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The genetic contribution to non-syndromic human obesity

Key Points

  • Obesity is an important disease globally, and has resulted in significant increases in morbidity and mortality in both the developed and developing worlds. There are many proposed explanations for the current obesity epidemic, but it is clear that genetics plays a significant part in whether a person becomes obese by affecting susceptibility to the current obesogenic environment, which is characterized by easy access to high-calorie food and reduced energy expenditure owing to decreased levels of physical activity in daily life.

  • Although the precise physiological basis of obesity remains unclear, skewed energy balance, abnormalities of fat storage and mobilization, and disordered feeding behaviour may all play a part.

  • Both genome-wide linkage scans and candidate gene association studies have had limited success in identifying genes underlying non-syndromic obesity, although genes responsible for monogenic obesity have been identified.

  • Recently, the genome-wide association scan method has been used to successfully identify many novel SNPs associated with non-syndromic obesity. These results have significantly increased the number of obesity-related loci for which there is strong statistical evidence at the genome-wide level.

  • The question remains why analysis of SNPs has not identified any variants of sufficiently large genetic effect to account for the level of heritability observed in obesity. Other forms of genomic variation may account for this, for example, low frequency SNPs, copy number variants and epigenetic modifications.

  • Key strategies for the future of genetic studies in obesity include improving subject selection, phenotype measurement, and genome-wide study design. A systems-based approach to synthesizing genome-wide data sets is likely to be a fruitful approach to identifying obesity genes.

Abstract

The last few years have seen major advances in common non-syndromic obesity research, much of it the result of genetic studies. This Review outlines the competing hypotheses about the mechanisms underlying the genetic and physiological basis of obesity, and then examines the recent explosion of genetic association studies that have yielded insights into obesity, both at the candidate gene level and the genome-wide level. With obesity genetics now entering the post-genome-wide association scan era, the obvious question is how to improve the results obtained so far using single nucleotide polymorphism markers and how to move successfully into the other areas of genomic variation that may be associated with common obesity.

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Figure 1: The leptin–melanocortin pathway.
Figure 2: Odds ratios for genes associated with obesity in genome-wide studies.

References

  1. 1

    Wang, Y., Beydoun, M. A., Liang, L., Caballero, B. & Kumanyika, S. K. Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity (Silver Spring) 16, 2323–2330 (2008).

    Google Scholar 

  2. 2

    Kelly, T., Yang, W., Chen, C. S., Reynolds, K. & He, J. Global burden of obesity in 2005 and projections to 2030. Int. J. Obes. (Lond.) 32, 1431–1437 (2008).

    CAS  Google Scholar 

  3. 3

    Sturm, R. Increases in morbid obesity in the USA: 2000–2005. Public Health 121, 492–496 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Ogden, C. L. et al. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA 295, 1549–1555 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Sturm, R. The effects of obesity, smoking, and drinking on medical problems and costs. Health Aff. (Millwood) 21, 245–253 (2002).

    Google Scholar 

  6. 6

    Stunkard, A. J., Foch, T. T. & Hrubec, Z. A twin study of human obesity. JAMA 256, 51–54 (1986). The first twin study of obesity that reported the substantial role of genetics.

    CAS  PubMed  Google Scholar 

  7. 7

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

    CAS  PubMed  Google Scholar 

  8. 8

    Turula, M., Kaprio, J., Rissanen, A. & Koskenvuo, M. Body weight in the Finnish Twin Cohort. Diabetes Res. Clin. Pract. 10 (Suppl. 1), S33–S36 (1990).

    PubMed  Google Scholar 

  9. 9

    Wardle, J., Carnell, S., Haworth, C. M. & Plomin, R. Evidence for a strong genetic influence on childhood adiposity despite the force of the obesogenic environment. Am. J. Clin. Nutr. 87, 398–404 (2008). A twin study showing that, even in an obesogenic environment, genetics has a significant effect on obesity.

    CAS  PubMed  Google Scholar 

  10. 10

    Redden, D. T. et al. Regional admixture mapping and structured association testing: conceptual unification and an extensible general linear model. PLoS Genet. 2, e137 (2006).

    PubMed  PubMed Central  Google Scholar 

  11. 11

    Williams, R. C., Long, J. C., Hanson, R. L., Sievers, M. L. & Knowler, W. C. Individual estimates of European genetic admixture associated with lower body-mass index, plasma glucose, and prevalence of type 2 diabetes in Pima Indians. Am. J. Hum. Genet. 66, 527–538 (2000).

    CAS  PubMed  Google Scholar 

  12. 12

    Sivitz, W. I., Fink, B. D. & Donohoue, P. A. Fasting and leptin modulate adipose and muscle uncoupling protein: divergent effects between messenger ribonucleic acid and protein expression. Endocrinology 140, 1511–1519 (1999).

