Logo of plosonePLoS OneView this ArticleSubmit to PLoSGet E-mail AlertsContact UsPublic Library of Science (PLoS)
PLoS One. 2012; 7(12): e51954.
Published online Dec 14, 2012. doi:  10.1371/journal.pone.0051954
PMCID: PMC3522587

Novel Genetic Loci Identified for the Pathophysiology of Childhood Obesity in the Hispanic Population

Dana C. Crawford, Editor

Abstract

Genetic variants responsible for susceptibility to obesity and its comorbidities among Hispanic children have not been identified. The VIVA LA FAMILIA Study was designed to genetically map childhood obesity and associated biological processes in the Hispanic population. A genome-wide association study (GWAS) entailed genotyping 1.1 million single nucleotide polymorphisms (SNPs) using the Illumina Infinium technology in 815 children. Measured genotype analysis was performed between genetic markers and obesity-related traits i.e., anthropometry, body composition, growth, metabolites, hormones, inflammation, diet, energy expenditure, substrate utilization and physical activity. Identified genome-wide significant loci: 1) corroborated genes implicated in other studies (MTNR1B, ZNF259/APOA5, XPA/FOXE1 (TTF-2), DARC, CCR3, ABO); 2) localized novel genes in plausible biological pathways (PCSK2, ARHGAP11A, CHRNA3); and 3) revealed novel genes with unknown function in obesity pathogenesis (MATK, COL4A1). Salient findings include a nonsynonymous SNP (rs1056513) in INADL (p = 1.2E-07) for weight; an intronic variant in MTNR1B associated with fasting glucose (p = 3.7E-08); variants in the APOA5-ZNF259 region associated with triglycerides (p = 2.5-4.8E-08); an intronic variant in PCSK2 associated with total antioxidants (p = 7.6E-08); a block of 23 SNPs in XPA/FOXE1 (TTF-2) associated with serum TSH (p = 5.5E-08 to 1.0E-09); a nonsynonymous SNP (p = 1.3E-21), an intronic SNP (p = 3.6E-13) in DARC identified for MCP-1; an intronic variant in ARHGAP11A associated with sleep duration (p = 5.0E-08); and, after adjusting for body weight, variants in MATK for total energy expenditure (p = 2.7E-08) and in CHRNA3 for sleeping energy expenditure (p = 6.0E-08). Unprecedented phenotyping and high-density SNP genotyping enabled localization of novel genetic loci associated with the pathophysiology of childhood obesity.

Introduction

Obesity is a complex disease influenced by genetic and environmental factors and their interactions. The current surge in childhood obesity in the U.S. is attributable to an interaction between a genetic predisposition toward efficient energy storage and a permissive environment of readily available food and sedentary behaviors [1]. Genetic architecture of common polygenic childhood obesity remains largely unknown. In genetic studies, the phenotypic description of the obese child usually has been limited to body mass index (BMI). BMI represents a composite trait of fat free mass (FFM) and fat mass (FM) and thus loci influencing BMI may differ from more direct measures of adiposity. In addition, markers of biological processes underlying the development of obesity such as dietary intake, energy expenditure and nutrient partitioning may be more effectual in identifying causal genetic variants [2]. In epidemiology studies, childhood obesity has been shown to be genetically correlated with glucose intolerance, hypertension, dyslipidemia, insulin resistance, chronic inflammation, and risk for fatty liver disease [3], [4]. Identification of genes underlying these distinct patterns of association also may unravel important biological pathways involved in the pathophysiology of childhood obesity.

Genome-wide association studies (GWAS) have the potential to localize genetic loci contributing to obesity down to a few 100 kb [5]. In fact, a recent meta-analysis of the adult GIANT Consortium established 32 susceptibility BMI loci [6], several of which were confirmed in French and German children with extreme obesity [7] and European adults with early-onset obesity [8]. Two novel loci near OLFM4 and within HOXB5 were recently reported based on a meta-analysis of 14 pediatric studies of BMI [9]. These pediatric GWAS were confined to BMI and cohorts of European ancestry.

Here, we present findings from a GWAS designed to identify genetic variants influencing childhood obesity and its comorbidities in the Hispanic population. We have published evidence of heritability, pleiotropy amongst traits, and chromosomal regions implicated in obesity among Hispanic children in our VIVA LA FAMILIA Study [4], [10][15] In-depth phenotyping was performed to characterize the children, including anthropometry, body composition, growth, metabolites, hormones, inflammation, diet, energy expenditure and substrate utilization and physical activity. Our high-density SNP genotyping and phenotypes representing not only adiposity, but also biological processes associated with the development and consequences of childhood obesity enabled localization of novel genetic loci associated with the pathophysiology of childhood obesity.

Materials and Methods

The VIVA LA FAMILIA Study was designed to identify genetic variants influencing pediatric obesity and its comorbidities. Family recruitment and phenotyping were conducted in 2000–2005 in Houston, TX. All enrolled children and parents gave written informed consent or assent. The protocol was approved by the Institutional Review Boards for Human Subject Research for Baylor College of Medicine and Affiliated Hospitals and for Texas Biomedical Research Institute.

