The increasing prevalence of pediatric obesity around the world has become an area of scientific interest because of public health concern. Although since early stages of the lifespan body weight might be heavily influenced by an individual's behavior, epidemiological research highlights the involvement of genetic influences contributing to variation in fat accumulation and thus body composition. Results from genome-wide association studies and candidate gene approaches have identified specific regions across the human genome influencing obesity-related phenotypes. Reviewing the scientific literature provides support to the belief that at the conceptual level scientists understand that genes and environments do not act independently, but rather synergistically, and that such interaction might be the responsible factor for differences within and among populations. However, there is still limited understanding of genetic and environmental factors influencing fat accumulation and deposition among different populations, which highlights the need for innovative experimental designs, improved body composition measures and appropriate statistical methodology.
Pediatric obesity has become a worldwide public health concern,1 as it continues to increase in prevalence and severity. Early-onset adult health implications, such as diabetes, heart disease, non-alcoholic hepatic steatosis and some cancers,1 presenting among children worldwide has created a sense of alarm among parents, caretakers, health practitioners and public health professionals. Efforts toward understanding the contributors to the pediatric obesity epidemic are, perhaps more than ever, paramount within the scientific and clinical communities, and should be collectively directed toward the development of preventive strategies.
Beyond the estimation of the prevalence of pediatric overweight and obesity based on standardized weight measures, research has shown that children of different populations around the world differ in body composition parameters. In the United States, for example, African-American children have been reported to have lower levels of abdominal adiposity compared with children of European- or Hispanic-American descent.2, 3, 4 Chinese youth have more body fat and Asian Indians have higher levels of adiposity, more truncal fat and lower muscle mass5 compared with white youth. These observed differences during childhood indicate that, from early stages of development, the genetic makeup of individuals interacts with environmental factors (influenced by the population-specific locations, cultures, social settings and practices) to create variation in fat acquisition and accumulation among populations. This paper reviews existing scientific literature supporting genetic contributors to pediatric obesity, with a focus on population differences in obesity-related phenotypes and methodological considerations.
Materials and methods
For this review, we evaluated and summarized the results of studies involving genetics and ancestral genetic background, and obesity-related phenotypes in the pediatric population. We identified these studies through searches in Medline (PubMed), Web of Science and Google for original research and review articles written in english, published between January 1990 and July 2011 using the following search terms independently and/or in combination: children, pediatric obesity, body mass index, adiposity, body fat, polymorphism, genetics, cardiovascular disease, heart disease, diabetes, insulin resistance and lipids. We also searched within the reference lists of identified papers to locate additional studies. We identified 79 articles from this original search. We also searched the online catalog of Genome Wide Association Studies (http://www.genome.gov/26525384).
Genetic determinants and population differences in pediatric obesity
A variety of study designs have been used to determine the heritability (that is, proportion of variation in a phenotype that is explained by genetic factors) of body mass index (BMI=kg/m2),6 including adoption studies,7, 8 and twin and virtual twin studies.9, 10 Although estimates vary according to methodology and sample characteristics, approximately 65% of the variation in BMI can be explained by genetic factors.6, 7, 10, 11 Evidence suggests that heritability estimates are similarly high for measures of obesity other than BMI, particularly those of fat distribution.12, 13 It has also been suggested that the heritability of obesity-related traits increases with age,14 implying that at early stages of the lifespan body weight is highly influenced by behavioral, social, parental and environmental factors, highlighting a potentially crucial time for intervention.
