Genome-wide association studies of obesity and metabolic syndrome

Molecular and Cellular Endocrinology xxx (2012) xxx–xxx

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Molecular and Cellular Endocrinology journal homepage: www.elsevier.com/locate/mce

Review

Genome-wide association studies of obesity and metabolic syndrome Tove Fall, Erik Ingelsson ⇑ Dept. of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden

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Article history: Available online xxxx Keywords: Body mass index Genetics Genome-wide association studies Metabolic syndrome Obesity

a b s t r a c t Until just a few years ago, the genetic determinants of obesity and metabolic syndrome were largely unknown, with the exception of a few forms of monogenic extreme obesity. Since genome-wide association studies (GWAS) became available, large advances have been made. The first single nucleotide polymorphism robustly associated with increased body mass index (BMI) was in 2007 mapped to a gene with for the time unknown function. This gene, now known as fat mass and obesity associated (FTO) has been repeatedly replicated in several ethnicities and is affecting obesity by regulating appetite. Since the first report from a GWAS of obesity, an increasing number of markers have been shown to be associated with BMI, other measures of obesity or fat distribution and metabolic syndrome. This systematic review of obesity GWAS will summarize genome-wide significant findings for obesity and metabolic syndrome and briefly give a few suggestions of what is to be expected in the next few years. Ó 2012 Elsevier Ireland Ltd. All rights reserved.

Contents 1.

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Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Scope of the review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2. Obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3. Metabolic syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4. Pre-GWAS genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1. Monogenic and syndromic obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2. Common obesity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.3. Metabolic syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5. Genome-wide association studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results from genome-wide association studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Genome-wide association studies of body mass index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1. European descent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2. Other ethnic groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Genome-wide studies of extreme obesity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Genome-wide association studies of waist circumference and waist/hip ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1. European descent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2. Other ethnic groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Genome-wide association studies of fat mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Genome-wide association studies of the metabolic syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fine-mapping and function of obesity-related variants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Fine-mapping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Mechanistic studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Function of identified obesity loci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abbreviations: BMI, body mass index; CHARGE, cohorts for heart and aging research in genomic epidemiology; GWAS, genome-wide association study; eQTL, expression quantitative trait locus; GIANT, Genetic Investigation of ANthropometric Traits; IV, instrumental variable; MetS, metabolic syndrome; MeSH, medical subject headings; MR, Mendelian randomisation; NCEP, National Cholesterol Education Program; QTL, quantitative trait locus; T2D, type 2 diabetes; WC, waist circumference; WHR, waist–hip ratio; WHO, World Health Organization. ⇑ Corresponding author. Tel.: +46 8 524 823 34; fax: +46 8 31 49 75. E-mail address: [email protected] (E. Ingelsson). 0303-7207/$ - see front matter Ó 2012 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.mce.2012.08.018

Please cite this article in press as: Fall, T., Ingelsson, E. Genome-wide association studies of obesity and metabolic syndrome. Molecular and Cellular Endocrinology (2012), http://dx.doi.org/10.1016/j.mce.2012.08.018

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T. Fall, E. Ingelsson / Molecular and Cellular Endocrinology xxx (2012) xxx–xxx

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4.3.1. FTO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2. MC4R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3. GIPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion and future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. What have we learned from GWAS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Mendelian randomization studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Risk prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4. More GWAS in obesity? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5. Gene-environment interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. Supplementary data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Gene list ADAMTS9 ADAM metallopeptidase with thrombospondin type 1 motif, 9 APOA5 apolipoprotein A–V APOC1 apolipoprotein C-I BDNF brain-derived neurotrophic factor BRAP BRCA1 associated protein BUD13 BUD13 homolog C12orf51 chromosome 12 open reading frame 51 CADM2 cell adhesion molecule 2 CDKAL1 CDK5 regulatory subunit associated protein 1-like 1 CETP cholesteryl ester transfer protein, plasma CPEB4 cytoplasmic polyadenylation element binding protein 4 DNM3/PIGC dynamin 3 - phosphatidylinositol glycan anchor biosynthesis, class C ENPP1 ecto-nucleotide pyrophosphatase 1 ETV5 ets variant 5 FAIM2 Fas apoptotic inhibitory molecule 2 FANCL Fanconi anemia, complementation group L FLJ35779 POC5 centriolar protein homolog FTO fat mass and obesity associated GAD2 glutamic acid decarboxylase GLUT2 solute carrier family 2 of the facilitated glucose transporter GNPDA2 glucosamine-6-phosphate deaminase 2 GP2 glycoprotein 2 (zymogen granule membrane) GPRC5B G protein-coupled receptor, family C, group 5, member B GRB14 growth factor receptor-bound protein 14 HOXC13 homeobox C13 INSIG2 insulin induced gene 2 IRS1 insulin receptor substrate 1 ITPR2/SSPN inositol 1,4,5-trisphosphate receptor, type 2 /sarcospan (Kras oncogene-associated gene) KCNMA1 potassium large conductance calcium-activated channel, subfamily M, alpha member 1 KCTD15 potassium channel tetramerisation domain containing 15 KLF9 Kruppel-like factor 9 LPL lipoprotein lipase LRP1B low density lipoprotein receptor-related protein 1B LRRN6C leucine rich repeat neuronal 6C LY86 lymphocyte antigen 86 LYPLAL1 lysophospholipase-like 1 MAF musculoaponeurotic fibrosarcoma oncogene homolog (avian) MAP2K5 mitogen-activated protein kinase kinase 5 MC4R melanocortin 4 receptor MRPS22 mitochondrial ribosomal protein S22 MSRA methionine sulfoxide reductase A MTCH2 mitochondrial carrier 2 MTIF3 mitochondrial translational initiation factor 3 NEGR1 neuronal growth regulator 1 NFE2L3 nuclear factor (erythroid-derived 2)-like 3

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NISCH/STAB1 nischarin/stabilin 1 NRXN3 neurexin 3 NUDT3 nudix (nucleoside diphosphate linked moiety X)-type motif 3 PAX5 paired box 5 PCSK1 proprotein convertase subtilisin/kexin type 1 PLCG1 phospholipase C, gamma 1 PRKD1 protein kinase D1 PTBP2 polypyrimidine tract binding protein 2 QPCTL/GIPR glutaminyl-peptide cyclotransferase-like/gastric inhibitory polypeptide receptor POMC/ADCY3 proopiomelanocortin/adenylate cyclase 3 RPL27A ribosomal protein L27a RSPO3 R-spondin 3 SEC16B SEC16 homolog B SH2B1 SH2B adaptor protein 1 SLC39A8 solute carrier family 39 (zinc transporter), member 8 SLC6A14 solute carrier family 6 member 14 SPRY2 sprouty homolog 2 TBX15/WARS2 T-box 15/tryptophanyl tRNA synthetase 2, mitochondrial TFAP2B transcription factor AP-2 beta (activating enhancer binding protein 2 beta) TMEM160 transmembrane protein 160 TMEM18 transmembrane protein 18 TNNI3K TNNI3 interacting kinase TRIB1 tribbles homolog 1 VEGFA vascular endothelial growth factor A ZNF259 zinc finger protein 259 ZNF608 zinc finger protein 608 ZNRF3-KREMEN1 zinc and ring finger 3/kringle containing transmembrane protein 1

1. Background 1.1. Scope of the review There has been a striking evolution in techniques used for genotyping over the last decade, which has enabled genotyping of large sets of genetic variants in each individual to decreasing costs. The field of obesity genetics has drawn much attention, and some of the largest consortia and study materials are found in this field. This review aims to summarize the most important studies and findings within GWAS of obesity and metabolic syndrome, and to briefly give a few suggestions of what is to be expected in the next few years.

Please cite this article in press as: Fall, T., Ingelsson, E. Genome-wide association studies of obesity and metabolic syndrome. Molecular and Cellular Endocrinology (2012), http://dx.doi.org/10.1016/j.mce.2012.08.018