    CAS  PubMed  Google Scholar 

  13. 13

    Rahmouni, K. & Morgan, D. A. Hypothalamic arcuate nucleus mediates the sympathetic and arterial pressure responses to leptin. Hypertension 49, 647–652 (2007).

    CAS  PubMed  Google Scholar 

  14. 14

    Lowell, B. B. et al. Development of obesity in transgenic mice after genetic ablation of brown adipose tissue. Nature 366, 740–742 (1993). The first report to show that loss of BAT in transgenic mice leads to obesity.

    CAS  PubMed  Google Scholar 

  15. 15

    Ghorbani, M., Claus, T. H. & Himms-Hagen, J. Hypertrophy of brown adipocytes in brown and white adipose tissues and reversal of diet-induced obesity in rats treated with a β3-adrenoceptor agonist. Biochem. Pharmacol. 54, 121–131 (1997).

    CAS  PubMed  Google Scholar 

  16. 16

    Nedergaard, J., Bengtsson, T. & Cannon, B. Unexpected evidence for active brown adipose tissue in adult humans. Am. J. Physiol. Endocrinol. Metab. 293, E444–E452 (2007).

    CAS  PubMed  Google Scholar 

  17. 17

    van Marken Lichtenbelt, W. D. et al. Cold-activated brown adipose tissue in healthy men. N. Engl. J. Med. 360, 1500–1508 (2009).

    CAS  PubMed  Google Scholar 

  18. 18

    Cypess, A. M. et al. Identification and importance of brown adipose tissue in adult humans. N. Engl. J. Med. 360, 1509–1517 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Virtanen, K. A. et al. Functional brown adipose tissue in healthy adults. N. Engl. J. Med. 360, 1518–1525 (2009).

    CAS  Google Scholar 

  20. 20

    Ozata, M., Ozdemir, I. C. & Licinio, J. Human leptin deficiency caused by a missense mutation: multiple endocrine defects, decreased sympathetic tone, and immune system dysfunction indicate new targets for leptin action, greater central than peripheral resistance to the effects of leptin, and spontaneous correction of leptin-mediated defects. J. Clin. Endocrinol. Metab. 84, 3686–3695 (1999).

    CAS  PubMed  Google Scholar 

  21. 21

    Henry, B. A., Dunshea, F. R., Gould, M. & Clarke, I. J. Profiling postprandial thermogenesis in muscle and fat of sheep and the central effect of leptin administration. Endocrinology 149, 2019–2026 (2008).

    CAS  PubMed  Google Scholar 

  22. 22

    Seale, P. et al. PRDM16 controls a brown fat/skeletal muscle switch. Nature 454, 961–967 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Tseng, Y. H. et al. New role of bone morphogenetic protein 7 in brown adipogenesis and energy expenditure. Nature 454, 1000–1004 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Spalding, K. L. et al. Dynamics of fat cell turnover in humans. Nature 453, 783–787 (2008).

    CAS  PubMed  Google Scholar 

  25. 25

    Freedman, D. S. et al. Childhood overweight and family income. MedGenMed 9, 26 (2007).

    PubMed  PubMed Central  Google Scholar 

  26. 26

    Lofgren, P. et al. Long-term prospective and controlled studies demonstrate adipose tissue hypercellularity and relative leptin deficiency in the postobese state. J. Clin. Endocrinol. Metab. 90, 6207–6213 (2005).

    PubMed  Google Scholar 

  27. 27

    O'Rahilly, S. & Farooqi, I. S. Human obesity: a heritable neurobehavioral disorder that is highly sensitive to environmental conditions. Diabetes 57, 2905–2910 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Zhang, Y. et al. Positional cloning of the mouse obese gene and its human homologue. Nature 372, 425–432 (1994). The paper that identified the first gene underlying obesity and that brought obesity research into the modern age.

    CAS  PubMed  Google Scholar 

  29. 29

    Montague, C. T. et al. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature 387, 903–908 (1997). The first reported evidence that monogenic obesity exists in humans.

    CAS  PubMed  Google Scholar 

  30. 30

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

    CAS  PubMed  Google Scholar 

  31. 31

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

    CAS  PubMed  Google Scholar 

  32. 32

    Jackson, R. S. et al. Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene. Nature Genet. 16, 303–306 (1997).

    CAS  PubMed  Google Scholar 

  33. 33

    Vaisse, C., Clement, K., Guy-Grand, B. & Froguel, P. A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nature Genet. 20, 113–4 (1998). This paper, together with reference 34, first identified MC4R gene variants as the most prevalent form of monogenic human obesity.

