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The genetics of human obesity
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REVIEW ARTICLE The genetics of human obesity Q12
JILL WAALEN LA JOLLA, CALIFORNIA
The heritability of obesity has long been appreciated and the genetics of obesity has been the focus of intensive study for decades. Early studies elucidating genetic factors involved in rare monogenic and syndromic forms of extreme obesity focused attention on dysfunction of hypothalamic leptin–related pathways in the control of food intake as a major contributor. Subsequent genome-wide association studies of common genetic variants identified novel loci that are involved in more common forms of obesity across populations of diverse ethnicities and ages. The subsequent search for factors contributing to the heritability of obesity not explained by these 2 approaches (‘‘missing heritability’’) has revealed additional rare variants, copy number variants, and epigenetic changes that contribute. Although clinical applications of these findings have been limited to date, the increasing understanding of the interplay of these genetic factors with environmental conditions, such as the increased availability of high calorie foods and decreased energy expenditure of sedentary lifestyles, promises to accelerate the translation of genetic findings into more successful preventive and therapeutic interventions. (Translational Research 2014;-:1–9) Abbreviations: --- ¼ ---
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INTRODUCTION
Submitted for publication February 20, 2014; revision submitted May 17, 2014; accepted for publication May 20, 2014.
technologic revolution in genotyping that has paralleled the obesity epidemic has allowed investigation of genetic factors in unprecedented detail—from single genes that cause rare forms of obesity to multiple genetic factors involved in more common forms. Such investigations have extended beyond identification of single-nucleotide variants associated with obesityrelated traits across populations to rare variants, copy number variations (CNVs), and epigenetic changes that help explain a substantial portion of the heritability of these traits. These technologic advances have also enabled more detailed study of the interaction of the genetic and environmental factors contributing to the obesity epidemic, which promise to point to more effective interventions for both prevention and treatment.
Reprint requests: Jill Waalen, The Scripps Research Institute and the Scripps Translational Science Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037; e-mail: [email protected].
HERITABILITY
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n the search for causes of the recent obesity epidemic, emphasis has been placed on the ‘‘obesogenic environment’’ and lifestyle changes that have resulted in increased access to calories and decreased energy expenditure. Although the hereditary aspects of obesity have long been recognized, the contribution of genetics to this relatively rapid population-wide change is also becoming increasingly evident. The
From the The Scripps Research Institute and the Scripps Translational Science Institute, La Jolla, California.
1931-5244/$ - see front matter Ó 2014 Mosby, Inc. All rights reserved. http://dx.doi.org/10.1016/j.trsl.2014.05.010
The inherited propensity toward obesity has been supported by numerous family and twin studies, many of which preceded the identification of specific genes by 1
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decades. One early twin study quantifying the heritability of a large number of anthropometric measures, including height, weight, and waist circumference, found body weight to have one of the highest heritability rates of approximately 70%, with nearly equally high heritability of waist circumference of 65%.1 Although estimates in subsequent studies have varied, the heritability of obesity is now widely accepted as being between 40% and 70%.2 However, it is clear that the heritability estimates can be dramatically altered by consideration of environmental factors. Physical activity, in particular, has been found to have a powerful influence on the heritability of obesity-related traits. Heritability of fat mass in Finnish twins, for example, has been reported to be 90% among twins with the low physical activity, but only 20% among the most active pairs of twins.3 Age effects are also striking, with the greatest heritability of body mass index (BMI), for example, manifesting in adolescence (starting around the age of 11 years) and peaking in young adulthood (around the age of 20 years).4 More detailed exploration of the effects of these and other influences, such as ethnicity and sex, are now being incorporated into studies with increasing emphasis on understanding gene-environment interactions. MONOGENIC OBESITY MODELS
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Although the current focus of obesity genetics research is on the complexity of the multiple genes and gene-gene/gene-environment interactions likely to be involved in common forms of human obesity, the discovery of single genes responsible for obesity in animal models, albeit rare causes of obesity in humans, provided the earliest breakthroughs in identifying mechanisms involved in human obesity. The identification of these genes pointed to the crucial role of hormonal and neural networks in regulating adiposity, particularly in the appetite control centers of the hypothalamus. The first of these breakthroughs came through an obese mouse strain, ob/ob, which had been discovered at the Jackson Laboratories in 1949.5 Mice of this strain were found to grow to weights of up to 3 times that of other mouse strains, and early crossbreeding studies revealed that the obesity trait was inherited in a recessive manner. Infusion of blood from normal mice into ob/ob mice resulted in the normalization of the weight of the obese mice, indicating that the inherited alteration involved a lack of a factor present in normal mice.6,7 Using positional cloning techniques, Friedman et al8 identified the specific gene responsible for obesity in the ob/ob mice in 1994. The discovery was followed by the identification of the encoded protein, which when injected into ob/ob mice restored them to normal
weight. The protein, named leptin after the Greek ‘‘leptos’’ meaning thin, is released by growing white fat cells and binds to receptors in the hypothalamus resulting in decreased appetite and food intake, consistent with a fed state. Mutations in Lep (leptin gene) in mice, and its human homolog LEP, lead to a deficiency of leptin and thus a decreased ability to inhibit appetite. The discovery of LEP was followed by the discovery of the gene responsible for obesity in another strain of obese mice with diabetes, db/db.9 The responsible gene was found to encode the leptin receptor, expressed in the hypothalamus among other regions of the brain, and was named LEPR. Other obesity-causing genes in humans similarly discovered from their homologs in obese animal include proopiomelanocortin (POMC), prohormone convertase 1 (PCSK1), and melanocortin 4 receptor (MC4R). Like LEP and LEPR, these genes are involved in the leptin-melanocortin signaling pathway, wherein leptin acts via its receptors to inhibit food intake through effects on POMC and agoutirelated protein/neuropeptide Y neurons in the arcuate Q3 nucleus of the hypothalamus. MC4R is activated in the paraventricular nucleus through this pathway by a-melanocyte–stimulating hormone, a cleavage product of the POMC transcript resulting from the activity of the PCSK1 enzyme to signal satiety.10 The identification of leptin and its associated pathways was initially met with enthusiasm for its potential as a therapeutic target for treating all forms of obesity. It subsequently has become apparent, however, that most obese humans are not leptin-deficient, but rather have very high levels of leptin and are leptin-resistant.11,12 Congenital hypoleptinemia because of mutations of these genes was also found to be rare, affecting only a few families in the world. In these few individuals, however, recombinant leptin therapy has been very effective in treating obesity.13 And, although not considered an effective treatment for common obesity per se, the use of leptin therapy in diabetes is now being explored.14 In contrast to the severe obesity observed in the rare homozygotes for the deleterious mutations in the genes involved in the leptin pathway, heterozygotes for mutations of MC4R, LEP, LEPR, and POMC exhibit a less severe and nonfully penetrant form of obesity.15-19 MC4R mutations are the most common, with a population prevalence of at least 0.05% and a prevalence of 0.5%–1% among obese adults and 1%–6% among obese children.20 SYNDROMIC OBESITY
