2. 2.

    , & Lipogenesis in adipose tissue from genetically obese rats. Metabolism 19, 839–848 (1970).

  3. 3.

    & Gluttony. 1. An experimental study of overeating low- or high-protein diets. Am. J. Clin. Nutr. 20, 1212–1222 (1967).

  4. 4.

    , & Inducible metabolic abnormalities during development of obesity. Annu. Rev. Med. 22, 235–250 (1971).

  5. 5.

    La differenciation sexuelle; facteur determinant des formes de l'obesite [French]. Presse Med. 55, 339 (1947).

  6. 6.

    , , & Prevalence of obesity among adults and youth: United States, 2011–2014. Centers for Disease Control and Prevention (2015).

  7. 7.

    , , & Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA 311, 806–814 (2014).

  8. 8.

    & Cigarette smoking, nicotine, and body weight. Clin. Pharmacol. Ther. 90, 164–168 (2011).

  9. 9.

    et al. The response to long-term overfeeding in identical twins. N. Engl. J. Med. 322, 1477–1482 (1990).

  10. 10.

    et al. The response to exercise with constant energy intake in identical twins. Obes. Res. 2, 400–410 (1994).

  11. 11.

    , , , & 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).

  12. 12.

    , , & Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am. J. Clin. Nutr. 87, 801–809 (2008).

  13. 13.

    et al. Maintaining a high physical activity level over 20 years and weight gain. JAMA 304, 2603–2610 (2010).

  14. 14.

    , , , & Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA 289, 1785–1791 (2003).

  15. 15.

    et al. Physical activity, sedentary time, and obesity in an international sample of children. Med. Sci. Sports Exerc. 47, 2062–2069 (2015).

  16. 16.

    et al. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N. Engl. J. Med. 360, 859–873 (2009).

  17. 17.

    , & Physical activity and obesity prevention: a review of the current evidence. Proc. Nutr. Soc. 64, 229–247 (2005).

  18. 18.

    , & Predictors of body composition and body energy changes in response to chronic overfeeding. Int. J. Obes. (Lond.) 38, 236–242 (2014).

  19. 19.

    & The thermic effect of food and obesity: a critical review. Obes. Res. 5, 622–631 (1997).

  20. 20.

    , , & Detecting and investigating substrate cycles in a genome-scale human metabolic network. FEBS J. 279, 3192–3202 (2012).

  21. 21.

    , & 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.

  22. 22.

    , , & 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).

  23. 23.

    Sounding Board. A possible metabolic basis for the control of body weight. N. Engl. J. Med. 302, 400–405 (1980).

  24. 24.

    et al. Reduced rate of energy expenditure as a risk factor for body-weight gain. N. Engl. J. Med. 318, 467–472 (1988).

  25. 25.

    & in Handbook of Obesity: Epidemiology, Etiology, and Pathophysiology Vol. 1 (eds Bray, G. A. & Bouchard, C.) 267–280 (CRC Press, 2014).

  26. 26.

    , , & 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).

  27. 27.

    Revisiting leptin's role in obesity and weight loss. J. Clin. Invest. 118, 2380–2383 (2008).

  28. 28.

    Thyroid and obesity: an intriguing relationship. J. Clin. Endocrinol. Metab. 95, 3614–3617 (2010).

  29. 29.

    Insulin resistance: an adaptation for weight maintenance. Lancet 340, 1452–1453 (1992).

  30. 30.

    et al. Partial leptin deficiency and human adiposity. Nature 414, 34–35 (2001).

  31. 31.

    & in Handbook of obesity: epidemiology, etiology, and physiopathology Vol. 1 (eds Bray, G. A. Bouchard, C.) 193–201 (CRC Press, 2014).

  32. 32.

    , , & Should the sympathetic nervous system be a target to improve cardiometabolic risk in obesity? Am. J. Physiol. Heart Circ. Physiol. 309, H244–H258 (2015).

  33. 33.

    The role of leptin in human obesity and disease: a review of current evidence. Ann. Intern. Med. 130, 671–680 (1999).

  34. 34.

    et al. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature 387, 903–908 (1997).

  35. 35.

    Obesity and androgens: facts and perspectives. Fertil. Steril. 85, 1319–1340 (2006).

  36. 36.

    , & Growth hormone in obesity. Int. J. Obes. Relat. Metab. Disord. 23, 260–271 (1999).

  37. 37.