The VIVA LA FAMILIA study design and methodology have been described in detail elsewhere4. GWAS was performed on 815 children from 263 Hispanic families. The number of families by sibships was: 8 (one child), 40 (two children), 155 (three children), 48 (four children), 6 (five children), 3 (six children), 2 (seven children) and 1 (eight children). Each family was ascertained on an obese proband, defined as a BMI>95th percentile, between the ages 4–19 y. Once identified, the obese proband and all siblings, 4 to 19 y of age, and their parents were invited to the Children’s Nutrition Research Center for a tour and full explanation of the study prior to consenting. The cross-sectional, longitudinal study design consisted of baseline measurements, with a one-year follow-up to track children’s growth and body compositional changes. In-depth baseline phenotyping included vital signs, anthropometry and body composition, diet and physical fitness, 24-h calorimetry, eating behavior, physical activity, fasting blood sampling for DNA and other biochemistries.

Briefly, blood pressure, heart rate and temperature were taken using an automated monitor. Anthropometric measurements were performed using standardized techniques according to Lohman [16]. Body composition was determined by dual-energy x-ray absorptiometry. Repeated measures after one year were used to compute growth velocities and changes in FM, FFM and energy storage [17]. Methods used to measure fasting blood and 24-h urinary biochemistries are described elsewhere [14], [18], [19]. A multiple-pass 24-h dietary recall was recorded on two occasions using Nutrition Data System (NDS) [20]. Eating behavior was assessed with a dinner meal and eating in the absence of hunger [21]. Room respiration calorimetry was used to make 24-h measurements of energy expenditure and substrate oxidation [13]. Maximum oxygen consumption (VO2max) and heart rate maximum (HRmax) were measured on a treadmill [22]. Actiwatch accelerometers were used to measure frequency, duration and intensity of physical activity [23].

Genotyping

The Illumina HumanOmni1-Quad v1.0 BeadChips were used to genotype 1.1 million single nucleotide polymorphisms (SNPs) in 815 children enrolled in the VIVA LA FAMILIA Study. Genotype calls were obtained after scanning on the Illumina BeadStation 500GX and analysis using the GenomeStudio software. Our genotyping error rate (based on duplicates) was 2 per 100,000 genotypes. Illumina has included sample-dependent and -independent controls on their BeadChips to test for accuracy of the procedure. The average call rate for all SNPs per individual sample was 97%.

SNP genotypes were checked for Mendelian consistency using the program SimWalk2 [24]. The estimates of the allele frequencies and their standard errors were obtained using SOLAR [25]. Specific SNPs were removed from analysis if they had call rates <95% (n = 6,596), were uncommon alleles in less than 5 participants (26,537), deviated from Hardy-Weinberg equilibrium (n = 0), or were monoallelic (n = 56,448). The number of SNPs that passed quality control and were included in the GWA analysis was 899,892.

Genome-wide Association Analysis

Measured genotype analysis (MGA) was performed using the SOLAR program [25]. All phenotypes were transformed by inverse normalization to meet assumptions of normality. We obtained residuals using linear regression models adjusted for age, sex, their interaction and higher order terms. Because energy expenditure is strongly influenced by body weight across the age range of 4 to 19 years of age, total energy expenditure and sleeping energy expenditure were additionally adjusted for body weight. Also, observed energy intakes consumed in a snack and dinner were adjusted for total energy expenditure or estimated energy requirement, again to compensate for the wide range of ages in our cohort.

Each SNP genotype was converted in SOLAR to a covariate measure equal to 0, 1, or 2 copies of the minor allele (or, for missing genotypes, the weighted covariate based on imputation). These covariates were included in the variance-components mixed models for measured genotype analyses [26] versus null models that incorporated the random effect of kinship and fixed effects such as age, sex, their interaction and higher order terms. For the initial GWA screen, we tested each SNP covariate independently as a 1 degree of freedom likelihood ratio test. The p-value threshold for genome-wide significance (alpha = 0.05) was set at 1.01×10−7. The p-value threshold for genome-wide significance was computed for our family-based cohort that takes into account pedigree structure. The effective number of SNPs given linkage disequilibrium (LD) was calculated by the method of Moskvina and Schmidt [27] as implemented in SOLAR. LD was computed in SOLAR using all available information (all genotyped SNPs on all individuals). The average ratio of SNP effective number/actual number obtained from analysis of 1,989 non-overlapping bins of SNPs was used to calculate the genome-wide effective number of tests and thus the significance threshold for genome-wide association. We performed quantitative transmission disequilibrium test (implemented in SOLAR) to test for population stratification.

