REVIEW URRENT C OPINION
Genetics of nonsyndromic obesity Yung Seng Lee a,b,c
Purpose of review Common obesity is widely regarded as a complex, multifactorial trait influenced by the ‘obesogenic’ environment, sedentary behavior, and genetic susceptibility contributed by common and rare genetic variants. This review describes the recent advances in understanding the role of genetics in obesity. Recent findings New susceptibility loci and genetic variants are being uncovered, but the collective effect is relatively small and could not explain most of the BMI heritability. Yet-to-be identified common and rare variants, epistasis, and heritable epigenetic changes may account for part of the ‘missing heritability’. Evidence is emerging about the role of epigenetics in determining obesity susceptibility, mediating developmental plasticity, which confers obesity risk from early life experiences. Genetic prediction scores derived from selected genetic variants, and also differential DNA methylation levels and methylation scores, have been shown to correlate with measures of obesity and response to weight loss intervention. Genetic variants, which confer susceptibility to obesity-related morbidities like nonalcoholic fatty liver disease, were also discovered recently. Summary We can expect discovery of more rare genetic variants with the advent of whole exome and genome sequencing, and also greater understanding of epigenetic mechanisms by which environment influences genetic expression and which mediate the gene–environment interaction. Keywords adiposity, epigenetics, genetics, obesity
INTRODUCTION Obesity is increasingly prevalent, fuelled by industrialization, modern sedentary lifestyle, and abundance of inexpensive energy dense foods [1,2]. Obesity can be classified into two categories from a medical genetic viewpoint, namely syndromic obesity and nonsyndromic obesity. Syndromic obesity refers to heritable genetic conditions with constellation of dysmorphic findings and birth defects, and often neurodevelopmental abnormalities, with obesity as one of the many features, such as Prader Willi syndrome and Bardet–Biedl syndrome. Nonsyndromic obesity, on the contrary, encompasses common (or polygenic) obesity and some forms of monogenic obesity, in which obesity is the predominant and often sole discernible phenotypic feature [3]. This review will be confined to the recent advances in understanding the genetics of nonsyndromic obesity. Common obesity is widely viewed as a complex, multifactorial condition with high heritability, which evolved from interaction between the modern ‘obesogenic’ environment and the individual’s genetic susceptibility to excessive weight gain [4]. www.co-pediatrics.com
Twins and family studies indicate that 40–80% of the variance in the BMI is attributable to genetic factors [5–12]. Some individuals are more prone to excessive adiposity accumulation, whereas others appear to be protected from weight gain despite exposure to the same obesogenic environment, supporting the existence of biological mechanisms, or genetic factors, which determine the individual’s response. The relative contribution of the environment and genetic susceptibility toward the pathogenesis of obesity varies between different obese individuals, and genetic predisposition to weight a
Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, bKhoo Teck Puat-National University Children’s Medical Institute, National University Hospital, National University Health System and cSingapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore Correspondence to A/Professor Yung Seng Lee, MBBS, MMed, PhD, MRCP, FRCPCH, FAMS Department of Paediatrics, Yong Loo Lin School of Medicine, NUHS Tower Block, Level 12, 1E Kent Ridge Road, Singapore 119228, Singapore. Tel: +65 67724112; fax: +65 67797486; e-mail: [email protected] Curr Opin Pediatr 2013, 25:666–673 DOI:10.1097/MOP.0b013e3283658fba Volume 25 Number 6 December 2013
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KEY POINTS Although many obesity susceptibility loci and genetic variants are being found, the collective effect is relatively small and could not explain most of the BMI heritability. Evidence is emerging about the role of epigenetics in determining obesity susceptibility, mediating developmental plasticity, which confers obesity risk from early life experiences. Obesity risk and response to weight loss intervention may be predicted by prediction scores derived from genetic variants and differential DNA methylation levels of genes. Genetic variants can confer susceptibility to obesityrelated morbidities like NAFLD.
gain may account for severity of phenotype, such as the age of onset [9], and response to efforts to lose weight [13,14].