Identification of genes involved in human obesity has also been driven by the study of obesity occurring as part of distinct syndromes, such as Prader-Willi
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syndrome, that, in addition to extreme obesity, often include cognitive deficits, distinct malformations, and unusual behaviors. Most genes associated with obesity in these syndromes are related to central nervous system appetite control centers and support the role of hyperphagia as the underlying cause of the associated obesity. Prader-Willi syndrome, the most common of these syndrome, which is characterized by extreme hyperphagia and early central obesity accompanied by hypotonicity and significant cognitive deficits, involves loss of expression of imprinted paternal genes on 15q11–13, mostly (75%) because of deletions on paternal chromosome. Recent studies have narrowed the critical region down to the C/D box small nucleolar RNA genes SNORD116 cluster.21,22 Studies are ongoing to determine how these noncoding RNAs—which control expression levels, splicing, and modification of other RNAs—contribute to the hypothalamic dysfunction that is the likely basis of the hyperphagia and obesity of the syndrome. Genes linked to obesity in at least 5 other syndromes have been associated with the function or formation of primary cilia, subcellular organelles, which serve a sensory function for most cell types. All 15 BBS genes that have been linked to various forms of Bardet-Biedl syndrome, for example, harbor variant alleles that result in abnormal cilia.23 Cilia-related genes associated with obesity in 4 other syndromes include ALMS1 in Alstrom syndrome24; RAB23, encoding a protein, which is a member of the Rab family of small guanosine triphosphatases at the primary cilium, in Carpenter syndrome25,26; CEP19, encoding a ciliary protein and identified in a family with multigenerational morbid obesity27; and TUB, encoding a protein present in the ciliary base of photoreceptors and cells in the hypothalamus, with mutations identified in a family with early onset obesity accompanied by retinopathy.28 Other genes associated with obesity occurring as part of well-defined syndromes have other subcellular targets. Borjeson-Forssman-Lehmann syndrome, a rare X-linked obesity syndrome, has been linked to mutations in a widely expressed zinc-finger gene, PHF6, which encodes a protein that localizes to the cell nucleus and nucleolus.29 The VPS13B (COH1) gene associated with Cohen syndrome encodes a Golgi matrix protein.30 Obesity in Albright’s hereditary osteodystrophy has been linked to the GNAS1 gene encoding a guanine nucleotide-binding protein that stimulates adenylcyclase activity.31 Additionally, partial deficiencies of SIM1 (single minded), BDNF (brain-derived neurotrophic factor), and NTRK2 (neurotrophic tyrosine receptor kinase encoding the TrK protein, the receptor for BDNF) genes, are associatedwithsyndromicfeaturesinvolvedinthefunctioningof
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the hypothalamus downstream of MC4R-expressing neurons and leading severe hyperphagic obesity.32-34 Haplodeficiency of BDNF has also been implicated in the obesity occurring in a subset of patients with WAGR (Wilms tumor, aniridia, genitourinary malformations, and retardation) syndrome.35 GENOME-WIDE ASSOCIATION STUDIES
Since the beginning of the genome-wide association study (GWAS) era in 2005, a number of large GWASs have been conducted on obesity and related traits in humans. The hypothesis-free approach involves testing the associations of millions of common variants (singlenucleotide polymorphisms [SNPs] or single-nucleotide variants) with obesity and other obesity-related traits by comparing SNP frequencies in obese cases vs normal weight controls (or along a spectrum of BMI or other adiposity measurement) and has led to the identification of many genetic loci robustly associated with these traits. To date, there are 32 established loci for BMI,36-41 and 14 loci associated with BMI-adjusted waist-to-hip ratio (WHR),42 representing the risk for increased abdominal adiposity independent of overall obesity, that have been identified in populations of European descent, usually combining the sexes. Four additional loci were identified by GWASs within an Asian population.43 More recent GWASs have focused on determining sex differences in genetic associations, differences among other ethnic populations, and in longitudinal cohorts looking for potential differential effects by age. These studies have confirmed many of the GWAS-identified obesity variants across populations and populationspecific variants. The 3 most significantly associated SNPs for BMI among populations of European ancestry identified in multiple large GWASs, including the Genetic Investigation of Athropometric Traits meta-analysis involving more than 240,000 subjects, are an SNP within intron 1 of the fat mass and obesity-associated (FTO) gene, one near TMEM18 (transmembrane protein 18), and one near MC4R.40 These loci have also been found to be significantly associated with BMI in GWASs of other populations, including young children (mean age, 10 years),44 adolescents, and young adults45,46 and, in the case of FTO and MC4R, individuals of African descent47 and young adults with extremely elevated BMI.48 Additional population-specific BMI-associated variants were also found in these studies, including novel variants identified in GALNT10 (encoding UDPN-acetylgalactosaminyltransferase 10) and MIR148A (a microRNA-encoding gene) among persons of African ancestry47 and COL6A5,44 OLFM4, and HOXB546 in obese children.
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A different set of loci has been identified by GWAS as associated with BMI-adjusted WHR, a trait more directly related to adiposity than BMI. Of the 14 WHR-associated loci identified in the Genetic Investigation of Athropometric Traits GWAS meta-analysis, 7 were found to have a significant effect in women only.42 The sex-specific effects were found among genes generally involved in insulin sensitivity (PPARG, VEGFA, ADAMTS9, and GRB14) and lipid-related traits (LYPLAL1, MAP3K1, and GRB14). The PPARG finding may be relevant to type 2 diabetes therapy as sex differences in response to the PPARG agonist pioglitazone have been reported.49 Six of the WHR- and WC-related loci found in the GWAS of subjects of European ancestry were also significantly associated with these BMI-adjusted traits in a GWAS of subjects of African ancestry (TBX15-WARS2, GRB14, ADAMTS9, LY86, RSPO3, and ITPR2-SSPN), with loci in 2 additional genes identified specifically in this population, LHX and RREB1, found to be suggestive.50 Although limited overlap has been found among genes associated with BMI and those associated with more direct measures of adiposity, such as WHR, in GWASs performed to date, FTO and TMEM18 are among the 8 loci to be associated with both.51 Also, although a consensus is developing among studies as to the most commonly associated genes across all cohorts, the function of most of these genes in causing obesity, including these 2 most common loci, is not well understood. Animal studies have supported the likely role of some of these genes in food intake related areas of the brain. At least 14 obesity risk genes (FTO, MC4R, BDNF, TMEM18, KCTD15, NEGR1, NRXN3, ETV5, MTCH2, SEC16B, TFAP2B, GNPDA2, FAIM2, and LYPLAL1) are expressed in the hypothalamus of both obese and lean rats, supporting a potential central effect of these genes on energy homeostasis. Four of these genes (TMEM18, BDNF, MTCH2, and NEGR1), however, have also been to be involved in adipocyte differention.52 It is also of note that several loci associated with increased BMI across multiple studies are located near genes that have already been linked to monogenic forms of early onset obesity with hypothalamic dysfunction and hyperphagia as a common feature (MC4R, SH2B1, BDNF, and POMC), suggesting that this mechanism may also contribute to more common forms of obesity. FTO has been one of the most highly investigated obesity-associated genes consistently identified by GWAS; however, other than its high levels of expression in the brain, particularly the hypothalamus, its role has not been clear. In addition, the noncoding variants of the gene associated with obesity have not been demonstrated to affect FTO gene function. Insights into this