Family-based data have been pivotal in the identification of genetic determinants of body adiposity in humans. Linkage analyses have provided insights into genetics of early-onset obesity, identifying strongly associated genes with large phenotypic effects such as the leptin receptor (LEPR),15 brain-derived neurotrophic factor (BDNF) and melanocortin-4 receptor (MC4R).16 Some of these genes have also been associated with phenotypes involving energy balance, as exemplified by the association of multiple rare variants in MC4R with both energy intake and expenditure among Hispanic children.17 However, linkage methodology is limited in detecting genes of small effects, which likely underlie the genetic basis of complex traits such as obesity. Consequently, although linkage scans have led to the identification of several hundred genomic regions, as summarized elsewhere,18 most of the genes identified have failed to be replicated.19
More recently, genome-wide association studies (GWAS) have utilized large samples of individuals genotyped at approximately 500 thousand single nucleotide polymorphisms spread throughout the genome. To date, several GWAS for BMI have been performed and replicated utilizing populations of mainly Northern-European ancestry,20, 21, 22, 23, 24 finding risk alleles in or near approximately 32 genes25 (see Table 1). Another commonly used method for identifying genetic variants is the candidate gene approach where selection of genes is based on the known physiological role in pathways associated with obesity (for example, fat deposition, energy expenditure, food intake and metabolism). Over the past 20 years, studies incorporating this approach have identified a large number of potential genes contributing to variation in obesity-related traits.26 However, of the 127 candidate genes identified, few have been replicated across multiple studies. A summary of findings for genes associated with obesity using the candidate gene approach is presented in Table 2. Although many genetic contributors to adiposity have been identified, there is surprisingly very little overlap between those regions identified by candidate gene studies and those identified by GWAS.18
The search for genetic factors specifically influencing pediatric obesity-related outcomes has primarily focused on replicated candidate genes in adult studies. Among the studies that have examined genetic associations among children, most report equivocal results. For example, a large twin study in the United Kingdom of ten candidate genes reported to be associated with BMI in adults, found no evidence for an association in children at ages four, seven or ten.27 Conversely, a study of bi-ethnic US children investigating 11 different candidate genes involved in energy balance and adipogenesis observed multiple associations.28 Other candidate genes have been identified in pediatric populations around the world, as indicated in Table 3, with some (e.g. FTO-fat mass and obesity-associated protein) reported to increase obesity risk with increase in age.14 The lack of replication across genetic studies raises issues related to mechanistic differences in metabolic pathways owing to heterogeneity of samples, in terms of impact of genetic influence across the lifespan, different environmental exposures and potential differences in genetic ancestral background.
Although there has been some progress in our understanding of the potential genetic determinants of obesity-related traits, there are still questions regarding the mechanisms of action of these genetic variants. Many of the variants discovered in GWAS are in or near genes expressed in the central nervous system and hypothalamus, suggesting a role in the regulation of food intake and energy expenditure.22 So far, the majority of risk alleles are found in noncoding regions, often in areas that are several kilobases away from the coding region of a gene. In addition, some of the genes associated with obesity might influence different phenotypes in a particular population, as seen in a recent example where MC4R, but not FTO, was found to be associated with self-reported eating behavior in European children and adults.29 In order to refine the genetic basis for obesity, large-scale GWAS should be followed by a multitude of small-scale studies to fine-map candidate regions. This will also determine whether the identified variants can be replicated with more explicit and better-defined phenotypes, and whether the variants contribute similarly among different sample populations, providing insights into possible mechanisms. The current decrease in cost associated with gene mapping makes it more feasible to obtain sequence data, enhancing research efforts. However, as we collectively move forward toward improving genetic analysis, new layers of complexity are likely to surface, including understanding of gene expression modification by environmental and/or developmental factors and the contributions of different epigenetic response owing to evolutionary developmental variation across populations.