COMMON DISEASE-COMMON VARIANT HYPOTHESIS The common disease-common variant hypothesis proposed that multiple common genetic variants (defined as minor allele frequency between 5– 50%) collectively contribute to the risk of obesity. The search for these genetic variants historically started with candidate gene studies, which identified candidates based on our knowledge of physiological weight regulation mechanisms gleaned from observations in extreme human obesity or transgenic mouse models. The approach assesses the association between an allele or a set of alleles with obesity-related traits in the population, but its dependence on our existing knowledge is a major limitation, and only a small number of these candidate genetic variants were found to be associated with susceptibility to obesity [15]. A recent example is the association between nucleobindin 2 gene NUCB2 common variant rs757081 (c.1012C>G resulting in Q338E) and adiposity. NUCB2 protein is a precursor of nesfatin-1, which was identified as a hypothalamic neuropeptide expressed in key hypothalamic nuclei and might be associated with the melanocortin signaling pathway regulating energy homeostasis, with anorectic effect demonstrated in murine studies [16–19]. This candidate gene was studied in 1049 obese white adults and 315 normal weight controls, and the common variant rs757081 is associated with obesity in men with an odds ratio (OR) of 1.42 [95% confidence interval (CI): 1.027– 1.955] [20]. Our study of 142 severely obese Chinese
children and 384 nonobese children found that the GG genotype was significantly less frequent in the obese group [21]. The OR for obese participants carrying CC and CG genotypes was 2.29 (95% CI, 1.17–4.49) in the dominant model, CC genotype 2.86 (95% CI, 1.41–5.81) in the additive model, and C allele 1.57 (95%CI, 1.17–2.1). The findings were replicated in an independent cohort of 372 obese and 390 nonobese Chinese children, in which the odds of obese participants with CC and CG genotypes were 1.69 (95%CI, 1.12–2.55). Within the cohort of 384 nonobese children, participants with GG genotype had significantly lower BMI and percentage ideal weight for height (WFH) at 5 and 8 years of age. Participants with lower birth weights also had more pronounced difference in WFH and BMI at 5 and 10 years of age between GG participants vs. CC/CG participants. It was postulated that the GG genotype is protective against excessive weight gain, and factors which predispose to excessive weight gain, such as higher birth weights, may ameliorate the effect. The candidate gene approach was followed by the hypothesis generating genome wide linkage studies, which screen the whole genome of related individuals with polymorphic markers to identify chromosomal regions that cosegregate with obesityrelated traits. The term ‘hypothesis generating’ is used because this analysis is exploratory and can uncover genetic loci not previously thought to be important, and in the process specific hypotheses can be formulated and tested or validated separately. However, this approach could identify loci, but failed to pinpoint causal genetic variants. The arrival of the revolutionary genome wide association studies (GWAS) brought a lot of hope of finally uncovering these obesity susceptibility variants. This hypothesis generating approach uses high-density scans, large sample size, and two stage study design, facilitated by high-throughput genotyping technologies, and at least 52 loci associated with obesityrelated traits have been discovered thus far, with at least 32 loci associated with BMI [22 ]. However, the effects of these loci on BMI and obesity risk are small; collectively, the 32 loci associated with BMI explained only 1.45% of the inter-individual BMI variation [22 ]. The best known locus, FTO, has the largest effect, with each risk allele increasing BMI by 0.39 kg/m2 and obesity risk by 1.2 times. Despite the high frequency of obesity risk allele in the population, FTO only explained 0.34% of the inter-individual variation in BMI [23]. The predictive value of these 32 BMI loci for obesity risk and BMI was only modest; in a model also consisting of age, age2 and sex, the area under the receiver operating characteristic curve was 0.575. Traditional prediction of
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obesity risk, such as using parental and childhood obesity, does much better, giving risk of 2.2–15.3 [24]. Ideally, the combination of traditional predictors with the genetic predictors will perform better. Nonetheless, these loci may be useful to help clinical care in other ways. A recent study reported 168 overweight and obese adolescents participating in a lifestyle intervention and nutrition education weight loss programme who were analyzed for nine obesity-related genetic variants chosen from previous GWAS, namely MC4R (rs1778231), FTO (rs9939609, rs7204609), PPARG (rs1801282), TMEM18 (rs7561317), IL6 (rs1800795), and ADPOQ (rs8223955, rs2241766, rs1501299), and a genetic prediction score was derived for each participant [25 ]. The study demonstrated not only that the score showed significant association with BMIstandard deviation score (SDS) and fat mass at baseline, but that participants with lower prediction score have greater weight loss and improvement in metabolic profile compared with participants with higher scores. The potential clinical application will be in stratified medicine, in which patients with poor prediction score may be candidates for other more intensive regimens or treatment options such as adjunct pharmaceutical therapies and even bariatric surgery, and the success of this application can only be known in a proper randomized clinical trial. &
‘Missing heritability’ Thus, despite the success of GWAS, these loci only accounted for a fraction of the phenotyping variance, giving rise to the enigma of the ‘missing heritability’. It was proposed that this ‘missing heritability’ may be because of the overestimation of heritability, underestimation of effect sizes of common alleles, yet-to-be identified common and rare alleles, epistasis, and transgenerational epigenetic changes [26 ]. As it stands, the sample sizes used for the discovery of these obesity risk loci are very large, and any further increase in sample size may find more common alleles but their expected effect sizes will be small. Epistasis refers to the phenomenon of genotype–genotype interactions in which the effect of a genetic variant on a trait is not independent of the effect of another genetic variant on the same trait. Here, the assumption that the collective effects of the obesity risk alleles are merely additive may not be valid, and genetic interactions may greatly inflate the apparent heritability. The proportion of heritability explained by a set of variants is the ratio of the heritability because of these variants (numerator), estimated directly from their observed effects, to the total heritability &
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(denominator), inferred indirectly from population data [27]. The explanation for missing heritability may not lie in the numerator (as-yet undiscovered variants), but a substantial portion of missing heritability could arise from overestimation of the denominator, or total heritability, because of genetic interactions. The total heritability may be much smaller, and the proportion of heritability explained much larger, and thus current estimates of missing heritability are not meaningful, because these ignore genetic interactions.