T. Fall, E. Ingelsson / Molecular and Cellular Endocrinology xxx (2012) xxx–xxx

1.2. Obesity Obesity is one of the largest global health problems and is associated with increased morbidity and mortality mediated by its association to several conditions, such as type 2 diabetes (T2D) and cardiovascular disease (WHO, 2011). The rapid economic progress in several developing countries has resulted in lifestyle changes, both in diet and physical activity (Shen et al., 2012), which in combination with aging populations have resulted in a worldwide epidemic of obesity. When there is an abundance of food in many countries, genetic components of obesity are getting more important in determining the individual risk of obesity. Family, twin, and adoption studies indicate that adiposity is highly heritable with the estimated genetic contribution to body mass index (BMI) in a range of 20–84% (reviewed in (Maes et al., 1997)). Obesity relates to the total fat mass of an individual and this is preferably measured by direct fat-measuring methods using imaging techniques, but is usually approximated by surrogate measurements such as the BMI (weight/height2) or waist circumference (WC), due to practical and economic reasons. This is especially true for genetic studies due to the very small effect sizes of susceptibility loci for common complex diseases. Also, a recent study did not find any support for replacing BMI with other more direct measurements of obesity for the risk assessment of coronary heart disease and diabetes (Taylor et al., 2010). Waist circumference and waist–hip ratio (WHR) are more correlated to intra-abdominal fat content (central adiposity) than BMI, and are considered strong risk factors for T2D (Kamel et al., 2000). 1.3. Metabolic syndrome Metabolic syndrome (MetS) constitutes of a clustering of several metabolic disturbances that increase risk for cardiovascular disease, such as central obesity, dyslipidemia, insulin resistance and/or glucose intolerance, and elevated blood pressure (Grundy et al., 2005; NCEP, 2001). In 1988, Reaven hypothesized that insulin resistance was the factor linking type 2 diabetes, essential hypertension and coronary heart disease (Reaven, 1988). He called the clustering of risk factors ‘syndrome X’, but eventually the terms ‘insulin resistance syndrome’ (Haffner et al., 1992) and more recently ‘metabolic syndrome’ (Alberti and Zimmet, 1998; Alberti et al., 2006; Balkau and Charles, 1999; Einhorn et al., 2003; Grundy et al., 2005; NCEP, 2001) have been increasingly utilized. Over the past decade, several definitions of MetS have been introduced. At present, the most widely used definitions for research, as well as clinical practice, seems to be the original and modified National Cholesterol Education Program (NCEP) criteria (Grundy et al., 2005; NCEP, 2001). However, many experts have questioned whether there is any value of diagnosing the metabolic syndrome in contrast to diagnosing and treating the individual abnormalities, such as dyslipidemia and hypertension. Further, it has also been argued that value of the metabolic syndrome must be considered not in pathophysiologic terms, but as a pragmatic approach to obtain a better clinical outcome. That said, studies of related individuals have found evidence of familial aggregation of the different components of the syndrome, including blood lipid concentration, blood pressure and glycemic traits (Carmelli et al., 1994; Mitchell et al., 1996), and heritability estimates of the metabolic syndrome range from 13% to 27% (Bellia et al., 2009; Henneman et al., 2008), which has prompted studies to find genetic determinants of MetS. 1.4. Pre-GWAS genetics 1.4.1. Monogenic and syndromic obesity During the 1990’s, the understanding of genetic determinants of body weight regulation in humans was heavily relying on the

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study of monogenic rodent models of obesity. For most of the monogenic forms of obesity in murine models mapped by positional cloning, human counterparts were identified by screening children with extreme childhood obesity for the genetic defects identified in mice. Loss-of-function mutations causing deficiencies of appetite-regulating hormones or their receptors such as leptin (Montague et al., 1997), leptin receptor (Clement et al., 1998), pro-opiomelanocortin (POMC) (Krude et al., 1998) and melanocortin 4 receptor (MC4R) (Vaisse et al., 1998; Yeo et al., 1998) are examples of such monogenic syndromes, and accounts for a minor proportion of the children with severe, young-onset obesity. Defects in MC4R are the most common known monogenic form of childhood obesity, but accounts only for about 6% of the cases of monogenic obesity in children (Farooqi et al., 2003). Because this severity of obesity is only seen in rare instances, these loss-of-function variants are uncommon in the general population. Syndromic obesity is when obesity occurs in the clinical context of a distinct set of associated clinical phenotypes. These syndromes have earlier been viewed as monogenic, but lately, several studies have pointed out a heterogeneous genetic background, including errors in genomic imprinting in the Prader–Willi syndrome (Butler, 2011) and mutations in over 15 different genes in the Bardet–Biedl syndrome (Chen et al., 2011). 1.4.2. Common obesity Genetic variants associated with ‘‘common polygenic obesity’’ have been extensively searched for using candidate gene approaches and genome-wide linkage studies; unfortunately with little success as the markers found associated with obesity varied heavily across ethnic groups and have been found difficult to replicate. Furthermore, the candidate gene approach has been unsuccessful in general, for several reasons including too small study samples, between study-heterogeneity, lack of adjustment for multiple testing, lack of replication, and often suboptimal biological hypotheses. Examples of genetic associations that have been difficult to replicate, and that have not been found through GWAS methods so far include polymorphisms in or near genes encoding solute carrier family 6 member 14 (SLC6A14) (Suviolahti et al., 2003), glutamic acid decarboxylase (GAD2) (Boutin et al., 2003), and ecto-nucleotide pyrophosphatase 1 (ENPP1) (Meyre et al., 2005). 1.4.3. Metabolic syndrome The pre-GWAS literature on shared genetic variants for the components of metabolic syndrome is quite sparse. Kissebah et al. performed genome-wide linkage analysis in 500 families and found signals for quantitative trait loci (QTLs) on both chromosomes 3 and 17, in regions harboring the solute carrier family 2 of the facilitated glucose transporter (GLUT2) locus and the adiponectin locus (Kissebah et al., 2000). Lehman et al. (2005) reported a bivariate linkage study combining pairs of components of metabolic syndrome assessing association to a broad region on chromosome 7 that had been implicated in a number of metabolic traits. They found evidence for association of a region on 7q11.23 for a number of combinations. This region has later been shown to be associated to lipoprotein particle size in a GWAS by Chasman et al. (2009). 1.5. Genome-wide association studies The first draft of the human genome was published by the Human Genome Project (McPherson et al., 2001) and the first comprehensive map of common haplotypes was reported by the HapMap project (2005). Most base pairs in the human genome are the same for all humans, and genetic association studies are focused on those where variation between humans are reported (single nucleotide polymorphisms, SNPs). The completion of the Human

Please cite this article in press as: Fall, T., Ingelsson, E. Genome-wide association studies of obesity and metabolic syndrome. Molecular and Cellular Endocrinology (2012), http://dx.doi.org/10.1016/j.mce.2012.08.018

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Genome Project and the HapMap project in conjunction with the development of high-throughput genotyping techniques, as well as statistical and computational methods enabled large-scale GWAS, in which a large number of genetic variants are tested for association with the trait of interest. To adjust for the vast number of tests performed in GWAS, procedures such as multiple testing correction and replication in independent samples are undertaken to minimize the number of false discoveries. Associations between the trait of interest and a SNP that hold for these procedures are called ‘‘genome-wide significant’’. Currently, associations of common variants reaching significance levels of P 6 5  10 8 are considered genome-wide- significant. The cut-off is based on the estimated effective number of independent tests in the genome if all common SNPs in HapMap were tested (directly genotyped or imputed) (Panagiotou and Ioannidis, 2012).

2. Methods For this systematic review of GWAS of obesity and metabolic syndrome, we aimed to include all original genome-wide association studies, including meta-analyses of GWAS, with one or more of the following outcomes: BMI, obesity (binary trait), body fat distribution (WHR, WC), body fat percentage/amount and metabolic syndrome. We performed a PubMed Search entering the following search terms: (‘‘Genome-Wide Association Study’’ [Mesh] OR ‘‘genomewide’’ OR ‘‘whole-genome’’) AND (‘‘Obesity’’ [Mesh] OR ‘‘Body Mass Index’’ [Mesh] OR ‘‘Body Fat Distribution’’ [Mesh] OR ‘‘Metabolic Syndrome X’’ [Mesh] OR ‘‘Waist–Hip Ratio’’ [Mesh] OR ‘‘Waist Circumference’’ [Mesh] OR ‘‘waist circumference’’ OR ‘‘body fat percentage’’ OR ‘‘fat mass’’) with the limits: Humans, English and Publication Date from 2006/01/01 to 2012/03/01. This search yielded 489 hits. Because recent publications may not yet have been MeSH (Medical Subject Headings)-indexed, we also

Fig. 1. Schematic overview of the selection process for this systematic review.

performed the following additional search: (‘‘genome-wide’’ OR ‘‘whole-genome’’) AND (‘‘obesity’’ OR ‘‘body mass index’’ OR ‘‘BMI’’ OR ‘‘body fat distribution’’ OR ‘‘metabolic syndrome’’ OR ‘‘waist–hip ratio’’ OR ‘‘waist circumference’’ OR ‘‘body fat percentage’’ OR ‘‘fat mass’’) with limits: Publication Date from 2011/01/01 to 2012/03/01. This search yielded 293 hits of which 187 were unique to the second search, giving a total number of 676 publications found in any or both searches. We reviewed all 676 publications and excluded those that: (i) were not being performed in humans; (ii) studied outcomes not within the scope of this review, such as non-obesity phenotypes, obesity within a specific disease group, and two studies focusing on gene–environment interaction and epistasis; (iii) were not original GWAS studies or meta-analyses thereof; and/or (iv) utilized GWAS data for examining copy number variations and large deletions. The 39 included and 637 excluded papers are listed in Supplementary Table 1. Fig. 1 outlines the selection process, including the number of studies excluded for different reasons. The 39 included studies were reviewed in depth to find SNPs associated with to BMI, waist circumference, waist-hip ratio, fat percentage, fat mass or metabolic syndrome in GWAS with p 6 5  10 8. Table 1 gives an overview of all these SNPs, including reported allele frequencies and effect sizes. Loci discovered in study samples with extreme obesity are summarized in Table 2. SNPs associated with metabolic syndrome are discussed only in the text.

