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Genetics

Genetic Susceptibility to Obesity and Related Traits in Childhood and Adolescence

Influence of Loci Identified by Genome-Wide Association Studies

  1. Marcel den Hoed1,
  2. Ulf Ekelund1,2,
  3. Søren Brage1,
  4. Anders Grontved3,
  5. Jing Hua Zhao1,
  6. Stephen J. Sharp1,
  7. Ken K. Ong1,
  8. Nicholas J. Wareham1 and
  9. Ruth J.F. Loos1
  1. 1Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge, U.K.;
  2. 2School of Health and Medical Sciences, Örebro University, Örebro, Sweden;
  3. 3Institute of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.
  1. Corresponding author: Ruth J.F. Loos, ruth.loos{at}mrc-epid.cam.ac.uk.
Diabetes 2010 Nov; 59(11): 2980-2988. https://doi.org/10.2337/db10-0370
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Abstract

OBJECTIVE Large-scale genome-wide association (GWA) studies have thus far identified 16 loci incontrovertibly associated with obesity-related traits in adults. We examined associations of variants in these loci with anthropometric traits in children and adolescents.

RESEARCH DESIGN AND METHODS Seventeen variants representing 16 obesity susceptibility loci were genotyped in 1,252 children (mean ± SD age 9.7 ± 0.4 years) and 790 adolescents (15.5 ± 0.5 years) from the European Youth Heart Study (EYHS). We tested for association of individual variants and a genetic predisposition score (GPS-17), calculated by summing the number of effect alleles, with anthropometric traits. For 13 variants, summary statistics for associations with BMI were meta-analyzed with previously reported data (Ntotal = 13,071 children and adolescents).

RESULTS In EYHS, 15 variants showed associations or trends with anthropometric traits that were directionally consistent with earlier reports in adults. The meta-analysis showed directionally consistent associations with BMI for all 13 variants, of which 9 were significant (0.033–0.098 SD/allele; P < 0.05). The near-TMEM18 variant had the strongest effect (0.098 SD/allele P = 8.5 × 10−11). Effect sizes for BMI tended to be more pronounced in children and adolescents than reported earlier in adults for variants in or near SEC16B, TMEM18, and KCTD15, (0.028–0.035 SD/allele higher) and less pronounced for rs925946 in BDNF (0.028 SD/allele lower). Each additional effect allele in the GPS-17 was associated with an increase of 0.034 SD in BMI (P = 3.6 × 10−5), 0.039 SD, in sum of skinfolds (P = 1.7 × 10−7), and 0.022 SD in waist circumference (P = 1.7 × 10−4), which is comparable with reported results in adults (0.039 SD/allele for BMI and 0.033 SD/allele for waist circumference).

CONCLUSIONS Most obesity susceptibility loci identified by GWA studies in adults are already associated with anthropometric traits in children/adolescents. Whereas the association of some variants may differ with age, the cumulative effect size is similar.

Over the past three decades, the prevalence of obesity has reached epidemic proportions not only in adults, but in children and adolescents alike (1,2). A high BMI during childhood and adolescence often persists into adulthood (3–5) and has been independently associated with cardiovascular risk factors, coronary heart disease events, and all-cause mortality (2,6–9). Family and twin studies have estimated that 40–70% of the variance in obesity-related traits is due to genetic factors (10,11). Longitudinal twin studies have shown that the genetic contribution to BMI increases from childhood to adolescence (12–14), and cross-sectional twin studies suggest that the heritability of BMI is higher in adolescence than during adulthood (15,16).

Six genome-wide association (GWA) studies in adults of white European descent have thus far identified 16 obesity susceptibility loci; 12 loci were consistently associated with BMI (17–22), and 4 loci were identified in GWA studies for waist circumference. Only variants in the FTO and near-MC4R loci have as of yet convincingly been associated with obesity-related traits in children and adolescents (12,18,20,23–27). Two studies have examined the effect of variants in GWA-derived loci other than FTO and MC4R in children and adolescents (20,28). However, both studies focused only on BMI and neither study examined the association of all 16 obesity susceptibility loci or their cumulative effect. Examining the association of these obesity susceptibility loci with measures of adiposity in childhood and adolescence may provide insight into their impact on obesity risk early in life. Furthermore, it has been suggested that physical activity modifies the association of genetic variation with general adiposity in adults (29–31). Thus far, this has not been demonstrated in children.

In this study, we examined whether obesity susceptibility loci identified by GWA studies in adults are associated with anthropometric traits and risk of obesity in children and adolescents from the European Youth Heart Study (EYHS). To increase statistical power and to compare effect sizes in children/adolescents and adults, we additionally meta-analyzed our findings with those reported by others (20,28). Furthermore, we examined the cumulative effect of variants in the 16 loci on anthropometric traits in EYHS and tested whether the association between genetic predisposition and anthropometric traits is modified by physical activity.

RESEARCH DESIGN AND METHODS

Study population and anthropometry.

The EYHS is a school-based, mixed longitudinal study of pre- and early pubertal children and adolescents aged 9.7 ± 0.4 and 15.5 ± 0.5 years, respectively (32). Participants were randomly selected via application of a two-stage sampling strategy in four countries (Denmark, Estonia, Norway, and Portugal). The present study includes 1,252 children and 790 adolescents from Denmark and Estonia (944 boys and 1,098 girls) for whom data on anthropometric traits were available at baseline (Table 1). DNA was not available for the other two EYHS centers.

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TABLE 1

Descriptive characteristics of children and adolescents of the EYHS stratified by sex

Body mass and height were measured using standard procedures, with participants dressed in light clothing and barefoot (33). The BMI was standardized according to BMI reference charts derived by Cole's LMS method (34). Thickness of skinfolds was measured at four locations (triceps brachi, biceps brachi, sub-scapula and supra-iliaca in millimeters) (35) and was combined to obtain the sum of skinfolds. Waist circumference was measured using a metal anthropometric tape midway between the lower rib margin and the iliac crest at the end of a gentle expiration. Sexual maturity was assessed using the five-stage Tanner scale for breast development in girls and pubic hair in boys (Table 1) (36).

