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Progress in the genetics of common obesity and type 2 diabetes

Published online by Cambridge University Press:  26 February 2010

Karani S. Vimaleswaran  and
Ruth J.F. Loos
Karani S. Vimaleswaran
Affiliation:
Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Cambridge, UK.
Ruth J.F. Loos*
Affiliation:
Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Cambridge, UK.
*
*Corresponding author: Ruth Loos, MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's HospitalBox 285, Hills Road, Cambridge, CB2 OQQ, UK. E-mail: ruth.loos@mrc-epid.cam.ac.uk
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Abstract

The prevalence of obesity and diabetes, which are heritable traits that arise from the interactions of multiple genes and lifestyle factors, continues to rise worldwide, causing serious health problems and imposing a substantial economic burden on societies. For the past 15 years, candidate gene and genome-wide linkage studies have been the main genetic epidemiological approaches to identify genetic loci for obesity and diabetes, yet progress has been slow and success limited. The genome-wide association approach, which has become available in recent years, has dramatically changed the pace of gene discoveries. Genome-wide association is a hypothesis-generating approach that aims to identify new loci associated with the disease or trait of interest. So far, three waves of large-scale genome-wide association studies have identified 19 loci for common obesity and 18 for common type 2 diabetes. Although the combined contribution of these loci to the variation in obesity and diabetes risk is small and their predictive value is typically low, these recently identified loci are set to substantially improve our insights into the pathophysiology of obesity and diabetes. This will require integration of genetic epidemiological methods with functional genomics and proteomics. However, the use of these novel insights for genetic screening and personalised treatment lies some way off in the future.

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Review Article
Information
Expert Reviews in Molecular Medicine , Volume 12 , January 2010 , e7
Copyright
Copyright © Cambridge University Press 2010

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References

References

1Flegal, K.M. et al. (2005) Excess deaths associated with underweight, overweight, and obesity. Journal of the American Medical Association 293, 1861-1867Google Scholar
2Ogden, C.L. et al. (2006) Prevalence of overweight and obesity in the United States, 1999–2004. Journal of the American Medical Association 295, 1549-1555Google Scholar
3Jee, S.H. et al. (2006) Body-mass index and mortality in Korean men and women. New England Journal of Medicine 355, 779-787Google Scholar
4Narayan, K.M. et al. (2003) Lifetime risk for diabetes mellitus in the United States. Journal of the American Medical Association 290, 1884-1890Google Scholar
5International Association for the Study of Obesity, http://www.iotf.org/Google Scholar
6Hill, J.O. et al. (2003) Obesity and the environment: where do we go from here? Science 299, 853-855Google Scholar
7Maes, H.H., Neale, M.C. and Eaves, L.J. (1997) Genetic and environmental factors in relative body weight and human obesity. Behavioural Genetics 27, 325-351Google Scholar
8Stunkard, A.J., Foch, T.T. and Hrubec, Z. (1986) A twin study of human obesity. Journal of the American Medical Association 256, 51-54Google Scholar
9Stunkard, A.J. et al. (1986) An adoption study of human obesity. New England Journal of Medicine 314, 193-198Google Scholar
10Permutt, M.A., Wasson, J. and Cox, N. (2005) Genetic epidemiology of diabetes. Journal of Clinical Investigation 115, 1431-1439Google Scholar
11Allison, D.B. et al. (1996) Heritability of body mass index among an international sample of monozygotic twins reared apart. International Journal of Obesity and Related Metabolic Disorders 20, 501-506Google Scholar
