Obesity

A number sign (#) is used with this entry because obesity is predominantly a polygenic and multifactorial trait. Genetic variation in some genes have been associated with susceptibility to obesity as a monogenic trait (see body mass index (BMI), 606641).

Autosomal recessive disorders with obesity as a predominant feature include leptin deficiency (614962), leptin receptor deficiency (614963), prohormone convertase-1 deficiency (600955), and proopiomelanocortin deficiency (609734); associated features in these disorders include hypogonadotropic hypogonadism, hypoadrenalism, and short stature.

There are also syndromes associated with obesity such as Prader-Willi syndrome (176270), Bardet-Biedl syndrome (BBS; see 209900), and Cohen syndrome (216550), among others.

For a review of the molecular basis of obesity, see Barsh et al. (2000). Bell et al. (2005) provided a comprehensive review of the genetics of human obesity.

For a review of the molecular understanding of adaptive thermogenesis, see Lowell and Spiegelman (2000).

Barness et al. (2007) reviewed the genetic, molecular, and environmental aspects of obesity and discussed the myriad associated complications, including hypertension (145500), dyslipidemia, endothelial dysfunction, type 2 diabetes mellitus (125853) and impaired glucose tolerance, acanthosis nigricans (100600), hepatic steatosis, premature puberty (see 176400), hypogonadism and polycystic ovary syndrome, obstructive sleep disorder, orthopedic complications, cholelithiasis, and pseudotumor cerebri (243200).

Pathogenesis

Both Roberts et al. (1988) and Ravussin et al. (1988) presented evidence that reduced energy expenditure is a major 'risk factor' in obesity. The study by Roberts et al. (1988), done in infants from birth to 1 year of age, measured total energy expenditure and metabolizable energy intake over a period of 7 days when the infants were 3 months old, and the postprandial metabolic rate when they were 0.1 and 3 months old. The results were related to weight gain in the first year of life. No significant difference was found between infants who became overweight by the age of 1 year (50% of infants born to overweight mothers) and those who did not, with respect to weight, length, skin-fold thicknesses, metabolic rate at 0.1 and 3 months of age, and metabolizable energy intake at 3 months. However, total energy expenditure at 3 months of age was 20.7% lower in infants who became overweight than in the other infants. In a study done in southwestern American Indians, Ravussin et al. (1988) found that energy expenditure correlated with the rate of change in body weight over a 2-year follow-up period and that, among 94 sibs from 36 families, 24-hour energy expenditure aggregated in families.

The 2 predominant populations of microbiota in both the mouse and human gut are members of the bacterial groups known as the Firmicutes and the Bacteroidetes. Ley et al. (2006) found that genetically obese mice (ob/ob) had 50% fewer Bacteroidetes and correspondingly more Firmicutes than their lean wildtype sibs. They also showed that the relative proportion of Bacteroidetes was decreased in obese people in comparison with lean people, and that this proportion increased with weight loss on 2 types of low-calorie diet. Ley et al. (2006) concluded that obesity has a microbial component, which might have potential therapeutic implications. Turnbaugh et al. (2006) demonstrated through metagenomic and biochemical analyses that these changes affect the metabolic potential of the mouse gut microbiota. Their results indicated that the obese microbiome has an increased capacity to harvest energy from the diet. Furthermore, this trait was transmissible: colonization of germ-free mice with an 'obese microbiota' resulted in a significantly greater increase in total body fat than colonization with a 'lean microbiota.' Turnbaugh et al. (2006) concluded that their results identified the gut microbiota as an additional contributing factor to the pathophysiology of obesity.

Spalding et al. (2008) showed that adipocyte number is a major determinant of fat mass in adults. However, the number of fat cells stays constant in adulthood in lean and obese individuals, even after marked weight loss, indicating that the number of adipocytes is set during childhood and adolescence. To establish the dynamics within the stable population of adipocytes in adults, the authors measured adipocyte turnover by analyzing genomic DNA for the integration of (14)C derived from above-ground nuclear bomb tests. Findings indicated that approximately 10% of fat cells are renewed annually at all adult ages and levels of body mass index. Neither adipocyte death nor generation rate was altered in early-onset obesity, suggesting a tight regulation of fat cell number in this condition during adulthood.