apparent paradox have been provided by a recent study showing that BMI-associated FTO variants cause changes in the expression of another relatively distant gene, IRX3, approximately 2 megabases away.53 Further experiments by the group making this discovery showed that Irx3 knockout mice have a 25%–30% weight reduction compared with wild-type mice. In addition, when fed a high fat diet, the knockouts did not gain weight, compared with a 63% weight gain in wild-type mice. Although the IRX3 gene had not been previously identified as associated with BMI, earlier studies had shown IRX3 overexpression in adipocytes of patients experiencing profound weight loss after bariatric surgery.54 Thus, evidence to date implicates the gene in central and peripheral mechanisms of altered metabolism related to weight gain. THE ‘‘MISSING HERITABILITY’’ OF OBESITY
Although GWASs on obesity and related traits have been a rich source for candidate genes and pathways to expand the search for causal pathways and therapeutic targets, the explanatory power they provide has been somewhat disappointing. Effect sizes of the identified loci are smaller than anticipated, with the combined risk alleles explaining only a fraction (1.45%) of the heritability of BMI. The FTO locus has the largest effect of any single gene at only 0.34%. The poor predictive value of the currently identified loci for obesity is in contrast to the performance of risk calculators based on other characteristics. For example for childhood obesity, a risk score based on 39 common BMI-associated SNPs has an area under the receiver operating characteristic curve of 0.59 compared with a risk calculator based on parental BMI, child birth weight, maternal gestational weight gain, smoking status, number of individuals living in the household, and maternal occupation, which had an area under the receiver operating characteristic curve of 0.85.55 Several of the clinical parameters in this model, however, have important genetic components not incorporated into current genetic risk calculators. This problem of missing heritability describes the gap between risk explained by known SNPs identified by GWASs (,2% in the case of BMI) and the estimated heritability (40%–70% from twin and other family studies). One of the major underlying issues of missing heritability is that SNP selection for GWAS genotyping arrays reflects the common disease–common variant hypothesis, wherein the heritability of common diseases is thought to be driven by additive effects of a few common allelic variants. Most SNPs represented on GWAS arrays have a minor allele frequency (MAF) of 1%–5% and the proportion of heritability captured depends on how well
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causal variants are tagged by these SNPs. New types of analyses, such as genome-wide complex trait analysis (GCTA), analysis of uncommon (MAF 0.5%–1%) or rare (MAF ,0.5%) variants and structural variants not detected by GWAS arrays, and epigenetic analysis, are helping to fill that gap. Gene-environment interactions, which are increasingly being explored, also have the potential to dramatically alter the heritability equation. Genome-wide complex trait analysis. GCTA uses data from GWASs in aggregate rather than on an SNP by SNP basis. That is, rather than quantifying associations with the given trait for each SNP, GCTA quantifies the total additive genetic effect of common SNPs by taking advantage of the fact that the degree of genetic resemblance for common SNPs at the whole-genome level is normally distributed among unrelated individuals. Thus, the influence of SNPs that individually do not reach genome-wide statistical significance, but contribute to heritability, is included in the estimate. This approach has accounted for up to 16% of the heritability of BMI and obesity in adults56 and 30% of the heritability in children57 using common SNPs. Rare variants. One strategy for assessing the association of rare variants not represented on GWAS platforms with traits such as obesity has been to use gene-centric platforms that include variants of selected genes that have an MAF ,1%. One such platform is the ITMATBroad-Candidate Gene Association Resource, a microarray chip with approximately 50,000 SNPs for 2100 metabolic- and cardiovascular disease-related loci, with a focus on rare variants and those likely to have functional consequences based on publically available resequencing data. SNPs on the chip have a lower limit MAF of 0.5%. A study using this platform for associations with BMI confirmed 8 of 10 previously identified loci, including 2 new variants in the BDNF and MC4R genes.58 A similar study for adiposity-related traits found 3 new loci associated with WHR (TMCC1, HOXC10, and PEMT) with an additional 2 that had significant associations in females only (SHC1 and ATBDB4).59 Complete sequencing of specific genes of interest has identified additional rare variants. New functional variants in the MC4R gene associated with BMI, for example, have been found with this approach.60 Similar studies have recently been carried out for SIM1 gene, identifying functional variants in both Prader-Willi–like syndrome and nonsyndromic morbid obesity in adults.61,62 In addition to targeted complete sequencing, whole genome and whole exome sequencing studies are now being undertaken. In a recent study using whole exome sequencing of 4 individuals with extreme familial forms of obesity, for example, 2 novel LEPR mutations
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were found.63 Some functional variants found in this manner may explain the associations found with more common SNPs with no apparent functionality. Copy number variants. CNVs, in which segments of chromosomes encompassing large parts of genes or multiple genes are either replicated or deleted, represent another source of the heritability that is missed by GWAS studies. Studies of the association of CNVs with obesity traits to date, often involving extreme obesity phenotypes with or without syndromic features, have identified candidate regions near the NEGR1 locus and chromosome 10q11.22,64 as well as on chromosomes 11q1165 and 10q26.366 among others. A region within chromosome 16p11.2 is particularly well studied, deletions of which are associated with obesity and duplications associated with an underweight phenotype.67-69 Complete sequencing of associated regions that can be replicated in multiple studies’ promises to identify the critical variants and potentially new loci underlying these associations, as with SIM1 found in the 16q16 region associated with Prader-Willi–like syndrome. Epigenetics. Epigenetic mechanisms also contribute to the heritability of obesity. These mechanisms include 3 general categories by which gene expression can be modulated without modification of the sequence, blocking of transcription, by direct methylation of DNA; modification of histones by methylation, acetylation, or phosphorylation; or microRNA. The agouti gene, associated with obesity and other traits in mice, is a classic example of epigenetic control of gene expression.70 The gene is expressed in hair follicles of mice resulting in production of a yellow pigment. In normal mice, the agouti gene is highly methylated leading to complete gene repression. A spontaneous insertion into the agouti gene of a common mouse genome retrotransposon causes dysregulation of the methylation, leading to a spectrum of possible phenotypes including yellow hair and extreme obesity because of ectopic agouti protein expression in the hypothalamus. This protein binds to the MC4R, resulting in massive hyperphagia. The potential for such mechanisms to play a role in obesity in humans was supported by a study finding variably methylated regions of CpG sites near genes previ- Q9 ously implicated in body weight, with the SORCS1 gene having the strongest association.71 Another recent study found lower levels of DNA methylation of the IGF2 gene—a pattern associated with childhood obesity72— in offspring of obese fathers compared with those of nonobese fathers, providing evidence of the transmissibility of these obesity-related modifications.73 The association of DNA methylation with obesity is also being examined on a genome-wide basis. A recent