EPIGENETICS Strong evidence is accumulating for developmental origins of health and diseases, including the obese phenotype. The environment, especially early life experiences in utero and in the early neonatal period, can alter gene expression and influence the eventual phenotype, and this is believed to be mediated by the process of epigenetics. Epigenetics refers to the study of changes to gene expression which are not because of changes in DNA sequence, but are under regulation of two major mechanisms at transcriptional level, namely methylation of cytosine residues of CpG islands of DNA and acetylation of histone proteins, and by small noncoding RNAs (siRNAs) regulating DNA methyl transferases and histone deacetylases at posttranscriptional level [28,29]. Many genes had CpG islands in their promoter regions, and promoter methylation and/or histone modifications prevent access by transcription factors and result in gene silencing. Although most epigenetic changes are transient and not heritable, some are transgenerational [30–32]. Epigenetic changes occur most commonly during gestation and the neonatal period, in which these early life experiences result in disease susceptibility in later life, and these changes can be carried over by cell divisions and heritable. The evidence of developmental plasticity conferring obesity susceptibility came from the Dutch famine study, which studied the offspring of women who conceived during the Dutch famine of 1944, when all food supplies to German-occupied Netherlands were embargoed [33,34]. During this period, famously known as the ‘Hongerwinter’, adult rations dropped to 400– 800 kcal/day. Increased prevalence of obesity was observed in the offspring of mothers who were malnourished during pregnancy, especially in the first two trimesters for 19-year-old men, and the first trimester for women at 50 years of age. Recently, the Southampton Women Survey study team published the first report of methylation status of a gene promoter in utero, which affected subsequent adiposity in late childhood [35 ]. The methylation &
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pattern of the CpGs in the promoters of 78 candidate genes in umbilical cord-derived DNA obtained at birth were correlated with the adiposity of the children when they turned 9 years old, and the retinoid X receptor-a gene methylation had significant association with sex-adjusted fat mass and was replicated in an independent cohort. Another study was recently published in which the gene expression profile was determined in peripheral blood samples of 24 individuals of The Preterm Birth Growth Study and compared between the upper and lower tertiles of BMI [36]. This generated differentially expressed genes, which were then used to pursue DNA methylation analysis in the cord blood DNA of 178 individuals of the Avon Longitudinal Study of Parents and Children, with body composition data collected at about 9–10 years of age. Methylation in nine genes studied was associated with at least one index of body composition (BMI, fat mass, lean mass, and height) at age 9 years, although only one of these associations (ALPL with height) remained after correction for multiple testing. Another recent study demonstrated that epigenetic mechanisms may mediate and determine response to weight loss intervention [37 ]. One hundred and seven obese or overweight adolescents underwent a 10-week weight loss intervention programme, and were classified into high or low responders. Methylation microarray followed by relevant validation performed to search for baseline epigenetic differences of DNA obtained from venous blood sample between the two groups found differential methylation levels in five regions located in or near AQP9, DUSP22, HIPK3, TNNT1, and TNNI3 genes. The calculated methylation score was also significantly associated with changes in weight, BMI-SDS, and body fat mass loss after the intervention. More studies are underway and will shed more light on the role of epigenetics as the mechanism by which physiological and environmental factors can influence genomic responses, which will help us to understand the complex interplay of genetic susceptibility, epigenetic modulation, and environment, and provide further molecular mechanistic explanation of the gene–environment interaction. &
RARE VARIANT-COMMON DISEASE HYPOTHESIS The rare variant-common disease hypothesis proposed that rare variants contribute significantly to complex traits like obesity. Rare variants (defined as minor allelic frequency of 40 years). This is most likely because of exposure to an increasingly obesogenic environment over the decades. The MC4R has two single nucleotide polymorphisms (SNPs), Val103Ile and Ile25Leu, which were thought to be nonfunctional, but an in-vitro study revealed these two variants could be possible gain of function mutations [58]. A large European study (n ¼ 16797) of nine cohorts [59], a UK study (n ¼ 8304) of three cohorts as well as meta-analysis [60], and a meta-analysis of six east Asian studies (n ¼ 3526) [61] reported that these polymorphisms are protective against obesity. However, the Volume 25 Number 6 December 2013
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prevalence of the 103Ile variant in the UK cohorts ranged from 2 to 4%, and the carriers had an estimated 18% lower risk of obesity, compared with higher prevalence of 5.8–7.2% in east Asian populations, with an estimated 31% less risk of obesity. Thus, the impact of the protective alleles in different populations and their contribution to populationrisk reduction for obesity differ significantly. The MC3R is a seven-transmembrane G-proteincoupled receptor [62] which shares the same agonist melanocyte stimulating hormone as MC4R, but is expressed in a more restricted distribution in the hypothalamic nuclei known to regulate energy homeostasis [63], and has a dominant role in inhibition of energy storage [64,65]. There are at least 11 rare missense and nonsense MC3R mutations reported to be associated with human obesity [66–68]. However, unlike MC4R deficiency, most of the reports did not demonstrate extensive cosegregation of the MC3R mutations with obesity in family studies in the classic dominant or codominant fashion. Instead, it is proposed that MC3R rare variants could contribute to common obesity as per the rare variant-common disease hypothesis, in which the MC3R variants/mutations may contribute to human obesity as predisposing factors at the very least, modified by other genetic, epigenetic, and environmental factors [66]. This notion is further supported by the two common nonsynonymous SNPs Thr6Lys and Val81Ile of MC3R (in near complete linkage disequilibrium). At least two studies of obese children from different populations have independently demonstrated the association of these SNPs with childhood adiposity, supported by reduced variant MC3R activity in vitro, compared with wildtype MC3R [66,69]. Compared with similarly obese children with wildtype MC3R, AfroAmerican and white obese children homozygous for the MC3R variants had higher BMI-SDS score, body fat mass, and percentage body fat [69], and Singaporean Asian children with the variants similarly had higher percentage ideal WFH, percentage body fat, and leptin levels, supported by an additive effect in which heterozygotes had intermediate phenotype [66]. Studies on three cohorts of normal and overweight children 6–19 years old (n ¼ 302) found that those with the variants had greater energy intake when given ad libitum meals, and did not demonstrate altered resting or total energy expenditure [70]. The frequency of the common variants differs greatly between obese children of the ethnic groups: Afro-Americans 53% heterozygous and 16% homozygous, whites 18 and 2%, Chinese 36 and 3%. Thus, the impact and contribution of these MC3R variants to increased adiposity vary from population to population.