    CAS  PubMed  Google Scholar 

  34. 34

    Yeo, G. S. et al. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nature Genet. 20, 111–112 (1998).

    CAS  PubMed  Google Scholar 

  35. 35

    Holder, J. L. Jr, Butte, N. F. & Zinn, A. R. Profound obesity associated with a balanced translocation that disrupts the SIM1 gene. Hum. Mol. Genet. 9, 101–108 (2000).

    CAS  PubMed  Google Scholar 

  36. 36

    Friedel, S. et al. Mutation screen of the brain derived neurotrophic factor gene (BDNF): identification of several genetic variants and association studies in patients with obesity, eating disorders, and attention-deficit/hyperactivity disorder. Am. J. Med. Genet. B Neuropsychiatr. Genet. 132B, 196–199 (2005).

    Google Scholar 

  37. 37

    Yeo, G. S. et al. A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nature Neurosci. 7, 1187–1189 (2004).

    CAS  PubMed  Google Scholar 

  38. 38

    Rankinen, T. et al. The human obesity gene map: the 2005 update. Obesity (Silver Spring) 14, 529–644 (2006).

    Google Scholar 

  39. 39

    Boutin, P. et al. GAD2 on chromosome 10p12 is a candidate gene for human obesity. PLoS Biol. 1, E68 (2003).

    PubMed  PubMed Central  Google Scholar 

  40. 40

    Meyre, D. et al. Variants of ENPP1 are associated with childhood and adult obesity and increase the risk of glucose intolerance and type 2 diabetes. Nature Genet. 37, 863–867 (2005).

    CAS  PubMed  Google Scholar 

  41. 41

    Suviolahti, E. et al. The SLC6A14 gene shows evidence of association with obesity. J. Clin. Invest. 112, 1762–72 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Durand, E. et al. Polymorphisms in the amino acid transporter solute carrier family 6 (neurotransmitter transporter) member 14 gene contribute to polygenic obesity in French Caucasians. Diabetes 53, 2483–2486 (2004).

    CAS  PubMed  Google Scholar 

  43. 43

    Saunders, C. L. et al. Meta-analysis of genome-wide linkage studies in BMI and obesity. Obesity (Silver Spring) 15, 2263–2275 (2007).

    Google Scholar 

  44. 44

    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). The first obesity gene identified through a GWA study, although the study was for type 2 diabetes rather than obesity.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Bell, C. G., Walley, A. J. & Froguel, P. The genetics of human obesity. Nature Rev. Genet. 6, 221–34 (2005).

    CAS  PubMed  Google Scholar 

  46. 46

    Jiang, Y. et al. Common variants in the 5′ region of the leptin gene are associated with body mass index in men from the National Heart, Lung, and Blood Institute Family Heart Study. Am. J. Hum. Genet. 75, 220–230 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Li, W. D. et al. Sequence variants in the 5′ flanking region of the leptin gene are associated with obesity in women. Ann. Hum. Genet. 63, 227–234 (1999).

    CAS  PubMed  Google Scholar 

  48. 48

    Chagnon, Y. C. et al. Associations between the leptin receptor gene and adiposity in middle-aged Caucasian males from the HERITAGE family study. J. Clin. Endocrinol. Metab. 85, 29–34 (2000).

    CAS  PubMed  Google Scholar 

  49. 49

    Roth, H. et al. Transmission disequilibrium and sequence variants at the leptin receptor gene in extremely obese German children and adolescents. Hum. Genet. 103, 540–546 (1998).

    CAS  PubMed  Google Scholar 

  50. 50

    Mizuta, E. et al. Leptin gene and leptin receptor gene polymorphisms are associated with sweet preference and obesity. Hypertens. Res. 31, 1069–1077 (2008).

    CAS  PubMed  Google Scholar 

  51. 51

    Challis, B. G. et al. A missense mutation disrupting a dibasic prohormone processing site in pro-opiomelanocortin (POMC) increases susceptibility to early-onset obesity through a novel molecular mechanism. Hum. Mol. Genet. 11, 1997–2004 (2002).

    CAS  PubMed  Google Scholar 

  52. 52

    Benzinou, M. et al. Common nonsynonymous variants in PCSK1 confer risk of obesity. Nature Genet. 40, 943–945 (2008).

    CAS  PubMed  Google Scholar 

  53. 53

    Hinney, A. et al. Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLoS ONE 2, e1361 (2007). The first GWA study to specifically recruit obese subjects.

    PubMed  PubMed Central  Google Scholar 

  54. 54

    Dubern, B. et al. Mutational analysis of melanocortin-4 receptor, agouti-related protein, and alpha-melanocyte-stimulating hormone genes in severely obese children. J. Pediatr. 139, 204–209 (2001).