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study analyzing genome-wide methylation in 1 discovery and 2 replication cohorts in a total of 2587 subjects of European descent identified 5 methylation sites associated with BMI, 3 of which were in the same intron of 1 gene, HIF3A (hypoxia inducible transcription factor 3A).74 Increased methylation at these sites was found in both blood cells and adipose tissue of subjects with high BMI and was associated with changes in HIF3A expression, further supporting the involvement of this gene pathway in yet-to-be-defined processes related to weight gain. Diet and exercise have been associated with differential methylation of genes, providing a mechanism for gene-environment interactions, including the ability to counteract acquired or imprinted epigenomic patterns regulating obesity-related genes. For example, significantly different patterns of DNA methylation were found in the overall genome and near-specific genes (AQP9, DUSP22, HIPK3, TNNT1, and TNNI3) among adolescents who lost weight in response to an intervention, which included diet and exercise compared with those who did not.75 In another study measuring DNA methylation and gene expression in skeletal muscle biopsies from sedentary adults who underwent acute exercise, whole genome methylation and methylation of promoters of specific genes—PPARG, PGC-1a, and PDK4—decreased in an exercise intensity–dependent manner, accompanied by increased expression of these genes.76 Six months of exercise intervention has also been shown to alter the epigenome of adipose tissue, including changes in methylation of 2 obesity-related genes, CPEB4 and SDCCAG8, which corresponded to changes in the expression of these genes.77 Gene-gene interactions (epistasis). Just as associations of genetic variants with BMI may be altered by interactions with environmental factors, such as diet and exercise, unrecognized interaction between variants of different genes also likely contribute significantly to the missing heritability of obesity and its related traits. Although such interactions have been identified for select candidate genes, mostly in the pre-GWAS era, systematic approaches to identify these interactions on a genome-wise basis are still being developed. Reports on the development of some of these approaches have used BMI as an outcome, but the results are largely yet to be replicated.78,79 TRANSLATION INTO CLINICAL PRACTICE
Although the discoveries related to the genetic and epigenetic mechanisms contributing to obesity have obvious and exciting potential for clinical application, translation of these discoveries into prevention and treatment strategies has been limited to date. One area in which new research findings and approaches can be
directly applied is in the screening of patients presenting with clinical features suggesting a syndromic form of obesity. These patients can now be screened using either targeted or genome-wide copy number analysis through array comparative genome hybridization.80 Given the poor predictive value of genetic risk scores derived from the GWAS-determined common genetic variants associated with common obesity, more generalized genetic screening currently has little clinical utility. However, a pioneering study focusing on individuals who self-identify as having weight problems suggests patients may benefit even from the limited information provided by these tests.81 In the study, in-depth interviews of 7 middle-aged overweight British women (BMI . 25) volunteering to be tested for FTO gene mutation status revealed that those with the higher risk variants experienced relief and saw the result as confirming their assumptions that they were susceptible to weight gain for external reasons. However, they also expressed increased motivation to overcome their genetic predisposition. Those with lower risk variants, after reporting brief disappointment, focused on alternative explanations and recognized the positive side of not having the genetic predisposition, whereas realizing that other genes could be responsible. Despite the limited practical value of the screening results, all individuals in the study reported deriving some benefit from the information. One area of particular clinical interest has been in using obesity-associated variants to predict outcomes of bariatric surgery. For example, in a study of the role of 11 common BMI-associated variants on weight loss, Sarzynski et al found an SNP within the FTO Q10 gene to be significantly associated with postsurgical weight loss. However, no variant tested was found to be associated with weight regain. In another study, significant differences in postbariatric surgery weight loss were found among patients categorized as low, intermediate, or high risk based on SNPs in 4 genes (INSIG-2, FTO, MC4R, and PCSK-1).82 More recently, a GWAS comparing patients at extremes of weight loss and weight gain after Roux-en-Y gastric bypass surgery identified 17 SNPs with potential biological relevance, clustering in or near PKHD1, HTR1A, NMBR, and IGFR1 genes.83 Although the studies to date in aggregate suggest great promise for targeting interventions such as bariatric surgery to those who will benefit most, they also make clear that the optimization of the genetic screens, as in other applications, will require continued study. CONCLUSIONS
Insights into the genetic contribution to obesity, including genetic interactions with the environment,
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are rapidly increasing with information being generated from multiple new sources. GWASs are being extended beyond those of European ancestry to many other ethnicities and differential associations by age, sex, and other characteristics are being explored. Techniques for assessing the contribution of rare variants, including new types of analysis of GWAS data such as GTCA and assays for whole exome and whole genome analysis, are becoming common. In addition, studies of epigenetics and epigenomics are providing insights not only to the heritability of obesity-related traits, but also to how the environment can influence the heritability. In parallel to studies on these basic mechanisms, clinicians are eager to use the knowledge gained to better target prevention and treatment strategies for their patients. Although these types of translational studies are only beginning, the intense efforts across the spectrum of research in the genetics of obesity promise to accelerate their clinical application and bring greater understanding to the underlying factors of the obesity epidemic at a population level. ACKNOWLEDGMENTS
Conflict of interests: None. REFERENCES
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34. Faivre L, Cormier-Daire V, Lapierre JM, et al. Deletion of the SIM1 gene (6q16.2) in a patient with a Prader-Willi-like phenotype. J Med Genet 2002;39:594–6. 35. Han JC, Liu QR, Jones M, et al. Brain-derived neurotrophic factor and obesity in the WAGR syndrome. N Engl J Med 2008;359: 918–27. 36. Frayling TM, Timpson NJ, Weedon MN, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 2007; 316:889–94. 37. Loos RJ, Lindgren CM, Li S, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 2008;40:768–75. 38. Thorleifsson G, Walters GB, Gudbjartsson DF, et al. Genomewide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet 2009;41:18–24. 39. Lindgren CM, Heid IM, Randall JC, et al. Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution. PLoS Genet 2009;5:e1000508. 40. Speliotes EK, Willer CJ, Berndt SI, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010;42:937–48. 41. Willer CJ, Speliotes EK, Loos RJ, et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 2009;41:25–34. 42. Heid IM, Jackson AU, Randall JC, et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 2010;42:949–60. 43. Wen W, Cho YS, Zheng W, et al. Meta-analysis identifies common variants associated with body mass index in east Asians. Nat Genet 2012;44:307–11. 44. Namjou B, Keddache M, Marsolo K, et al. EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children. Front Genet 2013;4:268. eCollection 2013. 45. Graff M, Ngwa JS, Workalemahu T, et al. Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course. Hum Mol Genet 2013;22:3597–607. 46. Bradfield JP, Taal HR, Timpson NJ, et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet 2012;44:526–31. 47. Monda KL, Chen GK, Taylor KC, et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat Genet 2013;45:690–6. 48. Paternoster L, Evans DM, Nohr EA, et al. Genome-wide population-based association study of extremely overweight young adults—the GOYA study. PLoS ONE 2011;6:e24303. 49. Arnetz L, Dorkhan M, Alvarsson M, Brismar K, Ekberg NR. Gender differences in non-glycemic responses to improved insulin sensitivity by pioglitazone treatment in patients with type 2 diabetes. Acta Diabetol 2014;51:185–92. 50. Liu CT, Monda KL, Taylor KC, et al. Genome-wide association of body fat distribution in African ancestry populations suggests new loci. PLoS Genet 2013;9:e1003681. 51. Speakman JR. Functional analysis of seven genes linked to body mass index and adiposity by genome-wide association studies: a review. Hum Hered 2013;75:57–79. 52. Bernhard F, Landgraf K, Kloting N, et al. Functional relevance of genes implicated by obesity genome-wide association study signals for human adipocyte biology. Diabetologia 2013;56:311–22. 53. Smemo S, Tena JJ, Kim KH, et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 2014;507:371–5.