Overall physical activity and the fraction of time spent on moderate and vigorous intensity physical activity (>2,000 cpm [ref. 37]) were measured in daily life during 2 weekdays and 2 weekend days with a validated MTI Actigraph accelerometer (Manufacturing Technology, Fort Walton Beach, FL) (38). For the present study, physical activity data were available for 870 children and 413 adolescents (Table 1).

The study was approved by the local scientific committees and was performed in accordance with the Declaration of Helsinki. All parents gave written informed consent for their child to participate, and all children and adolescents gave verbal consent.

Genotyping.

Seventeen SNPs in the 16 obesity susceptibility loci (17–22) identified by recent GWA studies were genotyped: rs2815752, rs10913469, rs2605100, rs6548238, rs7647305, rs10938397, rs987237, rs545854, rs1488830, rs925946, rs10838738, rs7138803, rs10146997, rs8055138, rs1121980, rs17782313, and rs11084753 (NEGR, SEC16B, LYPLAL1, TMEM18, ETV5, GNPDA2, TFAP2B, MSRA, BDNF, MTCH2, BCDIN3D, NRXN3, SH2B1, FTO, MC4R, and KCTD15 loci, respectively) (supplementary Table 1, available in the online appendix [http://diabetes.diabetesjournals.org/cgi/content/full/db10-0370/DC1]). Two variants in the BDNF locus were included (rs1488830 [BDNF SNP 1]) and rs925946 [BDNF SNP 2]; linkage disequilibrium r2 = 0.10) because these variants were previously independently associated with BMI (19).

Markers rs7647305, rs10938397, and rs1121980 were genotyped using Custom TaqMan SNP Genotyping assays according to the manufacturer's protocol (Applied Biosystems, Warrington, U.K.). The remaining markers were genotyped using a Sequenom iPLEX platform (Sequenom, San Diego, CA) as previously described (39).

All variants passed quality-control criteria with a call rate >95% and a blind duplicate concordance rate of 100%. The distributions of all variants were in Hardy-Weinberg equilibrium, as determined by a χ2 test with 1 d.f. (supplementary Table 1).

Statistical analyses.

Before testing for associations, all traits were transformed to normal distributions, with a mean of zero and an SD of 1 in all participants combined using inverse normal transformation. Effect sizes can be interpreted as changes in Z scores, which allows comparison across traits and with effect sizes previously reported in adults.

The association of each SNP with BMI, sum of skinfolds, and waist circumference was tested using linear regression assuming an additive effect. Associations with height were examined to evaluate whether SNPs were specifically associated with adiposity or with body size in general. The effect alleles were those that increased BMI in adults in the original GWA studies (17–22). Logistic regression was used to test the association of each SNP with the risk of obesity and overweight versus not overweight. Assessing the risk by comparing with nonobese instead of not overweight did not change the results. Obesity (N = 105) and overweight (N = 309) were defined using age- and sex-specific thresholds of BMI (≥95th and ≥85th percentiles, respectively [ref. 34]).

A genetic predisposition score (GPS) was calculated by summing the number of effect alleles carried by each individual (GPS-17). The GPS-17 was normally distributed, with the majority of individuals (73.8%) carrying 13–18 of the 34 possible effect alleles. Only 2.9% of the individuals carried 10 or fewer effect alleles, and 3.2% carried ≥21 (Fig. 1). We did not weight the effect alleles by their effect size, which has been suggested to have only a limited effect (40), to allow comparison with the nonweighted score reported for adults of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk study. The latter represents the largest population-based study (N = 20,431) thus far in which the association of variants in all 12 loci identified in GWA studies for BMI have been examined in a consistent manner and the only study that has additionally reported their cumulative association with BMI and waist circumference (39). Given its large sample size, effect sizes are likely stable and representative. An alternative GPS (GPS-12) was calculated in EYHS, which contained 12 variants representing the 12 loci discovered in GWA studies for BMI (Fig. 3) (39). Associations of GPS-17 and GPS-12 with continuous anthropometric traits and the risk of obesity and overweight were examined using general linear model and logistic regression analyses, respectively. Differences in effect size of the GPS-12 between children/adolescents and adults were examined by estimating the amount of heterogeneity between the two groups.

FIG. 1.
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FIG. 1.

Distribution of the GPS-17 and cumulative effects of the effect alleles from the 17 obesity susceptibility variants on inverse normally transformed BMI, sum of skinfolds, and waist circumference.

All analyses were adjusted for sex, age, age-group, country, and maturity, including BMI, which was still significantly associated with age and sex after application of the LMS method. Associations with sum of skinfolds and waist circumference were additionally adjusted for height. For the GPS-17, interactions with sex, age-group, country, habitual physical activity, and the time spent on moderate and vigorous intensity physical activity were tested via inclusion of product terms in the model.

To increase the statistical power to detect an association, a meta-analysis using inverse variance weighted–fixed-effect models was performed for single-SNP associations with BMI. Summary statistics from the current study were meta-analyzed with those from two recently reported studies examining a cohort of children of the Children's Hospital of Philadelphia (CHP) (N = 6,078) (28) and the Avon Longitudinal Study of Parents and Children (ALSPAC) (N = 4,951) (20) for SNPs in loci identified in GWA studies for BMI. The meta-analysis included a maximum sample of 13,071 children and adolescents for variants in or near NEGR1, TMEM18, GNPDA2, MTCH2, SH2B1, FTO, MC4R, and KCTD15, for which data were available from EYHS, ALSPAC, and CHP, and 8,120 children and adolescents for variants in or near SEC16B, ETV5, BDNF and BCIN3D, for which data were available from EYHS and CHP only. Five SNPs were identical to those studied in the EYHS. For eight SNPs, the CHP and/or ALSPAC studies had reported results on proxy SNPs that were either in complete linkage disequilibrium (r2 = 1; three SNPs) or high linkage disequilibrium (r2 = 0.8–1.0; four SNPs). For the KCTD15 locus, the CHP study reported on a variant of which linkage disequilibrium with the variant chosen in EYHS and ALSPAC was somewhat lower but still acceptable (r2 = 0.64) (supplementary Table 2). Effect sizes observed in the meta-analysis in children and adolescents were compared with those reported recently in adults from the EPIC-Norfolk study, a large population-based sample (N = 20,431) in which the associations of variants in the 12 obesity susceptibility loci identified in GWA studies for BMI with BMI and waist circumference were recently reported (39).