In addition to individual genes, genetic ancestral background appears to contribute to the variation in adiposity at the population level. Genetic research has demonstrated within-group variation in genetic ancestry proportions among populations, particularly those in the United States, along with a relationship with disease risk.30 Some studies have reported significant associations between genetic admixture and measures of adiposity in specific groups, particularly in African-American women31, 32 and Hispanic individuals of Afro-Caribbean descent.33, 34 The results of the association of genetic admixture and obesity-related measures in North American Hispanics have not been consistent,32, 35, 36, 37, 38 and among Native Americans, only two studies have examined the relationship between BMI and genetic admixture (both finding a negative relationship between European admixture and BMI).35, 39 Regardless of the reported associations, it is important to consider the existence of studies failing to replicate these findings, thus raising concern about methodology and experimental design, the use of categories (used for racial/ethnic classification), and the importance of evaluating gene by environmental interactions in population studies.40, 41, 42, 43 Nevertheless, the evaluation of ancestral genetic background has helped in explaining observed racial/ethnic differences in weight and adiposity, particularly in the United States.44, 45, 46
Behavioral genetic factors
Although individual exposure to the obesigenic environment does not necessarily influence choice, the observed increase of heritability throughout the lifespan may result from genetic predisposition to behaviors promoted by an environment of great accessibility to foods and limited opportunities for physical activity. Recent evidence has demonstrated that behavioral practices respond to genetic factors. For example, heritability estimates for feeding behaviors such as food cue responsiveness and eating rate have been reported.47, 48 Apart from eating behaviors, physiological factors such as dopamine,49 serotonin50 and cannabinoid receptor candidate genes have also been identified as contributing to eating behavior and thus, body weight regulation in children.51 Genetic determinants of physical activity performance and fitness phenotypes have been reported with >200 estimated associated DNA variants.52 Although the majority of these studies were conducted in adults, it is highly possible that associations translate to the pediatric population and, in fact, could control physiological and/or behavioral process(es) that cause(s) children to take a certain body composition trajectory.
The variation of behavioral traits among populations cannot be explained solely by genetics. There are many behavioral practices mediated by factors such as culture, family practices, accessibility to resources, spirituality, social status and economical solvency among others that contribute to obesity-related behaviors. Childhood behaviors represent a significant influence on body composition trajectory, and thus long-term overall health. Hence, there is a call for research initiatives targeted toward the understanding of how cultural, social and economical practices impact behavioral choice, and how these factors interact with biological parameters throughout the childhood continuum to signal processes of fat accumulation.
Study design considerations
As reviewed, most of the identified genetic contributors to obesity-related traits have been discovered through population research. The study of population differences in obesity-related traits serves as a portal to the understanding of etiology of individual variability. Such understanding becomes relevant in the development of prevention strategies and treatment for those at a higher risk for disease. As we continue to explore the scientific discovery funnel that moves from population biology down to individual treatment, it is pertinent to continue efforts to improve population research.
As previously stated, few studies in the field of obesity have investigated the interrelation between genes and environment. However, acquiring an insight into the development of obesity as a disease throughout the lifespan will rely on the implementation of experimental designs with appropriate measurements and statistical methodology to evaluate the action and interaction of biological and non-biological parameters. As the scientific community collectively moves toward eliminating the global burden of pediatric obesity, the following methodological aspects deserve consideration when designing studies targeted to understanding the genetic etiology childhood obesity and population differences:
The contributions of genes and environment to overweight and obesity act synergistically (or antagonistically) rather than independently, and thus a clear dichotomy between genetic and environmental influences on related traits cannot exist. There is evidence demonstrating how the environment interacts with genetic makeup and expression to influence a trait. Epigenetic mechanisms have also been documented, whereby environmental factors physically affect gene expression without altering the DNA sequence. Of particular interest is evidence showing the impact of the intra-uterine environment and birth weight on differences in early-onset obesity.53, 54, 55 The importance of defining a priori what researchers mean by environment in a particular study should also be stressed when designing studies, paying particular attention to the limitations in accurately quantifying those factors traditionally considered as environmental in clinical research. Understanding how environmentally related exposures and parent-of-origin effects contribute to genetic expression and disease risk is an exciting avenue for future research, which will provide additional insight into the etiology of pediatric obesity and population differences. Furthermore, the recent identification of a set of genetic variants by GWAS is likely to make this task more manageable, by simply investigating whether and how these genetic associations are mediated by environmental, behavioral factors and vice versa.56
Genetic influences likely vary amongst populations. Many of the associations identified in GWAS, for instance, have been conducted among European adults. It is not known whether these associations will apply equally to other racial/ethnic populations or other age groups. In the case of the well-documented FTO gene, for instance, a particular SNP associated with obesity has been identified and replicated in Chinese and Mexican populations,57, 58 despite a large difference in the frequency of the risk allele between the two populations (12.6% vs 45%).57 However, although associations between FTO and obesity have recently been found in other worldwide populations,59, 60, 61, 62, 63, 64 no association between FTO variants and BMI were found among a West African population in Gambia, or in an Oceanic population.65, 66 Similarly, in children, a recent study evaluating the contributions of the MC4R variant (a recent finding of GWAS in adults) to childhood obesity was replicated in children of European descent, but not of African descent.67 As the identification of specific genetic variants responsible for obesity progresses, it will be important to examine how the effect of these variants differs between populations, and to identify population- and age-specific pathways to obesity.