3. Results from genome-wide association studies 3.1. Genome-wide association studies of body mass index 3.1.1. European descent The first GWAS of BMI, published in Science in 2006, was performed in 694 participants of the Framingham Heart Study using a microarray with 100,000 SNPs chip (Herbert et al., 2006). A SNP in the INSIG2 locus was identified in the discovery and replicated in four independent samples. However, this SNP was not significant at the genome-wide threshold that we use today, and subsequent large-scale studies have not replicated this finding (Dina et al., 2007b; Loos et al., 2007). The first BMI locus with robust statistical significance identified through GWAS was published by Frayling et al. (2007), who found an association with a variant in the FTO gene as a by-product of a GWAS of T2D. They set out to identify genes associated with T2D using an array with 500,000 SNPs in 924 cases and 2938 population controls. However, when the locus with the strongest association to T2D was adjusted for BMI, the effect was abolished, indicating that the effect was due to a primary effect on BMI, not T2D. They replicated the finding by genotyping a total of 39,000 individuals and modeling the effect of the FTO variant on BMI as a continuous trait. The FTO gene, which at the time had unknown function, was originally described in a laboratory mouse with Fused TOes (Fto) (Peters et al., 1999). With the discovery of the association with human obesity, the HUGO Gene Nomenclature Committee changed the name to fat mass and obesity associated (FTO) (Groop, 2007). Simultaneously, Dina et al. (2007a) published another study that also identified FTO as an obesity susceptibility locus. The authors stumbled over this association when examining 48 SNPs as markers of population stratification in what they thought were intergenic regions. One of these SNPs actually mapped the FTO locus and showed strong association with extreme childhood obesity. They also replicated their finding by genotyping 2900 obese individuals and 5100 controls of European origin. The effect size of FTO (0.34% of the total variance according to the most recent large-scale meta-analysis (Speliotes et al., 2010))

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8

.

Nearest gene

Full gene name

SNP

Chr

Trait

Firstauthor

EAFa (%)

Beta (replication)a

Nb

Best pvaluea

ADAMTS9

ADAM metallopeptidase with thrombospondin type 1 motif, 9

rs6795735

3

Heid

41

0.026*

161,642

9.79E-14

BDNF

Brain-derivedneurotrophicfactor

C12orf51 CADM2 CDKAL1

Chromosome 12 openreadingframe 51 Cell adhesion molecule 2 CDK5 regulatory subunit associated protein 1-like 1

CPEB4

Cytoplasmicpolyadenylation element binding protein 4

rs10767664 rs4923461 rs6265 rs2030323 rs2074356 rs13078807 rs9356744 rs2206734 rs6861681

11 11 11 11 12 3 6 6 5

Speliotes Thorleifsson Wen Okada Cho Speliotes Wen Okada Heid

78 84c 44 60 85 20 58 59 34

0.19 0.04** 0.05** 0.04** 0.005 0.10 0.03** 0.04** 0.019*

204,158 72,003 65,406 62,245 16,703 237,404 83,048 62,245 162,886

2.67E-25 3.20E-11 3.56E-13 3.80E-16 7.8E–12 2.45E-11 2.00E-11 1.40E-11 1.91E-09

DNM3/PIGC

Dynamin 3 – phosphatidylinositol glycan anchor biosynthesis, class C

rs1011731

1

Heid

57

0.026*

169,112

9.51E-18

ETV5

ets variant 5

FAIM2 FANCL FLJ35779 FTO

Fas apoptoticinhibitorymolecule 2 Fanconi anemia, complementation group L POC5 centriolar protein homolog Fat mass and obesity associated

GNPDA2 GP2 GPRC5B GRB14

Glucosamine-6-phosphate deaminase 2 Glycoprotein 2 (zymogengranulemembrane) G protein-coupled receptor, family C, group 5, member B Growth factor receptor-bound protein 14

rs9816226 rs7647305 rs7138803 rs887912 rs2112347 rs1558902 rs1121980 rs1558902 rs9939609 rs8050136 rs17817449 rs12149832 rs8050136 rs1558902 rs9939609 rs10938397 rs12597579 rs12444979 rs10195252

3 3 12 2 5 16 16 16 16 16 16 16 16 16 16 4 16 16 2

Speliotes Thorleifsson Speliotes Speliotes Speliotes Speliotes Loos Heard-Costa Frayling, Willer Thorleifsson Wen Okada Kilpeläinen Heard-Costa Frayling Speliotes, Willer Wen Speliotes Heid

82 77c 38 29 63 42 NR NR 41 41c 17 20 40 NR 39 43 80 87 60

0.14 0.04** 0.12 0.10 0.10 0.39 0.06** NR 0.33 0.08** 0.08** 0.09** 0.047* NR 0.08** 0.18 0.04** 0.17 0.036*

196,221 77,453 200,064 242,807 231,729 192,344 16,876b 31,373b 114,643 72,003 65,406 62,245 70,642 31,373b 21,463 197,008 83,048 239,715 179,568

1.69E-18 7.20E-11 1.63E-17 2.10E-13 5.03E-13 2.3E-111 3.60E-08 3.72E-24 4.90E-74 1.10E-47 4.60E-27 4.80E-22 2.70E-26 4.60E-19 4.00E-09 1.60E-29 1.02E-08 3.27E-21 2.09E-24

HOXC13

Homeobox C13

rs1443512

12

Heid

24

0.03*

189,518

6.38E-17

IRS1 ITPR2/SSPN

rs2943650 rs718314

2 12

Kilpeläinen Heid

36 74

0.025* 0.03*

76,150 184,670

3.80E-11 1.14E-17

KCTD15

Insulin receptor substrate 1 Inositol 1,4,5-trisphosphate receptor, type 2 /sarcospan (Kras oncogeneassociated gene) Potassium channel tetramerisation domain containing 15

rs29941

19

WHR adjusted for BMI BMI BMI BMI BMI WHR BMI BMI BMI WHR adjusted for BMI WHR adjusted for BMI BMI BMI BMI BMI BMI BMI BMI BMI BMI BMI BMI BMI Fat percentage WAIST WAIST BMI BMI BMI WHR adjusted for BMI WHR adjusted for BMI Fat percentage WHR adjusted for BMI BMI

67

0.06

192,872

3.32E-08

KLF9 LRP1B LRRN6C LY86

Kruppel-like factor 9 Low density lipoprotein receptor-related protein 1B Leucine rich repeat neuronal 6C Lymphocyte antigen 86

rs11084753 rs11142387 rs2890652 rs10968576 rs1294421

19 9 2 9 6

Speliotes, Thorleifsson Willer Okada Speliotes Speliotes Heid

67 46 18 31 39

0.06 0.03** 0.09 0.11 0.028*

101,526 62,245 209,068 216,916 179,343

4.50E-12 1.30E-09 9.27E-11 2.65E-13 1.75E-17

LYPLAL1

Lysophospholipase-like 1

rs2605100 rs4846567

1 1

Lindgren Heid

69 72

0.001 0.032*

47,633 168,987

2.60E-08 6.89E-21

MAP2K5

Mitogen-activated protein kinase kinase 5

MC4R

Melanocortin 4 receptor

rs2241423 rs4776970 rs571312 rs12970134

15 15 18 18

Speliotes Wen Speliotes Thorleifsson

78 22 24 29c

0.13 0.03** 0.23 0.04**

227,950 83,048 203,600 72,003

2.76E-18 2.33E-09 2.22E-40 1.20E-12

BMI BMI BMI BMI WHR adjusted for BMI WHR WHR adjusted for BMI BMI BMI BMI BMI

5

(continued on next page)

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Table 1 SNPs reported to be associated to BMI, waist circumference, waist–hip ratio, fat percentage or fat mass in GWAS with p 6 5  10

Nearest gene

6

Full gene name

SNP

Chr

Trait

Firstauthor

EAFa (%)

Beta (replication)a

Nb

Best pvaluea

18 18 18 18 3 8 11 11 13 1 1 7

BMI BMI BMI WAIST BMI WAIST BMI BMI BMI BMI BMI WHR adjusted for BMI WHR adjusted for BMI BMI BMI Fat mass BMI BMI BMI BMI BMI BMI BMI BMI WHR adjusted for BMI BMI BMI BMI BMI BMI BMI Fat percentage WHR adjusted for BMI BMI Waist BMI BMI BMI BMI BMI WHR adjusted for BMI BMI WHR adjusted for BMI

Loos, Willer Wen Okada Chambers Melka Lindgren Speliotes Willer Speliotes Speliotes, Willer Thorleifsson Heid

21 21 25 36 6 18 41 34 24 61 58a 21

0.20 0.06** 0.05** 0.74 NR 0.43 0.06 0.07 0.09 0.13 0.03** 0.043*

110,567 65,406 62,245 14,639 598d 80,210 191,943 110,737 198,577 198,380 72,003 190,781

1.10E-20 2.76E-15 1.80E-11 1.70E-09 4.60E-08 9.00E-09 1.24E-11 1.90E-11 9.48E-10 2.95E-20 1.20E-11 9.97E-25

Heid

94

0.036*

185,887

3.84E-10

Spelitotes Speliotes Melka Wen Speliotes Speliotes Speliotes Wen, Okada Speliotes Wen Speliotes Heid