Inheritance

Zonta et al. (1987) studied the genetic factors in obesity in a sample of nuclear families in northern Italy. Sixty-seven families consisted of the parents and sibs of all elementary school children considered to be obese and 112 families consisted of a similar sample of nonobese children and their parents and sibs. Several analyses suggested the presence of a dominant major gene with weak effect. Several other studies were reviewed. In a study of a Hutterite group, Paganini-Hill et al. (1981) found evidence for a major gene in the determination of 'bulk factor.' In a study in the Danish Adoption Register, Stunkard et al. (1986) found a strong relation between the weight class (thin, median weight, overweight, or obese) and the body-mass index of the biologic parents--for the mothers, p less than 0.0001; for the fathers, p less than 0.02. No relation was found between the weight class of the adoptees and the body-mass index of their adoptive parents. Twin studies (Medlund et al., 1976) also indicated an important role of genetic factors. Stunkard et al. (1986) emphasized that the studies should not discourage persons or their physicians from treating obesity but rather that the genetic information should be a guide to the maintenance of a relatively high level of physical activity and appropriate diet.

In 774 adults in 59 pedigrees ascertained through cases of cardiovascular disease, Hasstedt et al. (1989) studied the genetics of a relative-fat-pattern index (RFPI), i.e., the ratio of subscapular skinfold thickness to the sum of subscapular and suprailiac skinfold thicknesses. Likelihood analysis supported recessive inheritance of an allele with a frequency of 46%, which elevated mean RFPI from 0.412 to 0.533 when homozygous. The analysis apportioned the variance in RFPI as 42.3% due to the major locus, 9.5% due to polygenic inheritance, and 48.2% due to random environmental effects.

Bouchard et al. (1990) subjected 12 pairs of identical male twins to overfeeding by 1000 kcal per day, 6 days a week, for a period of 100 days. The variance among pairs in response to overfeeding was about 3 times greater than that within pairs. With respect to the changes in regional fat distribution and amount of abdominal visceral fat, the differences were particularly striking, there being about 6 times as much variance among pairs as within pairs. Bouchard et al. (1990) suggested that the explanation lay in the involvement of genetic factors that govern the tendency to store energy as either fat or lean tissue and the various determinants of resting expenditure of energy.

Moll et al. (1991) investigated the role of genetic and environmental factors in determining variability in ponderosity (body weight relative to height). Ponderosity was measured by body mass index (BMI; kg per sq m) in the mothers, fathers, and sibs of 284 school children in Muscatine, Iowa. Moll et al. (1991) concluded that there was strong support for a single recessive locus with a major effect that accounted for almost 35% of the adjusted variation in BMI. Polygenic loci accounted for an additional 42% of the variation. Approximately 23% of the adjusted variation was not explained by genetic factors. Thus, according to their analysis, more than 75% of the variation was explained by genetic factors that included a single recessive locus. Approximately 6% of persons in the population were predicted to have 2 copies of the recessive gene, while 37% were predicted to have 1 copy of the gene.

Mapping

Obesity

On the basis of accumulating evidence that obesity has a substantial genetic component, Norman et al. (1997) performed a genomewide search for linkage of DNA markers to percent body fat in Pima Indians, a population with a very high prevalence of obesity. Single-marker linkages to percent body fat were evaluated by sib pair analysis for quantitative traits. From these analyses, the best evidence of genes influencing body fat came from markers at 11q21-q22 and 3p24.2-p22. Neither linkage achieved a lod score of 3.0, however.

To evaluate potential epistatic interactions among 5 regions, on chromosomes 7, 10, and 20, that had been linked to obesity phenotypes, Dong et al. (2003) conducted pairwise correlation analyses based on alleles shared identical by descent (IBD) for independent obese affected sib pairs, and determined family-specific nonparametric linkage (NPL) scores in 244 families. The correlation analyses were also conducted separately, by race, through use of race-specific allele frequencies. Both the affected sib pair-specific IBD-sharing probability and the family-specific NPL score revealed that there were strong positive correlations between 10q (88-97 cM) and 20q (65-83 cM), through single-point and multipoint analyses with 3 obesity thresholds across African American and European American samples. The results from multiple methods and correlated phenotypes were considered consistent with epistatic interactions between loci on chromosomes 20 and 10 playing a role in extreme human obesity. See BMIQ9 (602025) and BMIQ10 (607514) for a discussion of the loci on 20q and 10q, respectively.