Body composition trajectory responds to a cumulative process that initiates, in many cases, at early stages of the life course and continues throughout development. It is not completely clear at what time during the developmental or aging process the variants identified as contributors to obesity initiate their influence on fat accumulation; although some evidence suggest that their effects do indeed manifest themselves early in the lifespan.68, 69 However, the obesity risk attributable to gene variants has been reported to increase with age.14 In order to optimize treatment and prevention plans, more research is needed to understand how such gene–environment interactions change across the lifespan.
Appropriate statistical methodology is needed to address the complexity of genetic interactions that influence variability in measures related to obesity. Relative heritability estimates for BMI average about 65%; however, genetic variants identified by GWAS explain a disappointingly small proportion of the expected genetic variation.70, 71 For example, although evidence supports an additive genetic contribution to BMI so that individuals with ⩾13 risk alleles have greater BMI when compared with individuals of similar height with ⩽3 risk alleles,22 a very large proportion of the total expected genetic variation remains unexplained by genetic variants identified to date.72 Despite recent methodological advances,73, 74 there is still an imminent need to develop statistical approaches that allow the evaluation of individual and interactive contributions of genetic, physiological and environmental factors. There is also a need for the simultaneous statistical evaluation of multiple and interactive genes, and their integration with other genomic data, including gene expression, high density SNP genotyping and sequencing. Newly developed statistical approaches are being used to simultaneously consider thousands of genetic markers across the genome, promising to identify relevant genomic regions and to explain a higher proportion of the expected heritability of obesity-related phenotypes.75 Follow-up of genetic association studies with functional studies that could uncover the molecular and physiological mechanisms underlying obesity phenotypes will serve as a crucial future step in the development of preventive and therapeutic strategies.
As common variants have explained a small proportion of variation in BMI, there has been considerable debate as to whether rarer variants could explain much of the so-called missing heritability.71 As efforts proceed toward the identification of rare variants, various methods are being developed and tried with the goal of measuring and assessing rare variants, such as by collapsing them in some predefined regions of interest and evaluating their association with phenotypes.76 Rare variants could be measured via imputation using reference samples from the HapMap and the 1000 Genomes Projects, or by sequencing candidate regions, targeted exome regions or entire genomes. These methods are still in their infancy, and they will ultimately be guided by how they perform in explaining and predicting phenotypes.
More precise and accurate measurements are needed to qualify and quantify fat acquisition in epidemiological studies. Although the use of BMI as a predictor of disease risk has been of great importance for public health, it is clear that BMI does not equally and accurately represent health status of individuals across populations. It is well documented that although some Asian populations have lower prevalence of BMI-defined obesity, adiposity levels (particularly in the abdominal compartment) is greater than other groups with higher BMI. For effective epidemiological research, it is important to examine more specific and clinically relevant phenotypes, such as measures of body composition, waist circumference and metabolic parameters.
A major risk factor for adult obesity is excessive fat accumulation during childhood. Differences in body composition have been consistently documented among members of diverse populations beginning early in an individual's lifespan. Genetic background is strongly implicated in fat accumulation, and underlies variation in obesity susceptibility. Although control mechanisms for body composition appear to have a strong genetic etiology, body fat accumulation is also influenced by external stimuli.
Success in identifying the etiology of pediatric obesity among diverse populations will depend on the development of appropriate statistical methods, accurate phenotypic measurements and sophisticated experimental designs that take into account the developmental origins and trajectory of body composition, as well as social, cultural and economical influences.
It is evident that our understanding of population differences in pediatric obesity will depend on our ability to take into account the continuous and cumulative process of fat accumulation and acquisition in individuals. Precise and population-specific classification of body composition will provide clarity to research endeavors, directing scientific discovery toward the identification of possible underlying physiological mechanisms, and ultimately improved prevention strategies.