    & Energy and macronutrient metabolism. Baillieres Clin. Endocrinol. Metab. 8, 527–548 (1994).

  38. 38.

    , , , & 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).

  39. 39.

    , , , & Tumour necrosis factor, a key role in obesity? FEBS Lett. 451, 215–219 (1999).

  40. 40.

    et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 392, 398–401 (1998).

  41. 41.

    , & The hormonal control of food intake. Cell 129, 251–262 (2007).

  42. 42.

    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).

  43. 43.

    , , & Glucagon-like peptide 1 promotes satiety and suppresses energy intake in humans. J. Clin. Invest. 101, 515–520 (1998).

  44. 44.

    et al. A receptor subtype involved in neuropeptide-Y-induced food intake. Nature 382, 168–171 (1996).

  45. 45.

    , & Cholecystokinin decreases food intake in rats. J. Comp. Physiol. Psychol. 84, 488–495 (1973).

  46. 46.

    et al. Agouti-related peptide-expressing neurons are mandatory for feeding. Nat. Neurosci. 8, 1289–1291 (2005).

  47. 47.

    et al. Glucagon-like peptide-1: a potent regulator of food intake in humans. Gut 44, 81–86 (1999).

  48. 48.

    & The role of the Agouti-related protein in energy balance regulation. Cell. Mol. Life Sci. 65, 2721–2731 (2008).

  49. 49.

    , & The role of peptide YY in appetite regulation and obesity. J. Physiol. 587, 19–25 (2009).

  50. 50.

    et al. Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nat. Genet. 19, 155–157 (1998).

  51. 51.

    et al. Adiponectin stimulates AMP-activated protein kinase in the hypothalamus and increases food intake. Cell Metab. 6, 55–68 (2007).

  52. 52.

    , , , & Brain serotonin system in the coordination of food intake and body weight. Pharmacol. Biochem. Behav. 97, 84–91 (2010).

  53. 53.

    et al. Attenuated peptide YY release in obese subjects is associated with reduced satiety. Endocrinology 147, 3–8 (2006).

  54. 54.

    & 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.

  55. 55.

    , , & The regulation of food intake by selective stimulation of the type 3 melanocortin receptor (MC3R). Peptides 27, 259–264 (2006).

  56. 56.

    The role of proopiomelanocortin (POMC) neurones in feeding behaviour. Nutr. Metab. (Lond.) 4, 18 (2007).

  57. 57.

    et al. A role for ghrelin in the central regulation of feeding. Nature 409, 194–198 (2001).

  58. 58.

    , & Characteristics of BDNF-induced weight loss. Exp. Neurol. 131, 229–238 (1995).

  59. 59.

    , & 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).

  60. 60.

    , , & 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).

  61. 61.

    Cholecystokinin and control of food intake. J. Nutr. 124, (Suppl. 8) S1327–S1333 (1994).

  62. 62.

    BDNF and the central control of feeding: accidental bystander or essential player? Trends Neurosci. 36, 83–90 (2013).

  63. 63.

    et al. Effect of galanin on food intake in rats: involvement of lateral and ventromedial hypothalamic sites. Am. J. Physiol. 264, R355–R361 (1993).

  64. 64.

    et al. Central nervous system control of food intake. Nature 404, 661–671 (2000).

  65. 65.

    , , & Hormonal regulation of food intake. Physiol. Rev. 85, 1131–1158 (2005).

  66. 66.

    et al. A role for glucagon-like peptide-1 in the central regulation of feeding. Nature 379, 69–72 (1996).

  67. 67.

    , , & The addictive dimensionality of obesity. Biol. Psychiatry 73, 811–818 (2013).

  68. 68.

    & Hap1 and GABA: thinking about food intake. Cell Metab. 3, 388–390 (2006).

  69. 69.

    & 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).

  70. 70.

    & 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.

  71. 71.

    & Cell biology of fat storage. Mol. Biol. Cell 27, 2523–2527 (2016).

  72. 72.

    & Brown and beige fat: development, function and therapeutic potential. Nat. Med. 19, 1252–1263 (2013).

  73. 73.

    , , , & Suppression of whole body and regional lipolysis by insulin: effects of obesity and exercise. J. Clin. Endocrinol. Metab. 84, 3886–3895 (1999).

  74. 74.

    Potential role of TNFalpha and lipoprotein lipase as candidate genes for obesity. J. Nutr. 127, 1917S–1922S (1997).

  75. 75.