54. Dankel SN, Fadnes DJ, Stavrum AK, et al. Switch from stress response to homeobox transcription factors in adipose tissue after profound fat loss. PLoS ONE 2010;5:e11033. 55. Morandi A, Meyre D, Lobbens S, et al. Estimation of newborn risk for child or adolescent obesity: lessons from longitudinal birth cohorts. PLoS ONE 2012;7:e49919. 56. Yang J, Manolio TA, Pasquale LR, et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 2011;43:519–25. 57. Llewellyn CH, Trzaskowski M, Plomin R, Wardle J. Finding the missing heritability in pediatric obesity: the contribution of genomewide complex trait analysis. Int J Obes (Lond) 2013;37:1506–9. 58. Guo Y, Lanktree MB, Taylor KC, Hakonarson H, Lange LA, Keating BJ. Gene-centric meta-analyses of 108 912 individuals confirm known body mass index loci and reveal three novel signals. Hum Mol Genet 2013;22:184–201. 59. Yoneyama S, Guo Y, Lanktree MB, et al. Gene-centric metaanalyses for central adiposity traits in up to 57 412 individuals of European descent confirm known loci and reveal several novel associations. Hum Mol Genet 2014;23:2498–510. 60. Rovite V, Petrovska R, Vaivade I, et al. The role of common and rare MC4R variants and FTO polymorphisms in extreme form of obesity. Mol Biol Rep 2014;41:1491–500. 61. Ramachandrappa S, Raimondo A, Cali AM, et al. Rare variants in single-minded 1 (SIM1) are associated with severe obesity. J Clin Invest 2013;123:3042–50. 62. Bonnefond A, Raimondo A, Stutzmann F, et al. Loss-of-function mutations in SIM1 contribute to obesity and Prader-Willi-like features. J Clin Invest 2013;123:3037–41. 63. Gill R, Cheung YH, Shen Y, et al. Whole-exome sequencing identifies novel LEPR mutations in individuals with severe early onset obesity. Obesity (Silver Spring) 2014;22:576–84. 64. Wheeler E, Huang N, Bochukova EG, et al. Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity. Nat Genet 2013;45: 513–7. 65. Jarick I, Vogel CI, Scherag S, et al. Novel common copy number variation for early onset extreme obesity on chromosome 11q11 identified by a genome-wide analysis. Hum Mol Genet 2011;20:840–52. 66. Yang TL, Guo Y, Shen H, et al. Copy number variation on chromosome 10q26.3 for obesity identified by a genome-wide study. J Clin Endocrinol Metab 2013;98:E191–5. 67. Walters RG, Jacquemont S, Valsesia A, et al. A new highly penetrant form of obesity due to deletions on chromosome 16p11.2. Nature 2010;463:671–5. 68. Jacquemont S, Reymond A, Zufferey F, et al. Mirror extreme BMI phenotypes associated with gene dosage at the chromosome 16p11.2 locus. Nature 2011;478:97–102. 69. Walters RG, Coin LJ, Ruokonen A, et al. Rare genomic structural variants in complex disease: lessons from the replication of associations with obesity. PLoS ONE 2013;8:e58048. 70. Dolinoy DC. The agouti mouse model: an epigenetic biosensor for nutritional and environmental alterations on the fetal epigenome. Nutr Rev 2008;66(Suppl 1):S7–11. 71. Feinberg AP, Irizarry RA, Fradin D, et al. Personalized epigenomic signatures that are stable over time and covary with body mass index. Sci Transl Med 2010;2:49ra67. 72. Soubry A, Schildkraut JM, Murtha A, et al. Paternal obesity is associated with IGF2 hypomethylation in newborns: results from a Newborn Epigenetics Study (NEST) cohort. BMC Med 2013;11:29. 73. Perkins E, Murphy SK, Murtha AP, et al. Insulin-like growth factor 2/H19 methylation at birth and risk of overweight and obesity in children. J Pediatr 2012;161:31–9.
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79. Tyler AL, Lu W, Hendrick JJ, Philip VM, Carter GW. CAPE: an R package for combined analysis of pleiotropy and epistasis. PLoS Comput Biol 2013;9:e1003270. 80. El-Sayed Moustafa JS, Froguel P. From obesity genetics to the future of personalized obesity therapy. Nat Rev Endocrinol 2013;9:402–13. 81. Meisel SF, Wardle J. Responses to FTO genetic test feedback for obesity in a sample of overweight adults: a qualitative analysis. Genes Nutr 2014;9:374. 82. Parikh M, Hetherington J, Sheth S, et al. Frequencies of obesity susceptibility alleles among ethnically and racially diverse bariatric patient populations. Surg Obes Relat Dis 2013;9:436–41. 83. Rinella ES, Still C, Shao Y, et al. Genome-wide association of single-nucleotide polymorphisms with weight loss outcomes after Roux-en-Y gastric bypass surgery. J Clin Endocrinol Metab 2013; 98:E1131–6.
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REVIEW ARTICLE The genetics of human obesity Q12
JILL WAALEN LA JOLLA, CALIFORNIA
The heritability of obesity has long been appreciated and the genetics of obesity has been the focus of intensive study for decades. Early studies elucidating genetic factors involved in rare monogenic and syndromic forms of extreme obesity focused attention on dysfunction of hypothalamic leptin–related pathways in the control of food intake as a major contributor. Subsequent genome-wide association studies of common genetic variants identified novel loci that are involved in more common forms of obesity across populations of diverse ethnicities and ages. The subsequent search for factors contributing to the heritability of obesity not explained by these 2 approaches (‘‘missing heritability’’) has revealed additional rare variants, copy number variants, and epigenetic changes that contribute. Although clinical applications of these findings have been limited to date, the increasing understanding of the interplay of these genetic factors with environmental conditions, such as the increased availability of high calorie foods and decreased energy expenditure of sedentary lifestyles, promises to accelerate the translation of genetic findings into more successful preventive and therapeutic interventions. (Translational Research 2014;-:1–9) Abbreviations: --- ¼ ---
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INTRODUCTION
Submitted for publication February 20, 2014; revision submitted May 17, 2014; accepted for publication May 20, 2014.
technologic revolution in genotyping that has paralleled the obesity epidemic has allowed investigation of genetic factors in unprecedented detail—from single genes that cause rare forms of obesity to multiple genetic factors involved in more common forms. Such investigations have extended beyond identification of single-nucleotide variants associated with obesityrelated traits across populations to rare variants, copy number variations (CNVs), and epigenetic changes that help explain a substantial portion of the heritability of these traits. These technologic advances have also enabled more detailed study of the interaction of the genetic and environmental factors contributing to the obesity epidemic, which promise to point to more effective interventions for both prevention and treatment.
Reprint requests: Jill Waalen, The Scripps Research Institute and the Scripps Translational Science Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037; e-mail: [email protected].
HERITABILITY
I
n the search for causes of the recent obesity epidemic, emphasis has been placed on the ‘‘obesogenic environment’’ and lifestyle changes that have resulted in increased access to calories and decreased energy expenditure. Although the hereditary aspects of obesity have long been recognized, the contribution of genetics to this relatively rapid population-wide change is also becoming increasingly evident. The
From the The Scripps Research Institute and the Scripps Translational Science Institute, La Jolla, California.
