Introduction

The obesity phenotype has been increasing in the last decades and the causes for this complex disorder are thought to be related with an imbalance between energy intake and energy expenditure due to changes in lifestyles including exposure to an obesogenic environment.1 Furthermore, it is estimated that the heritable predisposition to obesity may range from 40 to 70%.2, 3, 4

In <7 years, genome-wide association studies (GWASs) have successfully identified >50 genetic loci, which were unequivocally associated with obesity-related traits.5 The first locus associated with obesity was the fat mass and obesity associated (FTO) gene by Frayling et al.,6 which is the most replicated gene across the world, both in adult and in children samples,7, 8, 9 including the Portuguese population.10 Subsequently, several other studies emerged associating single-nucleotide polymorphisms (SNPs) in several genes across the genome, most of them in adults,11, 12 and a few in children.13 Nevertheless, candidate and replication studies of obesity loci among different populations emerge as an important step to identify and clarify which variants are indeed associated with obesity. The frequency of obesity-susceptibility alleles varies between populations, and these allele distributions could be a consequence of population-specific obesogenic environments associated with specific demography and cultural histories. Replication studies are also relevant to determine which polymorphisms previously associated with obesity in adults are also linked in children, and, in a final instance, to better understand the complexity of obesity susceptibility.

In this study, a sample of Portuguese children was tested for the association of obesity and obesity-related quantitative traits with ten polymorphisms in nine candidate genes including, methionine sulfoxide reductase A (MSRA), transcription factor AP-2 beta (TFAP2B), melanocortin 4 receptor (MC4R), neurexin 3 (NRXN3), peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A), transmembrane protein 18 (TMEM18), homolog of S. cerevisiae Sec16 (SEC16B), homeobox B5 (HOXB5) and olfactomedin 4 (OLFM4).

Materials and methods

Study subjects

The original sample consisted of 1433 Portuguese children of European descent, aging between 6 and 12 years old, randomly selected from several public schools in the central region of Portugal.14 From this original sample, three body mass index (BMI) groups, using age- and sex-specific BMI cutoffs provided by the International Obesity Task Force,15 were attained in a total of 730 children, including: (1) 154 obese subjects (resulting from the BMI in adult’s cutpoints ⩾30 kg m−2); (2) 320 overweight subjects (resulting from the BMI in adult’s cutpoints between 25 and 29 kg m−2); and (3) 256 lean controls randomly selected from the initial group of 959 children with BMI <25 kg m−2.

The study protocol was approved by Direção-Geral de Inovação e de Desenvolvimento Curricular, the ethical Committee of the Portuguese Ministry of Education, and was conducted in accordance with the institutional and ethical guidelines of the University of Coimbra. Written informed consent was previously obtained from the children’s parents.

Anthropometric measurements

All children underwent anthropometric measurements of height, weight, waist and hip circumference using a standardized protocol. Body weight (kg) and height (cm) were taken with participants dressed in lightweight clothing without shoes to determine the BMI. Waist circumference (WC) (cm) was measured midway between the lowest rib and the iliac crest, to the nearest 0.1 cm after inhalation and exhalation. Hip circumference (cm) was measured at the point over the buttocks yielding the maximum circumference. The BMI was calculated by dividing weight (in kg) by height (in m) squared (kg m−2). Abdominal obesity was defined using the sex and age specific ⩾90th WC percentile.16

Selection and genotyping of polymorphisms

Ten SNPs identified from the literature significantly associated with obesity or obesity-related traits in children of European origin were selected: rs17782313 and rs12970134 near MC4R, prominent in the literature; rs10146997 in NRXN3, rs8192678 in PPARGC1A, rs7561317 near TMEM18 and rs10913469 in SEC16B, poorly studied; rs545854 in MSRA and rs987237 in TFAP2B, associated in adult’s populations11 but never replicated in children; and rs9299 in HOXB5 and rs9568856 in OLFM4, recently associated with childhood obesity13 but never replicated.

The genomic DNA was extracted from buccal cells using the PureLink® Pro 96 Genomic DNA Kit (Invitrogen Corporation, Carlsbad, CA, USA), according to the instructions of the manufacturer.

Samples were genotyped for all SNPs by allelic discrimination assays using TaqMan probes (Applied Biosystems, Foster City, CA, USA). All PCRs were done in a volume of 20 μl containing in 1 × SsoFast Probes Supermix (Bio-Rad, Hercules, CA, USA), 0.5 μl of specific TaqMan SNP Genotyping Assays (20 × ) (Applied Biosystems) and about 40 ng of genomic DNA, according to the manufacturer’s instructions. Thermal cycling conditions were 10 min at 95 °C, and 35 cycles each of 95 °C for 15 s and 60 °C for 1 min. The fluorescence was observed through a MiniOpticon real time PCR system (Bio-Rad). To assess genotyping reproducibility, 10% of random samples were selected and re-genotyped for all SNPs with 100% concordance.