To detect potentially imprinted, obesity-related genetic loci, Dong et al. (2005) performed genomewide parent-of-origin linkage analyses under an allele-sharing model for discrete traits and under a family regression model for obesity-related quantitative traits. They studied a European American sample of 1,297 individuals from 260 families and also 2 smaller, independent samples for replication. For discrete trait analysis, they found evidence for a maternal effect in 10p12 (see BMIQ8; 603188) across the 3 samples, with lod scores of 5.69 (single point) and 4.52 (multipoint) for the pooled sample. For quantitative trait analysis, they found the strongest evidence for a maternal effect (single-point lod of 2.85; multipoint lod of 4.01 for BMI and 3.69 for waist circumference) in region 12q24 and for a paternal effect (single-point lod of 4.79; multipoint lod of 3.72 for BMI) in region 13q32, in the European American sample. The results suggested that parent-of-origin effects, perhaps including genomic imprinting, may play a role in human obesity.

Loos et al. (2008) performed a metaanalysis of data from 4 European population-based studies and 3 disease-case series, involving a total of 16,876 individuals of European descent, and confirmed the previously reported association between the FTO gene and BMI. They also found a significant association between rs17782313, located 188 kb downstream of the MC4R gene, and BMI in adults (p = 2.8 x 10(-15)) and children (p = 1.5 x 10(-8)). In case-control analyses, the odds for severe childhood obesity reached 1.30 (p = 8.0 x 10(-11)), and overtransmission of the risk allele to obese offspring was observed in 660 families. The authors concluded that common variants near the MC4R gene influence fat mass, weight, and obesity risk at the population level.

Qi et al. (2008) examined the associations of the MC4R variants rs17782313 (T-C) and rs17700633 (G-A) with dietary intakes, weight change, and diabetes risk in a prospective cohort of 5,724 women, 1,533 of whom had type 2 diabetes. Under an additive inheritance model, rs17782313 was significantly associated with high intake of total energy (p = 0.028), total fat (p = 0.008), and protein (p = 0.003); adjustment for age, BMI, diabetes status, and other covariates did not appreciably change the associations, and the associations between rs17782313 and higher BMI (p = 0.002) were independent of dietary intakes. Carriers of the rs17782313 C allele had 0.2 kg/m(2) greater 10-year increase in BMI from cohort baseline in 1976 to 1986 (p = 0.028) compared to noncarriers, and per C allele of rs17782313 was associated with a 14% increased risk of type 2 diabetes, adjusting for BMI and other covariates. The SNP rs17700633 was not significantly associated with dietary intake or obesity traits.

Meyre et al. (2009) analyzed genomewide association data from 1,380 Europeans with early-onset and morbid adult obesity and 1,416 age-matched normal-weight controls and confirmed association at rs17782313, with replication in an additional 14,186 European individuals (combined p = 4.8 x 10(-15)).

Renstrom et al. (2009) performed association studies between 9 SNPs from 9 target genes and obesity in 3,885 nondiabetic and 1,038 diabetic Swedish adults. In models with adipose mass traits, BMI or obesity as outcomes, the most strongly associated SNP rs1121980 was in the FTO gene. Five other SNPs, rs7498665 in the SH2B1 gene (608937), rs4752856 in the MTCH2 gene, rs17782313 in the MC4R gene, rs2815752 in the NEGR1 gene, and rs10938397 in the GNPDA2 gene were significantly associated with obesity.

Other Associations with Obesity

SNPs within APOE (107741) and TGF-beta-1 (190180) have been associated with the obesity phenotypes of fat mass, percentage fat mass, and lean mass.

A 3-allele haplotype of the ENPP1 gene (see 173335.0006) is associated with childhood and adult obesity and increased risk of glucose intolerance and type II diabetes (125853).

Nonsynonymous SNPs in the SDC3 gene (186357) have been associated with obesity in the Korean population.

Variation in the PCSK1 gene (162150.0005) influences risk of obesity.

Variation in the PYY gene (600781) may influence susceptibility to obesity.

Leanness

In a case-control study of 7,790 individuals, Andersen et al. (2005) found that the pro203 allele of PPARGC1B (608886.0001) was significantly less frequent among obese participants than normal or overweight subjects (p = 0.004). Andersen et al. (2005) concluded that variation of PPARGC1B may contribute to the pathogenesis of obesity, with the widespread ala203 allele being a risk factor for the development of this common disorder.

Lavebratt et al. (2005) genotyped 356 overweight or obese and 148 lean Swedish men for 4 SNPs in the AHSG genes (138680) and found that homozygosity for the AHSG*2 haplotype (see 138680.0001) conferred an increased risk for leanness (OR, 1.90; p = 0.027). The AHSG*2 haplotype had been associated with lower AHSG levels (Osawa et al., 2005). Lavebratt et al. (2005) suggested that a low level of AHSG is protective against fatness.