12Herskind, A.M. et al. (1996) Untangling genetic influences on smoking, body mass index and longevity: a multivariate study of 2464 Danish twins followed for 28 years. Human Genetics 98, 467-475Google Scholar
13Luke, A. et al. (2001) Heritability of obesity-related traits among Nigerians, Jamaicans and US black people. International Journal of Obesity and Related Metabolic Disorders 25, 1034-1041Google Scholar
14Rice, T. et al. (1999) Familial aggregation of body mass index and subcutaneous fat measures in the longitudinal Quebec Family Study. Genetic Epidemiology 16, 316-334Google Scholar
15Kaprio, J. et al. (1992) Concordance for type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia 35, 1060-1067Google Scholar
16Newman, B. et al. (1987) Concordance for type 2 (non-insulin-dependent) diabetes mellitus in male twins. Diabetologia 30, 763-768Google Scholar
17Poulsen, P. et al. (1999) Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance–a population-based twin study. Diabetologia 42, 139-145Google Scholar
18Köbberling, J. and Tillil, H. (1982) Empirical risk figures for first degree relatives of non-insulin dependent diabetics. In The Genetics of Diabetes Mellitus (Köbberling, J. and Tattersall, R., eds), pp. 201-209, Academic Press, London, UKGoogle Scholar
19Rankinen, T. et al. (2006) The Human Obesity Gene Map: The 2005 Update. Obesity Research 14, 529-644Google Scholar
20Huszar, D. et al. (1997) Targeted disruption of the melanocortin-4 receptor results in obesity in mice. Cell 88, 131-141Google Scholar
21Farooqi, I.S. et al. (2003) Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. New England Journal of Medicine 348, 1085-1095Google Scholar
22Xiang, Z. et al. (2006) Pharmacological characterization of 40 human melanocortin-4 receptor polymorphisms with the endogenous proopiomelanocortin-derived agonists and the agouti-related protein (AGRP) antagonist. Biochemistry 45, 7277-7288Google Scholar
23Heid, I.M. et al. (2005) Association of the 103I MC4R allele with decreased body mass in 7937 participants of two population based surveys. Journal of Medical Genetics 42, e21Google Scholar
24Geller, F. et al. (2004) Melanocortin 4 receptor gene variant I103 is negatively associated with obesity. American Journal of Human Genetics 74, 572-581Google Scholar
25Young, E.H. et al. (2007) The V103I polymorphism of the MC4R gene and obesity: population based studies and meta-analysis of 29,563 individuals. International Journal of Obesity 31, 1437-1441Google Scholar
26Stutzmann, F. et al. (2007) Non-synonymous polymorphisms in melanocortin-4 receptor protect against obesity: the two facets of a Janus obesity gene. Human Molecular Genetics 16, 1837-1844Google Scholar
27Clement, K. et al. (1995) Genetic variation in the b3-adrenergic receptor and an increased capacity to gain weight in patients with morbid obesity. New England Journal of Medicine 333, 352-354Google Scholar
28Widen, E. et al. (1995) Association of a Polymorphism in the {beta}3-Adrenergic-Receptor Gene with Features of the Insulin Resistance Syndrome in Finns. New England Journal of Medicine 333, 348-352Google Scholar
29Walston, J. et al. (1995) Time of Onset of Non-Insulin- Dependent Diabetes Mellitus and Genetic Variation in the {beta}3-Adrenergic-Receptor Gene. New England Journal of Medicine 333, 343-347Google Scholar
30Pietri-Rouxel, F. et al. (1997) The biochemical effect of the naturally occurring Trp64***Arg mutation on human beta3-adrenoceptor activity. European Journal of Biochemistry 247, 1174-1179Google Scholar
31Umekawa, T. et al. (1999) Trp64Arg mutation of beta3-adrenoceptor gene deteriorates lipolysis induced by beta3-adrenoceptor agonist in human omental adipocytes. Diabetes 48, 117-120Google Scholar
32Kurokawa, N. et al. (2008) The ADRB3 Trp64Arg variant and BMI: a meta-analysis of 44 833 individuals. International Journal of Obesity 32, 1240-1249Google Scholar
33Jackson, R.S. et al. (1997) Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene. Nature Genetics 16, 303-306Google Scholar
34Benzinou, M. et al. (2008) Common nonsynonymous variants in PCSK1 confer risk of obesity. Nature Genetics 40, 943-945Google Scholar