Statistical analyses were performed using SAS, version 9.1, for Windows (SAS Institute, Cary, NC). STATA software was used for the meta-analysis and to compare effect sizes of children/adolescents and adults (metan), as well as to determine the power to detect such a difference in effect size (sampsi) (version 10; StataCorp, College Station, TX). A two-sided P value ≤0.05 was considered statistically significant.

Quanto, v1.1.1 (http//hydra.usc.edu/gxe), was used to estimate the smallest effect size detectable with a power of 80% and an α-level of 5% (supplementary Fig. 1A), as well as to estimate the power to detect association as a function of the frequency of the effect allele—both assuming an additive model (supplementary Fig. 1B).

RESULTS

The association of 15 of the 17 obesity susceptibility SNPs with BMI, sum of skinfolds, and waist circumference was directionally consistent with results reported in the original GWA studies, 7 of which reached statistical significance (Table 2). Variants in/near TMEM18 and SEC16B showed the largest effect size for all three continuous traits (Table 2 and supplementary Fig. 2). For both loci, effect sizes were twofold larger for associations with BMI and sum of skinfolds than for waist circumference. For rs2605100 near LYPLAL1, effect sizes were fivefold larger for associations with sum of skinfolds and waist circumference than for association with BMI (Table 2 and supplementary Fig. 2). Associations with sum of skinfolds and waist circumference remained significant after additionally adjusting for BMI (effect size 0.047 and 0.050 SD/allele; P value = 0.0035 and 0.0095, respectively). Associations with obesity risk were most pronounced for variants near TMEM18 and ETV5 (supplementary Table 3 and supplementary Fig. 3). Variants in/near TMEM18 and BCIN3D were significantly associated with greater height (Table 2).

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TABLE 2

Associations of the individual obesity susceptibility SNPs and the GPS-12 and GPS-17 with anthropometric traits in children and adolescents of the EYHS (n = 2,042)

Based on effect allele frequencies (supplementary Table 1) and effect sizes for BMI reported earlier in children/adolescents (Fig. 2), the power to detect single SNP associations with anthropometric traits in EYHS alone ranged from <10% for variants in/near ETV5, BDNF (SNP 2), MTCH2, and SH2B1 to 80% for the SNP in FTO (supplementary Fig. 1). This may explain why few associations reached statistical significance. The meta-analysis in up to 13,071 children and adolescents, however, showed significant associations with BMI for 9 of 13 variants (Fig. 2). There was little heterogeneity in effect size across the three studies except for the near-NEGR1, SEC16B, FTO, and near-MC4R variants (Pheterogeneity = 0.13, 0.045, 0.006, and 0.069, respectively). The most pronounced effect on BMI was observed for the near-TMEM18 variant (0.098 SD/allele [95% CI 0.07–0.13]), followed by variants in or near FTO (0.076 [0.05–0.10]), SEC16B (0.068 [0.03–0.10]), and MC4R (0.067 [0.04–0.09]) (Fig. 2). Variants in/near ETV5, BDNF (SNP 2), MTCH2, and SH2B1 were not significantly associated with BMI in the meta-analysis.

FIG. 2.
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FIG. 2.

Meta-analysis for summary statistics of the association between variants in the obesity susceptibility loci with BMI in the EYHS, CHP (28), and ALSPAC (20). I2 and P values for heterogeneity between cohorts are provided. For associations within cohorts, effect sizes (B) and 95% CIs are shown; for the meta-analysis, P values for effect sizes are additionally provided.

The power to detect a difference in effect size between children/adolescents and adults was low (5–47%), and differences in effect size ranging from 0.045 to 0.069 SD/allele between age-groups could be detected with 80% power (supplementary Table 1). Whereas no differences reached statistical significance (Fig. 3), the associations tended to be more pronounced in children/adolescents than in adults for variants near KCTD15, SEC16B, and TMEM18 (Pheterogeneity = 0.086, 0.11, and 0.16, respectively). The effect size of the BDNF variant (rs925946), on the other hand, was twice as large in children and adolescents as that in adults (Pheterogeneity = 0.14) (Fig. 3).

FIG. 3.
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FIG. 3.

Association of variants in the obesity susceptibility loci with BMI after meta-analysis in a maximal sample of 13,071 (NEGR1, TMEM18, GNPDA2, MTCH2, SH2B1, FTO, MC4R, and KCTD15) or 8,120 (SEC16B, ETV5, BDNF, and BCIN3D) children and adolescents compared with 20,431 adults from the EPIC-Norfolk cohort. Effect sizes (B) and 95% CIs are shown; I2 and P values for heterogeneity between age-groups are additionally provided.

The GPS-17, which examines the cumulative effects of the 17 SNPs in EYHS, was significantly associated with BMI (effect size 0.034 SD/allele [95% CI 0.018–0.050]; P = 3.6 × 10−5), sum of skinfolds (0.039 [0.024 - 0.053]; P = 1.7 × 10−7), and waist circumference (0.022 [0.011–0.034]; P = 1.7 × 10−4) (Table 2), explaining 0.8, 1.1, and 0.4% of their variance, respectively. The 3.2% (N = 58) of individuals who carried 21 or more effect alleles had a BMI that was 0.51 SD, a sum of skinfolds that was 0.28 SD, and a waist circumference that was 0.35 SD larger than the 2.9% (N = 53) of individuals who carried 10 or fewer effect alleles (Fig. 1). The associations of the GPS-12, which includes only the 12 variants of the GPS used in the EPIC-Norfolk study for adults (N = 20,431), were slightly more pronounced; 0.044 SD/allele for BMI (95% CI 0.025–0.063), 0.043 for sum of skinfolds (0.026–0.061), and 0.025 for waist circumference (0.011–0.039). These effect sizes were similar to those reported for adults of the EPIC-Norfolk study (i.e., 0.039 SD/allele for BMI [95% CI 0.031–0.047] and 0.033 for waist circumference [0.025–0.041]) (39).