    CAS  PubMed  Google Scholar 

  55. 55

    Geller, F. et al. Melanocortin-4 receptor gene variant I103 is negatively associated with obesity. Am. J. Hum. Genet. 74, 572–581 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Heid, I. M. et al. Association of the 103I MC104R allele with decreased body mass in 7937 participants of two population based surveys. J. Med. Genet. 42, e21 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Stutzmann, F. et al. Non-synonymous polymorphisms in melanocortin-4 receptor protect against obesity: the two facets of a Janus obesity gene. Hum. Mol. Genet. 16, 1837–1844 (2007).

    CAS  PubMed  Google Scholar 

  58. 58

    Bouatia-Naji, N. et al. ACDC/adiponectin polymorphisms are associated with severe childhood and adult obesity. Diabetes 55, 545–550 (2006).

    CAS  PubMed  Google Scholar 

  59. 59

    Nakatani, K. et al. Adiponectin gene variation associates with the increasing risk of type 2 diabetes in non-diabetic Japanese subjects. Int. J. Mol. Med. 15, 173–177 (2005).

    CAS  PubMed  Google Scholar 

  60. 60

    Sutton, B. S. et al. Genetic analysis of adiponectin and obesity in Hispanic families: the IRAS Family Study. Hum. Genet. 117, 107–118 (2005).

    CAS  PubMed  Google Scholar 

  61. 61

    Vimaleswaran, K. S. et al. A novel association of a polymorphism in the first intron of adiponectin gene with type 2 diabetes, obesity and hypoadiponectinemia in Asian Indians. Hum. Genet. 123, 599–605 (2008).

    CAS  PubMed  Google Scholar 

  62. 62

    Benzinou, M. et al. Endocannabinoid receptor 1 gene variations increase risk for obesity and modulate body mass index in European populations. Hum. Mol. Genet. 17, 1916–1921 (2008).

    CAS  PubMed  Google Scholar 

  63. 63

    Thomas, G. N., Tomlinson, B. & Critchley, J. A. Modulation of blood pressure and obesity with the dopamine D2 receptor gene Taq I polymorphism. Hypertension 36, 177–182 (2000).

    CAS  PubMed  Google Scholar 

  64. 64

    Epstein, L. H. et al. Food reinforcement, the dopamine D2 receptor genotype, and energy intake in obese and nonobese humans. Behav. Neurosci. 121, 877–886 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    McCarthy, S. et al. Complex HTR2C linkage disequilibrium and promoter associations with body mass index and serum leptin. Hum. Genet. 117, 545–557 (2005).

    CAS  PubMed  Google Scholar 

  66. 66

    Pooley, E. C. et al. A 5-HT2C receptor promoter polymorphism (HTR2C - 759C/T) is associated with obesity in women, and with resistance to weight loss in heterozygotes. Am. J. Med. Genet. B Neuropsychiatr. Genet. 126B, 124–127 (2004).

    PubMed  Google Scholar 

  67. 67

    Fuemmeler, B. F. et al. Genes implicated in serotonergic and dopaminergic functioning predict BMI categories. Obesity (Silver Spring) 16, 348–355 (2008).

    CAS  Google Scholar 

  68. 68

    Heo, M. et al. A meta-analytic investigation of linkage and association of common leptin receptor (LEPR) polymorphisms with body mass index and waist circumference. Int. J. Obes. Relat. Metab. Disord. 26, 640–646 (2002).

    CAS  PubMed  Google Scholar 

  69. 69

    The International HapMap Consortium. The International HapMap Project. Nature 426, 789–796 (2003). The original description of the project to map the human variation that underpins much of the current human genetic studies.

  70. 70

    The International HapMap Consortium. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).

  71. 71

    The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

  72. 72

    McCarthy, M. I. et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Rev. Genet. 9, 356–369 (2008).

    CAS  PubMed  Google Scholar 

  73. 73

    Iyengar, S. K. & Elston, R. C. The genetic basis of complex traits: rare variants or “common gene, common disease”? Methods Mol. Biol. 376, 71–84 (2007).

    CAS  PubMed  Google Scholar 

  74. 74

    Willer, C. J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nature Genet. 41, 25–34 (2009). A meta-analysis of 15 GWA studies for BMI associations reporting six novel loci.

    CAS  PubMed  Google Scholar 

  75. 75

    Dina, C. et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nature Genet. 39, 724–726 (2007).

    CAS  PubMed  Google Scholar 

  76. 76

    The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007). A large-scale GWA study of seven common diseases, including type 2 diabetes.