GENETIC SUSCEPTIBILITY TO OBESITYRELATED MORBIDITIES Obesity is associated with a myriad of related morbidities, and there is increasing evidence of genetic susceptibility to these complications, raising the potential use of genetic information to identify susceptible patients. Nonalcoholic fatty liver disease (NAFLD) is one of these comorbidities, with the spectrum ranging from simple steatosis to steatohepatitis and even cirrhosis, and a number of studies have recently reported the susceptibility genetic variants. A study of 455 American obese children and adolescents from a study cohort who underwent fast gradient MRI to measure the hepatic fat content reported the GCKR variant rs1260326 was associated with elevated triglycerides, very low density lipoprotein, and fatty liver, whereas the PNPLA3 variant rs738409 was associated with fatty liver only, and both variants explained 32% of the variance of the hepatic fat fraction [71]. The same research group later reported significant association with rs2645424 of FDFT1 as well in 229 obese youth of the same cohort [72 ]. The association with PNPLA3 variant rs738409 was independently reported in a study of Taiwanese children [73]. The risk of NAFLD increased by 2.96 times (95% CI, 1.57–5.59) in the participants with CG alleles and by 5.84 times (95% CI, 2.59– 13.16) in those with GG alleles, compared with participants with CC alleles. The same group later genotyped variant rs8192678 peroxisome proliferator-activated receptor-g coactivator (PGC)-1a gene (PPARGC1A) risk A allele in 781 Taiwanese children and adolescents, and reported PPARGC1A rs8192678 risk A allele was an independent risk factor for developing NAFLD, with an OR of 1.740 (95% CI: 1.149, 2.637), after control for the effects of age-adjusted and sex-adjusted BMI, sex, and PNPLA3 rs738409 polymorphism [74 ]. Our recent study of 243 Singaporean children reported fasting serum visfatin, an adipokine thought to be secreted by visceral fat, correlated with measures of obesity and liver enzymes, and was elevated in obese children with abnormal glucose tolerance, and also NAFLD [75 ]. Two upstream SNPs of the visfatin gene –3187G>A (rs11977021) and –1537C>T (rs61330082) (in complete linkage disequilibrium) were significantly associated with adverse cardiometabolic parameters. The AA genotype of –3187G>A (rs11977021) was associated with higher percentage ideal WFH, and higher frequencies of severe SBP (25.6 vs. 9.9%) and severe hypertension (27.9 vs. 10.7%) as compared with the GA genotype. The AA genotype was also associated with higher serum visfatin (6.17 0.76ng/ ml vs. 3.92 0.44ng/ml) and higher triglyceride (1.39 0.08mmol/l vs. 1.19 0.07mmol/l) as compared with the GG genotype.
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CONCLUSION The search for genetic factors causing obesity is challenging, but the availability of advanced platforms and technologies has certainly accelerated our quest to piece together the complex genetic architecture. Like an unfinished jigsaw puzzle, every piece of evidence serves to complete the picture, and we can look forward to better understanding the molecular circuitry governing weight regulation through the discovery of the obesity susceptibility genes and their products, which in turn can be targets for drug development. Acknowledgements None. Conflicts of interest There are no conflicts of interest.