1931-5244/$ - see front matter Ó 2014 Mosby, Inc. All rights reserved. http://dx.doi.org/10.1016/j.trsl.2014.05.010
The inherited propensity toward obesity has been supported by numerous family and twin studies, many of which preceded the identification of specific genes by 1
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decades. One early twin study quantifying the heritability of a large number of anthropometric measures, including height, weight, and waist circumference, found body weight to have one of the highest heritability rates of approximately 70%, with nearly equally high heritability of waist circumference of 65%.1 Although estimates in subsequent studies have varied, the heritability of obesity is now widely accepted as being between 40% and 70%.2 However, it is clear that the heritability estimates can be dramatically altered by consideration of environmental factors. Physical activity, in particular, has been found to have a powerful influence on the heritability of obesity-related traits. Heritability of fat mass in Finnish twins, for example, has been reported to be 90% among twins with the low physical activity, but only 20% among the most active pairs of twins.3 Age effects are also striking, with the greatest heritability of body mass index (BMI), for example, manifesting in adolescence (starting around the age of 11 years) and peaking in young adulthood (around the age of 20 years).4 More detailed exploration of the effects of these and other influences, such as ethnicity and sex, are now being incorporated into studies with increasing emphasis on understanding gene-environment interactions. MONOGENIC OBESITY MODELS
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Although the current focus of obesity genetics research is on the complexity of the multiple genes and gene-gene/gene-environment interactions likely to be involved in common forms of human obesity, the discovery of single genes responsible for obesity in animal models, albeit rare causes of obesity in humans, provided the earliest breakthroughs in identifying mechanisms involved in human obesity. The identification of these genes pointed to the crucial role of hormonal and neural networks in regulating adiposity, particularly in the appetite control centers of the hypothalamus. The first of these breakthroughs came through an obese mouse strain, ob/ob, which had been discovered at the Jackson Laboratories in 1949.5 Mice of this strain were found to grow to weights of up to 3 times that of other mouse strains, and early crossbreeding studies revealed that the obesity trait was inherited in a recessive manner. Infusion of blood from normal mice into ob/ob mice resulted in the normalization of the weight of the obese mice, indicating that the inherited alteration involved a lack of a factor present in normal mice.6,7 Using positional cloning techniques, Friedman et al8 identified the specific gene responsible for obesity in the ob/ob mice in 1994. The discovery was followed by the identification of the encoded protein, which when injected into ob/ob mice restored them to normal
weight. The protein, named leptin after the Greek ‘‘leptos’’ meaning thin, is released by growing white fat cells and binds to receptors in the hypothalamus resulting in decreased appetite and food intake, consistent with a fed state. Mutations in Lep (leptin gene) in mice, and its human homolog LEP, lead to a deficiency of leptin and thus a decreased ability to inhibit appetite. The discovery of LEP was followed by the discovery of the gene responsible for obesity in another strain of obese mice with diabetes, db/db.9 The responsible gene was found to encode the leptin receptor, expressed in the hypothalamus among other regions of the brain, and was named LEPR. Other obesity-causing genes in humans similarly discovered from their homologs in obese animal include proopiomelanocortin (POMC), prohormone convertase 1 (PCSK1), and melanocortin 4 receptor (MC4R). Like LEP and LEPR, these genes are involved in the leptin-melanocortin signaling pathway, wherein leptin acts via its receptors to inhibit food intake through effects on POMC and agoutirelated protein/neuropeptide Y neurons in the arcuate Q3 nucleus of the hypothalamus. MC4R is activated in the paraventricular nucleus through this pathway by a-melanocyte–stimulating hormone, a cleavage product of the POMC transcript resulting from the activity of the PCSK1 enzyme to signal satiety.10 The identification of leptin and its associated pathways was initially met with enthusiasm for its potential as a therapeutic target for treating all forms of obesity. It subsequently has become apparent, however, that most obese humans are not leptin-deficient, but rather have very high levels of leptin and are leptin-resistant.11,12 Congenital hypoleptinemia because of mutations of these genes was also found to be rare, affecting only a few families in the world. In these few individuals, however, recombinant leptin therapy has been very effective in treating obesity.13 And, although not considered an effective treatment for common obesity per se, the use of leptin therapy in diabetes is now being explored.14 In contrast to the severe obesity observed in the rare homozygotes for the deleterious mutations in the genes involved in the leptin pathway, heterozygotes for mutations of MC4R, LEP, LEPR, and POMC exhibit a less severe and nonfully penetrant form of obesity.15-19 MC4R mutations are the most common, with a population prevalence of at least 0.05% and a prevalence of 0.5%–1% among obese adults and 1%–6% among obese children.20 SYNDROMIC OBESITY
Identification of genes involved in human obesity has also been driven by the study of obesity occurring as part of distinct syndromes, such as Prader-Willi
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syndrome, that, in addition to extreme obesity, often include cognitive deficits, distinct malformations, and unusual behaviors. Most genes associated with obesity in these syndromes are related to central nervous system appetite control centers and support the role of hyperphagia as the underlying cause of the associated obesity. Prader-Willi syndrome, the most common of these syndrome, which is characterized by extreme hyperphagia and early central obesity accompanied by hypotonicity and significant cognitive deficits, involves loss of expression of imprinted paternal genes on 15q11–13, mostly (75%) because of deletions on paternal chromosome. Recent studies have narrowed the critical region down to the C/D box small nucleolar RNA genes SNORD116 cluster.21,22 Studies are ongoing to determine how these noncoding RNAs—which control expression levels, splicing, and modification of other RNAs—contribute to the hypothalamic dysfunction that is the likely basis of the hyperphagia and obesity of the syndrome. Genes linked to obesity in at least 5 other syndromes have been associated with the function or formation of primary cilia, subcellular organelles, which serve a sensory function for most cell types. All 15 BBS genes that have been linked to various forms of Bardet-Biedl syndrome, for example, harbor variant alleles that result in abnormal cilia.23 Cilia-related genes associated with obesity in 4 other syndromes include ALMS1 in Alstrom syndrome24; RAB23, encoding a protein, which is a member of the Rab family of small guanosine triphosphatases at the primary cilium, in Carpenter syndrome25,26; CEP19, encoding a ciliary protein and identified in a family with multigenerational morbid obesity27; and TUB, encoding a protein present in the ciliary base of photoreceptors and cells in the hypothalamus, with mutations identified in a family with early onset obesity accompanied by retinopathy.28 Other genes associated with obesity occurring as part of well-defined syndromes have other subcellular targets. Borjeson-Forssman-Lehmann syndrome, a rare X-linked obesity syndrome, has been linked to mutations in a widely expressed zinc-finger gene, PHF6, which encodes a protein that localizes to the cell nucleus and nucleolus.29 The VPS13B (COH1) gene associated with Cohen syndrome encodes a Golgi matrix protein.30 Obesity in Albright’s hereditary osteodystrophy has been linked to the GNAS1 gene encoding a guanine nucleotide-binding protein that stimulates adenylcyclase activity.31 Additionally, partial deficiencies of SIM1 (single minded), BDNF (brain-derived neurotrophic factor), and NTRK2 (neurotrophic tyrosine receptor kinase encoding the TrK protein, the receptor for BDNF) genes, are associatedwithsyndromicfeaturesinvolvedinthefunctioningof
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the hypothalamus downstream of MC4R-expressing neurons and leading severe hyperphagic obesity.32-34 Haplodeficiency of BDNF has also been implicated in the obesity occurring in a subset of patients with WAGR (Wilms tumor, aniridia, genitourinary malformations, and retardation) syndrome.35 GENOME-WIDE ASSOCIATION STUDIES
Since the beginning of the genome-wide association study (GWAS) era in 2005, a number of large GWASs have been conducted on obesity and related traits in humans. The hypothesis-free approach involves testing the associations of millions of common variants (singlenucleotide polymorphisms [SNPs] or single-nucleotide variants) with obesity and other obesity-related traits by comparing SNP frequencies in obese cases vs normal weight controls (or along a spectrum of BMI or other adiposity measurement) and has led to the identification of many genetic loci robustly associated with these traits. To date, there are 32 established loci for BMI,36-41 and 14 loci associated with BMI-adjusted waist-to-hip ratio (WHR),42 representing the risk for increased abdominal adiposity independent of overall obesity, that have been identified in populations of European descent, usually combining the sexes. Four additional loci were identified by GWASs within an Asian population.43 More recent GWASs have focused on determining sex differences in genetic associations, differences among other ethnic populations, and in longitudinal cohorts looking for potential differential effects by age. These studies have confirmed many of the GWAS-identified obesity variants across populations and populationspecific variants. The 3 most significantly associated SNPs for BMI among populations of European ancestry identified in multiple large GWASs, including the Genetic Investigation of Athropometric Traits meta-analysis involving more than 240,000 subjects, are an SNP within intron 1 of the fat mass and obesity-associated (FTO) gene, one near TMEM18 (transmembrane protein 18), and one near MC4R.40 These loci have also been found to be significantly associated with BMI in GWASs of other populations, including young children (mean age, 10 years),44 adolescents, and young adults45,46 and, in the case of FTO and MC4R, individuals of African descent47 and young adults with extremely elevated BMI.48 Additional population-specific BMI-associated variants were also found in these studies, including novel variants identified in GALNT10 (encoding UDPN-acetylgalactosaminyltransferase 10) and MIR148A (a microRNA-encoding gene) among persons of African ancestry47 and COL6A5,44 OLFM4, and HOXB546 in obese children.