35Kernie, S.G., Liebl, D.J. and Parada, L.F. (2000) BDNF regulates eating behaviour and locomotor activity in mice. EMBO Journal 19, 1290-1300Google Scholar
36Rios, M. et al. (2001) Conditional deletion of brain-derived neurotrophic factor in the postnatal brain leads to obesity and hyperactivity. Molecular Endocrinology 15, 1748–57Google Scholar
37Gray, J. et al. (2006) Hyperphagia, severe obesity, impaired cognitive function, and hyperactivity associated with functional loss of one copy of the brain-derived neurotrophic factor (BDNF) gene. Diabetes 55, 3366-3371Google Scholar
38Shugart, Y.Y. et al. (2009) Two British women studies replicated the association between the Val66Met polymorphism in the brain-derived neurotrophic factor (BDNF) and BMI. European Journal of Human Genetics 17, 1050-1055Google Scholar
39Thorleifsson, G. et al. (2009) Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nature Genetics 41, 18-24CrossRefGoogle Scholar
40Benzinou, M. et al. (2008) Endocannabinoid receptor 1 gene variations increase risk for obesity and modulate body mass index in European populations. Human Molecular Genetics 17, 1916-1921Google Scholar
41Meyre, D. et al. (2007) ENPP1 K121Q polymorphism and obesity, hyperglycaemia and type 2 diabetes in the prospective DESIR Study. Diabetologia 50, 2090-2096Google Scholar
42Weedon, M.N. et al. (2006) No evidence of association of ENPP1 variants with type 2 diabetes or obesity in a study of 8,089 U.K. Caucasians. Diabetes 55, 3175-3179Google Scholar
43Lyon, H.N. et al. (2006) Common variants in the ENPP1 gene are not reproducibly associated with diabetes or obesity. Diabetes 55, 3180-3184Google Scholar
44Grarup, N. et al. (2006) Studies of the relationship between the ENPP1 K121Q polymorphism and type 2 diabetes, insulin resistance and obesity in 7,333 Danish white subjects. Diabetologia 49, 2097-2104Google Scholar
45Qi, L. et al. (2007) Interleukin-6 genetic variability and adiposity: associations in two prospective cohorts and systematic review in 26,944 individuals. Journal of Clinical Endocrinology and Metabolism 92, 3618-3625Google Scholar
46Jalba, M.S., Rhoads, G.G. and Demissie, K. (2008) Association of codon 16 and codon 27 beta 2-adrenergic receptor gene polymorphisms with obesity: a meta-analysis. Obesity 16, 2096-2106Google Scholar
47Norman, R.A. et al. (1997) Genomewide search for genes influencing percent body fat in Pima Indians: Suggestive linkage at chromosome 11q21-q22. American Journal of Human Genetics 60, 166-173Google Scholar
48Saunders, C.L. et al. (2007) Meta-analysis of genome-wide linkage studies in BMI and obesity. Obesity 15, 2263-2275Google Scholar
49Frayling, T.M. et al. (2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–94Google Scholar
50Scuteri, A. et al. (2007) Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLos Genetics 3, e115Google Scholar
51Dina, C. et al. (2007) Variation in FTO contributes to childhood obesity and severe adult obesity. Nature Genetics 39, 724-726Google Scholar
52Loos, R.J. et al. (2008) Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nature Genetics 40, 768–75Google Scholar
53Chambers, J.C. et al. (2008) Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nature Genetics 40, 716-718Google Scholar
54Willer, C.J. et al. (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nature Genetics 41, 25-34Google Scholar
55Li, S. et al. (2010) Cumulative effects and predictive value of common obesity-susceptibility variants identified by genome-wide association studies. American Journal of Clinical Nutrition 91, 184-190Google Scholar
56Meyre, D. et al. (2009) Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nature Genetics 41, 157-159Google Scholar
57Lindgren, C.M. et al. (2009) Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution. PLoS Genetics 5, e1000508Google Scholar
58Heard-Costa, N.L. et al. (2009) NRXN3 is a novel locus for waist circumference: a genome-wide association study from the CHARGE Consortium. PLoS Genetics 5, e1000539Google Scholar