Results

GWAS was performed on 815 children from 263 Hispanic families. Mean ± SD (range) was 25.2±7.5 (13.4 to 61.9) for the children’s BMI, 85.6±20.8 (4 to100) for BMI percentile and 1.52±1.01 (−3.0 to 4.5) for BMI z-score. Measured genotype analysis examined 129 obesity-related traits including BMI and adiposity as well as biological processes associated with the pathophysiology of childhood obesity; a description of the phenotypes is provided in Table S1. In our GWAS, population stratification was not significant and therefore did not confound our associations. A complete listing of all suggestive (p<1.0E-06) and genome-wide significant (p<1.0E-07) genetic variants and their associated traits are presented by chromosomal position in Table S2.

Anthropometry and Body Composition

BMI status, body composition and the growth process were assessed from repeated measurements of body weight, height, FFM and FM at baseline and after one-year (Table 1). A nonsynonymous SNP (rs1056513; G1178S (NP_005790.2)) in INADL on chromosome 1 attained near genome-wide significance (p = 1.2E-07) for weight, and BMI, FFM, FM, trunk FM, and hip circumference (p = 8.3E-06 to 1.6E-07). SNP rs1056513 is common (MAF = 0.50) and accounted for 3% of the variance in body weight and body composition in this cohort. Weight z-score change was significantly associated with an intronic variant in COL4A1 on chromosome 13 (p = 4.7E-08) (Supporting Information Figure S1). Linear growth (height change) was associated with a variant in the 5′UTR region of TSEN34 on chromosome 19 (p = 4.5E-08).

Table 1
Measured genotype analysis for anthropometric and body composition traits.

Endometabolic Traits

Genome-wide significant variants were identified for several endometabolic traits (Table 2). An intronic variant in MTNR1B on chromosome 11 was strongly associated with fasting glucose (p = 3.7E-08). Intronic and 3′UTR variants in the APOA5-ZNF259 region on chromosome 11 were associated with triglycerides (p = 2.5-4.8E-08). An intronic variant in PCSK2 on chromosome 20 was associated with total antioxidants (p = 7.6E-08). Variants in the flanking 3′UTR regions of RNASE1 on chromosome 14 and ASS1P11 on chromosome 7 were associated with 24-h urinary nitrogen excretion and creatinine excretion, respectively (p = 8.2-8.4E-08). An intronic SNP in GCH1 on chromosome 14 was associated with 24-h urinary dopamine: creatinine ratio (p = 6.3E-08). A nonsynonymous SNP (rs3733402; S143T (NP_000883.2)) in KLKB1 on chromosome 4 was identified for serum free IGF-1. An intronic SNP in MPRIP on chromosome 17 was associated with serum IGFBP-3 (p = 7.2E-08). A block of twenty-three SNPs in the flanking 5′UTR region of XPA/FOXE1 (TTF-2) on chromosome 9 were highly associated with serum TSH (p = 5.5E-08 to 1.0E-09) and found to be in strong linkage disequilibrium with one another (R = 0.91−1.00).

Table 2
Measured genotype analysis for endometabolic traits.

Inflammation Markers

Genome-wide significant variants were identified for inflammation markers (Table 3). Highly significant associations for a nonsynonymous SNP (rs12075; G42D (NP_002027.2) (p = 1.3E-21) and an intronic SNP (p = 3.6E-13) in DARC on chromosome 1 were identified for MCP-1. Coding variants in GREB1 on chromosome 2 (p = 6.5E-08) and DFNB31 on chromosome 9 (p = 2.0E-08) were also identified for MCP-1. A variants in the 3′UTR for CCR3 was highly associated with MCP1, as well as an intronic SNP in RASGEF1A (p = 4.6-9.6E-08). A variant in the intronic region of ABO was strongly associated with IL-6 (p = 2.0E-08).

Table 3
Measured genotype analysis for inflammation markers.

Diet, Energy Expenditure, Substrate Utilization and Physical Activity

Genome-wide significant variants were associated with energy intake, energy expenditure and substrate utilization, and physical activity (Table 4). An intronic variant in TMEM229B on chromosome 14 was associated with ad libitum energy intake at dinner (p = 5.1E-08). An intronic variant in ARHGAP11A on chromosome 15 was associated with sleep duration (p = 5.0E-08). A SNP in the intronic region of C21orf34 was detected for respiratory quotient (RQ) during sleep (p = 5.3E-08). A variant was identified for accelerometer-measured light activity in the 5′UTR region of RPL7P3 on chromosome 9 (p = 7.5E-08) and sedentary-light activity in 3′UTR of CTCFL on chromosome 20 (p = 3.6E-08). In addition, adjusting for body weight, highly significant variants were identified for total energy expenditure (p = 2.7E-08) for MATK on chromosome 19 and sleeping energy expenditure (p = 6.0E-08) for CHRNA3 on chromosome 15.

Table 4
Measured genotype analysis for diet, energy expenditure, substrate utilization and physical activity.

Discussion

Extensive phenotyping and high-density SNP genotyping enabled localization of novel genetic loci associated with the pathophysiology of obesity in Hispanic children. Our unprecedented phenotypes represent not only adiposity, but also biological processes underlying the development of childhood obesity. The number of genome-wide significant genetic variants detected substantiates our analytical strategy and statistical power to identify variants.