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A different set of loci has been identified by GWAS as associated with BMI-adjusted WHR, a trait more directly related to adiposity than BMI. Of the 14 WHR-associated loci identified in the Genetic Investigation of Athropometric Traits GWAS meta-analysis, 7 were found to have a significant effect in women only.42 The sex-specific effects were found among genes generally involved in insulin sensitivity (PPARG, VEGFA, ADAMTS9, and GRB14) and lipid-related traits (LYPLAL1, MAP3K1, and GRB14). The PPARG finding may be relevant to type 2 diabetes therapy as sex differences in response to the PPARG agonist pioglitazone have been reported.49 Six of the WHR- and WC-related loci found in the GWAS of subjects of European ancestry were also significantly associated with these BMI-adjusted traits in a GWAS of subjects of African ancestry (TBX15-WARS2, GRB14, ADAMTS9, LY86, RSPO3, and ITPR2-SSPN), with loci in 2 additional genes identified specifically in this population, LHX and RREB1, found to be suggestive.50 Although limited overlap has been found among genes associated with BMI and those associated with more direct measures of adiposity, such as WHR, in GWASs performed to date, FTO and TMEM18 are among the 8 loci to be associated with both.51 Also, although a consensus is developing among studies as to the most commonly associated genes across all cohorts, the function of most of these genes in causing obesity, including these 2 most common loci, is not well understood. Animal studies have supported the likely role of some of these genes in food intake related areas of the brain. At least 14 obesity risk genes (FTO, MC4R, BDNF, TMEM18, KCTD15, NEGR1, NRXN3, ETV5, MTCH2, SEC16B, TFAP2B, GNPDA2, FAIM2, and LYPLAL1) are expressed in the hypothalamus of both obese and lean rats, supporting a potential central effect of these genes on energy homeostasis. Four of these genes (TMEM18, BDNF, MTCH2, and NEGR1), however, have also been to be involved in adipocyte differention.52 It is also of note that several loci associated with increased BMI across multiple studies are located near genes that have already been linked to monogenic forms of early onset obesity with hypothalamic dysfunction and hyperphagia as a common feature (MC4R, SH2B1, BDNF, and POMC), suggesting that this mechanism may also contribute to more common forms of obesity. FTO has been one of the most highly investigated obesity-associated genes consistently identified by GWAS; however, other than its high levels of expression in the brain, particularly the hypothalamus, its role has not been clear. In addition, the noncoding variants of the gene associated with obesity have not been demonstrated to affect FTO gene function. Insights into this
apparent paradox have been provided by a recent study showing that BMI-associated FTO variants cause changes in the expression of another relatively distant gene, IRX3, approximately 2 megabases away.53 Further experiments by the group making this discovery showed that Irx3 knockout mice have a 25%–30% weight reduction compared with wild-type mice. In addition, when fed a high fat diet, the knockouts did not gain weight, compared with a 63% weight gain in wild-type mice. Although the IRX3 gene had not been previously identified as associated with BMI, earlier studies had shown IRX3 overexpression in adipocytes of patients experiencing profound weight loss after bariatric surgery.54 Thus, evidence to date implicates the gene in central and peripheral mechanisms of altered metabolism related to weight gain. THE ‘‘MISSING HERITABILITY’’ OF OBESITY
Although GWASs on obesity and related traits have been a rich source for candidate genes and pathways to expand the search for causal pathways and therapeutic targets, the explanatory power they provide has been somewhat disappointing. Effect sizes of the identified loci are smaller than anticipated, with the combined risk alleles explaining only a fraction (1.45%) of the heritability of BMI. The FTO locus has the largest effect of any single gene at only 0.34%. The poor predictive value of the currently identified loci for obesity is in contrast to the performance of risk calculators based on other characteristics. For example for childhood obesity, a risk score based on 39 common BMI-associated SNPs has an area under the receiver operating characteristic curve of 0.59 compared with a risk calculator based on parental BMI, child birth weight, maternal gestational weight gain, smoking status, number of individuals living in the household, and maternal occupation, which had an area under the receiver operating characteristic curve of 0.85.55 Several of the clinical parameters in this model, however, have important genetic components not incorporated into current genetic risk calculators. This problem of missing heritability describes the gap between risk explained by known SNPs identified by GWASs (,2% in the case of BMI) and the estimated heritability (40%–70% from twin and other family studies). One of the major underlying issues of missing heritability is that SNP selection for GWAS genotyping arrays reflects the common disease–common variant hypothesis, wherein the heritability of common diseases is thought to be driven by additive effects of a few common allelic variants. Most SNPs represented on GWAS arrays have a minor allele frequency (MAF) of 1%–5% and the proportion of heritability captured depends on how well
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causal variants are tagged by these SNPs. New types of analyses, such as genome-wide complex trait analysis (GCTA), analysis of uncommon (MAF 0.5%–1%) or rare (MAF ,0.5%) variants and structural variants not detected by GWAS arrays, and epigenetic analysis, are helping to fill that gap. Gene-environment interactions, which are increasingly being explored, also have the potential to dramatically alter the heritability equation. Genome-wide complex trait analysis. GCTA uses data from GWASs in aggregate rather than on an SNP by SNP basis. That is, rather than quantifying associations with the given trait for each SNP, GCTA quantifies the total additive genetic effect of common SNPs by taking advantage of the fact that the degree of genetic resemblance for common SNPs at the whole-genome level is normally distributed among unrelated individuals. Thus, the influence of SNPs that individually do not reach genome-wide statistical significance, but contribute to heritability, is included in the estimate. This approach has accounted for up to 16% of the heritability of BMI and obesity in adults56 and 30% of the heritability in children57 using common SNPs. Rare variants. One strategy for assessing the association of rare variants not represented on GWAS platforms with traits such as obesity has been to use gene-centric platforms that include variants of selected genes that have an MAF ,1%. One such platform is the ITMATBroad-Candidate Gene Association Resource, a microarray chip with approximately 50,000 SNPs for 2100 metabolic- and cardiovascular disease-related loci, with a focus on rare variants and those likely to have functional consequences based on publically available resequencing data. SNPs on the chip have a lower limit MAF of 0.5%. A study using this platform for associations with BMI confirmed 8 of 10 previously identified loci, including 2 new variants in the BDNF and MC4R genes.58 A similar study for adiposity-related traits found 3 new loci associated with WHR (TMCC1, HOXC10, and PEMT) with an additional 2 that had significant associations in females only (SHC1 and ATBDB4).59 Complete sequencing of specific genes of interest has identified additional rare variants. New functional variants in the MC4R gene associated with BMI, for example, have been found with this approach.60 Similar studies have recently been carried out for SIM1 gene, identifying functional variants in both Prader-Willi–like syndrome and nonsyndromic morbid obesity in adults.61,62 In addition to targeted complete sequencing, whole genome and whole exome sequencing studies are now being undertaken. In a recent study using whole exome sequencing of 4 individuals with extreme familial forms of obesity, for example, 2 novel LEPR mutations