21 21 22 41 4 59 80 50 47 45 52 52

0.13 0.06 NR 0.04** 0.17 0.06 0.15 0.04** 0.14 0.03** 0.06 0.045*

183,022 249,777 598d 83,048 241,667 243,013 194,564 83,048 230,748 83,048 249,791 190,746

9.07E-10 3.30E-08 9.30E-09 5.13E-09 2.23E-10 3.11E-09 7.56E-15 5.93E-14 5.02E-20 1.35E-13 3.54E-09 1.84E-40

Speliotes Wen Okada Speliotes Willer, Thorleifsson Speliotes Kilpeläinen Heid

19 20 22 40 41 7 32 37

0.22 0.06** 0.04** 0.15 0.15 0.19 0.023* 0.031*

179,414 65,406 62,245 204,309 116,497 245,378 70,831 186,790

7.68E-22 9.47E-20 3.40E-09 9.54E-20 2.20E-14 7.08E-13 3.2  10 8 8.69E-25

Speliotes Lindgren Speliotes Speliotes Willer Thorleifsson Speliotes Heid

18 16 67 83 84 84c 43 56

0.13 0.49 0.09 0.31 0.26 0.06** 0.07 0.039*

195,776 118,691 233,512 197,806 114,643 72,003 227,900 172,559

1.24E-19 1.90E-11 1.22E-11 3.71E-46 3.20E-26 4.20E-17 2.51E-13 5.88E-25

Speliotes Heid

48 57

0.07 0.019*

241,999 170,997

1.57E-09 1.10E-11

MRPS22 MSRA MTCH2

Mitochondrialribosomal protein S22 Methioninesulfoxidereductase A Mitochondrialcarrier 2

MTIF3 NEGR1

Mitochondrialtranslational initiation factor 3 Neuronal growth regulator 1

NFE2L3

Nuclear factor (erythroid-derived 2)-like 3

rs17782313 rs6567160 rs2331841 rs12970134 rs7638110 rs7826222 rs3817334 rs10838738 rs4771122 rs2815752 rs2568958 rs1055144

NISCH/STAB1

Nischarin/stabilin 1

rs6784615

3

NRXN3 NUDT3 PAX5 PCSK1 PRKD1 PTBP2 QPCTL/GIPR

Neurexin 3 Nudix (nucleoside diphosphate linked moiety X)-type motif 3 Paired box 5 Proproteinconvertasesubtilisin/kexin type 1 Protein kinase D1 Polypyrimidinetractbinding protein 2 Glutaminyl-peptide cyclotransferase-like/gastric inhibitory polypeptide receptor

POMC/ADCY3

Proopiomelanocortin/adenylatecyclase 3

RPL27A RSPO3

Ribosomal protein L27a R-spondin 3

rs10150332 rs206936 rs16933812 rs261967 rs11847697 rs1555543 rs2287019 rs11671664 rs713586 rs6545814 rs4929949 rs9491696

14 6 9 5 14 1 19 19 2 2 11 6

SEC16B

SEC16 homolog B

SH2B1

SH2B adaptor protein 1

SLC39A8 SPRY2 TBX15/WARS2

Solute carrier family 39 (zinc transporter), member 8 Sprouty homolog 2 T-box 15/tryptophanyltRNA synthetase 2, mitochondrial

rs543874 rs574367 rs516636 rs7359397 rs7498665 rs13107325 rs534870 rs984222

1 1 1 16 16 4 13 1

TFAP2B

Transcription factor AP-2 beta (activating enhancer binding protein 2 beta)

TMEM160 TMEM18

Transmembrane protein 160 Transmembrane protein 18

TNNI3K VEGFA

TNNI3 interactingkinase Vascular endothelial growth factor A

rs987237 rs987237 rs3810291 rs2867125 rs6548238 rs7561317 rs1514175 rs6905288

6 6 19 2 2 2 1 6

ZNF608 ZNRF3KREMEN1

Zinc finger protein 608 Zinc and ring finger 3/kringle containing transmembrane protein 1

rs4836133 rs4823006

5 22

NR, not reported; Chr, chromosome; EAF, effect allele frequency. a From largest study sample reporting this SNP. b Sample size of discovery and replication in largest study reporting this SNP. c Allele frequency for largest cohort in discovery set. d From discovery set, replication not reported. * Inverse normally transformed phenotype. ** Z-score transformed phenotype.

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Table 1 (continued)

8

with obesity-related traits in studies of extremely obese individuals.

Nearest gene BDNF FAIM2 FTO

KCNMA1

MAF MC4R

Brain-derivedneurotrophicfactor Fas apoptoticinhibitorymolecule 2 Fat mass and obesity associated

Potassium large conductance calciumactivated channel, subfamily M, alpha member 1 Musculoaponeuroticfibrosarcoma oncogene homolog (avian) Melanocortin 4 receptor

NRXN3 TFAP2B

Neurexin 3 Transcription factor AP-2 beta (activating enhancer binding protein 2 beta)

MSRA TMEM18

Methioninesulfoxidereductase A Transmembrane protein 18

Nd

Best pvalue

10,338

5.2E-17 6.10E-09 1.2E-28

SNP

Chr

Trait

Firstauthor

EAF (cases) (%)

EAF (controls)

Cases (discovery)

Controls (discovery)

rs988712 rs7132908 rs1421085

11 12 16

Jiao Paternoster Meyre, Scherag

81a 44e 49a

73%a

164 2633 1380

163 2740 1416

rs9941349 rs17817449 rs9936385 rs2116830

16 16 16 10

Extreme obesity Extreme obesity Discovery in extreme obesity + replication in populationbased cohorts Extreme obesity Extreme obesity Extreme obesity Extreme obesity

Cotsapas Wang Paternoster Jiao

43e 48 42e 87a

3197 540 2740 163

*

76a

775 520 2633 164

rs1424233

16

Extreme obesity

Meyre

52a

47a

1380

1416

16,982b

a

a

1380 2633

1416 2740

b

4.8E-15 3.20E-08

rs17782313 rs8089364

18 19

rs17700144 rs11624704 rs734597

18 14 6

rs17150703 rs11127485

8 2

NR, not reported; Chr, chromosome; EAF, effect allele frequency. a Allele frequency for largest cohort in discovery set. b Replication set included both extreme cohorts and population-based subjects. c Replication set consisted of families to cases and controls in addition to discovery set. d Sample size of discovery and replication in largest study reporting this SNP. E EAF not reported for cases and controls separately. * No replication phase.

Extreme obesity Extreme population/linear regression BMI Extreme obesity WHR in extreme population Discovery in extreme obesity + replication in populationbased cohorts Extreme obesity Discovery in extreme obesity + replication in populationbased cohorts

42%a

36

*

16,982b

6.09E 12 2.50E-12 1.40E-13 2.82E-10

2256c *

10,338

Meyre Paternoster

28 30e

26

Scherag Wang Paternoster

NR NR 47e (replication set) NR NR

NR NR

1138 520 2633

1120 540 2740

5399 2256 69,108b

6.47E-11 2.67E-09 3.10E-08

NR NR

1,138 1,138

1120 1120

5399 29,483b

1.85E-08 9.97E-10

Scherag Scherag

16,982

3.8E-13

*

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Table 2 SNPs reported to be associated in GWAS with p 6 510