59Gerken, T. et al. (2007) The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science 318, 1469-1472Google Scholar
60Sanchez-Pulido, L. and Andrade-Navarro, M.A. (2007) The FTO (fat mass and obesity associated) gene codes for a novel member of the nonheme dioxygenase superfamily. BMC Biochemistry 8, 23Google Scholar
61Fredriksson, R. et al. (2008) The obesity gene, FTO, is of ancient origin, up-regulated during food deprivation and expressed in neurons of feeding-related nuclei of the brain. Endocrinology 149, 2062-2071Google Scholar
62Fischer, J. et al. (2009) Inactivation of the Fto gene protects from obesity. Nature 458, 894-898Google Scholar
63Stratigopoulos, G. et al. (2008) Regulation of Fto/Ftm gene expression in mice and humans. American Journal of Physiology – Regulatory, Integrative, and Comparative Physiology 294, R1185-1196Google Scholar
64Wahlen, K., Sjolin, E. and Hoffstedt, J. (2008) The common rs9939609 gene variant of the fat mass and obesity associated gene (FTO) is related to fat cell lipolysis. Journal of Lipid Research 49, 607-611Google Scholar
65Ren, D. et al. (2007) Neuronal SH2B1 is essential for controlling energy and glucose homeostasis. Journal of Clinical Investigation 117, 397-406Google Scholar
66Nakagawa, T. et al. (2003) Anti-obesity and anti-diabetic effects of brain-derived neurotrophic factor in rodent models of leptin resistance. International Journal of Obesity and Related Metabolic Disorders 27, 557-565Google Scholar
67Barroso, I. et al. (2003) Candidate gene association study in type 2 diabetes indicates a role for genes involved in beta-cell function as well as insulin action. PLoS Biology 1, E20Google Scholar
68Gaulton, K.J. et al. (2008) Comprehensive association study of type 2 diabetes and related quantitative traits with 222 candidate genes. Diabetes 57, 3136-3144Google Scholar
69Deeb, S.S. et al. (1998) A Pro12Ala substitution in PPARγ2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nature Genetics 20, 284-287Google Scholar
70Altshuler, D. et al. (2000) The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nature Genetics 26, 76-80Google Scholar
71Tönjes, A. et al. (2006) Association of Pro12Ala polymorphism in peroxisome proliferator-activated receptor gamma with Pre-diabetic phenotypes: meta-analysis of 57 studies on nondiabetic individuals. Diabetes Care 29, 2489-2497Google Scholar
72Gloyn, A.L. et al. (2003) Large-scale association studies of variants in genes encoding the pancreatic β-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52, 568-572Google Scholar
73Florez, J.C. et al. (2004) Haplotype structure and genotype-phenotype correlations of the sulfonylurea receptor and the islet ATP-sensitive potassium channel gene region. Diabetes 53, 1360-1368Google Scholar
74Van Dam, R.M. et al. (2005) Common variants in the ATP-sensitive K+ channel genes KCNJ11 (Kir6.2) and ABCC8 (SUR1) in relation to glucose intolerance: population-based studies and meta-analyses. Diabetic Medicine 22, 590-598Google Scholar
75Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research, Saxena, R. et al. (2007) Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331-1336Google Scholar
76Scott, L.J. et al. (2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341-1345Google Scholar
77Zeggini, E. et al. (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nature Genetics 40, 638-645Google Scholar
78Riggs, A.C. et al. (2005) Mice conditionally lacking the Wolfram gene in pancreatic islet beta cells exhibit diabetes as a result of enhanced endoplasmic reticulum stress and apoptosis. Diabetologia 48, 2313-2321Google Scholar
79Yamada, T. et al. (2006) WFS1-deficiency increases endoplasmic reticulum stress, impairs cell cycle progression and triggers the apoptotic pathway specifically in pancreatic beta-cells. Human Molecular Genetics 15, 1600-1609Google Scholar
80Minton, J.A. et al. (2002) Association studies of genetic variation in the WFS1 gene and type 2 diabetes in U.K. populations. Diabetes 51, 1287-1290Google Scholar
81Sandhu, M.S. et al. (2007) Common variants in WFS1 confer risk of type 2 diabetes. Nature Genetics 39, 951-953Google Scholar