Statistical analysis

The allelic and genotypic frequencies of all polymorphisms were estimated by direct counting. Hardy–Weinberg equilibrium probability values were achieved using an exact test.17 Logistic regression under an additive genetic model, allowing for analysis of a binary outcome (a case–control status), was used to test obesity and overweight phenotype polymorphism associations, adjusted for age and sex, by calculating odds ratios (ORs) with 95% of confidence intervals (CIs) and P-values. For MC4R rs17782313 and rs12970134 polymorphisms, linkage disequilibrium (r2) values and a case/control (normal vs obese) haplotype association were assessed. All these statistical analyses were done by using the set-based tests implemented on PLINK software v.1.07 (http://pngu.mgh.harvard.edu/purcell/plink/).18 For each obesity-related quantitative parameters (BMI, BMI Z-score, weight and WC), the non-parametric Kruskal−Wallis test was used to evaluate differences among the three genotypes in all the polymorphisms. Normality of the data was assessed using the Kolmogorov–Smirnov test. This statistical analysis was performed using the SPSS software (statistical package for the social sciences for Windows, version 18.0., SPSS inc., Chicago, IL, USA). A significant P-value was considered below 0.005 (0.05/10) by applying a Bonferroni correction for multiple testing, and a P-value between 0.005 and 0.05 has been considered as nominally significant.

QUANTO, v.1.1 power calculator (http://hydra.usc.edu/gxe/) was used to estimate the power of association as a function of the frequency of the effect allele assuming an additive model.19

Results

The anthropometric characteristics of the study subjects distributed by phenotype are shown in Table 1.

Table 1 General characteristics of the Portuguese children participants
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The genotyping success rate varied between 96.3 and 99.7%. The minor allele frequencies observed for all polymorphisms in the total sample were 15% for rs7561317-A (TMEM18), 16% for rs10913469-C (SEC16B), 36% for rs8192678-A (PPARGC1A), 18% for rs987237-G (TFAP2B), 16% for rs545854-C (MSRA), 12% for rs9568856-A (OLFM4), 18% for rs10146997-G (NRXN3), 32% for rs9299-C (HOXB5), 21% for 17782313-C (MC4R) and 22% for rs12970134-A (MC4R) (Table 2). These frequencies are in accordance with those found in the HapMap CEU population (http://www.ensembl.org/). Genotype distributions among the control group were in agreement with Hardy–Weinberg equilibrium for all the studied polymorphisms (P>0.05).

Table 2 Minor allele frequencies and Hardy–Weinberg equilibrium test of the 10 studied polymorphisms in the sampled Portuguese children and their associations with obesity-related quantitative traits
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We analyzed the obesity-related quantitative traits BMI, BMI Z-score, weight and WC among different genotypes for each of the studied polymorphisms. The mean values for the anthropometric traits among the three different genotypes are detailed in Table 2. The MC4R rs12970134 major G-allele was found nominally associated with increase in BMI (P=0.035), BMI Z-score (P=0.043) and WC (P=0.020), and borderline associated with weight (P=0.053). Near nominal associations were also found for the PPARGC1A rs8192678 minor A-allele with weight (P=0.061) and WC (P=0.093), and for the MSRA rs545854 major G-allele with BMI (P=0.055) and BMI Z-score (P=0.056). After correction for multiple testing no statistically significant associations were found.

Genotype distributions of obesity-related parameters for the MC4R rs12970134 polymorphism, which showed the highest statistical significant associations with the obesity-related traits, are detailed in Figure 1. Homozygotes for the minor A-allele have the lowest value distributions for all the analyzed quantitative parameters (BMI, BMI Z-score, WC and weight).

Figure 1
figure 1

Box plot demonstrating the distribution of untransformed body mass index (BMI), BMI Z-score, weight and waist circumference within each genotype group of MC4R rs12970134 polymorphism. Each box represents the anthropometric traits values between the 25th and 75th quartiles, and the dark line within the boxes indicates the median values. A full color version of this figure is available at the Journal of Human Genetics journal online.