  77. 77

    Loos, R. J. & Bouchard, C. FTO: the first gene contributing to common forms of human obesity. Obes. Rev. 9, 246–50 (2008).

    CAS  PubMed  Google Scholar 

  78. 78

    Gerken, T. et al. The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science 318, 1469–1472 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79

    Speakman, J. R., Rance, K. A. & Johnstone, A. M. Polymorphisms of the FTO gene are associated with variation in energy intake, but not energy expenditure. Obesity (Silver Spring) 16, 1961–1965 (2008).

    CAS  Google Scholar 

  80. 80

    Wardle, J., Llewellyn, C., Sanderson, S. & Plomin, R. The FTO gene and measured food intake in children. Int. J. Obes. (Lond.) (2008).

  81. 81

    Wardle, J. et al. Obesity associated genetic variation in FTO is associated with diminished satiety. J. Clin. Endocrinol. Metab. 93, 3640–3643 (2008).

    CAS  PubMed  Google Scholar 

  82. 82

    Wahlen, K., Sjolin, E. & Hoffstedt, J. The common rs9939609 gene variant of the fat mass- and obesity-associated gene FTO is related to fat cell lipolysis. J. Lipid Res. 49, 607–611 (2008).

    PubMed  Google Scholar 

  83. 83

    Andreasen, C. H. et al. Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes 57, 95–101 (2008).

    CAS  PubMed  Google Scholar 

  84. 84

    Loos, R. J. et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nature Genet. 40, 768–775 (2008).

    CAS  PubMed  Google Scholar 

  85. 85

    Chambers, J. C. et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nature Genet. 40, 716–188 (2008).

    CAS  PubMed  Google Scholar 

  86. 86

    Andreasen, C. H. et al. Non-replication of genome-wide based associations between common variants in INSIG2 and PFKP and obesity in studies of 18,014 Danes. PLoS ONE 3, e2872 (2008).

    PubMed  PubMed Central  Google Scholar 

  87. 87

    Qi, L., Kraft, P., Hunter, D. J. & Hu, F. B. The common obesity variant near MC4R gene is associated with higher intakes of total energy and dietary fat, weight change and diabetes risk in women. Hum. Mol. Genet. 17, 3502–8 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Herbert, A. et al. A common genetic variant is associated with adult and childhood obesity. Science 312, 279–283 (2006).

    CAS  PubMed  Google Scholar 

  89. 89

    Dina, C. et al. Comment on “A common genetic variant is associated with adult and childhood obesity”. Science 315, 187b; author reply 187e (2007).

    Google Scholar 

  90. 90

    Rosskopf, D. et al. Comment on “A common genetic variant is associated with adult and childhood obesity”. Science 315, 187; author reply 187e (2007).

    CAS  PubMed  Google Scholar 

  91. 91

    Loos, R. J., Barroso, I., O'Rahilly, S. & Wareham, N. J. Comment on “A common genetic variant is associated with adult and childhood obesity”. Science 315, 187c; author reply 187e (2007).

    Google Scholar 

  92. 92

    Lyon, H. N. et al. The association of a SNP upstream of INSIG2 with body mass index is reproduced in several but not all cohorts. PLoS Genet. 3, e61 (2007).

    PubMed  PubMed Central  Google Scholar 

  93. 93

    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).

    PubMed  PubMed Central  Google Scholar 

  94. 94

    Meyre, D. et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nature Genet. 41, 157–159 (2009). The first GWA study for severe adult and child obesity reporting three novel loci.

    CAS  PubMed  Google Scholar 

  95. 95

    Su, A. I. et al. Large-scale analysis of the human and mouse transcriptomes. Proc. Natl Acad. Sci. USA 99, 4465–4470 (2002).

    CAS  PubMed  Google Scholar 

  96. 96

    Amigo, L. et al. Relevance of Niemann–Pick type C1 protein expression in controlling plasma cholesterol and biliary lipid secretion in mice. Hepatology 36, 819–828 (2002).

    CAS  PubMed  Google Scholar 

  97. 97

    Ikonen, E. Cellular cholesterol trafficking and compartmentalization. Nature Rev. Mol. Cell Biol. 9, 125–138 (2008).

    CAS  Google Scholar 

  98. 98

    Vance, J. E. Lipid imbalance in the neurological disorder, Niemann–Pick C disease. FEBS Lett. 580, 5518–5524 (2006).

    CAS  PubMed  Google Scholar 

  99. 99

    Xie, C., Turley, S. D., Pentchev, P. G. & Dietschy, J. M. Cholesterol balance and metabolism in mice with loss of function of Niemann–Pick C protein. Am. J. Physiol. 276, E336–E344 (1999).