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63. Roselli-Rehfuss L, Mountjoy KG, Robbins LS, et al. Identification of a receptor for gamma melanotropin and other proopiomelanocortin peptides in the hypothalamus and limbic system. Proc Natl Acad Sci U S A 1993; 90: 8856–8860. 64. Chen AS, Marsh DJ, Trumbauer ME, et al. Inactivation of the mouse melanocortin-3 receptor results in increased fat mass and reduced lean body mass. Nat Genet 2000; 26:97–102. 65. Butler AA, Kesterson RA, Khong K, et al. A unique metabolic syndrome causes obesity in the melanocortin-3 receptor-deficient mouse. Endocrinology 2000; 141:3518–3521. 66. Lee YS, Poh LK, Kek BL, Loke KY. The role of melanocortin 3 receptor gene in childhood obesity. Diabetes 2007; 56:2622–2630. 67. Mencarelli M, Dubern B, Alili R, et al. Rare melanocortin-3 receptor mutations with in vitro functional consequences are associated with human obesity. Hum Mol Genet 2011; 20:392–399. 68. Calton MA, Ersoy BA, Zhang S, et al. Association of functionally significant Melanocortin-4 but not Melanocortin-3 receptor mutations with severe adult obesity in a large North American case-control study. Hum Mol Genet 2009; 18:1140–1147. 69. Feng N, Young SF, Aguilera G, et al. Co-occurrence of two partially inactivating polymorphisms of MC3R is associated with pediatric-onset obesity. Diabetes 2005; 54:2663–2667. 70. Savastano DM, Tanofsky-Kraff M, Han JC, et al. Energy intake and energy expenditure among children with polymorphisms of the melanocortin-3 receptor. Am J Clin Nutr 2009; 90:912–920. 71. Santoro N, Zhang CK, Zhao H, et al. Variant in the glucokinase regulatory protein (GCKR) gene is associated with fatty liver in obese children and adolescents. Hepatology 2012; 55:781–789. 72. Santoro N, Feldstein AE, Enoksson E, et al. The association between hepatic & fat content and liver injury in obese children and adolescents: effects of ethnicity, insulin resistance, and common gene variants. Diabetes Care 2013; 36:1353–1360. Comprehensive study about the discovery of a susceptibility genetic variant to obesity-related NAFLD. 73. Lin YC, Chang PF, Hu FC, et al. A common variant in the PNPLA3 gene is a risk factor for nonalcoholic fatty liver disease in obese Taiwanese children. J Pediatr 2011; 158:740–744. 74. Lin YC, Chang PF, Chang MH, Ni YH. A common variant in the peroxisome && proliferator-activated receptor-gamma coactivator-1alpha gene is associated with nonalcoholic fatty liver disease in obese children. Am J Clin Nutr 2013; 97:326–331. Comprehensive study about the discovery of a susceptibility genetic variant to obesity-related NAFLD. 75. Ooi SQ, Chan RM, Poh LK, et al. Visfatin and its genetic variants are & associated with obesity-related morbidities and cardiometabolic risk in severely obese children. Pediatr Obes 2013. [Epub ahead of print] Study about the discovery of susceptibility genetic variants to adverse cardiometabolic parameters in obese children.
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Genetics of nonsyndromic obesity Yung Seng Lee a,b,c
Purpose of review Common obesity is widely regarded as a complex, multifactorial trait influenced by the ‘obesogenic’ environment, sedentary behavior, and genetic susceptibility contributed by common and rare genetic variants. This review describes the recent advances in understanding the role of genetics in obesity. Recent findings New susceptibility loci and genetic variants are being uncovered, but the collective effect is relatively small and could not explain most of the BMI heritability. Yet-to-be identified common and rare variants, epistasis, and heritable epigenetic changes may account for part of the ‘missing heritability’. Evidence is emerging about the role of epigenetics in determining obesity susceptibility, mediating developmental plasticity, which confers obesity risk from early life experiences. Genetic prediction scores derived from selected genetic variants, and also differential DNA methylation levels and methylation scores, have been shown to correlate with measures of obesity and response to weight loss intervention. Genetic variants, which confer susceptibility to obesity-related morbidities like nonalcoholic fatty liver disease, were also discovered recently. Summary We can expect discovery of more rare genetic variants with the advent of whole exome and genome sequencing, and also greater understanding of epigenetic mechanisms by which environment influences genetic expression and which mediate the gene–environment interaction. Keywords adiposity, epigenetics, genetics, obesity
INTRODUCTION Obesity is increasingly prevalent, fuelled by industrialization, modern sedentary lifestyle, and abundance of inexpensive energy dense foods [1,2]. Obesity can be classified into two categories from a medical genetic viewpoint, namely syndromic obesity and nonsyndromic obesity. Syndromic obesity refers to heritable genetic conditions with constellation of dysmorphic findings and birth defects, and often neurodevelopmental abnormalities, with obesity as one of the many features, such as Prader Willi syndrome and Bardet–Biedl syndrome. Nonsyndromic obesity, on the contrary, encompasses common (or polygenic) obesity and some forms of monogenic obesity, in which obesity is the predominant and often sole discernible phenotypic feature [3]. This review will be confined to the recent advances in understanding the genetics of nonsyndromic obesity. Common obesity is widely viewed as a complex, multifactorial condition with high heritability, which evolved from interaction between the modern ‘obesogenic’ environment and the individual’s genetic susceptibility to excessive weight gain [4]. www.co-pediatrics.com
Twins and family studies indicate that 40–80% of the variance in the BMI is attributable to genetic factors [5–12]. Some individuals are more prone to excessive adiposity accumulation, whereas others appear to be protected from weight gain despite exposure to the same obesogenic environment, supporting the existence of biological mechanisms, or genetic factors, which determine the individual’s response. The relative contribution of the environment and genetic susceptibility toward the pathogenesis of obesity varies between different obese individuals, and genetic predisposition to weight a
Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, bKhoo Teck Puat-National University Children’s Medical Institute, National University Hospital, National University Health System and cSingapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore Correspondence to A/Professor Yung Seng Lee, MBBS, MMed, PhD, MRCP, FRCPCH, FAMS Department of Paediatrics, Yong Loo Lin School of Medicine, NUHS Tower Block, Level 12, 1E Kent Ridge Road, Singapore 119228, Singapore. Tel: +65 67724112; fax: +65 67797486; e-mail: [email protected] Curr Opin Pediatr 2013, 25:666–673 DOI:10.1097/MOP.0b013e3283658fba Volume 25 Number 6 December 2013
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KEY POINTS Although many obesity susceptibility loci and genetic variants are being found, the collective effect is relatively small and could not explain most of the BMI heritability. Evidence is emerging about the role of epigenetics in determining obesity susceptibility, mediating developmental plasticity, which confers obesity risk from early life experiences. Obesity risk and response to weight loss intervention may be predicted by prediction scores derived from genetic variants and differential DNA methylation levels of genes. Genetic variants can confer susceptibility to obesityrelated morbidities like NAFLD.
gain may account for severity of phenotype, such as the age of onset [9], and response to efforts to lose weight [13,14].