The GPS-17 did not show a significant association with height (effect size 0.012 SD/allele [95% CI −0.004 to 0.029]; P = 0.15), whereas the GPS-12 did (0.023 [0.003–0.043]; P = 0.024) (Table 2). The latter association was substantially attenuated after removing the near-TMEM18 and BCIN3D variants from the score (effect size 0.015 SD/allele; P value 0.19), suggesting that the association was largely driven by these variants. The association of the GPS-17 and the risk of obesity and overweight showed that each additional effect allele was associated with a 1.12-fold increased odds of obesity (95% CI 1.04–1.22) and a 1.09-fold increased odds of overweight (1.04–1.15) (supplementary Table 3). Consistent with the observation for continuous traits, the GPS-12 showed somewhat more pronounced effects than the GPS-17, with 1.18-fold (1.08–1.30) and 1.13-fold (1.06–1.20) increased odds for obesity and overweight per additional effect allele, respectively. These effects were similar to those reported for adults, i.e., 1.11 (1.08–1.14) and 1.06 (1.04–1.07) for obesity and overweight, respectively (39).

We found no evidence for sex-, age-group–, or country-specific effects of the GPS-17 on BMI, sum of skinfolds, or waist circumference or on the risk of obesity or overweight (P > 0.4 for product terms). Furthermore, no significant interactions were observed between either overall physical activity or the fraction of time spent on moderate and vigorous intensity physical activity and the GPS-17 for any of these anthropometric traits (P > 0.15 for product terms).

DISCUSSION

Nine of 13 variants in the obesity susceptibility loci identified by GWA studies in adults also showed significant associations with BMI in a meta-analysis of up to 13,071 children and adolescents. In the EYHS, for which we had data on 16 obesity susceptibility loci, BMI, skinfolds, and waist circumference, effect sizes were similar across traits for most variants. Each additional effect allele in the GPS-17, which combined the data of 17 variants in 16 obesity susceptibility loci, increased BMI by 0.034 SD, sum of skinfolds by 0.039 SD, and waist circumference by 0.022 SD.

Four of the 13 variants included in the meta-analysis for BMI showed a moderate to high heterogeneity in effect size between studies (I2 > 50%), which is more than would be expected based on chance. The near-NEGR1 and SEC16B variants were more strongly associated with BMI in EYHS than reported earlier in children and adolescents (20,28), whereas the variants in/near FTO and MC4R were strongly and significantly associated with BMI in ALSPAC (20) and CHP (28) but not in EYHS.

Overall, the effect size for BMI was largest for the near-TMEM18 variant (0.098 SD/allele), followed by the FTO, SEC16B, and near-MC4R variants (0.076, 0.068, and 0.067 SD/allele, respectively), and ranged from 0.033 to 0.055 SD/allele for the five remaining variants that reached significance. In adults, the FTO locus has the largest effect of all currently established obesity susceptibility loci (19,20). Our study was not sufficiently powered to examine whether differences in effect size between the near-TMEM18 and FTO variants were statistically significant. Larger studies, as well as longitudinal studies, are needed to identify changes in effect sizes during the life course. A recent longitudinal study on the life course effects of the FTO and near-MC4R loci suggests that, at least for these loci, effects strengthen during childhood and adolescence, peak at age 20 years, and weaken during adulthood (24).

Of the four variants that were not significantly associated with BMI, the association of rs925946 in BDNF (P = 0.076) was directionally consistent with that observed in adults (19,39,41), whereas for the near-MTCH2 variant the association was completely absent. For variants near ETV5 and SH2B1, results remain inconclusive; associations were not significant in the meta-analysis but were directionally consistent with those reported earlier in adults (19,20,39,41). Studies of larger samples may be required to confirm an association of these variants with anthropometric traits in children.

In EYHS, rs2605100 was associated with sum of skinfolds and waist circumference but not with BMI. This SNP, which is located ∼259 kb upstream of LYPLAL1, was also associated with waist circumference but not with BMI in adult women (22). The present study extends these results, strongly suggesting that the locus represents a pure abdominal obesity hit that is already seen in children. This is an important finding because waist circumference is independently associated with the risk of death in adults (42) and is already associated with elevated concentrations of lipids and insulin in children (43). Contrary to the original study in adults, no evidence was observed for a sex-specific effect of the near-LYPLAL1 variant on waist circumference (Pinteraction term = 0.50) in children and adolescents. The LYPLAL1 gene encodes a lysophospholipase-like 1 protein that may act as a triglyceride lipase and is upregulated in subcutaneous adipose tissue of obese individuals (44).

No significant differences in effect size were observed for BMI between children/adolescents and adults of the EPIC-Norfolk study (39). However, effect sizes tended to be 1.4- to 2.8-fold higher in children/adolescents than in adults for variants in/near SEC16B, TMEM18, and KCTD15 and twofold lower for rs925946 in BDNF. This discrepancy may reflect a truly different association with adiposity between age-groups but may also result from a lack of comparability of the phenotype. The GPS-12 showed that the cumulative or average effect of 12 obesity-susceptibility loci on BMI and waist circumference was very similar in children/adolescents and adults (39).

As was reported earlier in adults of the EPIC-Norfolk study, the GPS-12 and GPS-17 tended to be more strongly associated with BMI than with waist circumference. The GPS-17 and GPS-12 both explained ∼1% of the inter-individual variation in BMI and sum of skinfolds and ∼0.4% of the variation in waist circumference. This suggests that the predictive value for risk of obesity based on these variants is likely very low in children and adolescents, consistent with observations in adults (39). In the EYHS, effect sizes were larger for the GPS-12 than the GPS-17 for all anthropometric traits. Apparently, SNPs identified in GWA studies for BMI are on average more strongly associated with (abdominal) obesity than SNPs identified in GWA studies for waist circumference in children/adolescents.