Molecular Genetics

Nishigori et al. (2001) identified mutations in the NR0B2 gene (604630) that segregated with mild or moderate early-onset obesity in Japanese subjects.

To identify potential genetic contributors to the quantitative trait body weight, Ahituv et al. (2007) resequenced coding exons and splice junctions of 58 genes in 379 obese and 378 lean individuals. This 96-Mb survey included 21 genes associated with monogenic forms of obesity in human or mice, as well as 37 genes that function in body weight-related pathways. They found that the monogenic obesity-associated gene group was enriched for rare nonsynonymous variants unique to the obese population compared with the lean population. In addition, computational analysis predicted a greater fraction of deleterious variants within the obese cohort. Together, these data suggested that multiple rare alleles contribute to obesity in the population and provide a medical sequencing-based approach to detecting them.

The accumulation of mildly deleterious missense mutations in individual human genomes is proposed as a genetic basis for complex diseases. The plausibility of this hypothesis depends on quantitative estimates of the prevalence of mildly deleterious de novo mutations and polymorphic variants in humans and on the intensity of selective pressure against them. Kryukov et al. (2007) combined analysis of mutations causing human mendelian diseases as cataloged in the Human Genome Mutation Database (HGMD) (Stenson et al., 2003) with analysis of human-chimpanzee divergence and systematic data on human genetic variation and found that approximately 20% of new missense mutations in humans result in a loss of function, whereas approximately 27% are effectively neutral. Thus the remaining 53% of new missense mutations have mildly deleterious effects. These mutations give rise to many low-frequency deleterious allelic variants in the human population, as is evident from a new dataset of 37 genes sequenced in more than 1,500 individual human chromosomes. Up to 70% of low frequency missense alleles are mildly deleterious and are associated with a heterozygous fitness loss in the range of 0.001-0.003. Thus, the low allele frequency of an amino acid variant can, by itself, serve as a predictor of its functional significance. The observation that the majority of human rare nonsynonymous variants are deleterious, and thus are of significance to function and phenotype, suggests a strategy for candidate gene association studies. Disease populations are expected to have a higher rate of rare amino acid variants in genes involved in disease than are healthy control populations. This difference can be easily detected in a deep resequencing study. Obviously, this strategy would be highly inefficient if the majority of coding variants at low frequency were neutral. Kryukov et al. (2007) concluded that their analysis provides an explanation for the success of studies, such as the one of Ahituv et al. (2007), which demonstrate an excess of rare missense variants in individuals with phenotypes associated with disease risk.

A heterozygous missense mutation in the POMC gene (176830.0004) was associated with severe childhood obesity in 2 unrelated children and segregated with obesity in the 3-generation family of 1 of the children.

A heterozygous missense mutation in the CART gene (602606.0001) was associated with obesity in a 3-generation Italian family.

Willer et al. (2009) performed a metaanalysis of 15 genomewide association studies for BMI comprising 32,387 participants and followed up top signals in 14 additional cohorts comprising 59,082 participants. They strongly confirmed association with FTO and MC4R and identified 6 additional loci (P less than 5 x 10(-8)): TMEM18 (613220), KCTD15 (615240), GNPDA2 (613222), SH2B1 (608937), MTCH2 (613221), and NEGR1 (613173) (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the CNS, emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.

Animal Model

The 'diabetes' mouse (db) and the 'obese' mouse (ob) are indistinguishable phenotypically when bred on the same mouse strain. The db gene maps to mouse chromosome 4, however, in a region that shows extensive conservation of synteny and gene order with human 1p32-p31 (Bahary et al., 1990; Bahary et al., 1991). On the basis of syntenic homology, there thus might be a human obesity gene on chromosome 1p near oncogene JUN (165160). Other recessive mouse models of obesity, such as 'tubby' (TUB; 601197) and 'fat' (fat), may also be in conserved regions. The tub gene was found to lie 2.4 cM from the Hbb gene (141900). Jones et al. (1992) suggested that the human homolog of 'tubby' resides in 11p15 and that the Hbb locus in the human could be used as a linkage marker for studies of familial obesity in humans.

See leptin (164160) for a discussion of the human homolog of the murine 'obesity' (ob) locus.