Genome-wide significant and suggestive genetic variants were associated with anthropometric indices, body composition and growth rate. The common nonsynonymous SNP rs1056513 in INADL accounted for 3% of the variance in body weight and body composition, and is highly conserved across mammalian species. INADL encodes a PDZ domain-containing protein thought to play a role in tight junctions and adipocyte differentiation [28]. Weight z-score change observed over one-year was associated with an intronic variant in COL4A1, which encodes a basement membrane collagen. Height change was associated with a 5′UTR variant inTSEN34, which is involved in tRNA splicing, a fundamental process required for cell growth and division [29].

Fasting serum glucose was associated with an intronic variant (MAF = 0.20; effect size 4.8%) in MTNR1B which encodes one of the melatonin receptors expressed in the retina and brain. Corroborating our findings, this variant (rs10830963) has been strongly associated with fasting glucose levels in adults [30], [31] and children [32][35]. Melatonin, the ligand to MTNR1B, has an inhibitory effect on insulin secretion resulting in elevated fasting glucose.

Fasting serum triglycerides were associated with variants in the intron and 3′UTRs within the APOA5-ZNF259 region. APOA5 is an important determinant of circulating triglyceride levels [36]. SNPs detected in our GWAS have been associated with triglycerides in other populations [37]. In the Kosrae population, triglyceride levels were associated with seven SNPs near APOC3/A5 [38]. Variants in the APOA5-ZNF259 region (including rs3741298) were associated with HDL-C and ApoA-1 response to therapy with statins and fenofibric acid in patients with dyslipidemia [39].

Association with total antioxidants was shown for a variant in PCSK2, a proprotein convertase that is involved in proteolytic processing of neuropeptide and hormone precursors. PCSK2 is highly expressed in the islets of Langerhans, where it plays a role in the conversion of proinsulin to insulin.

A LD block of 23 SNPs in the XPA/FOXE1 (TTF-2) region was highly associated with fasting serum TSH (MAF = 0.25-0.29, effect size = 2.9-3.8%). The association is likely attributed to FOXE1 rather than XPA which is involved in DNA excision repair [40] and the skin disease xeroderma pigmentosum [41]. FOXE1 or thyroid transcription factor 2 (TTF-2) belongs to the ‘forkhead’ gene family and is involved in promoting the migration process or in repressing differentiation of the thyroid follicular cells until migration has occurred [42] and has been associated with thyroid cancer [43][46]. In the Kosrae population, plasma TSH levels were strongly associated with 10 SNPs in a region encompassing TTF-2 on chromosome 9 [38]. In a cohort of Caucasian adults, genetic variation in FOXE1(TTF-2) showed significant effects on free T4 levels and borderline effects on serum TSH [47]. Three of the SNPs (rs925488, rs1877431, rs1588635) detected in the VIVA cohort was also reported by Lowe et.al (38); there was no overlap with Medici et al (43). Also, three of the SNPs (rs2805809, rs2668804, rs2808693) reported by Lowe et al. (38) was in high linkage disequilibrium (1.00) with rs2805771, rs2808699, rs7875482 detected in the VIVA cohort. This is a region of high LD, located in the 5′UTR-region of the gene which has been shown to influence transcriptional regulation of FOXE1(TTF-2). The functional variant is likely to be situated at this locus, but its exact localization remains to be elucidated in future studies, involving in-depth resequencing.

A unifying role of inflammation in chronic diseases including cardiovascular disease, diabetes, hypertension and obesity is emerging. In the VIVA GWAS, variants in six genes were associated with proinflammatory markers. Our findings support a major role of Duffy antigen receptor for chemokines (DARC) in the regulation of the circulating levels of the cysteine-cysteine (CC) chemokine, MCP-1 (effect size = ~10%). In circulation, MCP-1 is bound to erythrocyte DARC that acts as a chemokine receptor/reservoir of proinflammatory cytokines [48]. Our strongest association for MCP-1 was with a nonsynonymous, highly conservedSNP in DARC (rs12075; MAF = 0.44) which replicated results from the Framingham Heart Study GWAS in Caucasian adults [49]. MCP-1 was also associated with SNPs in GREB1, DFNB31, RASGEF1A, and CCR3. Huber et al. found increased expression of CCR3 in subcutaneous and visceral adipose tissue in obese patients compared to lean controls [50]. Studies by Schnabel et al [49] and Naitza et al. [51] found suggestive associations of serum MCP-1 with CCR2 which is also a MCP-1 receptor and is in the same region of chromosome 3 as CCR3, and referred to as the CCR2/CCR3 cytokine receptor gene cluster.