Full size image

Logistic regression analysis, in an additive model, revealed that MC4R rs12970134 major G-allele was nominally associated with the obesity risk (OR=1.477; P=0.029) and that TFAP2B rs987237 major A-allele and TMEM18 rs7561317 major G-allele are near nominally associated with the risk of obesity (OR=1.455; P=0.056; and OR=1.416; P=0.092, respectively) (Table 3). Only the PPARGC1A rs8192678 polymorphism was found nominally associated with the overweight phenotype (OR=1.297; P=0.041) (Table 3).

Table 3 Allele frequencies of the 10 studied polymorphisms in the Portuguese children, and their associations with risk of obesity among phenotypic groups
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We further investigated the difference in the genotype distribution between cases and controls for abdominal obesity by logistic regression analysis. In the total of 730 children, 112 revealed abdominal obesity (using sex and age specific ⩾90th WC percentile). From the 10 polymorphisms studied, only TMEM18 rs7561317 (major G-allele) showed nominal association with increased abdominal obesity (OR=1.589; 95% CI, 1.02–2.50; P=0.042).

The haplotype analysis for the two MC4R rs17782313 and rs12970134 polymorphisms, located at a distance of 33.5 Kb and in high linkage disequilibrium (r2=0.74), revealed that the common TG haplotype was associated with the risk of obesity (P=0.043) (with frequency of 81.2% in the obese group vs 75.0% in the control group).

Discussion

Understanding the genetic basis of obesity in children could be used as a first step to develop possible preventive measures. Recent GWASs have identified many (>50) different genetic variants conferring susceptibility to obesity.20 However, the modest association with the obesity risk observed for most variants implies that replication studies in different populations are required to detect and confirm such signals of association, eliminating false positives that may arise by chance or systematic bias.

Focused on 10 polymorphisms across the genome (located in or near the MSRA, TFAP2B, NRXN3, PPARGC1A, TMEM18, SEC16B, HOXB5, OLFM4 and MC4R genes), previously associated by GWAS with obesity-related outcomes in populations of European origin, we conducted a genetic association study to investigate their role in the susceptibility of obesity in a sample of Portuguese children. Using obesity-related quantitative traits to assess whether the genotypes predict the trait value, we identified the MC4R rs12970134 loci nominally associated with several obesity-related traits, and two loci (PPARGC1A and MSRA) near nominally associated with at least one anthropometric parameter (Table 2). In addition, using logistic regression analyses, the MC4R rs12970134 polymorphism was found nominally associated (P=0.029) with the risk of obesity and the TFAP2B rs987237 polymorphism showed borderline significant association (P=0.056) with the obese phenotype. Thus, this study highlights these four polymorphisms as potential genetic markers of the obesity phenotype in this Portuguese children sample.

The MC4R gene is known to be the most common cause of monogenic obesity in extreme childhood obesity,5 but also its flanking genomic region has been the third strongest implicated in polygenic obesity. The expression of MC4R is restricted to the hypothalamus involved in food intake regulation.21 Until now, several variations of this gene were established with BMI and/or WC, showing an independent role in body variation. Polymorphisms rs12970134 and rs17782313, located 154 kb and 188 kb, respectively, downstream of the MC4R gene, were found associated with obesity and obesity-related traits in several studies in Asian and European populations, both in children and in adults.22, 23, 24, 25, 26, 27 In the present study, nominal significant associations were found between MC4R rs12970134 and BMI, BMI Z-score and WC (P<0.05), as also with the risk of obesity (P=0.029). However, our findings do not replicate previous reports that show the minor A-allele associated with increased risk in BMI and WC in children of European22, 24, 25, 26, 28 or Asian27 descent. Instead, in the present study, Portuguese children showed the minor rs12970134 A-allele significantly associated with lower BMI (P=0.035), BMI Z-score (P=0.043), WC (P=0.020), and also with a lower risk for the obesity phenotype (OR=0.677; P=0.029). For this polymorphism, the minor A-allele frequency was 18.5% in the obese group vs 25.1% in the control group. For the second MC4R polymorphism, rs17782313, previous studies showed the C-allele associated with childhood obesity (increasing BMI in ±0.22 kg m−2),23 however, in the Portuguese children, no statistical association was found with any obesity-related trait. The haplotype analysis showed the MC4R rs17782313/rs12970134 TG haplotype associated with the risk of obesity, confirming the potential role of rs12970134 major G-allele in the etiology of obesity in our sample of Portuguese children.