Aitman (2003) noted that the approach Schadt et al. (2003) used to study the genetics of obesity in mice was useful in understanding of the molecular pathogenesis of complex diseases. They used mice derived from matings of 2 standard inbred strains and performed gene expression profiles with microarrays in second-generation mice to determine the extent to which approximately 24,000 genes were differentially expressed in the liver tissues of fat and lean mice, as measured by the levels of mRNA. The data were used to form expression signatures of high or low adiposity. The analysis excluded many obvious genes that might have previously been considered strong candidates and identified new genes--including those that encode major urinary protein-1, a protein glycosyltransferase, and a cation-transporting ATPase--that may have been primary determinants of obesity in the obese mice.

Ozcan et al. (2004) used cell culture and mouse models to show that obesity causes endoplasmic reticulum (ER) stress. This stress in turn leads to suppression of insulin receptor signaling through hyperactivation of c-Jun N-terminal kinase (JNK; see 601158) and subsequent serine phosphorylation of insulin receptor substrate-1 (IRS1; 147545). Mice deficient in X box-binding protein-1 (XBP1; 194355), a transcription factor that modulates the ER stress response, develop insulin resistance. Ozcan et al. (2004) concluded that ER stress is a central feature of peripheral insulin resistance and type II diabetes (125853) at the molecular, cellular, and organismal levels.

Bera et al. (2008) generated mice homozygous for partial inactivation of the Ankrd26 gene (610855) and observed the development of extreme obesity, insulin resistance, and a dramatic increase in body size. The obesity was associated with hyperphagia with no reduction in energy expenditure or activity. The authors noted that the human ANKRD26 gene is located on chromosome 10p12, where Dong et al. (2005) had found linkage to obesity in European American individuals.

Chen et al. (2008) developed an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, they identified gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population resulted in the identification of a macrophage-enriched metabolic network (MEMN) supported as having a causal relationship with disease traits associated with metabolic syndrome (see 605552). Three genes in this network, lipoprotein lipase (LPL; 609708), lactamase beta (LACTB; 608440), and protein phosphatase 1-like (PPM1L; 611931), were validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Given the prediction that LPL and LACTB have a causal relationship with obesity, Chen et al. (2008) recorded weight, fat mass, and lean mass for Lpl heterozygous null mice, Lactb transgenic mice, and wildtype littermate controls every 2 weeks starting at 11 weeks of age using quantitative nuclear magnetic resonance (NMR). As predicted, the growth curves for the Lpl heterozygous null and Lactb transgenic animals were significantly different from those of controls, with the fat mass/lean mass ratio difference generally increasing over time. At the final quantitative NMR measurement the fat mass/lean mass ratios in the Lpl heterozygous null mouse and the Lactb transgenic mice were increased by 22% and 20%, respectively, over the wildtype controls (p = 1.09 x 10(-5) and p = 4.48 x 10(-5)), respectively. LPL is the principal enzyme responsible for the hydrolysis of circulating triglycerides and is active in differentiated macrophages, consistent with its presence in the MEMN. LACTB is a serine protease with high similarity to bacterial lactamase, which metabolizes peptidoglycan in the bacterial cell wall. LACTB has been detected in mitochondria as part of the mitochondrial ribosomal complex. Interestingly, a strain of rat that exhibits late-onset obesity contains a mutation in the S26 subunit of the mitochondrial ribosome (611988), at least partially explaining the obesity phenotype.

Yang et al. (2009) generated knockout or transgenic mouse models for 9 candidate genes for abdominal obesity and observed that perturbation of 8 of the 9 genes, including Lpl (609708), Tgfbr2 (190182), Lactb (608440), Zpf90 (609451), Gas7 (603127), Gpx3 (138321), Me1 (154250), and C3ar1 (605246), resulted in significant changes in obesity-related traits such as fat/muscle ratios, body weight, adiposity, individual fat pad masses, or plasma lipids. Liver expression signatures revealed alterations in common pathways and subnetworks that relate to metabolic pathways, suggesting that obesity is driven by a gene network instead of a single gene.

Perry et al. (2008) identified prominent expression of the Girk4 gene (600734) in mouse hypothalamus, with most pronounced expression in the ventromedial, paraventricular, and arcuate nuclei, neuron populations implicated in energy homeostasis. Girk4-null mice were predisposed to late-onset obesity. By 9 months, Girk4-null mice were approximately 25% heavier than wildtype controls due to greater body fat. Before the development of overweight, Girk4-null mice exhibited a tendency toward greater food intake and an increased propensity to work for food in an operant task. Girk4-null mice also exhibited reduced net energy expenditure, despite displaying elevated resting heart rates and core body temperatures. These data implicated GIRK4-containing channels in signaling crucial to energy homeostasis and body weight.