In contrast with previous results in adults, the GPS-12 was additionally associated with height. However, this association was largely attenuated after removal of the near-TMEM18 and BCIN3D variants from the score. Moreover, the effect size of the GPS-12 for BMI was almost twice that of height, indicating a larger effect on body mass than on height. Objectively measured habitual physical activity did not modify the association of the GPS-17 with anthropometric traits in EYHS. This may result from a relatively high level of physical activity in children and adolescents compared with adults. Physical activity measured by Actigraph in EYHS was comparable with earlier reports in children from the ALSPAC and SPEEDY cohorts (45,46) but higher than reported in adults (47,48). Alternatively, the lack of interaction may result from the relatively small sample in which objective data on physical activity were available.

At this stage, little is known about the mechanisms responsible for the association of these loci with anthropometric traits. Given that NEGR1, TMEM18, GNPDA2, FTO, MC4R and KCTD15 are all expressed at high levels in the hypothalamus (20,49,50), the associations may result from a neuronal effect on energy balance. However, many of these loci are located near multiple genes, and before a neuronal influence on energy balance can be confirmed, the causal variants will have to be identified.

In conclusion, common variants in obesity susceptibility loci identified by GWA studies in adults have, on average, similar effect sizes on anthropometric traits and risk of obesity in children and adolescents, with variants in the TMEM18 locus showing the largest effect. Although the association of some variants may not be constant throughout life, this discrepancy levels off when their cumulative effect is evaluated.

ACKNOWLEDGMENTS

This study received funding from the following sources: the Danish Heart Foundation; the Danish Medical Research Council Health Foundation; the Danish Council for Sports Research; the Foundation in Memory of Asta Florida Bolding Renée Andersen; the Faculty of Health Sciences, University of Southern Denmark; and the Estonian Science Foundation and the Medical Research Council, U.K.

No potential conflicts of interest relevant to this article were reported.

M.d.H. designed the study, outlined the analysis plan, analyzed the data, interpreted the results, wrote the manuscript, and contributed to the discussion. U.E. contributed to the discussion and reviewed and edited the manuscript. S.B. contributed to the discussion and reviewed and edited the manuscript. A.G. contributed to the discussion and reviewed and edited the manuscript. J.H.Z. contributed to the discussion and reviewed and edited the manuscript. S.J.S. contributed to the discussion and reviewed and edited the manuscript. K.K.O. contributed to the discussion and reviewed and edited the manuscript. N.J.W. contributed to the discussion and reviewed and edited the manuscript. R.J.F.L. designed the study, outlined the analysis plan, contributed to the discussion, and reviewed and edited the manuscript.

We thank the volunteers in the EYHS, who gave their time to take part in this study. We also acknowledge the EYHS study teams who collected the data used in these analyses.

Footnotes

  • The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

  • Received March 16, 2010.
  • Accepted August 6, 2010.
  • © 2010 by the American Diabetes Association.