COMMON DISEASE-COMMON VARIANT HYPOTHESIS The common disease-common variant hypothesis proposed that multiple common genetic variants (defined as minor allele frequency between 5– 50%) collectively contribute to the risk of obesity. The search for these genetic variants historically started with candidate gene studies, which identified candidates based on our knowledge of physiological weight regulation mechanisms gleaned from observations in extreme human obesity or transgenic mouse models. The approach assesses the association between an allele or a set of alleles with obesity-related traits in the population, but its dependence on our existing knowledge is a major limitation, and only a small number of these candidate genetic variants were found to be associated with susceptibility to obesity [15]. A recent example is the association between nucleobindin 2 gene NUCB2 common variant rs757081 (c.1012C>G resulting in Q338E) and adiposity. NUCB2 protein is a precursor of nesfatin-1, which was identified as a hypothalamic neuropeptide expressed in key hypothalamic nuclei and might be associated with the melanocortin signaling pathway regulating energy homeostasis, with anorectic effect demonstrated in murine studies [16–19]. This candidate gene was studied in 1049 obese white adults and 315 normal weight controls, and the common variant rs757081 is associated with obesity in men with an odds ratio (OR) of 1.42 [95% confidence interval (CI): 1.027– 1.955] [20]. Our study of 142 severely obese Chinese
children and 384 nonobese children found that the GG genotype was significantly less frequent in the obese group [21]. The OR for obese participants carrying CC and CG genotypes was 2.29 (95% CI, 1.17–4.49) in the dominant model, CC genotype 2.86 (95% CI, 1.41–5.81) in the additive model, and C allele 1.57 (95%CI, 1.17–2.1). The findings were replicated in an independent cohort of 372 obese and 390 nonobese Chinese children, in which the odds of obese participants with CC and CG genotypes were 1.69 (95%CI, 1.12–2.55). Within the cohort of 384 nonobese children, participants with GG genotype had significantly lower BMI and percentage ideal weight for height (WFH) at 5 and 8 years of age. Participants with lower birth weights also had more pronounced difference in WFH and BMI at 5 and 10 years of age between GG participants vs. CC/CG participants. It was postulated that the GG genotype is protective against excessive weight gain, and factors which predispose to excessive weight gain, such as higher birth weights, may ameliorate the effect. The candidate gene approach was followed by the hypothesis generating genome wide linkage studies, which screen the whole genome of related individuals with polymorphic markers to identify chromosomal regions that cosegregate with obesityrelated traits. The term ‘hypothesis generating’ is used because this analysis is exploratory and can uncover genetic loci not previously thought to be important, and in the process specific hypotheses can be formulated and tested or validated separately. However, this approach could identify loci, but failed to pinpoint causal genetic variants. The arrival of the revolutionary genome wide association studies (GWAS) brought a lot of hope of finally uncovering these obesity susceptibility variants. This hypothesis generating approach uses high-density scans, large sample size, and two stage study design, facilitated by high-throughput genotyping technologies, and at least 52 loci associated with obesityrelated traits have been discovered thus far, with at least 32 loci associated with BMI [22 ]. However, the effects of these loci on BMI and obesity risk are small; collectively, the 32 loci associated with BMI explained only 1.45% of the inter-individual BMI variation [22 ]. The best known locus, FTO, has the largest effect, with each risk allele increasing BMI by 0.39 kg/m2 and obesity risk by 1.2 times. Despite the high frequency of obesity risk allele in the population, FTO only explained 0.34% of the inter-individual variation in BMI [23]. The predictive value of these 32 BMI loci for obesity risk and BMI was only modest; in a model also consisting of age, age2 and sex, the area under the receiver operating characteristic curve was 0.575. Traditional prediction of
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obesity risk, such as using parental and childhood obesity, does much better, giving risk of 2.2–15.3 [24]. Ideally, the combination of traditional predictors with the genetic predictors will perform better. Nonetheless, these loci may be useful to help clinical care in other ways. A recent study reported 168 overweight and obese adolescents participating in a lifestyle intervention and nutrition education weight loss programme who were analyzed for nine obesity-related genetic variants chosen from previous GWAS, namely MC4R (rs1778231), FTO (rs9939609, rs7204609), PPARG (rs1801282), TMEM18 (rs7561317), IL6 (rs1800795), and ADPOQ (rs8223955, rs2241766, rs1501299), and a genetic prediction score was derived for each participant [25 ]. The study demonstrated not only that the score showed significant association with BMIstandard deviation score (SDS) and fat mass at baseline, but that participants with lower prediction score have greater weight loss and improvement in metabolic profile compared with participants with higher scores. The potential clinical application will be in stratified medicine, in which patients with poor prediction score may be candidates for other more intensive regimens or treatment options such as adjunct pharmaceutical therapies and even bariatric surgery, and the success of this application can only be known in a proper randomized clinical trial. &
‘Missing heritability’ Thus, despite the success of GWAS, these loci only accounted for a fraction of the phenotyping variance, giving rise to the enigma of the ‘missing heritability’. It was proposed that this ‘missing heritability’ may be because of the overestimation of heritability, underestimation of effect sizes of common alleles, yet-to-be identified common and rare alleles, epistasis, and transgenerational epigenetic changes [26 ]. As it stands, the sample sizes used for the discovery of these obesity risk loci are very large, and any further increase in sample size may find more common alleles but their expected effect sizes will be small. Epistasis refers to the phenomenon of genotype–genotype interactions in which the effect of a genetic variant on a trait is not independent of the effect of another genetic variant on the same trait. Here, the assumption that the collective effects of the obesity risk alleles are merely additive may not be valid, and genetic interactions may greatly inflate the apparent heritability. The proportion of heritability explained by a set of variants is the ratio of the heritability because of these variants (numerator), estimated directly from their observed effects, to the total heritability &
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(denominator), inferred indirectly from population data [27]. The explanation for missing heritability may not lie in the numerator (as-yet undiscovered variants), but a substantial portion of missing heritability could arise from overestimation of the denominator, or total heritability, because of genetic interactions. The total heritability may be much smaller, and the proportion of heritability explained much larger, and thus current estimates of missing heritability are not meaningful, because these ignore genetic interactions.