A genome-wide association scan meta-analysis conducted by Lindgren et al.29 found that the G-allele of the MSRA rs545854 polymorphism was associated with WC (P=8.9 × 10−9) in adults. Bille et al.11 also found significant association between this polymorphism and WC (OR=1.08; P=0.02). In our study, nominal borderline significant associations with BMI (P=0.055) and BMI Z-score (P=0.056) were observed. The biological function between MSRA locus and adiposity remains unclear.29

PPARGC1A is a transcriptional co-activator that has been implicated in the regulation of genes involved in energy metabolism.30 The Gly482Ser missense mutation (rs8192678), predicted by a G-to-A transition at position +1564 in exon 8 of the PPARGC1A gene, was found associated with obesity indices in middle-aged women of a cross-sectional Austrian population30 and with abdominal obesity in Chinese adults.31 In our study, near nominal associations were obtained with weight (P=0.061) and WC (P=0.093) for the 482Ser variant.

The molecular function of the TMEM18 gene product is to bind DNA to suppress transcription; it could be differently expressed in the hypothalamus and is possibly involved in the regulation of feeding.32 Polymorphism rs7561317, located about 22 kb downstream of TMEM18, is the second best associated locus with BMI after FTO gene.5 A GWAS conducted by Thorleifsson et al.33 found that the rs7561317 GG genotype increases BMI in ±0.70 kg m−2 and is associated with obesity. In the present study, significant associations were not found between any obesity-related trait and the TMEM18 rs7561317 polymorphism; however, our findings are directionally consistent with previous studies conducted in children and adolescents, as the rs7561317 major G-allele was found marginally associated with the risk of obesity (OR=0.706; P=0.092).

The TFAP2B gene is suggested to be involved in global adipocyte response to positive energy balance.29 The minor G-allele of rs987237 polymorphism was previously found associated with increased WC (P=1.9 × 10−11) and BMI (P=7.0 × 10−12) in a meta-analysis of 16 GWASs within adults of European ancestry;29 and also in children it was found associated with increased WC (P=3.5 × 10−2) and BMI (P=0.06).28 Our data in Portuguese children do not replicate previous findings while we observed a nominal borderline significant association of the major rs987237 A-allele with the risk of obesity (OR=0.692; P=0.056).

In the present study, the major MC4R rs12970134-G and TFAP2B rs987237 A-alleles showed, respectively, nominal and near-nominal significant associations with the risk of obesity, but the direction of effect was reverse when comparing with that in the original reports. Despite opposite direction on the effect of a risk allele is highly unlikely, this was observed in several studies including between different populations34 but also inside a same population.35 For the TFAP2B rs987237 polymorphism, the original significant association was found in adults,29 and to the authors knowledge, this study with Portuguese participants is the first replication report involving children. Therefore, the opposite direction of association for rs987237-A risk allele could be due to differences between children and adults regarding natural physiological differences.

All genes studied in this work are considered as candidates for the risk of developing obesity. Most of them are involved in homeostasis and energy metabolism, nevertheless the casual effect of these polymorphisms in the pathogenesis of obesity remains unclear. In the present study with Portuguese children, only the MC4R rs12970134 polymorphism showed nominal significant association (P<0.05) with obesity and most of obesity-related traits. In a previous study for the FTO gene using the same cohort of individuals,10 significant associations were also found between the rs9939609 minor A-allele and increased risk for several anthropometric traits, including weight (P=0.019), BMI (P=0.018), BMI Z-score (P=0.011) and WC (P=0.016), in concordance with reports worldwide. Thus, both loci FTO and MC4R appear to have a key role in the obesity phenotype in Portuguese children. Most of the analyzed polymorphisms in this present study showed no nominal effects in obesity-related traits, but this difference with the previous findings may be due partly to the sample size, which may have been insufficient to replicate the original findings. The estimated power of association observed ranges from 6 to 72%, but at least for MC4R rs12970134 polymorphism, the variant most associated with obesity in our sample, the obtained power (72%) is close to values (⩾80%) commonly considered as sufficient to detect genetic variant interaction effects.

In conclusion, among the 10 loci reported in this study, polymorphisms in or near MC4R, PPARGC1A, MSRA and TFAP2B genes could be assumed to have a role in the risk of obesity in this population sample of Portuguese children. While we could not replicate the original findings concerning the direction effect of the MC4R rs12970134 and TFAP2B rs987237 risk alleles our results deserve confirmatory studies in other populations. Moreover, our data may show that the polymorphisms provided here could have a modest role in the obesity etiology in children, at least when comparing with the FTO gene, suggesting the existence of other unknown loci involved in the obesity susceptibility. Our replication study could also have public health significance while genes playing an essential role in energy homeostasis, such as PPARGC1A or TFAP2B, suggested by our data as obesity-related genes, may be used as targets for obesity treatment. Further investigations in the near future regarding genetic associations and functional roles of these polymorphisms should be helpful to confirm its implication in the development of obesity and if they could be attractive targets for therapeutic agents.