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

REFERENCES

  1. ↵
    1. Christakis NA,
    2. Fowler JH
    . The spread of obesity in a large social network over 32 years. N Engl J Med 2007;357:370–379
    OpenUrlCrossRefPubMedWeb of Science
  2. ↵
    1. Baker JL,
    2. Olsen LW,
    3. Sorensen TI
    . Childhood body-mass index and the risk of coronary heart disease in adulthood. N Engl J Med 2007;357:2329–2337
    OpenUrlCrossRefPubMedWeb of Science
  3. ↵
    1. Serdula MK,
    2. Ivery D,
    3. Coates RJ,
    4. Freedman DS,
    5. Williamson DF,
    6. Byers T
    . Do obese children become obese adults? A review of the literature. Prev Med 1993;22:167–177
    OpenUrlCrossRefPubMedWeb of Science
  4. ↵
    1. Whitaker RC,
    2. Wright JA,
    3. Pepe MS,
    4. Seidel KD,
    5. Dietz WH
    . Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med 1997;337:869–873
    OpenUrlCrossRefPubMedWeb of Science
  5. ↵
    1. Freedman DS,
    2. Khan LK,
    3. Dietz WH,
    4. Srinivasan SR,
    5. Berenson GS
    . Relationship of childhood obesity to coronary heart disease risk factors in adulthood: the Bogalusa Heart Study. Pediatrics 2001;108:712–718
    OpenUrlAbstract/FREE Full Text
  6. ↵
    1. Freedman DS,
    2. Dietz WH,
    3. Srinivasan SR,
    4. Berenson GS
    . The relation of overweight to cardiovascular risk factors among children and adolescents: the Bogalusa Heart Study. Pediatrics 1999;103:1175–1182
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Tounian P,
    2. Aggoun Y,
    3. Dubern B,
    4. Varille V,
    5. Guy-Grand B,
    6. Sidi D,
    7. Girardet JP,
    8. Bonnet D
    . Presence of increased stiffness of the common carotid artery and endothelial dysfunction in severely obese children: a prospective study. Lancet 2001;358:1400–1404
    OpenUrlCrossRefPubMedWeb of Science
  8. ↵
    1. Must A,
    2. Jacques PF,
    3. Dallal GE,
    4. Bajema CJ,
    5. Dietz WH
    . Long-term morbidity and mortality of overweight adolescents: a follow-up of the Harvard Growth Study of 1922 to 1935. N Engl J Med 1992;327:1350–1355
    OpenUrlCrossRefPubMedWeb of Science
  9. ↵
    1. Franks PW,
    2. Hanson RL,
    3. Knowler WC,
    4. Sievers ML,
    5. Bennett PH,
    6. Looker HC
    . Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med 2010;362:485–493
    OpenUrlCrossRefPubMedWeb of Science
  10. ↵
    1. Maes HH,
    2. Neale MC,
    3. Eaves LJ
    . Genetic and environmental factors in relative body weight and human adiposity. Behav Genet 1997;27:325–351
    OpenUrlCrossRefPubMedWeb of Science
  11. ↵
    1. Stunkard AJ,
    2. Foch TT,
    3. Hrubec Z
    . A twin study of human obesity. JAMA 1986;256:51–54
    OpenUrlCrossRefPubMedWeb of Science
  12. ↵
    1. Haworth CM,
    2. Carnell S,
    3. Meaburn EL,
    4. Davis OS,
    5. Plomin R,
    6. Wardle J
    . Increasing heritability of BMI and stronger associations with the FTO gene over childhood. Obesity (Silver Spring) 2008;16:2663–2668
    OpenUrlCrossRefPubMed
  13. ↵
    1. Lajunen HR,
    2. Kaprio J,
    3. Keski-Rahkonen A,
    4. Rose RJ,
    5. Pulkkinen L,
    6. Rissanen A,
    7. Silventoinen K
    . Genetic and environmental effects on body mass index during adolescence: a prospective study among Finnish twins. Int J Obes (Lond) 2009;33:559–567
    OpenUrlCrossRefPubMed
  14. ↵
    1. Silventoinen K,
    2. Rokholm B,
    3. Kaprio J,
    4. Sorensen TI
    . The genetic and environmental influences on childhood obesity: a systematic review of twin and adoption studies. Int J Obes (Lond) 2010;34:29–40
    OpenUrlCrossRefPubMed
  15. ↵
    1. Pietilainen KH,
    2. Kaprio J,
    3. Rissanen A,
    4. Winter T,
    5. Rimpela A,
    6. Viken RJ,
    7. Rose RJ
    . Distribution and heritability of BMI in Finnish adolescents aged 16y and 17y: a study of 4884 twins and 2509 singletons. Int J Obes Relat Metab Disord 1999;23:107–115
    OpenUrlCrossRefPubMedWeb of Science
  16. ↵
    1. Wardle J,
    2. Carnell S,
    3. Haworth CM,
    4. 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
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. Frayling TM,
    2. Timpson NJ,
    3. Weedon MN,
    4. Zeggini E,
    5. Freathy RM,
    6. Lindgren CM,
    7. Perry JR,
    8. Elliott KS,
    9. Lango H,
    10. Rayner NW,
    11. Shields B,
    12. Harries LW,
    13. Barrett JC,
    14. Ellard S,
    15. Groves CJ,
    16. Knight B,
    17. Patch AM,
    18. Ness AR,
    19. Ebrahim S,
    20. Lawlor DA,
    21. Ring SM,
    22. Ben-Shlomo Y,
    23. Jarvelin MR,
    24. Sovio U,
    25. Bennett AJ,
    26. Melzer D,
    27. Ferrucci L,
    28. Loos RJ,
    29. Barroso I,
    30. Wareham NJ,
    31. Karpe F,
    32. Owen KR,
    33. Cardon LR,
    34. Walker M,
    35. Hitman GA,
    36. Palmer CN,
    37. Doney AS,
    38. Morris AD,
    39. Smith GD,
    40. Hattersley AT,
    41. McCarthy MI
    . 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
    OpenUrlAbstract/FREE Full Text
  18. ↵
    1. Loos RJ,
    2. Lindgren CM,
    3. Li S,
    4. Wheeler E,
    5. Zhao JH,
    6. Prokopenko I,
    7. Inouye M,
    8. Freathy RM,
    9. Attwood AP,
    10. Beckmann JS,
    11. Berndt SI,
    12. Jacobs KB,
    13. Chanock SJ,
    14. Hayes RB,
    15. Bergmann S,
    16. Bennett AJ,
    17. Bingham SA,
    18. Bochud M,
    19. Brown M,
    20. Cauchi S,
    21. Connell JM,