8

T. Fall, E. Ingelsson / Molecular and Cellular Endocrinology xxx (2012) xxx–xxx

was less than researchers had guessed that the top hit would have – a lesson shared with other complex traits. Therefore, in order to maximize statistical power, large-scale international collaboration and formation of genetic consortia was needed, as well as metaanalysis of existing data. In 2008, Loos et al. published a meta-analysis of genome-wide association data from 16,876 individuals of European descent. They replicated the FTO finding and also found an additional strong signal 188 kb downstream of the MC4R locus. This was replicated in 60,000 adults and 6000 children aged 7– 11 years (Loos et al., 2008). The 2009 paper from the GIANT consortium (Genetic Investigation of ANthropometric Traits) by Willer et al. (2009) included a meta-analysis of GWAS from >32,000 individuals with independent replication in >59,000 individuals. They confirmed the FTO and MC4R loci as BMI loci and identified six additional loci as genome-wide significant: TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1. NEGR1 did reach significance only when results were combined with those from another study by Thorleifsson et al. (2009), published in the same issue of Nature Genetics. The latter study from deCODE was a GWAS on weight and BMI, mainly based on Icelandic individuals, but also including some other populations of Northern European descent with a total of 34,416 individuals in the discovery sample. They replicated their findings in 5586 de novo genotyped Danish subjects and in the discovery GWAS from the GIANT study. The results confirmed known associations to BMI of variants in or near the loci FTO, MC4R, as well as for the loci NEGR1, TMEM18, KCTD15 and SH2B1, which were common findings with the simultaneous paper by Willer et al. They also reported additional two new loci: ETV5 and BDNF (Thorleifsson et al., 2009). The concordance between the reported findings from Willer et al. and Thorleifsson et al. may be explained by similar sample sizes, replication of top hits in the other study’s discovery set, as well as relatively large effect sizes for the common SNPs identified, with the exception of KCTD15. The unique findings of each study (GNPDA2, and ETV5 and BDNF, respectively) may be explained by the large number of individuals from the Icelandic population in one of the studies, which may be regarded as a large founder population, somewhat different genotyping platforms used by the two studies, and by other study-specific factors. In 2010, Speliotes et al. published the results of the largest BMI GWAS undertaken to date which again was a meta-analysis from the GIANT consortium examining 2.8 million SNPs in 124,000 individuals with targeted follow up of the 42 strongest signals in 126,000 additional individuals. The discovery stage included individual study samples from previous GWAS efforts described above (Loos et al., 2008; Thorleifsson et al., 2009; Willer et al., 2009), and a large number of additional, new studies. They confirmed 14 known susceptibility loci for BMI and weight, one known WHR locus, and identified 18 new loci associated with BMI (p 6 5  10 8). As a consequence of the increased power of this study, variants with lower minor allele frequency and/or smaller effect sizes compared to previously identified variants were discovered. Using simulation methods, they also predicted the numbers of common variants with similar effects on BMI that remains to be identified to >250. Together, the 32 confirmed BMI loci explained 1.45% of the inter-individual variation in BMI, with the FTO SNP accounting for the largest proportion of the variance (0.34%). To estimate the cumulative effect of the 32 variants on BMI, a weighted genetic susceptibility score summing the number of BMI-increasing alleles was constructed. For each unit increase in the genetic susceptibility score, approximately equivalent to one additional risk allele, BMI increased by 0.17 kg/m2, equivalent to a 435–551 g gain in body weight in adults of 160–180 cm in height. The difference in average BMI between individuals with the highest genetic susceptibility score (P38 BMI-increasing alleles) and those with the lowest (621 BMI-increasing alleles) was 2.73 kg/m2, equivalent to a

6.99–8.85 kg body weight difference in adults 160–180 cm in height. Still, it should be noted that the predictive value for obesity risk as indicated by C-statistics of the 32 variants combined was modest, although statistically significant (Speliotes et al., 2010). They further explored whether the 32 SNPs were in LD with common missense SNPs or copy number variants (CNVs). Non-synonymous variants in LD with the lead signals were present in a number of genes, for example in the BDNF, GIPR and MTCH2 genes. In addition, the rs7359397 signal was in LD with coding variants in several genes in the SH2B1/SULT1A region. Furthermore, two of the lead SNPs tagged common CNVs in NEGR1 and GPRC5. Several GWAS of BMI in subjects of European descent have used samples from isolated populations, hoping to benefit from the lower variation of these populations. Such studies include the study by Scuteri et al. (2007), which pointed to FTO as a susceptibility variant around the same time as the Frayling et al. and Dina et al. publications using a sample of 4700 individuals from Sardinia; Sabatti et al. (2009), which used a sample 5000 individuals from a founder population in North Finland and the study by Polasek et al. (2009) which included 900 individuals from an isolated island in Croatia. None of these three studies were successful in reporting associations with BMI at the conventional genome-wide significance. Moreover, some GWAS have made use of family-based cohorts, where DNA has often been collected originally for other purposes, such as genome-wide linkage studies. In GWAS, statistical adjustment for family clustering is necessary, and while statistical power is somewhat lower per individual when using family-based data; there are also advantages in dealing with population stratification. Examples of such studies are the early GWAS from the Framingham Heart Study; Florez et al. (2007) and Fox et al. (2007) that both included about 1300 individuals from 300 families. Other examples are the study by Liu et al. (2010) that included 11,500 Australian twins and family members. Johansson et al. (2010) based their study on 4200 individuals from 5 genetically isolated cohorts with family data from Swedish, South Tyrolean, Croatian, Orkney Islands and Dutch populations. None of the abovementioned family-based studies were successful in reporting associations at conventional genome-wide significance level. 3.1.2. Other ethnic groups In 2012, two large-scale GWAS from South Asia were published in the same issue of Nature Genetics. They had discovery samples of 26,000–28,000 individuals, and were thus by far the largest GWAS of BMI in individuals of non-European descent. The study by Wen et al. (2012) included 27,715 East Asians (Chinese, Korean, Indonesian) in their discovery sample, which was followed by in silico and de novo replication in 37,691 and 17,642 additional East Asians, respectively. They replicated seven previously identified loci (FTO, SEC16B, MC4R, GIPR-QPCTL, ADCY3-DNAJC27, BDNF and MAP2K5) with p 6 5  10 8 and identified three novel loci in or near the CDKAL1, PCSK1 and GP2 genes. The other study by Okada et al. (2012) included a discovery sample of 26,620 Japanese subjects with replication in an additional sample of 7900 Japanese individuals, as well as in the discovery sample from Wen et al. (2012). Results showed genome-wide significant association of five previous reported loci (SEC16B, BDNF, FTO, MC4R and GIPR) as well as CDKAL1, which also was reported by Wen et al., and the novel KLF9 locus. The two studies jointly reported four new loci, although their samples were smaller than the GIANT study by Speliotes et al. Reasons for this discrepancy could be genetic differences between the populations, such as different LD structure and different allele frequencies, resulting in better statistical power for some loci in these ethnicities. This is highlighted by the fact that for several of the susceptibility loci common to Asian and European populations, such as for FTO, the effect allele frequency differ substantially (see Table 1). Different food and lifestyle factors could also

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9

KCNMA1*, BDNF 22.2 ± 1.8

Loci so far not reported to be associated with obesity in the general population. *

Current and life-time BMI < 25 BMI > 40, age > 16; recruited through advertisements or in myocardial infarction studies

44.7 ± 4.7

FTO, NRXN3 20.8 ± 1.8 Current and life-time BMI < 25 Current BMI > 35, must been > 40 during life. Female cohort with a few males

49.4 ± 8.8

FTO, MC4R, FAIM2, TFAP2B Females: 22.5 (18.6–30.1) Males: 21.3 (18.4–24.7) Random sample of the remaining cohort Pregnant woman:BMI > 96.4 percentile after adj for age and parity. Men:BMI > 31 at age 20 (99th percentile) sampled from 1943–1977)

Females: 36.3(33.8–43.9) Males:32.5 (31.6–34.1)

Unknown 50.4 ± 8.5 Population-based

German: 33.2 ± 6.7 German and French cohorts.

Bariatric surgery patients

Adult studies Cotsapas et al. (2009) Paternoster et al. (2011) Wang et al. (2011) Jiao et al. (2011)

German: 18.3 ± 1.1

FTO

FTO, MC4R, MSRA, TMEM18 Adults:21.8 + 1.8 French: 17.6 ± 2.3 Adults: 47.3 ± 7.6 French: 29.5 ± 6.5 Children and adolescents BMI P 97th percentile

Normal weight or lean controls

Children: 17.6 + 2.3 Children: 29.5 ± 6.4

FTO, MC4R, MAF*

18.3 ± 1.1

Young adults, 78% self-reported underweight at adolescence Children BMI < 90th percentile. Adults BMI < 25, repeated measurements

33.4 ± 6.8

Scherag et al. (2010)

Inclusion criteria of cases First author

Table 3 Phenotypes used in extreme obesity designs.

A number of GWAS have been performed in extremely/morbidly obese individuals with normal-weight controls. Two of the studies (Hinney et al., 2007; Scherag et al., 2010) included children and adolescents in their discovery sets; four focused on adults (Cotsapas et al., 2009; Jiao et al., 2011; Paternoster et al., 2011; Wang et al., 2011) and one included both adults and children in their discovery stage (Meyre et al., 2009). The criteria for extreme obesity in the respective studies were either based on fixed, clinically established cut points for obesity (e.g. obesity class II, BMI P 35) or relative cut points based on percentiles of the trait distributions (e.g. BMI > 99th percentile). However, irrespective of these definitions, BMI distributions of the extremely obese in the different studies have been comparable, with mean BMI for obese children ranging from 29.5 to 33.4 and in adults from 44.7 to 50, with the exception of the study by Paternoster et al. (2011), where the mean BMI was lower, only 36.3 in females and 32.5 in males. The mean BMI of controls was also comparable across studies. The inclusion criteria for cases and controls, as well as mean BMI of the different studies are shown in Table 3. The first study to use an extremes approach was published by Hinney et al. (2007) who performed a GWAS of early onset extreme obesity based on 487 extremely obese young German individuals and 442 healthy lean German controls with replication in 644 independent families. No associations with p P 5  10 8 were found. Meyre et al. (2009) analyzed GWAS data from 1380 children and adults with familiar obesity and from 1416 age-matched normal-weight controls, with replication in 14,186 European subjects, originating either from obesity case-control studies or a population-based cohort. In addition to FTO and MC4R, they detected genome-wide significant association of obesity with a SNP near MAF (p = 3.8  10 13). Cotsapas et al. (2009) performed a GWAS of 775 cases of extreme obesity that underwent bariatric surgery and 3197 controls with unknown BMI, and found association with variants in FTO. Scherag et al. (2010) studied German and French extremely obese children (BMI P 97 percentile) and compared to controls (n = 2258 individuals) with follow-up first in 3141 individuals, and then in a larger population of children and adults (n = 31,182). Part of their material overlapped with that published in Meyre et al. (2009). Apart from the previously identified loci FTO, MC4R and TMEM18, they detected a genome-wide significant locus near the MSRA gene that was previously reported to be associated with WC (Lindgren et al., 2009).