    CAS  PubMed  Google Scholar 

  100. 100

    Liu, Y. J. et al. Genome-wide association scans identified CTNNBL1 as a novel gene for obesity. Hum. Mol. Genet. 17, 1803–1813 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101

    Cauchi, S. & Froguel, P. TCF7L2 genetic defect and type 2 diabetes. Curr. Diab. Rep. 8, 149–155 (2008).

    CAS  PubMed  Google Scholar 

  102. 102

    Ross, S. E. et al. Inhibition of adipogenesis by Wnt signaling. Science 289, 950–953 (2000).

    CAS  PubMed  Google Scholar 

  103. 103

    Liu, F. et al. Wnt-β-catenin signaling initiates taste papilla development. Nature Genet. 39, 106–112 (2007).

    CAS  PubMed  Google Scholar 

  104. 104

    Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nature Genet. 41, 18–24 (2009).

    CAS  PubMed  Google Scholar 

  105. 105

    Froguel, P. & Blakemore, A. I. The power of the extreme in elucidating obesity. N. Engl. J. Med. 359, 891–893 (2008).

    CAS  PubMed  Google Scholar 

  106. 106

    Lasky-Su, J. et al. On the replication of genetic associations: timing can be everything! Am. J. Hum. Genet. 82, 849–858 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107

    Cookson, W., Liang, L., Abecasis, G., Moffatt, M. & Lathrop, M. Mapping complex disease traits with global gene expression. Nature Rev. Genet. 10, 184–194 (2009).

    CAS  PubMed  Google Scholar 

  108. 108

    Li, H. et al. Transcriptomic and metabonomic profiling of obesity-prone and obesity-resistant rats under high fat diet. J. Proteome Res. 7, 4775–4783 (2008).

    CAS  PubMed  Google Scholar 

  109. 109

    Boden, G. et al. Increase in endoplasmic reticulum stress-related proteins and genes in adipose tissue of obese, insulin-resistant individuals. Diabetes 57, 2438–2444 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. 110

    Zondervan, K. T. & Cardon, L. R. Designing candidate gene and genome-wide case–control association studies. Nature Protoc. 2, 2492–2501 (2007).

    CAS  Google Scholar 

  111. 111

    Rao, D. C. An overview of the genetic dissection of complex traits. Adv. Genet. 60, 3–34 (2008).

    CAS  PubMed  Google Scholar 

  112. 112

    Teo, Y. Y. Common statistical issues in genome-wide association studies: a review on power, data quality control, genotype calling and population structure. Curr. Opin. Lipidol. 19, 133–143 (2008).

    CAS  PubMed  Google Scholar 

  113. 113

    Iles, M. M. What can genome-wide association studies tell us about the genetics of common disease? PLoS Genet. 4, e33 (2008).

    PubMed  PubMed Central  Google Scholar 

  114. 114

    Cupples, L. A. Family study designs in the age of genome-wide association studies: experience from the Framingham Heart Study. Curr. Opin. Lipidol. 19, 144–150 (2008).

    CAS  PubMed  Google Scholar 

  115. 115

    Manolio, T. A., Bailey-Wilson, J. E. & Collins, F. S. Genes, environment and the value of prospective cohort studies. Nature Rev. Genet. 7, 812–820 (2006).

    CAS  PubMed  Google Scholar 

  116. 116

    Lowe, J. K. et al. Genome-wide association studies in an isolated founder population from the Pacific Island of Kosrae. PLoS Genet. 5, e1000365 (2009).

    PubMed  PubMed Central  Google Scholar 

  117. 117

    Blakemore, A. I. et al. A rare variant in the visfatin gene (NAMPT/PBEF1) is associated with protection from obesity. Obesity (Silver Spring) (in the press).

  118. 118

    Khor, C. C. et al. A Mal functional variant is associated with protection against invasive pneumococcal disease, bacteremia, malaria and tuberculosis. Nature Genet. 39, 523–528 (2007).

    CAS  PubMed  Google Scholar 

  119. 119

    Nejentsev, S., Walker, N., Riches, D., Egholm, M. & Todd, J. A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120

    Jones, S. et al. Exomic sequencing identifies PALB2 as a pancreatic cancer susceptibility gene. Science 324, 217 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121

    Stranger, B. E. et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315, 848–853 (2007). The first paper to describe the effects of copy number variation on gene expression.

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122

    de Smith, A. J. et al. Array CGH analysis of copy number variation identifies 1,284 new genes variant in healthy white males: implications for association studies of complex diseases. Hum. Mol. Genet. 16, 2783–2794 (2007).

    CAS  PubMed  Google Scholar 

  123. 123

    Feuk, L., Carson, A. R. & Scherer, S. W. Structural variation in the human genome. Nature Rev. Genet. 7, 85–97 (2006).