Fontaine KR, Redden DT, Wang C, Westfall AO, Allison DB . Years of life lost due to obesity. JAMA 2003; 289: 187–193.
Afghani A, Goran MI . Racial differences in the association of subcutaneous and visceral fat on bone mineral content in prepubertal children. Calcif Tissue Int 2006; 79: 383–388.
Gower BA, Nagy TR, Goran MI . Visceral fat, insulin sensitivity, and lipids in prepubertal children. Diabetes 1999; 48: 1515–1521.
Lee S, Kuk JL, Hannon TS, Arslanian SA . Race and gender differences in the relationships between anthropometrics and abdominal fat in youth. Obesity (Silver Spring) 2008; 16: 1066–1071.
Bhardwaj S, Misra A, Khurana L, Gulati S, Shah P, Vikram NK . Childhood obesity in Asian Indians: a burgeoning cause of insulin resistance, diabetes and sub-clinical inflammation. Asia Pac J Clin Nutr 2008; 17 (Suppl 1): 172–175.
Maes HH, Neale MC, Eaves LJ . Genetic and environmental factors in relative body weight and human adiposity. Behav Genet 1997; 27: 325–351.
Stunkard AJ, Sorensen TI, Hanis C, Teasdale TW, Chakraborty R, Schull WJ et al. An adoption study of human obesity. N Engl J Med 1986; 314: 193–198.
Segal NL, Feng R, McGuire SA, Allison DB, Miller S . Genetic and environmental contributions to body mass index: comparative analysis of monozygotic twins, dizygotic twins and same-age unrelated siblings. Int J Obes (Lond) 2009; 33: 37–41.
Haworth CM, Plomin R, Carnell S, Wardle J . Childhood obesity: genetic and environmental overlap with normal-range BMI. Obesity (Silver Spring) 2008; 16: 1585–1590.
Stunkard AJ, Harris JR, Pedersen NL, McClearn GE . The body-mass index of twins who have been reared apart. N Engl J Med 1990; 322: 1483–1487.
Wardle J, Carnell S, Haworth CM, Plomin R . Evidence for a strong genetic influence on childhood adiposity despite the force of the obesogenic environment. Am J Clin Nutr 2008; 87: 398–404.
Selby JV, Newman B, Quesenberry Jr CP, Fabsitz RR, King MC, Meaney FJ . Evidence of genetic influence on central body fat in middle-aged twins. Hum Biol 1989; 61: 179–194.
Katzmarzyk PT, Malina RM, Perusse L, Rice T, Province MA, Rao DC et al. Familial resemblance in fatness and fat distribution. Am J Hum Biol 2000; 12: 395–404.
Haworth CM, Carnell S, Meaburn EL, Davis OS, Plomin R, Wardle J . Increasing heritability of BMI and stronger associations with the FTO gene over childhood. Obesity (Silver Spring) 2008; 16: 2663–2668.
Clement K, Vaisse C, Lahlou N, Cabrol S, Pelloux V, Cassuto D et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature 1998; 392: 398–401.
Vaisse C, Clement K, Guy-Grand B, Froguel P . A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nat Genet 1998; 20: 113–114.
Cole SA, Butte NF, Voruganti VS, Cai G, Haack K, Kent Jr JW et al. Evidence that multiple genetic variants of MC4R play a functional role in the regulation of energy expenditure and appetite in Hispanic children. Am J Clin Nutr 2010; 91: 191–199.
Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, Walts B et al. The human obesity gene map: the 2005 update. Obesity (Silver Spring) 2006; 14: 529–644.
Saunders CL, Chiodini BD, Sham P, Lewis CM, Abkevich V, Adeyemo AA et al. Meta-analysis of genome-wide linkage studies in BMI and obesity. Obesity (Silver Spring) 2007; 15: 2263–2275.
Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM 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: 889–894.
Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet 2009; 41: 18–24.
Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 2009; 41: 25–34.
Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 2008; 40: 768–775.
Meyre D, Delplanque J, Chevre JC, Lecoeur C, Lobbens S, Gallina S et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet 2009; 41: 157–159.
Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010; 42: 937–948.
Rankinen T, Zuberi A, Chagnon YC, Weisnagel SJ, Argyropoulos G, Walts B et al. The human obesity gene map: the 2005 update. Obesity (Silver Spring) 2006; 14: 529–644.
Haworth CM, Butcher LM, Docherty SJ, Wardle J, Plomin R . No evidence for association between BMI and 10 candidate genes at ages 4, 7 and 10 in a large UK sample of twins. BMC Med Genet 2008; 9: 12.
Podolsky RH, Barbeau P, Kang HS, Zhu H, Treiber FA, Snieder H . Candidate genes and growth curves for adiposity in African- and European-American youth. Int J Obes (Lond) 2007; 31: 1491–1499.
Stutzmann F, Cauchi S, Durand E, Calvacanti-Proença C, Pigeyre M, Hartikainen AL et al. Common genetic variation near MC4R is associated with eating behaviour patterns in European populations. Int J Obes (Lond) 2009; 33: 373–378.
Shriver MD, Parra EJ, Dios S, Bonilla C, Norton H, Jovel C et al. Skin pigmentation, biogeographical ancestry and admixture mapping. Hum Genet 2003; 112: 387–399.
Fernandez JR, Shriver MD, Beasley TM, Rafla-Demetrious N, Parra E, Albu J et al. Association of African genetic admixture with resting metabolic rate and obesity among women. Obes Res 2003; 11: 904–911.
Tang H, Jorgenson E, Gadde M, Kardia SL, Rao DC, Zhu X et al. Racial admixture and its impact on BMI and blood pressure in African and Mexican Americans. Hum Genet 2006; 119: 624–633.
Fernandez J, Willig A, Jones A, Shriver MD, Beasley TM, Albu J et al. Genetic admixture is associated with visceral adipose tissue in Puerto Rican women. Int J Body Compos Res 2006; 4: 137–143.
Bonilla C, Shriver MD, Parra EJ, Jones A, Fernandez JR . Ancestral proportions and their association with skin pigmentation and bone mineral density in Puerto Rican women from New York city. Hum Genet 2004; 115: 57–68.
Klimentidis YC, Miller GF, Shriver MD . The relationship between European genetic admixture and body composition among Hispanics and Native Americans. Am J Hum Biol 2009; 21: 377–382.
Parra EJ, Hoggart CJ, Bonilla C, Dios S, Norris JM, Marshall JA et al. Relation of type 2 diabetes to individual admixture and candidate gene polymorphisms in the Hispanic American population of San Luis Valley, Colorado. J Med Genet 2004; 41: e116.
Sweeney C, Wolff RK, Byers T, Baumgartner KB, Giuliano AR, Herrick JS et al. Genetic admixture among Hispanics and candidate gene polymorphisms: potential for confounding in a breast cancer study? Cancer Epidemiol Biomarkers Prev 2007; 16: 142–150.
Ziv E, John EM, Choudhry S, Kho J, Lorizio W, Perez-Stable EJ et al. Genetic ancestry and risk factors for breast cancer among Latinas in the San Francisco Bay Area. Cancer Epidemiol Biomarkers Prev 2006; 15: 1878–1885.
Williams RC, Long JC, Hanson RL, Sievers ML, Knowler WC . Individual estimates of European genetic admixture associated with lower body-mass index, plasma glucose, and prevalence of type 2 diabetes in Pima Indians. Am J Hum Genet 2000; 66: 527–538.
Reiner AP, Carlson CS, Ziv E, Iribarren C, Jaquish CE, Nickerson DA . Genetic ancestry, population sub-structure, and cardiovascular disease-related traits among African-American participants in the CARDIA Study. Hum Genet 2007; 121: 565–575.
Gonzalez BE, Borrell LN, Choudhry S, Naqvi M, Tsai HJ, Rodriguez-Santana JR et al. Latino populations: a unique opportunity for the study of race, genetics, and social environment in epidemiological research. Am J Public Health 2005; 95: 2161–2168.
Halder I, Shriver MD . Measuring and using admixture to study the genetics of complex diseases. Hum Genomics 2003; 1: 52–62.