82Franks, P.W. et al. (2008) Replication of the association between variants in WFS1 and risk of type 2 diabetes in European populations. Diabetologia 51, 458-463Google Scholar
83Winckler, W. et al. (2007) Evaluation of common variants in the six known maturity-onset diabetes of the young (MODY) genes for association with type 2 diabetes. Diabetes 56, 685-693Google Scholar
84Gudmundsson, J. et al. (2007) Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nature Genetics 39, 977-983Google Scholar
85Nandi, A. et al. (2004) Mouse models of insulin resistance. Physiological Reviews 84, 623-647Google Scholar
86Almind, K. et al. (1993) Aminoacid polymorphisms of insulin receptor substrate-I in non-insulin-dependent diabetus mellitus. Lancet 342, 828-832Google Scholar
87Rung, J. et al. (2009) Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nature Genetics 41, 1110-1115Google Scholar
88Huang, Q.Y., Cheng, M.R. and Ji, S.L. (2006) Linkage and association studies of the susceptibility genes for type 2 diabetes. Yi Chuan Xue Bao 33, 573-589Google Scholar
89Guan, W. et al. (2008) Meta-analysis of 23 type 2 diabetes linkage studies from the International Type 2 Diabetes Linkage Analysis Consortium. Human Heredity 66, 35-49Google Scholar
90Grant, S.F. et al. (2006) Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nature Genetics 38, 320-323Google Scholar
91Reynisdottir, I. et al. (2003) Localization of a susceptibility gene for type 2 diabetes to chromosome 5q34-q35.2. American Journal of Human Genetics 73, 323-335Google Scholar
92Florez, J.C. (2007) The new type 2 diabetes gene TCF7L2. Current Opinion in Clinical Nutrition and Metabolic Care 10, 391-396Google Scholar
93Jin, T. and Liu, L. (2008) The Wnt signaling pathway effector TCF7L2 and type 2 diabetes mellitus. Molecular Endocrinology 22, 2383-2392Google Scholar
94Lyssenko, V. et al. (2007) Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. Journal of Clinical Investigation 117, 2155-2163Google Scholar
95Sladek, R. et al. (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881-885Google Scholar
96Steinthorsdottir, V. et al. (2007) A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nature Genetics 39, 770-775Google Scholar
97Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661-678Google Scholar
98Unoki, H. et al. (2008) SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. Nature Genetics 40, 1098-1102Google Scholar
99Yasuda, K. et al. (2008) Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nature Genetics 40, 1092-1097Google Scholar
100Wu, Y. et al. (2008) Common variants in CDKAL1, CDKN2A/B, IGF2BP2, SLC30A8 and HHEX/IDE genes are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Diabetes 57, 2834-2842Google Scholar
101Prokopenko, I. et al. (2009) Variants in MTNR1B influence fasting glucose levels. Nature Genetics 41, 77-81Google Scholar
102Bouatia-Naji, N. et al. (2009) A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nature Genetics 41, 89-94Google Scholar
103Lyssenko, V. et al. (2009) Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nature Genetics 41, 82-88Google Scholar
104Van Hoek, M. et al. (2008) Predicting type 2 diabetes based on polymorphisms from genome-wide association studies: a population-based study. Diabetes 57, 3122-3128Google Scholar
105Meigs, J.B. et al. (2008) Genotype score in addition to common risk factors for prediction of type 2 diabetes. New England Journal of Medicine 359, 2208-2219Google Scholar
106Lyssenko, V. et al. (2008) Clinical risk factors, DNA variants, and the development of type 2 diabetes. New England Journal of Medicine 359, 2220-2232Google Scholar
107Lango, H. et al. (2008) Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes 57, 3129-3135Google Scholar
108Ravussin, E. et al. (1994) Effects of a traditional lifestyle on obesity in Pima Indians. Diabetes Care 17, 1067-1074Google Scholar
109Esparza, J. et al. (2000) Daily energy expenditure in Mexican and USA Pima Indians: low physical activity as a possible cause of obesity. International Journal of Obesity 24, 55-59Google Scholar