Fasting serum IL-6 levels were associated with variants in the ABO gene that determines blood group. ABO blood group has been found to be associated with a number of biomarkers such as von Willebrand factor levels, Factor VIII levels, thrombomodulin, TNF-α and ICAM-1 [52], [53], and in our case IL-6. The mechanism by which the A and B alleles affect these biomarkers is uncertain. In Caucasians with and without type 1 diabetes, a variant rs579459 near the ABO blood group gene accounted for 19% of the variance in E-selectin levels [52]; in our GWAS, this same variant was associated with IL-6 levels (p = 1.7E-07; Table S2).

Total energy expenditure, adjusted for body weight, was significantly associated with rs12104221 in MATK which encodes a protein-tyrosine kinase involved in signal transduction pathways [54]. Sleeping energy expenditure, adjusted for body weight, was associated with rs8040868 in CHRNA3 (cholinergic receptor, neuronal nicotinic, alpha polypeptide 3), a member of a superfamily of ligand-gated ion channels that mediate fast signal transmission at synapses. After binding to acetylcholine, the receptor responds by opening ion-conducting channels across the plasma membrane, suggesting a plausible role of the coding variant (rs8040868) in energy metabolism [55]. Acetylcholine receptors activate proopiomelanocortin neurons that in turn activate melanocortin-4 receptors that are involved in the regulation of energy intake and expenditure [56], [57].

Sleep duration was associated with an intronic SNP in ARHGAP11A (rho GTPase activating protein 11A) that encodes a 1,023-amino acid protein that has a rhoGAP domain and tyrosine phosphorylation site. Evidence is emerging that obesity affects sleep, and that sleep patterns and disorders may have an effect on weight [58]. Although the mechanism is unclear, sleep disturbances are characteristic of Prader Willi Syndrome, caused by a deletion in 15q11-q13 that encompasses ARHGAP11A [59].

Sedentary-light physical activity was associated with a variant in CTCFL, an 11-zinc-finger factor involved in gene regulation. CTCFL forms methylation-sensitive insulators that regulate X-chromosome inactivation which may play a role in epigenetic regulation [60].

Variants in the 11 genes known to cause extreme early-onset obesity also may contribute to milder forms of obesity [61], [62]. None of the genotyped variants in genes for monogenic obesity reached genome-wide significance in our GWAS, although several variants in CRHR1, CRHR2, MCHR1, MC3R, MC4R and POMC were nominally associated. Similarly, variants in or nearby the susceptibility genes for obesity did not attain genome-wide significance but several - CHST8, KCTD15, MTCH2, SFRS10, SH2B1 and TMEM18 - were nominally associated with obesity-related traits, consistent with GWAS in children of European-American [63] or European ancestry [9]. The lack of genome-wide significant findings for monogenic causes of obesity or susceptibility genes may be a function of our sample size and statistical power, or the presence of rare variants in the Hispanic population not represented in the Illumina platform.

The VIVA LA FAMILIA Study is unique in its consideration of a pediatric Hispanic population. We believe that our extensive phenotyping and genotyping enabled localization of novel genetic loci associated with obesity in Hispanic children, despite our relatively small sample size. Our phenotypes represent not only adiposity, but also biological processes underlying the development and consequences of childhood obesity. We applied a standard and stringent approach to our measured genotype analysis, but do agree replication is desirable. We believe we have identified genes that warrant further investigation.

In conclusion, unprecedented in-depth phenotyping and high-density SNP genotyping enabled the localization of novel genetic loci associated with the pathophysiology of obesity in Hispanic children. Identified genome-wide significant loci: 1) corroborated genes implicated in other studies (MTNR1B, ZNF259/APOA5, XPA/FOXE1 (TTF-2), DARC, CCR3, ABO); 2) localized novel genes in plausible biological pathways (PCSK2, ARHGAP11A, CHRNA3); and 3) revealed novel genes with unknown function in obesity pathogenesis (MATK, COL4A1). As with other GWAS, the variants identified are likely not the actual causal variants but rather markers for genomic regions or loci in which the causal variants lie. Characterization of the underlying functional genetic variants contributing to this serious public health problem in Hispanic children will involve additional study.

Supporting Information

Figure S1

GWAS Manhattan plots are displayed for three phenotypes: total energy expenditure, adjusted for body weight, measured by 24-h room calorimetry; fasting serum thyroid stimulating hormone; and 1-y change in weight z-score. The genomic coordinates are shown along the X-axis, and the negative logarithm of the association p-value for each SNP on the Y-axis.

(TIFF)

Table S1

Description of the phenotypes used in the genome-wide association study.

(DOCX)

Table S2

Suggestive and genome-wide significant genetic variants identified by measured genotype analysis.

(DOCX)

Acknowledgments

We thank all the families who participated in the VIVA LA FAMILIA Study. The authors wish to acknowledge the contributions of Grace-Ellen Meixner, B.S. and Maria del Pilar Villegas, B.S., M.S. for technical assistance while working on this project at the Texas Biomedical Research Institute.