Paradies YC, Montoya MJ, Fullerton SM . Racialized genetics and the study of complex diseases: the thrifty genotype revisited. Perspect Biol Med 2007; 50: 203–227.
Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH . Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988-1994. Arch Pediatr Adolesc Med 2003; 157: 821–827.
Harris KM, Gordon-Larsen P, Chantala K, Udry JR . Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood. Arch Pediatr Adolesc Med 2006; 160: 74–81.
Ogden CL, Carroll MD, Flegal KM . High body mass index for age among US children and adolescents, 2003-2006. JAMA 2008; 299: 2401–2405.
Carnell S, Wardle J . Appetitive traits in children. New evidence for associations with weight and a common, obesity-associated genetic variant. Appetite 2009; 53: 260–263.
Llewellyn CH, van Jaarsveld CH, Boniface D, Carnell S, Wardle J . Eating rate is a heritable phenotype related to weight in children. Am J Clin Nutr 2008; 88: 1560–1566.
Smith GP . Ontogeny of ingestive behavior. Dev Psychobiol 2006; 48: 345–359.
Lau FC, Bagchi M, Sen C, Roy S, Bagchi D . Nutrigenomic analysis of diet-gene interactions on functional supplements for weight management. Curr Genomics 2008; 9: 239–251.
Benzinou M, Chevre JC, Ward KJ, Lecoeur C, Dina C, Lobbens S et al. Endocannabinoid receptor 1 gene variations increase risk for obesity and modulate body mass index in European populations. Hum Mol Genet 2008; 17: 1916–1921.
Wolfarth B, Bray MS, Hagberg JM, Pérusse L, Rauramaa R, Rivera MA et al. The human gene map for performance and health-related fitness phenotypes: the 2004 update. Med Sci Sports Exerc 2005; 37: 881–903.
Kral JG, Biron S, Simard S, Hould FS, Lebel S, Marceau S et al. Large maternal weight loss from obesity surgery prevents transmission of obesity to children who were followed for 2 to 18 years. Pediatrics 2006; 118: e1644–e1649.
Oken E, Gillman MW . Fetal origins of obesity. Obes Res 2003; 11: 496–506.
Heijmans BT, Tobi EW, Stein AD, Putter H, Blauw GJ, Susser ES et al. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci USA 2008; 105: 17046–17049.
Vimaleswaran KS, Li S, Zhao JH, Luan J, Bingham SA, Khaw KT et al. Physical activity attenuates the body mass index-increasing influence of genetic variation in the FTO gene. Am J Clin Nutr 2009; 90: 425–428.
Chang YC, Liu PH, Lee WJ, Chang TJ, Jiang YD, Li HY et al. Common variation in the fat mass and obesity-associated (FTO) gene confers risk of obesity and modulates BMI in the Chinese population. Diabetes 2008; 57: 2245–2252.
Villalobos-Comparan M, Teresa Flores-Dorantes M, Teresa Villarreal-Molina M, Rodríguez-Cruz M, García-Ulloa AC, Robles L et al. The FTO gene is associated with adulthood obesity in the Mexican population. Obesity (Silver Spring) 2008; 16: 2296–2301.
Al Attar SA, Pollex RL, Ban MR, Young TK, Bjerregaard P, Anand SS et al. Association between the FTO rs9939609 polymorphism and the metabolic syndrome in a non-Caucasian multi-ethnic sample. Cardiovasc Diabetol 2008; 7: 5.
Cha SW, Choi SM, Kim KS, Park BL, Kim JR, Kim JY et al. Replication of genetic effects of FTO polymorphisms on BMI in a Korean population. Obesity (Silver Spring) 2008; 16: 2187–2189.
Ng MC, Park KS, Oh B, Tam CH, Cho YM, Shin HD et al. Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes 2008; 57: 2226–2233.
Omori S, Tanaka Y, Takahashi A, Hirose H, Kashiwagi A, Kaku K et al. Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes 2008; 57: 791–795.
Tan JT, Dorajoo R, Seielstad M, Sim XL, Ong RT, Chia KS et al. FTO variants are associated with obesity in the Chinese and Malay populations in Singapore. Diabetes 2008; 57: 2851–2857.