Inclusion criteria of controls

3.2. Genome-wide studies of extreme obesity

Studies including children and adolescent Hinney et al. Adolescents at extreme obesity clinic (2007) Meyre et al. Children BMI > 97th percentile (from reference population) with familial (2009) obesity. Adults BMI > 40 and familial obesity

BMI cases Mean ± SD

BMI controls Mean ± SD

Genome-wide loci?

contribute to different effect sizes in different populations, if gene– environment interactions are present. Before 2012, six GWAS of BMI in other ethnic groups than those of European descent were reported. None of these six studies were successful in reporting associations with BMI at p 6 5  10 8. These six studies were based on cohorts of African American descent (Ng et al., 2012), Filipino descent (Croteau-Chonka et al., 2011), Asians with different ethnicity living in Singapore (Dorajoo et al., 2011), Japanese (Hiura et al., 2010), American Indians (Malhotra et al., 2011) and Hispanic Americans (Norris et al., 2009). The lack of genome-wide significant findings in these studies may be explained by the relatively small sample size (all discovery samples were of less than 1800 individuals). The exception is the study from Singapore by Dorajoo et al., which included 10,000 individuals from five different GWAS. However, the power of that that study may have suffered by the inclusion of several ethnic groups such as Chinese, Malay and Indians. Also, even with a sample size of 10,000, the only association that would reach genome-wide significance in individuals of European descent would typically be the FTO locus.

No

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In 2011, Paternoster et al. studied obese young men and women drawn from the extremes of the BMI distribution from two very large Danish cohorts, and equal numbers of population-based controls randomly sampled from the same cohorts (ncases = 2633; ncontrols = 2740) (Paternoster et al., 2011). They reached genome-wide significance for SNPs in or near the FTO, MC4R and FAIM2. In addition, in a pooled meta-analysis of their discovery sample and three other population-based cohorts with a total of 29,181 subjects, they found another genome-wide association signal near TFAP2B, previously reported to be associated with BMI (Speliotes et al., 2010) and WC (Lindgren et al., 2009). Jiao et al. (2011) performed a GWAS in 164 morbidly obese subjects (BMI > 40 kg/m2) and 163 Swedish subjects (>45 years) who had always been lean, with replication in independent cohorts comprising of 4214 obese and 5417 lean or population-based control individuals. The results from the six case-control samples were meta-analyzed and a new obesity locus, KCNMA1, was identified (p = 2.8  10 10). An already established locus, BDNF, was also found to be associated with extreme obesity (Jiao et al., 2011). Wang et al. (2011) performed a GWAS in 520 cases (BMI P 35) and 540 control subjects (BMI < 25) on measures of obesity and obesity-related traits with follow-up in 2256 individuals. They reported significant association of FTO to obesity and of NRXN3 to WHR, which are both known signals. In summary, results from GWAS of extreme obesity indicate that common genetic variation associated with extreme forms of obesity is at least partly overlapping with that of overall BMI (Fig. 2). This may suggest that sampling of individuals with the extreme phenotypes from a larger study population may be an efficient method for identifying common variants that influence quantitative traits. A smaller sample ascertained through extreme phenotypes can detect genuine associations in a GWAS by utilizing the higher statistical power when sampling from the extremes, but the sample sizes needed are still substantial. It also indicates that that subjects with extreme obesity can often be at the high end

of a spectrum of common polygenic obesity, even though separate genetic mechanisms can also contribute to a part of the cases, such as those included in monogenic obesity. Rare variants in genes associated with monogenic obesity (e.g. leptin receptor) may also have a role in common obesity, but GWAS is not designed to find them, and future projects using sequencing methods will be needed to address this possibility. A few loci have uniquely been found by extreme obesity GWAS; whether these associations are spurious, for example due to population stratification, or whether they represent true findings that are unique to morbid obesity needs to be addressed in further studies. 3.3. Genome-wide association studies of waist circumference and waist/hip ratio Accumulation of abdominal fat is a risk factor for cardiovascular disease and type 2 diabetes. In a large study by Pischon et al. (2008), the effect of increased WHR on cardiovascular death remained significant after adjusting for BMI. Definition of the mechanisms involved in the regulation of fat distribution and visceral fat mass is therefore important in the understanding of obesity and its complications. Since BMI, WC and WHR are heavily correlated, GWAS for any of these traits are likely to map some variants associated with the other traits. One strategy to capture mechanisms involved in fat distribution more than obesity itself is to adjust WHR (or WC) for BMI. These results can be looked at as the genotype association to the part of WHR that is not explained by BMI. 3.3.1. European descent In the original FTO paper by Frayling, 2007, the FTO variant was associated to both WC and BMI, but not height. In June 2009, two papers focusing on fat distribution were published in the same issue of PLoS Genetics. Lindgren et al. from the GIANT consortium performed a meta-analysis of 16 GWAS (n = 38,580) with data on WC and WHR. They selected 26 SNPs for follow-up, where the

Fig. 2. Summary of loci found by genome-wide association studies to be associated with body mass index (BMI), waist circumference (waist), waist–hip ratio (WHR), extreme obesity phenotypes (extremes) or BMI-adjusted WHR (BMIadjWHR) with p < 5  10 8.

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association to WC and/or WHR was strong, and at the same time the variants were not so strongly associated to BMI and height. Follow-up studies identified two loci; these mapped near TFAP2B (WC, p = 1.9  10 11) and MSRA (WC, p = 8.9  10 9). A third locus, near LYPLAL1, was associated with WHR in women only (p = 2.6  10 8) (Lindgren et al., 2009). The other paper came from the CHARGE consortium where GWAS from eight cohorts were meta-analyzed (n = 31,000) confirming FTO to affect WC (and BMI) (Heard-Costa et al., 2009). In 2010, Heid et al. from the GIANT consortium published a meta-analysis of 32 genome-wide association studies of WHR adjusted for BMI (n = 77,167) with follow-up of 16 loci in an additional 29 studies (n = 113,636). With this approach, they wanted to identify variants unique to fat distribution independently of BMI. They identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (p = 1.9  10 9 to p = 1.8  10 40) and the known signal at LYPLAL1. Seven of these loci showed different associations in men and women, all with a stronger effect on WHR in women than men (p for sex difference = 1.9  10 3 to p = 1.2  10 13). The authors concluded that these findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and that there seems to be strong gene-by-sex interactions in the regulation of fat distribution. These 14 loci together explain 1.34% of the variance in WHR in women, but only 0.46% of the variance in WHR in men (Heid et al., 2011). One CNV-tagging SNP (rs1294421 in LY86) was reported and one SNP (rs6784615, at the NISCH-STAB1 locus) was correlated with non-synonymous changes in two nearby genes, DNAH1 and GLYCTK (p.Leu170Val). Fine-mapping and functional studies are required to determine whether the DNAH1 or GLYCTK SNPs, and the LY86 CNV are causal of the WHR associations at these loci. 3.3.2. Other ethnic groups In 2008, Chambers et al. carried out a GWAS in 2,684 Indian Asians with further testing in 12,000 individuals of Indian Asian or European ancestry. They found associations of a SNP near MC4R associated with WC (p = 1.7  10 9) (Chambers et al., 2008). Cho et al. (2009), published a GWAS on eight quantitative traits in >8000 Koreans. They found a genome-wide association for the SNP rs2074356 to WHR. The SNP was located in an intron of the gene C12orf51, but could tag several genes in the region, of which PTPN11 is the most plausible candidate (Cho et al., 2009). However, for both of these studies, the effects could potentially be explained by direct or indirect effects of obesity, and not fat distribution in itself, since results were not adjusted for BMI. Moreover, the finding of an association of C12orf51 with WHR is non-overlapping with GWAS from subjects of European descent, and future larger studies are needed to confirm the effect of this variant in different ethnicities. To summarize the findings for waist-related traits, these phenotypes have not been as extensively studied as BMI. Because WC is strongly correlated with BMI, the overlap of findings (Fig. 2) is not very surprising. However, genes regulating body shape, as could be measured by WHR, especially when adjusted for BMI, probably have a different mechanism of action, and therefore is the nonoverlap that is seen between the study by Heid et al. and known BMI loci also not surprising. Since WHR was adjusted for BMI, the loci need to be pleiotropic for such an overlap to occur. 3.4. Genome-wide association studies of fat mass GWAS of more direct measurements of adiposity and body composition, such as fat mass measured by DEXA (Dual-emission X-ray absorptiometry) or bioelectrical impedance analysis are