110Rampersaud, E. et al. (2008) Physical activity and the association of common FTO gene variants with body mass index and obesity. Archives of Internal Medicine 168, 1791-1797Google Scholar
111Andreasen, C.H. et al. (2008) Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes 57, 95-101Google Scholar
112Vimaleswaran, K.S. et al. (2009) Physical activity attenuates the body mass index-increasing influence of genetic variation in the FTO gene. American Journal of Clinical Nutrition 90, 425-428Google Scholar
113Florez, J.C. et al. (2006) TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. New England Journal of Medicine 355, 241-250Google Scholar
114Wang, J. et al. (2007) Variants of transcription factor 7-like 2 (TCF7L2) gene predict conversion to type 2 diabetes in the Finnish Diabetes Prevention Study and are associated with impaired glucose regulation and impaired insulin secretion. Diabetologia 50, 1192-1200Google Scholar
115Zeggini, E. et al. (2007) Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316, 1336-1341Google Scholar
116Chen, W.M. et al. (2008) Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels. Journal of Clinical Investigation 118, 2620-2628Google Scholar
117Bouatia-Naji, N. et al. (2008) A polymorphism within the G6PC2 gene is associated with fasting plasma glucose levels. Science 320, 1085-1088Google Scholar
118Tabor, H.K., Risch, N.J. and Myers, R.M. (2002) Candidate-gene approaches for studying complex genetic traits: practical considerations. Nature Review Genetics 3, 391-397Google Scholar
119Frayling, T.M. (2007) Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nature Review Genetics 8, 657-662Google Scholar
120McCarthy, M.I. and Zeggini, E. (2009) Genome-wide association studies in type 2 diabetes. Current Diabetes Report 9, 164-171Google Scholar
121International HapMap consortium, et al. (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851-861Google Scholar
122Magi, R. et al. (2007) Evaluating the performance of commercial whole-genome marker sets for capturing common genetic variation. BMC Genomics 8, 159Google Scholar
123de Bakker, P.I. et al. (2008) Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Human Molecular Genetics 17, R122–128Google Scholar

Further reading, resources and contacts

The International Association for the Study of Obesity (IASO) is a not-for-profit organisation for nation obesity associations. The link provides the latest information on prevalence data and new developments in scientific research into the prevention and management of obesity:

McCarthy, M.I. and Zeggini, E. (2009) Genome-wide association studies in type 2 diabetes. Current Diabetes Report 9,164-171Google Scholar
Li, S. and Loos, R.J. (2008) Progress in the genetics of common obesity: size matters. Current Opinion in Lipidology 19, 113-121Google Scholar
Andreasen, C.H. and Andersen, G. (2009) Gene-environment interactions and obesity-further aspects of genome-wide association studies. Nutrition 25, 998-1003Google Scholar
Ridderstråle, M. and Groop, L. (2009) Genetic dissection of type 2 diabetes. Molecular and Cellular Endocrinology 297, 10-17Google Scholar
O'Rahilly, S. (2009) Human genetics illuminates the paths to metabolic disease. Nature 462, 307-314Google Scholar
http://www.iotf.org/Google Scholar
McCarthy, M.I. and Zeggini, E. (2009) Genome-wide association studies in type 2 diabetes. Current Diabetes Report 9,164-171Google Scholar
Li, S. and Loos, R.J. (2008) Progress in the genetics of common obesity: size matters. Current Opinion in Lipidology 19, 113-121Google Scholar
Andreasen, C.H. and Andersen, G. (2009) Gene-environment interactions and obesity-further aspects of genome-wide association studies. Nutrition 25, 998-1003Google Scholar
Ridderstråle, M. and Groop, L. (2009) Genetic dissection of type 2 diabetes. Molecular and Cellular Endocrinology 297, 10-17Google Scholar
O'Rahilly, S. (2009) Human genetics illuminates the paths to metabolic disease. Nature 462, 307-314Google Scholar
http://www.iotf.org/Google Scholar