This work is a publication of the U.S. Department of Agriculture (USDA)/Agricultural Research Service (ARS) Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas. The contents of this publication do not necessarily reflect the views or policies of the USDA, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

Funding Statement

This work was supported by grants from the National Institutes of Health (NIH) (DK080457), and the USDA/ARS (Cooperative Agreement 6250-51000-053). Work performed at the Texas Biomedical Research Institute in San Antonio, Texas was conducted in facilities constructed with support from the Research Facilities Improvement Program of the National Center for Research Resources, NIH (C06 RR013556, C06 RR017515). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1. O'Rahilly S, Farooqi IS (2008) Human obesity as a heritable disorder of the central control of energy balance. Int J Obes (Lond) 32 Suppl 7S55–S61 [PubMed]
2. Muller MJ, Bosy-Westphal A, Krawczak M (2010) Genetic studies of common types of obesity: a critique of the current use of phenotypes. Obes Rev 11: 612–618 [PubMed]
3. Barlow SE, Dietz WH (1998) Obesity evaluation and treatment: expert committee recommendations. Pediatrics 102: e29. [PubMed]
4. Butte NF, Cai G, Cole SA, Comuzzie AG (2006) VIVA LA FAMILIA Study: genetic and environmental contributions to childhood obesity and its comorbidities in the Hispanic population. Am J Clin Nutr 84: 646–654 [PubMed]
5. Hakonarson H, Grant SF (2011) GWAS and its impact on elucidating the etiology of diabetes. Diabetes Metab Res Rev 10.1002/dmrr.1221. [Epub ahead of print]. [PubMed]
6. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937–948 [PMC free article] [PubMed]
7. Scherag A, Dina C, Hinney A, Vatin V, Scherag S, et al. (2010) 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 6: e1000916. [PMC free article] [PubMed]
8. Meyre D, Delplanque J, Chevre JC, Lecoeur C, Lobbens S, et al. (2009) Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet 41: 157–159 [PubMed]
9. Bradfield JP, Taal HR, Timpson NJ, Scherag A, Lecoeur C, et al. . (2012) A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet 10.1038/ng.2247. [Epub ahead of print]. [PMC free article] [PubMed]
10. Cai G, Cole SA, Freeland-Graves JH, MacCluer JW, Blangero J, et al. (2004) Genome-wide scans reveal quantitative trait loci on 8p and 13q related to insulin action and glucose metabolism: the San Antonio Family Heart Study. Diabetes 53: 1369–1374 [PubMed]
11. Cai G, Cole SA, Butte NF, Bacino CA, Diego V, et al. (2006) A quantitative trait locus on chromosome 18q for physical activity and dietary intake in Hispanic children. Obesity 14: 1596–1604 [PubMed]
12. Cai G, Cole SA, Butte NF, Voruganti VS, Comuzzie AG (2007) A quantitative trait locus (QTL) on chromosome 13q affects fasting glucose levels in Hispanic children. J Clin Endocrinol Metab 92: 4893–4896 [PubMed]
13. Cai G, Cole SA, Butte NF, Voruganti VS, Comuzzie AG (2008) Genome-wide scan revealed genetic loci for energy metabolism in Hispanic children and adolescents. Int J Obes (Lond) 32: 579–585 [PubMed]
14. Cai G, Cole SA, Butte NF, Smith CW, Mehta NR, et al. (2008) A genetic contribution to circulating cytokines and obesity in children. Cytokine 44: 242–247 [PMC free article] [PubMed]
15. Voruganti VS, Goring HH, Diego V, Cai G, Mehta N, et al. (2007) Genome-wide scan for plasma ghrelin detects linkage on chromosome 1p36 in Hispanic children: results from the Viva la Familia study. Pediatr Res 62: 445–450 [PubMed]
16. Lohman TG, Roche AF, Martorell R (1988) Anthropometric Standardization Reference Manual. Champaign: Human Kinetics.
17. Butte NF, Christiansen E, Sorensen TI (2007) Energy imbalance underlying the development of childhood obesity. Obesity 15: 3056–3066 [PubMed]
18. Butte NF, Comuzzie AG, Cole SA, Mehta NR, Cai G, et al. (2005) Quantitative genetic analysis of the metabolic syndrome in Hispanic children. Pediatr Res 58: 1243–1248 [PubMed]
19. Butte NF, Cai G, Cole SA, Wilson TA, Fisher JO, et al. (2007) Metabolic and behavioral predictors of weight gain in Hispanic children: the VIVA LA FAMILIA Study. Am J Clin Nutr 85: 1478–1485 [PubMed]
20. Johnson RK, Driscoll P, Goran MI (1996) Comparison of multiple-pass 24-hour recall estimates of energy intake with total energy expenditure determined by the doubly labeled water method in young children. J Am Diet Assn 96: 1140–1144 [PubMed]