Wing MR, Ziegler J, Langefeld CD, Ng MC, Haffner SM, Norris JM et al. Analysis of FTO gene variants with measures of obesity and glucose homeostasis in the IRAS Family Study. Hum Genet 2009; 125: 615–626.
Hennig BJ, Fulford AJ, Sirugo G, Rayco-Solon P, Hattersley AT, Frayling TM et al. FTO gene variation and measures of body mass in an African population. BMC Med Genet 2009; 10: 21.
Ohashi J, Naka I, Kimura R, Natsuhara K, Yamauchi T, Furusawa T et al. FTO polymorphisms in oceanic populations. J Hum Genet 2007; 52: 1031–1035.
Grant SF, Bradfield JP, Zhang H, Wang K, Kim CE, Annaiah K et al. Investigation of the locus near MC4R with childhood obesity in Americans of European and African ancestry. Obesity (Silver Spring) 2009; 17: 1461–1465.
Elks CE, Loos RJ, Sharp SJ, Langenberg C, Ring SM, Timpson NJ et al. Genetic markers of adult obesity risk are associated with greater early infancy weight gain and growth. PLoS Med 2010; 7: e1000284.
Zhao J, Bradfield JP, Li M, Wang K, Zhang H, Kim CE et al. The role of obesity-associated loci identified in genome-wide association studies in the determination of pediatric BMI. Obesity (Silver Spring) 2009; 17: 2254–2257.
Bogardus C . Missing heritability and GWAS utility. Obesity (Silver Spring) 2009; 17: 209–210.
Maher B . Personal genomes: the case of the missing heritability. Nature 2008; 456: 18–21.
Hofker M, Wijmenga C . A supersized list of obesity genes. Nat Genet 2009; 41: 139–140.
Ma S, Yang L, Romero R, Cui Y . Varying coefficient model for gene-environment interaction: a non-linear look. Bioinformatics 2011; 27: 2119–2126.
Mi X, Eskridge KM, George V, Wang D . Structural equation modeling of gene-environment interactions in coronary heart disease. Ann Hum Genet 2011; 75: 255–265.
de los CG, Gianola D, Allison DB . Predicting genetic predisposition in humans: the promise of whole-genome markers. Nat Rev Genet 2010; 11: 880–886.
Bansal V, Libiger O, Torkamani A, Schork NJ . Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 2010; 11: 773–785.
Walley AJ, Asher JE, Froguel P . The genetic contribution to non-syndromic human obesity. Nat Rev Genet 2009; 10: 431–442.
Hinney A, Nguyen TT, Scherag A, Friedel S, Brönner G, Müller TD et al. Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLoS ONE 2007; 2: e1361.
Scherag A, Dina C, Hinney A, Vatin V, Scherag S, Vogel CI et al. Two new loci for body-weight regulation identified in a joint analysis of genome-wide association studies for early-onset extreme obesity in French and german study groups. PLoS Genet 2010; 6: e1000916.
This work has been funded in part by NIH Grants R01-DK067426 and T32HL007457.
The authors declare no conflict of interest.
About this article
Cite this article
Fernandez, J., Klimentidis, Y., Dulin-Keita, A. et al. Genetic influences in childhood obesity: recent progress and recommendations for experimental designs. Int J Obes 36, 479–484 (2012). https://doi.org/10.1038/ijo.2011.236
- childhood obesity
- body composition
Behavior Genetics (2019)
Influence of Childhood and Adolescent Fat Development on Fat Mass Accrual During Emerging Adulthood: A 20-Year Longitudinal Study
Genetic Susceptibility for Childhood BMI has no Impact on Weight Loss Following Lifestyle Intervention in Danish Children
Leptin Receptor Gene Variant rs11804091 Is Associated with BMI and Insulin Resistance in Spanish Female Obese Children: A Case-Control Study
International Journal of Molecular Sciences (2017)
3-D imaging of islets in obesity: formation of the islet–duct complex and neurovascular remodeling in young hyperphagic mice
International Journal of Obesity (2016)