    CAS  PubMed  Google Scholar 

  124. 124

    Peiffer, D. A. et al. High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping. Genome Res. 16, 1136–1148 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125

    McCarroll, S. A. et al. Integrated detection and population-genetic analysis of SNPs and copy number variation. Nature Genet. 40, 1166–1174 (2008).

    CAS  PubMed  Google Scholar 

  126. 126

    Baross, A. et al. Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data. BMC Bioinformatics 8, 368 (2007).

    PubMed  PubMed Central  Google Scholar 

  127. 127

    Horsthemke, B. & Wagstaff, J. Mechanisms of imprinting of the Prader–Willi/Angelman region. Am. J. Med. Genet. A 146A, 2041–2052 (2008).

    CAS  PubMed  Google Scholar 

  128. 128

    Dong, C. et al. Possible genomic imprinting of three human obesity-related genetic loci. Am. J. Hum. Genet. 76, 427–437 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129

    Guo, Y. F. et al. Assessment of genetic linkage and parent-of-origin effects on obesity. J. Clin. Endocrinol. Metab. 91, 4001–4005 (2006).

    CAS  PubMed  Google Scholar 

  130. 130

    Stoger, R. The thrifty epigenotype: an acquired and heritable predisposition for obesity and diabetes? Bioessays 30, 156–166 (2008).

    PubMed  Google Scholar 

  131. 131

    Bibikova, M. et al. High-throughput DNA methylation profiling using universal bead arrays. Genome Res. 16, 383–393 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. 132

    Weber, M. et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nature Genet. 37, 853–862 (2005).

    CAS  PubMed  Google Scholar 

  133. 133

    English, S. B. & Butte, A. J. Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes. Bioinformatics 23, 2910–2917 (2007). The first attempt at a systems biology approach to integrating obesity research results identifies novel genes.

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134

    Gorber, S. C., Tremblay, M., Moher, D. & Gorber, B. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes. Rev. 8, 307–326 (2007).

    PubMed  Google Scholar 

  135. 135

    Wells, J. C., Ruto, A. & Treleaven, P. Whole-body three-dimensional photonic scanning: a new technique for obesity research and clinical practice. Int. J. Obes. (Lond.) 32, 232–238 (2008).

    CAS  Google Scholar 

  136. 136

    Ellis, K. J. et al. Body-composition assessment in infancy: air-displacement plethysmography compared with a reference 4-compartment model. Am. J. Clin. Nutr. 85, 90–95 (2007).

    CAS  PubMed  Google Scholar 

  137. 137

    Shen, W. & Chen, J. Application of imaging and other noninvasive techniques in determining adipose tissue mass. Methods Mol. Biol. 456, 39–54 (2008).

    PubMed  Google Scholar 

  138. 138

    Vlachos, I. S., Hatziioannou, A., Perelas, A. & Perrea, D. N. Sonographic assessment of regional adiposity. AJR Am. J. Roentgenol. 189, 1545–1553 (2007).

    PubMed  Google Scholar 

  139. 139

    Westerterp, K. R. & Goris, A. H. Validity of the assessment of dietary intake: problems of misreporting. Curr. Opin. Clin. Nutr. Metab. Care 5, 489–493 (2002).

    PubMed  Google Scholar 

  140. 140

    Swanson, M. Digital photography as a tool to measure school cafeteria consumption. J. Sch. Health 78, 432–437 (2008).

    PubMed  Google Scholar 

  141. 141

    Pencina, M. J., Millen, B. E., Hayes, L. J. & D'Agostino, R. B. Performance of a method for identifying the unique dietary patterns of adult women and men: the Framingham nutrition studies. J. Am. Diet Assoc. 108, 1453–1460 (2008).

    PubMed  PubMed Central  Google Scholar 

  142. 142

    Dialektakou, K. D. & Vranas, P. B. Breakfast skipping and body mass index among adolescents in Greece: whether an association exists depends on how breakfast skipping is defined. J. Am. Diet Assoc. 108, 1517–1525 (2008).

    PubMed  Google Scholar 

  143. 143

    Morton, G. J., Cummings, D. E., Baskin, D. G., Barsh, G. S. & Schwartz, M. W. Central nervous system control of food intake and body weight. Nature 443, 289–295 (2006).

    CAS  PubMed  Google Scholar 

  144. 144

    Henry, B. A. & Clarke, I. J. Adipose tissue hormones and the regulation of food intake. J. Neuroendocrinol. 20, 842–849 (2008).