21. Fisher JO, Cai G, Jaramillo S, Cole SA, Comuzzie AG, et al. (2007) Heritability of hyperphagic eating behavior and appetite-related hormones among Hispanic children. Obesity 15: 1484–1495 [PubMed]
22. Butte NF, Puyau MR, Adolph AL, Vohra FA, Zakeri I (2007) Physical activity in nonoverweight and overweight Hispanic children and adolescents. Med Sci Sports Exerc 39: 1257–1266 [PubMed]
23. Puyau MR, Adolph AL, Vohra FA, Zakeri I, Butte NF (2004) Prediction of activity energy expenditure using accelerometers in children. Med Sci Sports Exerc 36: 1625–1631 [PubMed]
24. Sobel E, Lange K (1996) Descent graphs in pedigree analysis: applications to haplotyping, location scores, and marker-sharing statistics. Am J Hum Genet 58: 1323–1337 [PMC free article] [PubMed]
25. Almasy L, Blangero J (1998) Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62: 1198–1211 [PMC free article] [PubMed]
26. Boerwinkle E, Chakraborty R, Sing CF (1986) The use of measured genotype information in the analysis of quantitative phenotypes in man. I. Models and analytical methods. Ann Hum Genet 50: 181–194 [PubMed]
27. Moskvina V, Schmidt KM (2008) On multiple-testing correction in genome-wide association studies. Genet Epidemiol 32: 567–573 [PubMed]
28. Zhong J, Krawczyk SA, Chaerkady R, Huang H, Goel R, et al. (2010) Temporal profiling of the secretome during adipogenesis in humans. J Proteome Res 9: 5228–5238 [PMC free article] [PubMed]
29. Wende H, Volz A, Ziegler A (2000) Extensive gene duplications and a large inversion characterize the human leukocyte receptor cluster. Immunogenetics 51: 703–713 [PubMed]
30. Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, et al. (2009) Variants in MTNR1B influence fasting glucose levels. Nat Genet 41: 77–81 [PMC free article] [PubMed]
31. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, et al. (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42: 105–116 [PMC free article] [PubMed]
32. Kelliny C, Ekelund U, Andersen LB, Brage S, Loos RJ, et al. (2009) Common genetic determinants of glucose homeostasis in healthy children: the European Youth Heart Study. Diabetes 58: 2939–2945 [PMC free article] [PubMed]
33. Bouatia-Naji N, Bonnefond A, Froguel P (2009) Inputs from the genetics of fasting glucose: lessons for diabetes. Med Sci 25: 897–902 [PubMed]
34. Reinehr T, Scherag A, Wang HJ, Roth CL, Kleber M, et al. (2011) Relationship between MTNR1B (melatonin receptor 1B gene) polymorphism rs10830963 and glucose levels in overweight children and adolescents. Pediatr Diabetes 12: 435–441 [PubMed]
35. Holzapfel C, Siegrist M, Rank M, Langhof H, Grallert H, et al. (2011) Association of a MTNR1B gene variant with fasting glucose and HOMA-B in children and adolescents with high BMI-SDS. Eur J Endocrinol 164: 205–212 [PubMed]
36. Pennacchio LA, Olivier M, Hubacek JA, Cohen JC, Cox DR, et al. (2001) An apolipoprotein influencing triglycerides in humans and mice revealed by comparative sequencing. Science 294: 169–173 [PubMed]
37. Sarwar N, Sandhu MS, Ricketts SL, Butterworth AS, Di AE, et al. (2010) Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies. Lancet 375: 1634–1639 [PMC free article] [PubMed]
38. Lowe JK, Maller JB, Pe'er I, Neale BM, Salit J, et al. (2009) Genome-wide association studies in an isolated founder population from the Pacific Island of Kosrae. PLoS Genet 5: e1000365. [PMC free article] [PubMed]
39. Brautbar A, Covarrubias D, Belmont J, Lara-Garduno F, Virani SS, et al. (2011) Variants in the APOA5 gene region and the response to combination therapy with statins and fenofibric acid in a randomized clinical trial of individuals with mixed dyslipidemia. Atherosclerosis 219: 737–742 [PubMed]
40. Tanaka K, Miura N, Satokata I, Miyamoto I, Yoshida MC, et al. (1990) Analysis of a human DNA excision repair gene involved in group A xeroderma pigmentosum and containing a zinc-finger domain. Nature 348: 73–76 [PubMed]
41. States JC, McDuffie ER, Myrand SP, McDowell M, Cleaver JE (1998) Distribution of mutations in the human xeroderma pigmentosum group A gene and their relationships to the functional regions of the DNA damage recognition protein. Hum Mutat 12: 103–113 [PubMed]
42. Manley NR, Capecchi MR (1998) Hox group 3 paralogs regulate the development and migration of the thymus, thyroid, and parathyroid glands. Dev Biol 195: 1–15 [PubMed]
43. Eriksson N, Tung JY, Kiefer AK, Hinds DA, Francke U, et al. (2012) Novel associations for hypothyroidism include known autoimmune risk loci. PLoS One 7: e34442. [PMC free article] [PubMed]