EPIGENETICS Strong evidence is accumulating for developmental origins of health and diseases, including the obese phenotype. The environment, especially early life experiences in utero and in the early neonatal period, can alter gene expression and influence the eventual phenotype, and this is believed to be mediated by the process of epigenetics. Epigenetics refers to the study of changes to gene expression which are not because of changes in DNA sequence, but are under regulation of two major mechanisms at transcriptional level, namely methylation of cytosine residues of CpG islands of DNA and acetylation of histone proteins, and by small noncoding RNAs (siRNAs) regulating DNA methyl transferases and histone deacetylases at posttranscriptional level [28,29]. Many genes had CpG islands in their promoter regions, and promoter methylation and/or histone modifications prevent access by transcription factors and result in gene silencing. Although most epigenetic changes are transient and not heritable, some are transgenerational [30–32]. Epigenetic changes occur most commonly during gestation and the neonatal period, in which these early life experiences result in disease susceptibility in later life, and these changes can be carried over by cell divisions and heritable. The evidence of developmental plasticity conferring obesity susceptibility came from the Dutch famine study, which studied the offspring of women who conceived during the Dutch famine of 1944, when all food supplies to German-occupied Netherlands were embargoed [33,34]. During this period, famously known as the ‘Hongerwinter’, adult rations dropped to 400– 800 kcal/day. Increased prevalence of obesity was observed in the offspring of mothers who were malnourished during pregnancy, especially in the first two trimesters for 19-year-old men, and the first trimester for women at 50 years of age. Recently, the Southampton Women Survey study team published the first report of methylation status of a gene promoter in utero, which affected subsequent adiposity in late childhood [35 ]. The methylation &
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pattern of the CpGs in the promoters of 78 candidate genes in umbilical cord-derived DNA obtained at birth were correlated with the adiposity of the children when they turned 9 years old, and the retinoid X receptor-a gene methylation had significant association with sex-adjusted fat mass and was replicated in an independent cohort. Another study was recently published in which the gene expression profile was determined in peripheral blood samples of 24 individuals of The Preterm Birth Growth Study and compared between the upper and lower tertiles of BMI [36]. This generated differentially expressed genes, which were then used to pursue DNA methylation analysis in the cord blood DNA of 178 individuals of the Avon Longitudinal Study of Parents and Children, with body composition data collected at about 9–10 years of age. Methylation in nine genes studied was associated with at least one index of body composition (BMI, fat mass, lean mass, and height) at age 9 years, although only one of these associations (ALPL with height) remained after correction for multiple testing. Another recent study demonstrated that epigenetic mechanisms may mediate and determine response to weight loss intervention [37 ]. One hundred and seven obese or overweight adolescents underwent a 10-week weight loss intervention programme, and were classified into high or low responders. Methylation microarray followed by relevant validation performed to search for baseline epigenetic differences of DNA obtained from venous blood sample between the two groups found differential methylation levels in five regions located in or near AQP9, DUSP22, HIPK3, TNNT1, and TNNI3 genes. The calculated methylation score was also significantly associated with changes in weight, BMI-SDS, and body fat mass loss after the intervention. More studies are underway and will shed more light on the role of epigenetics as the mechanism by which physiological and environmental factors can influence genomic responses, which will help us to understand the complex interplay of genetic susceptibility, epigenetic modulation, and environment, and provide further molecular mechanistic explanation of the gene–environment interaction. &
RARE VARIANT-COMMON DISEASE HYPOTHESIS The rare variant-common disease hypothesis proposed that rare variants contribute significantly to complex traits like obesity. Rare variants (defined as minor allelic frequency of 40 years). This is most likely because of exposure to an increasingly obesogenic environment over the decades. The MC4R has two single nucleotide polymorphisms (SNPs), Val103Ile and Ile25Leu, which were thought to be nonfunctional, but an in-vitro study revealed these two variants could be possible gain of function mutations [58]. A large European study (n ¼ 16797) of nine cohorts [59], a UK study (n ¼ 8304) of three cohorts as well as meta-analysis [60], and a meta-analysis of six east Asian studies (n ¼ 3526) [61] reported that these polymorphisms are protective against obesity. However, the Volume 25 Number 6 December 2013