    22. Cooper C,
    23. Smith GD,
    24. Day I,
    25. Dina C,
    26. De S,
    27. Dermitzakis ET,
    28. Doney AS,
    29. Elliott KS,
    30. Elliott P,
    31. Evans DM,
    32. Sadaf Farooqi I,
    33. Froguel P,
    34. Ghori J,
    35. Groves CJ,
    36. Gwilliam R,
    37. Hadley D,
    38. Hall AS,
    39. Hattersley AT,
    40. Hebebrand J,
    41. Heid IM,
    42. Lamina C,
    43. Gieger C,
    44. Illig T,
    45. Meitinger T,
    46. Wichmann HE,
    47. Herrera B,
    48. Hinney A,
    49. Hunt SE,
    50. Jarvelin MR,
    51. Johnson T,
    52. Jolley JD,
    53. Karpe F,
    54. Keniry A,
    55. Khaw KT,
    56. Luben RN,
    57. Mangino M,
    58. Marchini J,
    59. McArdle WL,
    60. McGinnis R,
    61. Meyre D,
    62. Munroe PB,
    63. Morris AD,
    64. Ness AR,
    65. Neville MJ,
    66. Nica AC,
    67. Ong KK,
    68. O'Rahilly S,
    69. Owen KR,
    70. Palmer CN,
    71. Papadakis K,
    72. Potter S,
    73. Pouta A,
    74. Qi L,
    75. Randall JC,
    76. Rayner NW,
    77. Ring SM,
    78. Sandhu MS,
    79. Scherag A,
    80. Sims MA,
    81. Song K,
    82. Soranzo N,
    83. Speliotes EK,
    84. Syddall HE,
    85. Teichmann SA,
    86. Timpson NJ,
    87. Tobias JH,
    88. Uda M,
    89. Vogel CI,
    90. Wallace C,
    91. Waterworth DM,
    92. Weedon MN,
    93. Willer CJ,
    94. Wraight,
    95. Yuan X,
    96. Zeggini E,
    97. Hirschhorn JN,
    98. Strachan DP,
    99. Ouwehand WH,
    100. Caulfield MJ,
    101. Samani NJ,
    102. Frayling TM,
    103. Vollenweider P,
    104. Waeber G,
    105. Mooser V,
    106. Deloukas P,
    107. McCarthy MI,
    108. Wareham NJ,
    109. Barroso I,
    110. Jacobs KB,
    111. Chanock SJ,
    112. Hayes RB,
    113. Lamina C,
    114. Gieger C,
    115. Illig T,
    116. Meitinger T,
    117. Wichmann HE,
    118. Kraft P,
    119. Hankinson SE,
    120. Hunter DJ,
    121. Hu FB,
    122. Lyon HN,
    123. Voight BF,
    124. Ridderstrale M,
    125. Groop L,
    126. Scheet P,
    127. Sanna S,
    128. Abecasis GR,
    129. Albai G,
    130. Nagaraja R,
    131. Schlessinger D,
    132. Jackson AU,
    133. Tuomilehto J,
    134. Collins FS,
    135. Boehnke M,
    136. Mohlke KL
    . Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 2008;40:768–775
    OpenUrlCrossRefPubMedWeb of Science
  19. ↵
    1. Thorleifsson G,
    2. Walters GB,
    3. Gudbjartsson DF,
    4. Steinthorsdottir V,
    5. Sulem P,
    6. Helgadottir A,
    7. Styrkarsdottir U,
    8. Gretarsdottir S,
    9. Thorlacius S,
    10. Jonsdottir I,
    11. Jonsdottir T,
    12. Olafsdottir EJ,
    13. Olafsdottir GH,
    14. Jonsson T,
    15. Jonsson F,
    16. Borch-Johnsen K,
    17. Hansen T,
    18. Andersen G,
    19. Jorgensen T,
    20. Lauritzen T,
    21. Aben KK,
    22. Verbeek AL,
    23. Roeleveld N,
    24. Kampman E,
    25. Yanek LR,
    26. Becker LC,
    27. Tryggvadottir L,
    28. Rafnar T,
    29. Becker DM,
    30. Gulcher J,
    31. Kiemeney LA,
    32. Pedersen O,
    33. Kong A,
    34. Thorsteinsdottir U,
    35. Stefansson K
    . Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet 2009;41:18–24
    OpenUrlCrossRefPubMedWeb of Science
  20. ↵
    1. Willer CJ,
    2. Speliotes EK,
    3. Loos RJ,
    4. Li S,
    5. Lindgren CM,
    6. Heid IM,
    7. Berndt SI,
    8. Elliott AL,
    9. Jackson AU,
    10. Lamina C,
    11. Lettre G,
    12. Lim N,
    13. Lyon HN,
    14. McCarroll SA,
    15. Papadakis K,
    16. Qi L,
    17. Randall JC,
    18. Roccasecca RM,
    19. Sanna S,
    20. Scheet P,
    21. Weedon MN,
    22. Wheeler E,
    23. Zhao JH,
    24. Jacobs LC,
    25. Prokopenko I,
    26. Soranzo N,
    27. Tanaka T,
    28. Timpson NJ,
    29. Almgren P,
    30. Bennett A,
    31. Bergman RN,
    32. Bingham SA,
    33. Bonnycastle LL,
    34. Brown M,
    35. Burtt NP,
    36. Chines P,
    37. Coin L,
    38. Collins FS,
    39. Connell JM,
    40. Cooper C,
    41. Smith GD,
    42. Dennison EM,
    43. Deodhar P,
    44. Elliott P,
    45. Erdos MR,
    46. Estrada K,
    47. Evans DM,
    48. Gianniny L,
    49. Gieger C,
    50. Gillson CJ,
    51. Guiducci C,
    52. Hackett R,
    53. Hadley D,
    54. Hall AS,
    55. Havulinna AS,
    56. Hebebrand J,
    57. Hofman A,
    58. Isomaa B,
    59. Jacobs KB,
    60. Johnson T,
    61. Jousilahti P,
    62. Jovanovic Z,
    63. Khaw KT,
    64. Kraft P,
    65. Kuokkanen M,
    66. Kuusisto J,
    67. Laitinen J,
    68. Lakatta EG,
    69. Luan J,
    70. Luben RN,
    71. Mangino M,
    72. McArdle WL,
    73. Meitinger T,
    74. Mulas A,
    75. Munroe PB,
    76. Narisu N,
    77. Ness AR,
    78. Northstone K,
    79. O'Rahilly S,
    80. Purmann C,
    81. Rees MG,
    82. Ridderstrale M,
    83. Ring SM,
    84. Rivadeneira F,
    85. Ruokonen A,
    86. Sandhu MS,
    87. Saramies J,
    88. Scott LJ,
    89. Scuteri A,
    90. Silander K,
    91. Sims MA,
    92. Song K,
    93. Stephens J,
    94. Stevens S,
    95. Stringham HM,
    96. Tung YC,
    97. Valle TT,
    98. Van Duijn CM,
    99. Vimaleswaran KS,
    100. Vollenweider P,
    101. Waeber G,
    102. Wallace C,
    103. Watanabe RM,
    104. Waterworth DM,
    105. Watkins N,
    106. Witteman JC,
    107. Zeggini E,
    108. Zhai G,
    109. Zillikens MC,
    110. Altshuler D,
    111. Caulfield MJ,
    112. Chanock SJ,
    113. Farooqi IS,
    114. Ferrucci L,