44. Denny JC, Crawford DC, Ritchie MD, Bielinski SJ, Basford MA, et al. (2011) Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. Am J Hum Genet 89: 529–542 [PMC free article] [PubMed]
45. Landa I, Ruiz-Llorente S, Montero-Conde C, Inglada-Perez L, Schiavi F, et al. (2009) The variant rs1867277 in FOXE1 gene confers thyroid cancer susceptibility through the recruitment of USF1/USF2 transcription factors. PLoS Genet 5: e1000637. [PMC free article] [PubMed]
46. Gudmundsson J, Sulem P, Gudbjartsson DF, Jonasson JG, Sigurdsson A, et al. (2009) Common variants on 9q22.33 and 14q13.3 predispose to thyroid cancer in European populations. Nat Genet 41: 460–464 [PMC free article] [PubMed]
47. Medici M, van der Deure WM, Verbiest M, Vermeulen SH, Hansen PS, et al. (2011) A large-scale association analysis of 68 thyroid hormone pathway genes with serum TSH and FT4 levels. Eur J Endocrinol 164: 781–788 [PubMed]
48. Rull A, Camps J, onso-Villaverde C, Joven J (2010) Insulin resistance, inflammation, and obesity: role of monocyte chemoattractant protein-1 (or CCL2) in the regulation of metabolism. Mediators Inflamm 2010: 326580. Epub 2010 Sep 23. [PMC free article] [PubMed]
49. Schnabel RB, Baumert J, Barbalic M, Dupuis J, Ellinor PT, et al. (2010) Duffy antigen receptor for chemokines (Darc) polymorphism regulates circulating concentrations of monocyte chemoattractant protein-1 and other inflammatory mediators. Blood 115: 5289–5299 [PMC free article] [PubMed]
50. Huber J, Kiefer FW, Zeyda M, Ludvik B, Silberhumer GR, et al. (2008) CC chemokine and CC chemokine receptor profiles in visceral and subcutaneous adipose tissue are altered in human obesity. J Clin Endocrinol Metab 93: 3215–3221 [PubMed]
51. Naitza S, Porcu E, Steri M, Taub DD, Mulas A, et al. (2012) A genome-wide association scan on the levels of markers of inflammation in Sardinians reveals associations that underpin its complex regulation. PLoS Genet 8: e1002480. [PMC free article] [PubMed]
52. Paterson AD, Lopes-Virella MF, Waggott D, Boright AP, Hosseini SM, et al. (2009) Genome-wide association identifies the ABO blood group as a major locus associated with serum levels of soluble E-selectin. Arterioscler Thromb Vasc Biol 29: 1958–1967 [PMC free article] [PubMed]
53. Pare G, Ridker PM, Rose L, Barbalic M, Dupuis J, et al. (2011) Genome-wide association analysis of soluble ICAM-1 concentration reveals novel associations at the NFKBIK, PNPLA3, RELA, and SH2B3 loci. PLoS Genet 7: e1001374. [PMC free article] [PubMed]
54. Bennett BD, Cowley S, Jiang S, London R, Deng B, et al. (1994) Identification and characterization of a novel tyrosine kinase from megakaryocytes. J Biol Chem 269: 1068–1074 [PubMed]
55. Woolf NJ, Butcher LL (1986) Cholinergic systems in the rat brain: III. Projections from the pontomesencephalic tegmentum to the thalamus, tectum, basal ganglia, and basal forebrain. Brain Res Bull 16: 603–637 [PubMed]
56. Mineur YS, Abizaid A, Rao Y, Salas R, DiLeone RJ, et al. (2011) Nicotine decreases food intake through activation of POMC neurons. Science 332: 1330–1332 [PMC free article] [PubMed]
57. Barsh GS, Schwartz MW (2002) Genetic approaches to studying energy balance: perception and integration. Nat Rev Genet 3: 589–600 [PubMed]
58. Kelly-Pieper K, Lamm C, Fennoy I (2011) Sleep and obesity in children: a clinical perspective. Minerva Pediatr 63: 473–481 [PubMed]
59. Torrado M, Araoz V, Baialardo E, Abraldes K, Mazza C, et al. (2007) Clinical-etiologic correlation in children with Prader-Willi syndrome (PWS): an interdisciplinary study. Am J Med Genet A 143: 460–468 [PubMed]
60. Klenova EM, Morse HC III, Ohlsson R, Lobanenkov VV (2002) The novel BORIS+CTCF gene family is uniquely involved in the epigenetics of normal biology and cancer. Semin Cancer Biol 12: 399–414 [PubMed]
61. Cole SA, Butte NF, Voruganti VS, Cai G, Haack K, et al. (2010) 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 91: 191–199 [PMC free article] [PubMed]
62. Cotsapas C, Speliotes EK, Hatoum IJ, Greenawalt DM, Dobrin R, et al. (2009) Common body mass index-associated variants confer risk of extreme obesity. Hum Mol Genet 18: 3502–3507 [PMC free article] [PubMed]
63. Zhao J, Grant SF (2011) Genetics of childhood obesity. J Obes 2011: 845148. Epub 2011 May 26. [PMC free article] [PubMed]

Articles from PLoS ONE are provided here courtesy of Public Library of Science
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...