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scarce. In our systematic search, we only identified three GWAS addressing these phenotypes. Liu et al. studied 1000 healthy subjects, whose fat mass was measured with DEXA. Their main finding, a novel locus for obesity, CTNNBL1, was associated to fat mass, but after correction for genomic inflation, the p-value went above the genome-wide-significance threshold (Liu et al., 2008), and the locus has not been consistently associated with obesity in previous studies among Europeans (Andreasen et al., 2009; Vogel et al., 2009). In 2011, a study by Kilpeläinen et al. was based on a meta-analysis of GWAS for body fat percentage measured either with bioimpedance analysis or DEXA from 36,626 individuals with follow-up in an additional 39,576 individuals. They found a strong association to FTO and identified two new loci associated with body fat percentage, one near IRS1 and one near SPRY2, which both have strong links to fat mass biology. Notably, the body fat-decreasing allele near IRS1 was also associated with an impaired metabolic profile, including insulin resistance, and the effect was more pronounced in men. The results suggest that genetic variation near IRS1 may be associated with a reduced ability to store subcutaneous fat, which may increase fatty acid uptake in liver and muscle, and induce insulin resistance. Melka et al. reported a study that was conducted in a FrenchCanadian founder population with 598 adolescents. They reported a genome-wide significant association of PAX5 and fat mass measured with bioimpedance, but their results did not hold up in their replication stage, necessitating further studies in other cohorts. 3.5. Genome-wide association studies of the metabolic syndrome Several extensive and successful efforts have been made to map variants associated with the components of MetS, using one component at the time such as blood pressure (Ehret et al., 2011), glycemic traits (Dupuis et al., 2010) and plasma lipid concentrations (Teslovich et al., 2010), but it exceeds the scope of this review to summarize those. Instead, we have focused on studies using MetS as a binary trait or those investigating the components using bivariate or multivariate methods. Bivariate methods may however be considered somewhat paradoxical, as three or more components are used to define the syndrome. Moreover, it should be noted that the scientific community is not in agreement that MetS is an entity in itself, more than a collection of risk factors for cardiometabolic disease; and its usefulness for biological understanding, as well as in the clinic has been widely debated (Alberti et al., 2009; Kahn et al., 2005). Zabaneh and Balding (2010) conducted a two-stage GWAS to identify common genetic variation altering risk of the metabolic syndrome and related phenotypes in Indian Asian men, who have a high prevalence of these conditions. In stage 1, approximately 317,000 SNPs were genotyped in 2700 individuals, from which 1500 SNPs were selected to be genotyped in an additional 2300 individuals. No evidence of a common genetic basis for metabolic syndrome traits was found in this study (Zabaneh and Balding, 2010). Another approach was used by Kraja et al. in 2011, where seven studies from the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent bivariate genome-wide association analyses of metabolic traits. Phenotypes for MetS were combined in all possible pairwise combinations, and individuals exceeding the thresholds for both traits of a pair were considered affected. Twenty-eight SNPs were associated with MetS or a pair of traits. These variants were located in or near 15 genes associated with binary pairwise traits or with MetS per se at the genome-wide significance level. All but two of these bivariate associations included a lipid abnormality. The authors suggested that these results show that genetic effects on lipid levels are more pronounced than for other traits. The most influential variants in the correlation among traits were in or near LPL, CETP, APOA5, ZNF259, BUD13,

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TRIB1, LOC100129500, and LOC100128154. Genes with variants influencing MetS per se included LPL, CETP, and the APOA-cluster (APOA5, ZNF259, and BUD13), which are known to play an important role in lipid metabolism (Kraja et al., 2011). Another approach to combine several components of MetS in GWAS was published in 2011 by Avery et al. using data from 19,486 European Americans and 6287 African Americans. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) including 19 quantitative traits were examined and analyzed with principal component analysis. They then applied a multivariate approach that related eight principal components from the six domains. In European Americans, they identified genomewide significant SNPs representing 15 loci with p < 5  10 8. Many of these loci were associated with only one trait domain and five of these were consistent with results in African Americans. Several of these were already known, for instance the association of central obesity with FTO. However, they identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. The strongest new pleiotropic signal in European Americans was observed for rs4420638 (p = 1.7  10 57), located near APOC1 and was associated with elevated plasma glucose (p = 8.7  10 4), atherogenic dyslipidemia (p = 1  10 31), vascular inflammation (p = 5  10 12), and central obesity (p = 1.2  10 6). Further replication is needed, and if these pleiotropic loci hold true, they may help in characterizing metabolic dysregulation and identify targets for intervention (Avery et al., 2011). 4. Fine-mapping and function of obesity-related variants 4.1. Fine-mapping To better understand the biology underlying an association between a genetic variant and obesity, the causal gene and functional variant needs to be identified. An SNP-phenotype association is just showing that variation in the region is associated with the outcome; in most cases we have not assessed the causal variants, but a variant in high LD with the causal one. In order to identify the causal variant, different approaches may be undertaken. One fine-mapping strategy is to re-sequence the known regions to map all variation. The identified variants can then be assessed in even larger samples for validation and to narrow down the region where the causal variant(s) are likely to be harboured based on where the strongest signals are seen. Another alternative, which has not been so extensively used in this area so far, is to combine data from different ethnic groups that may have different haplotype structures. An example of this approach is a study of the FTO region in African-derived populations (Hassanein et al., 2010). Many of the SNPs identified in GWAS are located in noncoding DNA within or between genes. Noncoding DNA variants may be involved in transcriptional and translational regulation of the protein-coding sequences. To assess whether variants alter gene expression, expression quantitative trait locus (eQTL) approaches may be undertaken where the quantity of RNA transcripts from genes in the region are measured in tissues of interest, and regressed on the number of minor alleles for each individual. One problem with this approach is that you often do not have access to relevant tissues; in the obesity field, samples of central nervous tissue from humans are often unavailable. 4.2. Mechanistic studies After identifying causal genes and variants, the next crucial step is to undertake mechanistic studies in order to understand the pathophysiology underlying the genotype-phenotype association. One way of doing this can be in animal model systems where

overexpression or knockdown of the relevant genes can provide important insights. This knockdown or overexpression of a gene may be restricted to a specific tissue of interest. In these transgenic animals, measurements of the phenotype of interest are performed and compared to wild-type animals. Mouse is the most commonly used animal for these purposes, but also simpler model systems like zebrafish may be used. Other approaches include human cell studies, transgenic bacterial studies, as well as in vivo characterization in humans. Such characterizations may include a more detailed phenotype than in the original GWAS, e.g. longitudinal studies or detailed measurements of obesity, such as fat mass measurements; combinations with other -omics methods; or interventions with pharmaceutical agents. 4.3. Function of identified obesity loci Although most of the associated SNPs from published GWAS are located in regions with several genes, they sometimes include a specific gene with an established connection to obesity (Speliotes et al., 2010), for example in the MC4R and POMC loci (as discussed in more detail above). Moreover, several of the likely causal genes for obesity are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity. Many susceptibility genes for human obesity are now believed to act primarily on the central regulation of food intake. To this end, Willer et al. (2009) found that the genes nearest the lead SNPs in their GWAS on BMI showed high expression in the hypothalamus, which is central to appetite regulation. Speliotes et al. (2010) found enrichment for pathways involving many neuronal processes, but also regulation of cellular metabolism and growth. Overall, in most of the obesity susceptibility loci, the causal genes are not definitely identified, and the mechanisms of action of most implicated genes are not well understood. Below is a short summary of the literature about the two susceptibility genes for obesity that were first identified; FTO and MC4R, as well as a more recently reported gene with potentially peripheral action, GIPR. 4.3.1. FTO Several SNPs in the FTO locus have been shown to be associated with obesity-related traits by GWAS. Fat mass and obesity associated gene, FTO, is located on chromosome 16 and encodes a protein with a double-stranded b-helix fold homologous to members of the non-heme and 2-oxoglutarate oxygenase superfamily. Members of this family are involved in post-translational modification, DNA repair and fatty acid metabolism (Sanchez-Pulido and Andrade-Navarro, 2007). The exact function of the protein is still unknown. However, some murine studies have shed light on the issue. The first report of the global loss of Fto in a mouse was an important landmark, giving robust evidence that loss of the Fto-gene has a strong effect upon energy homeostasis. Fischer et al. showed in their Nature paper from 2009 that loss of Fto in mice leads to postnatal growth retardation and a significant reduction in adipose tissue and lean body mass. The leanness of Fto-deficient mice developed as a consequence of increased energy output, despite decreased spontaneous exercise and increased food intake. Fto null mice also had increased mortality (Fischer et al., 2009). More recently, Church et al. studied a mouse model overexpressing Fto. In direct contrast to the null animal, the study showed that overexpression of Fto led to a dose-dependent increase in body and fat mass, irrespective of diet. Their results suggested that the increased body mass resulted primarily from increased food intake. Moreover, mice with increased Fto expression on a high-fat diet developed glucose intolerance (Church et al., 2010). Fto is strongly expressed in the brain, particularly within the hypothalamus, a key region for control of appetitive behavior (Gerken et al., 2007). The