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were found.63 Some functional variants found in this manner may explain the associations found with more common SNPs with no apparent functionality. Copy number variants. CNVs, in which segments of chromosomes encompassing large parts of genes or multiple genes are either replicated or deleted, represent another source of the heritability that is missed by GWAS studies. Studies of the association of CNVs with obesity traits to date, often involving extreme obesity phenotypes with or without syndromic features, have identified candidate regions near the NEGR1 locus and chromosome 10q11.22,64 as well as on chromosomes 11q1165 and 10q26.366 among others. A region within chromosome 16p11.2 is particularly well studied, deletions of which are associated with obesity and duplications associated with an underweight phenotype.67-69 Complete sequencing of associated regions that can be replicated in multiple studies’ promises to identify the critical variants and potentially new loci underlying these associations, as with SIM1 found in the 16q16 region associated with Prader-Willi–like syndrome. Epigenetics. Epigenetic mechanisms also contribute to the heritability of obesity. These mechanisms include 3 general categories by which gene expression can be modulated without modification of the sequence, blocking of transcription, by direct methylation of DNA; modification of histones by methylation, acetylation, or phosphorylation; or microRNA. The agouti gene, associated with obesity and other traits in mice, is a classic example of epigenetic control of gene expression.70 The gene is expressed in hair follicles of mice resulting in production of a yellow pigment. In normal mice, the agouti gene is highly methylated leading to complete gene repression. A spontaneous insertion into the agouti gene of a common mouse genome retrotransposon causes dysregulation of the methylation, leading to a spectrum of possible phenotypes including yellow hair and extreme obesity because of ectopic agouti protein expression in the hypothalamus. This protein binds to the MC4R, resulting in massive hyperphagia. The potential for such mechanisms to play a role in obesity in humans was supported by a study finding variably methylated regions of CpG sites near genes previ- Q9 ously implicated in body weight, with the SORCS1 gene having the strongest association.71 Another recent study found lower levels of DNA methylation of the IGF2 gene—a pattern associated with childhood obesity72— in offspring of obese fathers compared with those of nonobese fathers, providing evidence of the transmissibility of these obesity-related modifications.73 The association of DNA methylation with obesity is also being examined on a genome-wide basis. A recent
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study analyzing genome-wide methylation in 1 discovery and 2 replication cohorts in a total of 2587 subjects of European descent identified 5 methylation sites associated with BMI, 3 of which were in the same intron of 1 gene, HIF3A (hypoxia inducible transcription factor 3A).74 Increased methylation at these sites was found in both blood cells and adipose tissue of subjects with high BMI and was associated with changes in HIF3A expression, further supporting the involvement of this gene pathway in yet-to-be-defined processes related to weight gain. Diet and exercise have been associated with differential methylation of genes, providing a mechanism for gene-environment interactions, including the ability to counteract acquired or imprinted epigenomic patterns regulating obesity-related genes. For example, significantly different patterns of DNA methylation were found in the overall genome and near-specific genes (AQP9, DUSP22, HIPK3, TNNT1, and TNNI3) among adolescents who lost weight in response to an intervention, which included diet and exercise compared with those who did not.75 In another study measuring DNA methylation and gene expression in skeletal muscle biopsies from sedentary adults who underwent acute exercise, whole genome methylation and methylation of promoters of specific genes—PPARG, PGC-1a, and PDK4—decreased in an exercise intensity–dependent manner, accompanied by increased expression of these genes.76 Six months of exercise intervention has also been shown to alter the epigenome of adipose tissue, including changes in methylation of 2 obesity-related genes, CPEB4 and SDCCAG8, which corresponded to changes in the expression of these genes.77 Gene-gene interactions (epistasis). Just as associations of genetic variants with BMI may be altered by interactions with environmental factors, such as diet and exercise, unrecognized interaction between variants of different genes also likely contribute significantly to the missing heritability of obesity and its related traits. Although such interactions have been identified for select candidate genes, mostly in the pre-GWAS era, systematic approaches to identify these interactions on a genome-wise basis are still being developed. Reports on the development of some of these approaches have used BMI as an outcome, but the results are largely yet to be replicated.78,79 TRANSLATION INTO CLINICAL PRACTICE
Although the discoveries related to the genetic and epigenetic mechanisms contributing to obesity have obvious and exciting potential for clinical application, translation of these discoveries into prevention and treatment strategies has been limited to date. One area in which new research findings and approaches can be
directly applied is in the screening of patients presenting with clinical features suggesting a syndromic form of obesity. These patients can now be screened using either targeted or genome-wide copy number analysis through array comparative genome hybridization.80 Given the poor predictive value of genetic risk scores derived from the GWAS-determined common genetic variants associated with common obesity, more generalized genetic screening currently has little clinical utility. However, a pioneering study focusing on individuals who self-identify as having weight problems suggests patients may benefit even from the limited information provided by these tests.81 In the study, in-depth interviews of 7 middle-aged overweight British women (BMI . 25) volunteering to be tested for FTO gene mutation status revealed that those with the higher risk variants experienced relief and saw the result as confirming their assumptions that they were susceptible to weight gain for external reasons. However, they also expressed increased motivation to overcome their genetic predisposition. Those with lower risk variants, after reporting brief disappointment, focused on alternative explanations and recognized the positive side of not having the genetic predisposition, whereas realizing that other genes could be responsible. Despite the limited practical value of the screening results, all individuals in the study reported deriving some benefit from the information. One area of particular clinical interest has been in using obesity-associated variants to predict outcomes of bariatric surgery. For example, in a study of the role of 11 common BMI-associated variants on weight loss, Sarzynski et al found an SNP within the FTO Q10 gene to be significantly associated with postsurgical weight loss. However, no variant tested was found to be associated with weight regain. In another study, significant differences in postbariatric surgery weight loss were found among patients categorized as low, intermediate, or high risk based on SNPs in 4 genes (INSIG-2, FTO, MC4R, and PCSK-1).82 More recently, a GWAS comparing patients at extremes of weight loss and weight gain after Roux-en-Y gastric bypass surgery identified 17 SNPs with potential biological relevance, clustering in or near PKHD1, HTR1A, NMBR, and IGFR1 genes.83 Although the studies to date in aggregate suggest great promise for targeting interventions such as bariatric surgery to those who will benefit most, they also make clear that the optimization of the genetic screens, as in other applications, will require continued study. CONCLUSIONS
Insights into the genetic contribution to obesity, including genetic interactions with the environment,
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are rapidly increasing with information being generated from multiple new sources. GWASs are being extended beyond those of European ancestry to many other ethnicities and differential associations by age, sex, and other characteristics are being explored. Techniques for assessing the contribution of rare variants, including new types of analysis of GWAS data such as GTCA and assays for whole exome and whole genome analysis, are becoming common. In addition, studies of epigenetics and epigenomics are providing insights not only to the heritability of obesity-related traits, but also to how the environment can influence the heritability. In parallel to studies on these basic mechanisms, clinicians are eager to use the knowledge gained to better target prevention and treatment strategies for their patients. Although these types of translational studies are only beginning, the intense efforts across the spectrum of research in the genetics of obesity promise to accelerate their clinical application and bring greater understanding to the underlying factors of the obesity epidemic at a population level. ACKNOWLEDGMENTS
Conflict of interests: None. REFERENCES
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