    CAS  PubMed  Google Scholar 

  145. 145

    Rosen, E. D. & Spiegelman, B. M. Adipocytes as regulators of energy balance and glucose homeostasis. Nature 444, 847–853 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. 146

    Spiegelman, B. M. & Flier, J. S. Obesity and the regulation of energy balance. Cell 104, 531–543 (2001).

    CAS  PubMed  Google Scholar 

  147. 147

    Wegner, L. et al. Common variation in LMNA increases susceptibility to type 2 diabetes and associates with elevated fasting glycemia and estimates of body fat and height in the general population: studies of 7,495 Danish whites. Diabetes 56, 694–698 (2007).

    CAS  PubMed  Google Scholar 

  148. 148

    Baessler, A. et al. Genetic linkage and association of the growth hormone secretagogue receptor (ghrelin receptor) gene in human obesity. Diabetes 54, 259–267 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. 149

    Gylvin, T. et al. Functional SOCS1 polymorphisms are associated with variation in obesity in whites. Diabetes Obes. Metab. 11, 196–203 (2009).

    CAS  PubMed  Google Scholar 

  150. 150

    Talbert, M. E. et al. Polymorphisms near SOCS3 are associated with obesity and glucose homeostasis traits in Hispanic Americans from the Insulin Resistance Atherosclerosis Family Study. Hum. Genet. 125, 153–162 (2009).

    CAS  PubMed  Google Scholar 

  151. 151

    Zobel, D. et al. Variation in the gene encoding Kruppel-like factor 7 influences body fat: studies of 14,818 Danes. Eur. J. Endocrinol. 160, 603–609 (2009).

    CAS  PubMed  Google Scholar 

  152. 152

    Yanagiya, T. et al. Association of single-nucleotide polymorphisms in MTMR9 gene with obesity. Hum. Mol. Genet. 16, 3017–3026 (2007).

    CAS  PubMed  Google Scholar 

  153. 153

    Wermter, A. K. et al. Preferential reciprocal transfer of paternal/maternal DLK1 alleles to obese children: first evidence of polar overdominance in humans. Eur. J. Hum. Genet. 16, 1126–1134 (2008).

    CAS  PubMed  Google Scholar 

  154. 154

    Stone, S. et al. TBC1D1 is a candidate for a severe obesity gene and evidence for a gene/gene interaction in obesity predisposition. Hum. Mol. Genet. 15, 2709–20 (2006).

    CAS  PubMed  Google Scholar 

  155. 155

    Siddiq, A. et al. Single nucleotide polymorphisms in the neuropeptide Y2 receptor (NPY2R) gene and association with severe obesity in French white subjects. Diabetologia 50, 574–84 (2007).

    CAS  PubMed  Google Scholar 

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Acknowledgements

Obesity research in the authors' laborarories is funded by the Wellcome Trust and the Medical Research Council.

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Correspondence to Andrew J. Walley or Philippe Froguel.

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Glossary

Heritability

The proportion of the total phenotypic variation in a given characteristic that can be attributed to additive genetic effects. In the broad sense, heritability involves all additive and non-additive genetic variance, whereas in the narrow sense, it involves only additive genetic variance.

Admixture mapping

Genetic mapping using individuals whose genomes are mosaics of fragments that are descended from genetically distinct populations. This method exploits differences in allele frequencies in the founders to determine ancestry at a locus in order to map traits to specific populations.

Genome-wide association study

A hypothesis-free method of investigating the association between common genetic variation and disease. This type of analysis requires a dense set of markers (for example, SNPs) that capture a substantial proportion of common variation across the genome, and large numbers of study subjects.

Hypothalamus

A brain region located below the thalamus, forming the main portion of the ventral region of the diencephalon and functioning to regulate bodily temperature, certain metabolic processes and other autonomic activities.

QTL

A genetic locus that is identified through the statistical analysis of a quantitative trait, such as height or body weight.

Case–control study

This is the comparison of cases (individuals with disease) with controls (otherwise similar individuals who do not have the disease) to determine whether genetic marker allele frequencies differ between the two groups, that is, are associated with susceptibility to or protection from disease.

Minor allele frequency

The frequency of the less common allele of a biallelic genetic marker in a given population.

Prospective cohort

This is a group of subjects initially assessed for exposure to certain risk factors and then followed over time to evaluate the progression towards specific outcomes (often disease). This forms the basis of a longitudinal study.

Population substructure

This is the presence of hidden subgroups in a population caused by, for example, admixture, population stratification or inbreeding. If this is not accounted for it may lead to increased type 1 error and decreased statistical power.

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Walley, A., Asher, J. & Froguel, P. The genetic contribution to non-syndromic human obesity. Nat Rev Genet 10, 431–442 (2009). https://doi.org/10.1038/nrg2594

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