    & Lipolysis and lipid mobilization in human adipose tissue. Prog. Lipid Res. 48, 275–297 (2009).

  76. 76.

    et al. Adipocyte lipases and defect of lipolysis in human obesity. Diabetes 54, 3190–3197 (2005).

  77. 77.

    & Brown and beige fat in humans: thermogenic adipocytes that control energy and glucose homeostasis. J. Clin. Invest. 125, 478–486 (2015).

  78. 78.

    & Lipoprotein lipase: from gene to obesity. Am. J. Physiol. Endocrinol. Metab. 297, E271–E288 (2009).

  79. 79.

    , & Metabolic flexibility and insulin resistance. Am. J. Physiol. Endocrinol. Metab. 295, E1009–E1017 (2008).

  80. 80.

    , & Regulation of skeletal muscle mitochondrial fatty acid metabolism in lean and obese individuals. Am. J. Clin. Nutr. 89, 455S–462S (2009).

  81. 81.

    , & Is there a metabolic program in the skeletal muscle of obese individuals? J. Obes. 2011, 250496 (2011).

  82. 82.

    Muscle development and obesity: is there a relationship? Organogenesis 4, 158–169 (2008).

  83. 83.

    & Skeletal muscle metabolism and body fat content in men and women. Obes. Res. 3, 23–29 (1995).

  84. 84.

    , , & The impact of the gut microbiota on human health: an integrative view. Cell 148, 1258–1270 (2012).

  85. 85.

    et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).

  86. 86.

    & 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.

  87. 87.

    Body fat distribution and risk of cardiovascular disease: an update. Circulation 126, 1301–1313 (2012).

  88. 88.

    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).

  89. 89.

    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).

  90. 90.

    BMI, fat mass, abdominal adiposity and visceral fat: where is the 'beef'? Int. J. Obes. (Lond.) 31, 1552–1553 (2007).

  91. 91.

    & 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).

  92. 92.

    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).

  93. 93.

    , & in Handbook of Obesity: epidemiology, etiology, and Physiopathology Vol. 1 (eds Bray, G. A.& Bouchard, C.) 91–104 (Taylor & Francis Group, 2014).

  94. 94.

    , , & Inheritance of the amount and distribution of human body fat. Int. J. Obes. 12, 205–215 (1988).

  95. 95.

    & in The Genetics of Obesity (ed. Bouchard, C.) 49–61 (CRC Press Inc., 1994).

  96. 96.

    , & A twin study of human obesity. JAMA 256, 51–54 (1986).

  97. 97.

    et al. An adoption study of human obesity. N. Engl. J. Med. 314, 193–198 (1986).

  98. 98.

    , , & Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin. Sci. (Lond.) 130, 943–986 (2016).

  99. 99.

    & The hunger genes: pathways to obesity. Cell 161, 119–132 (2015).

  100. 100.

    , , , & T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes. Database (Oxford) 2013, bas061 (2013).

  101. 101.

    et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).

  102. 102.

    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.

  103. 103.

    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.

  104. 104.

    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.

  105. 105.

    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.

  106. 106.

    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).

  107. 107.

    et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

  108. 108.

    et al. Genome-wide association studies suggest sex-specific loci associated with abdominal and visceral fat. Int. J. Obes. (Lond.) 40, 662–674 (2016).

  109. 109.

    et al. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels. Nat. Commun. 7, 10494 (2016).

  110. 110.

    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.

  111. 111.

    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).

  112. 112.

    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).

  113. 113.

    et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat. Genet. 45, 690–696 (2013).

  114. 114.

    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).

  115. 115.

    , , & Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529–541 (2011).

  116. 116.

    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).

  117. 117.

    GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 348, 648–660 (2015).

  118. 118.

    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.

  119. 119.

    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).

  120. 120.

    et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 3, e115 (2007).

  121. 121.

    , , & The statistical properties of gene-set analysis. Nat. Rev. Genet. 17, 353–364 (2016).

  122. 122.

    , & 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.

  123. 123.

    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).

  124. 124.

    , , , & Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am. J. Hum. Genet. 86, 581–591 (2010).

  125. 125.

    et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).

  126. 126.

    & Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics 26, 1759–1765 (2010).

  127. 127.

    et al. A network-based approach to prioritize results from genome-wide association studies. PLoS ONE 6, e24220 (2011).

  128. 128.

    & Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives. Hum. Genet. 133, 125–138 (2014).