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finding that selective modulation of Fto levels in the hypothalamus in mice can influence food intake is consistent with the reported effects of FTO alleles on eating behavior in humans (Cecil et al., 2008; Haupt et al., 2009). 4.3.2. MC4R The melanocortin-4 receptor is a G protein-coupled, seven-transmembrane receptor expressed in the brain. Inactivation of this receptor by gene targeting results in mice that develop a maturityonset obesity syndrome associated with hyperphagia, hyperinsulinemia, hyperglycemia, mature-onset obesity and increased linear growth in mice (Huszar et al., 1997). In 1998, human genetic studies demonstrated that mutations in the MC4R gene can cause monogenic obesity (Vaisse et al., 1998; Yeo et al., 1998). In 2008, Loos et al. showed for the first time that variants near MC4R have one of the strongest effects for common polygenic obesity (Loos et al., 2008). Within the arcuate nucleus of the hypothalamus, POMC is posttranslationally cleaved to produce the alpha-melanocyte stimulating hormone, a peptide with anorexigenic (decreases appetite) effects upon activation of the melanocortin-4 receptor (MC4R) expressed on the surface of target neurons (Dores and Baron, 2011). Large progress has been made on the MC4R since 1993 when it was first cloned. Diverse approaches has led to the elucidation of numerous functions for the MC4R, including appetite regulation, cardiovascular function, glucose and lipid homeostasis, reproduction, addiction, pain, and mood (reviewed in (Tao, 2010)). The mechanism by which the obesity variants near MC4R identified by GWAS alter the risk of obesity is still unclear, but they are likely to alter MC4R function or expression. 4.3.3. GIPR In contrast to FTO and MC4R, which main effects are believed to be in the hypothalamus, GIPR, which encodes a receptor of gastric inhibitory polypeptide (GIP), suggests a role for peripheral biology in common obesity. Gastric inhibitory polypeptide is a hormone expressed in the duodenum and intestine that helps to stimulate insulin secretion after meal intake (incretin effect). Important in vivo support for the role of GIP in regulating body weight was provided with the finding that mice lacking GIP receptors were protected from high fat diet-induced obesity and insulin resistance (Miyawaki et al., 2002). More specific support for a primary insulin-independent role of GIP in promoting adiposity was found by intercrossing GIPr / mice with mice lacking insulin receptor substrate, IRS-1. These relatively insulin insensitive mice were found to have decreased adiposity compared to the IRS-1 knockout mice when fed a standard diet. These results suggest that GIP plays a crucial role in switching from fat oxidation to fat accumulation when insulin action is decreased (Zhou et al., 2005). The specific action of the variant close to GIPR by Speliotes et al. is not yet clear. A SNP encoding a missense variation in GIPR (rs1800437, Glu354Gln) in high LD with this BMI-increasing variant was also reported in a GWAS to decrease 2-h-post-OGTT glucose (Saxena et al., 2010). These findings were further evaluated by Lyssenko et al. (2011), who found that the A allele of GIPR rs10423928 was associated with impaired glucose- and GIP-stimulated insulin secretion and a decrease in BMI, lean body mass, and waist circumference. The decrease in BMI almost completely neutralized the effect of impaired insulin secretion on risk of type 2 diabetes. 5. Discussion and future directions 5.1. What have we learned from GWAS? GWAS have been a huge success for identification of genetic loci affecting obesity and components of the metabolic syndrome. A

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large advantage of GWAS methods are that they are is hypothesis-free. We have found many things that were not anticipated, such as the large number of loci with small effects on BMI. We have already learned much new biology, and the obesity genetics field has transformed dramatically in a few years. To have the knowledge that a certain genetic variant affects the risk of obesity may be useful in a number of ways, including improved biological understanding, discovery of potential drug targets, and risk prediction and stratification. Furthermore, we now understand much more about genetic architecture of obesity than before. Allelic heterogeneity, where many different mutations at the same loci infer disease may be the norm, not the exception. We should expect diversity between populations. We have learnt that hundreds of loci are involved in complex traits, and they may include both common and rare variants and that the contribution of each identified locus to a trait is usually very small. 5.2. Mendelian randomization studies The new genotype–phenotype associations can also form the foundation for causal investigations of disease, in so-called Mendelian randomization (MR) studies, where the random distribution of genotypes across generations is used for unconfounded assessment of causality. Such causal relationships are difficult to prove in other settings due to confounding in observational studies and the difficulties of compliance and appropriate blinding in intervention studies. Also, for many exposures, intervention studies are unethical or impractical. In MR analyses, a genetic variant (or several forming a genetic score) associated with an exposure of interest (e.g. BMI) is used as an instrumental variable (IV) to evaluate the causal relationship to the outcome of interest. Since genetic associations can be assumed to be relatively constant throughout the life course, the MR studies are regarded as assessing lifetime effect of the intermediate phenotypes (the exposure). Assumptions that need to be met for performing these analysis are no pleiotropic effects of the SNPs used as instrumental variables, no genetic confounding by population stratification, no other variants with effect on the outcome of interest in LD with the SNP used as instrument, and no functional genomic confounding due to developmental canalization and gene imprinting (Didelez and Sheehan, 2007). Variants within the FTO locus are well-suited for these purposes, and have been used in several large-scale MR experiments supporting a causal relationship of increased BMI to risk factors of CVD and metabolic diseases, such as increased C-reactive protein, systolic and diastolic blood pressure, fasting insulin, triglycerides, metabolic syndrome and decreased concentrations of high-density-lipoprotein cholesterol (Freathy et al., 2008; Kivimaki et al., 2008; Timpson et al., 2009, 2011; Welsh et al., 2010). It has also been used to assess the causal impact of BMI on the development of mental disorders (Kivimaki et al., 2011). A limitation of using single genetic variants with rather small effect as an instrumental variable is the requirement of extremely large number of subjects to achieve the desirable power (Freathy et al., 2008; Pierce et al., 2011). One tractable approach to address this issue, as well as several of the other issues (pleiotropy, population stratification) is to use a genetic risk score as the instrumental variable. 5.3. Risk prediction Since the effects of different BMI loci are small, a genetic risk score is often calculated for predictive studies. This score sums the number of risk alleles in a person, sometimes weighting them with the reported effect for each allele. However, since even the cumulative effect is small, the predictive ability is limited (Li et al., 2010). That said, since there is a large gap between heritability estimations and variability explained, and as this is decreasing

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by uncovering more loci, the risk prediction is likely to be more and more effective.

Framework Programme (FP7/2007-2013), ENGAGE Consortium, grant agreement HEALTH-F4-2007-201413.

5.4. More GWAS in obesity?

Appendix A. Supplementary data

There are ongoing large efforts to gather more data for even larger GWAS meta-analysis of obesity. The increasing number of samples will ultimately lead to an even higher number of validated susceptibility loci for obesity. Efforts are also underway to use 1000 Genomes data for imputation of genotypes, which can help narrowing down the known signals, as well as to identify new ones that have not been adequately assessed in previous studies based on HapMap-imputed data. We are also anticipating larger studies of other ethnic background than Northern Europeans. Other approaches that will be important for finding additional novel variants associated with obesity are whole-genome sequencing and exome sequencing. These methods are especially suitable to assess rarer variants and CNVs, and may uncover loci previously not implicated in obesity. However, these methods are still costly, and it will take some time until the large study samples that are likely to be needed for efficient studies have been sequenced. That said, the development in this area is fast. We believe that these methods are going to be dominating in the field of obesity genomics as we move beyond GWAS.

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.mce.2012.08.018.

5.5. Gene-environment interactions Several of the SNPs reported to be associated to body fat distribution by Heid and Lindgren (Heid et al., 2011; Lindgren et al., 2009) showed a strong interaction with sex. Variants that have a strong interaction effect with environmental factors may be difficult to find with the conventional GWAS design, which entirely disregards the differences in genotype-phenotype association between different environments (leading to lower statistical power for these loci). We anticipate that more studies will include environmental factors and examine them for interaction effects in obesity GWAS in the near future. It should however be noted that very large sample sizes will be needed for these analyses. One method is the joint 2-degree-of freedom test, that tests for interaction at the same time as for the main effect (Kraft et al., 2007); the results are also possible to meta-analyze (Manning et al., 2011). A recent example of interaction between obesity loci and environment is the study by Kilpeläinen et al. which showed that the influence of FTO was significantly lower in subject with high physical activity than control subjects (Kilpelainen et al., 2011). 6. Conclusion Until recently, genetic determinants of common obesity and metabolic syndrome were largely unknown. With the advent of genome-wide association studies, the field has taken huge steps forward in the understanding of the genetic underpinnings of obesity. We now know that common forms of obesity are highly polygenic, with each variant contributing with very small effects. GWAS have identified many regions in the genome that are associated with obesity, and in the next few years, a major task for the field will be to identify and characterize the causal genes and their effects on obesity. Acknowledgements This study was supported by grants from the Swedish Foundation for Strategic Research (ICA08-0047), the Swedish Research Council (Project Grants No. 2009-2298), the Swedish Heart-Lung Foundation (2010-0401) and The European Community’s Seventh

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Please cite this article in press as: Fall, T., Ingelsson, E. Genome-wide association studies of obesity and metabolic syndrome. Molecular and Cellular Endocrinology (2012), http://dx.doi.org/10.1016/j.mce.2012.08.018