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prevalence of the 103Ile variant in the UK cohorts ranged from 2 to 4%, and the carriers had an estimated 18% lower risk of obesity, compared with higher prevalence of 5.8–7.2% in east Asian populations, with an estimated 31% less risk of obesity. Thus, the impact of the protective alleles in different populations and their contribution to populationrisk reduction for obesity differ significantly. The MC3R is a seven-transmembrane G-proteincoupled receptor [62] which shares the same agonist melanocyte stimulating hormone as MC4R, but is expressed in a more restricted distribution in the hypothalamic nuclei known to regulate energy homeostasis [63], and has a dominant role in inhibition of energy storage [64,65]. There are at least 11 rare missense and nonsense MC3R mutations reported to be associated with human obesity [66–68]. However, unlike MC4R deficiency, most of the reports did not demonstrate extensive cosegregation of the MC3R mutations with obesity in family studies in the classic dominant or codominant fashion. Instead, it is proposed that MC3R rare variants could contribute to common obesity as per the rare variant-common disease hypothesis, in which the MC3R variants/mutations may contribute to human obesity as predisposing factors at the very least, modified by other genetic, epigenetic, and environmental factors [66]. This notion is further supported by the two common nonsynonymous SNPs Thr6Lys and Val81Ile of MC3R (in near complete linkage disequilibrium). At least two studies of obese children from different populations have independently demonstrated the association of these SNPs with childhood adiposity, supported by reduced variant MC3R activity in vitro, compared with wildtype MC3R [66,69]. Compared with similarly obese children with wildtype MC3R, AfroAmerican and white obese children homozygous for the MC3R variants had higher BMI-SDS score, body fat mass, and percentage body fat [69], and Singaporean Asian children with the variants similarly had higher percentage ideal WFH, percentage body fat, and leptin levels, supported by an additive effect in which heterozygotes had intermediate phenotype [66]. Studies on three cohorts of normal and overweight children 6–19 years old (n ¼ 302) found that those with the variants had greater energy intake when given ad libitum meals, and did not demonstrate altered resting or total energy expenditure [70]. The frequency of the common variants differs greatly between obese children of the ethnic groups: Afro-Americans 53% heterozygous and 16% homozygous, whites 18 and 2%, Chinese 36 and 3%. Thus, the impact and contribution of these MC3R variants to increased adiposity vary from population to population.
GENETIC SUSCEPTIBILITY TO OBESITYRELATED MORBIDITIES Obesity is associated with a myriad of related morbidities, and there is increasing evidence of genetic susceptibility to these complications, raising the potential use of genetic information to identify susceptible patients. Nonalcoholic fatty liver disease (NAFLD) is one of these comorbidities, with the spectrum ranging from simple steatosis to steatohepatitis and even cirrhosis, and a number of studies have recently reported the susceptibility genetic variants. A study of 455 American obese children and adolescents from a study cohort who underwent fast gradient MRI to measure the hepatic fat content reported the GCKR variant rs1260326 was associated with elevated triglycerides, very low density lipoprotein, and fatty liver, whereas the PNPLA3 variant rs738409 was associated with fatty liver only, and both variants explained 32% of the variance of the hepatic fat fraction [71]. The same research group later reported significant association with rs2645424 of FDFT1 as well in 229 obese youth of the same cohort [72 ]. The association with PNPLA3 variant rs738409 was independently reported in a study of Taiwanese children [73]. The risk of NAFLD increased by 2.96 times (95% CI, 1.57–5.59) in the participants with CG alleles and by 5.84 times (95% CI, 2.59– 13.16) in those with GG alleles, compared with participants with CC alleles. The same group later genotyped variant rs8192678 peroxisome proliferator-activated receptor-g coactivator (PGC)-1a gene (PPARGC1A) risk A allele in 781 Taiwanese children and adolescents, and reported PPARGC1A rs8192678 risk A allele was an independent risk factor for developing NAFLD, with an OR of 1.740 (95% CI: 1.149, 2.637), after control for the effects of age-adjusted and sex-adjusted BMI, sex, and PNPLA3 rs738409 polymorphism [74 ]. Our recent study of 243 Singaporean children reported fasting serum visfatin, an adipokine thought to be secreted by visceral fat, correlated with measures of obesity and liver enzymes, and was elevated in obese children with abnormal glucose tolerance, and also NAFLD [75 ]. Two upstream SNPs of the visfatin gene –3187G>A (rs11977021) and –1537C>T (rs61330082) (in complete linkage disequilibrium) were significantly associated with adverse cardiometabolic parameters. The AA genotype of –3187G>A (rs11977021) was associated with higher percentage ideal WFH, and higher frequencies of severe SBP (25.6 vs. 9.9%) and severe hypertension (27.9 vs. 10.7%) as compared with the GA genotype. The AA genotype was also associated with higher serum visfatin (6.17 0.76ng/ ml vs. 3.92 0.44ng/ml) and higher triglyceride (1.39 0.08mmol/l vs. 1.19 0.07mmol/l) as compared with the GG genotype.
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CONCLUSION The search for genetic factors causing obesity is challenging, but the availability of advanced platforms and technologies has certainly accelerated our quest to piece together the complex genetic architecture. Like an unfinished jigsaw puzzle, every piece of evidence serves to complete the picture, and we can look forward to better understanding the molecular circuitry governing weight regulation through the discovery of the obesity susceptibility genes and their products, which in turn can be targets for drug development. Acknowledgements None. Conflicts of interest There are no conflicts of interest.
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