  129. 129.

    , , & Network analysis of GWAS data. Curr. Opin. Genet. Dev. 23, 602–610 (2013).

  130. 130.

    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.

  131. 131.

    , , , & Fast and rigorous computation of gene and pathway scores from SNP-based summary statistics. PLoS Comput. Biol. 12, e1004714 (2016).

  132. 132.

    , & Functional neuroanatomy of the basal ganglia. Cold Spring Harb. Perspect. Med. 2, a009621 (2012).

  133. 133.

    & The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).

  134. 134.

    et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat. Genet. 44, 1084–1089 (2012).

  135. 135.

    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.

  136. 136.

    , , & GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

  137. 137.

    et al. The NIH roadmap epigenomics mapping consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

  138. 138.

    et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

  139. 139.

    & 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).

  140. 140.

    , Sanger Mouse Genetics Project & Linking tissues to phenotypes using gene expression profiles. Database (Oxford) 2014, bau017 (2014).

  141. 141.

    et al. Complement factor H Y402H decreases cardiovascular disease risk in patients with familial hypercholesterolaemia. Eur. Heart J. 30, 618–623 (2009).

  142. 142.

    Modifier genes in mice and humans. Nat. Rev. Genet. 2, 165–174 (2001).

  143. 143.

    , , & alpha Chain mutations with opposite effects on the gelation of hemoglobin S. J. Biol. Chem. 254, 8169–8172 (1979).

  144. 144.

    , , & Two mutations preventing PDZ-protein interactions of GluR1 have opposite effects on synaptic plasticity. Learn. Mem. 13, 562–565 (2006).

  145. 145.

    & Antagonistic pleiotropy as a widespread mechanism for the maintenance of polymorphic disease alleles. BMC Med. Genet. 12, 160 (2011).

  146. 146.

    , , , & 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).

  147. 147.

    et al. Inactivation of the Fto gene protects from obesity. Nature 458, 894–898 (2009).

  148. 148.

    , , , & N6-adenosine methylation in MiRNAs. PLoS ONE 10, e0118438 (2015).

  149. 149.

    et al. Genetic variation at the FTO locus influences RBL2 gene expression. Diabetes 59, 726–732 (2010).

  150. 150.

    et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

  151. 151.

    et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

  152. 152.

    et al. Hypomorphism of Fto and Rpgrip1l causes obesity in mice. J. Clin. Invest. 126, 1897–1910 (2016).

  153. 153.

    et al. AmiGO: online access to ontology and annotation data. Bioinformatics 25, 288–289 (2009).

  154. 154.

    et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

  155. 155.

    , & How far from the SNP may the causative genes be? Nucleic Acids Res. 44, 6046–6054 (2016).

  156. 156.

    et al. MicroRNA-455 regulates brown adipogenesis via a novel HIF1an-AMPK-PGC1alpha signaling network. EMBO Rep. 16, 1378–1393 (2015).

  157. 157.

    et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

  158. 158.

    & 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.

  159. 159.

    , & Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn. Sci. 15, 37–46 (2011).

  160. 160.

    Bio-Text Mining Group in Text-mined Hypertension, Obesity, and Diabetes Candidate Gene Database (Intelligent Agent Systems Lab, 2011).

  161. 161.

    et al. Rare genomic structural variants in complex disease: lessons from the replication of associations with obesity. PLoS ONE 8, e58048 (2013).

  162. 162.

    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).

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Acknowledgements

C.B. is partially funded by the John W. Barton Sr Chair in Genetics and Nutrition. S.G. and C.B. are partially supported by the NIH-funded COBRE grant (NIH 81P30GM118430-01). This work was also supported by the National Medical Research Council, Ministry of Health, Singapore (WBS R913200076263) to S.G. We thank X. Chai for help with some data retrieval and steps in the analysis and M. Peterson for assistance with the manuscript.

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  1. Cardiovascular and Metabolic Disorders Program and Center for Computational Biology, Duke–National University of Singapore Graduate Medical School, Singapore 169857, Singapore.

    • Sujoy Ghosh
  2. Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana 70808-4124, USA.

    • Claude Bouchard

Authors

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Contributions

Both authors contributed equally to all aspects of the article.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Sujoy Ghosh or Claude Bouchard.

Supplementary information

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    Supplementary information

    Supplementary information Figures S1–S7

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    Supplementary information

    Supplementary information Tables S1–S8

Glossary

Obesity

In people of European descent, obesity is defined as a body mass index of 30 kg m−2 or higher. By contrast, overweight refers to a BMI in the range of 25 to 29.9 kg m−2.

Energy balance

The relationship between the calories consumed from food and drink and the calories expended to meet daily energy requirements.

Effect sizes

The magnitude of the difference in allele frequencies between two groups or between group phenotype values. The estimate of effect size is typically expressed as an odds ratio for a case:control GWAS or as a regression coefficient for continuous traits, but there are many other ways to quantify an effect size.

Genetic variance

The contribution of genotypic differences among individuals to phenotypic variation in a population.

Common variants

Single nucleotide variations in genetic sequences where the less prevalent form (minor allele) occurs at a frequency of 1% or greater in the human population under investigation.

Metabolic rate

The rate at which metabolic energy is expended to meet the energy needs of the body. For instance, resting metabolic rate is the rate of calorie expenditure required to maintain the basic biological functions of the body at rest. It is commonly assumed that this rate of energy expenditure can be approximated by the rate of ATP production.

GWAS

(Genome-wide association study). An approach involving the simultaneous scanning of millions of markers (single nucleotide polymorphisms, SNPs) across the entire genome with the goal of discovering genetic variants that are associated with a particular disease or trait.

Body mass index

(BMI). Also known as the Quetelet Index, the BMI is a person's weight in kilograms divided by the square of their height in metres (kg m−2).

Body fat percentage

A representation of the proportion of total body mass that is stored as fat, primarily in adipose tissue plus small amounts in other tissues and organs. It is calculated as total fat mass divided by total body mass (× 100). Currently, it is most often derived from dual-energy X-ray absorptiometry (DXA) scanning, in which the fat and lean components of body mass are quantified.

Genome-wide significant

A term that typically applies to an association P-value for a single nucleotide polymorphism in a GWAS. A SNP with an association P-value <0.05, after correction for the number of SNPs tested (Bonferroni correction), is considered to be genome-wide significant. For 1 million SNPs tested, this equates to a SNP with nominal P-value of 5 × 10−8.

Expression quantitative trait loci

Regions of the genome containing DNA sequence variants that influence the expression level of one or more genes.

Regulatory marks

Chromatin modifications in gene regulatory regions, primarily involving post-translational modifications of DNA-associated histones (acetylation, methylation, phosphorylation and ubiquitylation).

Adiposity

Refers to the level of fat stored in the adipose tissue of the organism. Most of the lipids are stored in the form of triglycerides in adipose cells. A high level of adiposity implies a large accumulation of fat and is commonly seen in obesity while leanness is associated with a low level of adiposity.

Heritability

An estimate of the contribution of genetic variation to a phenotype among individuals in a given population.

Penetrance

In genetics, penetrance refers to the likelihood that a particular gene or allele will be expressed. Penetrance can be reduced or complete.

Minor allele frequency

The frequency of the less frequent allele at a given locus and in a given population.

Network analysis

An approach involving the analysis of gene networks. Gene networks are collections of functionally related genes (for example, due to co-expression, protein-protein interactions, gene regulatory networks, etc.) where the topological relationships between the genes are known.

DEPICT

(Data-driven Expression-Prioritized Integration for Complex Traits). An integrative tool that systematically prioritizes the most likely causal genes at associated loci and highlights tissues and pathways enriched for highly expressed loci-associated genes.

Pathway analysis

An approach where the unit of analysis is a gene set, also referred to as a pathway. A pathway is a collection of genes that are related to one another by some functional parameter. For GWAS, the goal of a pathway analysis is to identify gene sets that have a statistically significant excess of polymorphisms compared with random gene collections.

Guilt by association

The process of inferring the function of a molecule by virtue of its association with other molecules of known function. For genetic studies, the association often manifests as transcriptional co-expression or participation in the same transcriptional network.

DNase I hypersensitivity sites

Chromatin regions characterized by increased cleavage as revealed by the endonuclease DNase I. It represents a region of regulatory DNA typically located near transcription start sites, enhancers and silencers.

Quantile–quantile plots

Scatterplots created by plotting two sets of quantiles against one another. In the case of GWAS, this type of plot is often used to compare quantiles of the experimentally observed SNP association P-values versus quantiles calculated from a theoretical (normal) distribution.

About this article

Publication history

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DOI

https://doi.org/10.1038/nrg.2017.72

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