    115. Guralnik JM,
    116. Hattersley AT,
    117. Hu FB,
    118. Jarvelin MR,
    119. Laakso M,
    120. Mooser V,
    121. Ong KK,
    122. Ouwehand WH,
    123. Salomaa V,
    124. Samani NJ,
    125. Spector TD,
    126. Tuomi T,
    127. Tuomilehto J,
    128. Uda M,
    129. Uitterlinden AG,
    130. Wareham NJ,
    131. Deloukas P,
    132. Frayling TM,
    133. Groop LC,
    134. Hayes RB,
    135. Hunter DJ,
    136. Mohlke KL,
    137. Peltonen L,
    138. Schlessinger D,
    139. Strachan DP,
    140. Wichmann HE,
    141. McCarthy MI,
    142. Boehnke M,
    143. Barroso I,
    144. Abecasis GR,
    145. Hirschhorn JN
    . Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 2009;41:25–34
    OpenUrlCrossRefPubMedWeb of Science
  21. ↵
    1. Heard-Costa NL,
    2. Zillikens MC,
    3. Monda KL,
    4. Johansson A,
    5. Harris TB,
    6. Fu M,
    7. Haritunians T,
    8. Feitosa MF,
    9. Aspelund T,
    10. Eiriksdottir G,
    11. Garcia M,
    12. Launer LJ,
    13. Smith AV,
    14. Mitchell BD,
    15. McArdle PF,
    16. Shuldiner AR,
    17. Bielinski SJ,
    18. Boerwinkle E,
    19. Brancati F,
    20. Demerath EW,
    21. Pankow JS,
    22. Arnold AM,
    23. Chen YD,
    24. Glazer NL,
    25. McKnight B,
    26. Psaty BM,
    27. Rotter JI,
    28. Amin N,
    29. Campbell H,
    30. Gyllensten U,
    31. Pattaro C,
    32. Pramstaller PP,
    33. Rudan I,
    34. Struchalin M,
    35. Vitart V,
    36. Gao X,
    37. Kraja A,
    38. Province MA,
    39. Zhang Q,
    40. Atwood LD,
    41. Dupuis J,
    42. Hirschhorn JN,
    43. Jaquish CE,
    44. O'Donnell CJ,
    45. Vasan RS,
    46. White CC,
    47. Aulchenko YS,
    48. Estrada K,
    49. Hofman A,
    50. Rivadeneira F,
    51. Uitterlinden AG,
    52. Witteman JC,
    53. Oostra BA,
    54. Kaplan RC,
    55. Gudnason V,
    56. O'Connell JR,
    57. Borecki IB,
    58. van Duijn CM,
    59. Cupples LA,
    60. Fox CS.,
    61. North KE
    : NRXN3 is a novel locus for waist circumference: a genome-wide association study from the CHARGE Consortium. PLoS Genet 2009;5:e1000539
    OpenUrlCrossRefPubMed
  22. ↵
    1. Lindgren CM,
    2. Heid IM,
    3. Randall JC,
    4. Lamina C,
    5. Steinthorsdottir V,
    6. Qi L,
    7. Speliotes EK,
    8. Thorleifsson G,
    9. Willer CJ,
    10. Herrera BM,
    11. Jackson AU,
    12. Lim N,
    13. Scheet P,
    14. Soranzo N,
    15. Amin N,
    16. Aulchenko YS,
    17. Chambers JC,
    18. Drong A,
    19. Luan J,
    20. Lyon HN,
    21. Rivadeneira F,
    22. Sanna S,
    23. Timpson NJ,
    24. Zillikens MC,
    25. Zhao JH,
    26. Almgren P,
    27. Bandinelli S,
    28. Bennett AJ,
    29. Bergman RN,
    30. Bonnycastle LL,
    31. Bumpstead SJ,
    32. Chanock SJ,
    33. Cherkas L,
    34. Chines P,
    35. Coin L,
    36. Cooper C,
    37. Crawford G,
    38. Doering A,
    39. Dominiczak A,
    40. Doney AS,
    41. Ebrahim S,
    42. Elliott P,
    43. Erdos MR,
    44. Estrada K,
    45. Ferrucci L,
    46. Fischer G,
    47. Forouhi NG,
    48. Gieger C,
    49. Grallert H,
    50. Groves CJ,
    51. Grundy S,
    52. Guiducci C,
    53. Hadley D,
    54. Hamsten A,
    55. Havulinna AS,
    56. Hofman A,
    57. Holle R,
    58. Holloway JW,
    59. Illig T,
    60. Isomaa B,
    61. Jacobs LC,
    62. Jameson K,
    63. Jousilahti P,
    64. Karpe F,
    65. Kuusisto J,
    66. Laitinen J,
    67. Lathrop GM,
    68. Lawlor DA,
    69. Mangino M,
    70. McArdle WL,
    71. Meitinger T,
    72. Morken MA,
    73. Morris AP,
    74. Munroe P,
    75. Narisu N,
    76. Nordstrom A,
    77. Nordstrom P,
    78. Oostra BA,
    79. Palmer CN,
    80. Payne F,
    81. Peden JF,
    82. Prokopenko I,
    83. Renstrom F,
    84. Ruokonen A,
    85. Salomaa V,
    86. Sandhu MS,
    87. Scott LJ,
    88. Scuteri A,
    89. Silander K,
    90. Song K,
    91. Yuan X,
    92. Stringham HM,
    93. Swift AJ,
    94. Tuomi T,
    95. Uda M,
    96. Vollenweider P,
    97. Waeber G,
    98. Wallace C,
    99. Walters GB,
    100. Weedon MN,
    101. Witteman JC,
    102. Zhang C,
    103. Zhang W,
    104. Caulfield MJ,
    105. Collins FS,
    106. Davey Smith G,
    107. Day IN,
    108. Franks PW,
    109. Hattersley AT,
    110. Hu FB,
    111. Jarvelin MR,
    112. Kong A,
    113. Kooner JS,
    114. Laakso M,
    115. Lakatta E,
    116. Mooser V,
    117. Morris AD,
    118. Peltonen L,
    119. Samani NJ,
    120. Spector TD,
    121. Strachan DP,
    122. Tanaka T,
    123. Tuomilehto J,
    124. Uitterlinden AG,
    125. van Duijn CM,
    126. Wareham NJ,
    127. Hugh W,
    128. Waterworth DM,
    129. Boehnke M,
    130. Deloukas P,
    131. Groop L,
    132. Hunter DJ,
    133. Thorsteinsdottir U,
    134. Schlessinger D,
    135. Wichmann HE,
    136. Frayling TM,
    137. Abecasis GR,
    138. Hirschhorn JN,
    139. Loos RJ,
    140. Stefansson K,
    141. Mohlke KL,
    142. Barroso I,
    143. McCarthy MI
    . Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution. PLoS Genet 2009;5:e1000508
    OpenUrlCrossRefPubMed
  23. ↵
    1. Dina C,
    2. Meyre D,
    3. Gallina S,
    4. Durand E,
    5. Korner A,
    6. Jacobson P,
    7. Carlsson LM,
    8. Kiess W,
    9. Vatin V,
    10. Lecoeur C,
    11. Delplanque J,
    12. Vaillant E,
    13. Pattou F,
    14. Ruiz J,
    15. Weill J,
    16. Levy-Marchal C,
    17. Horber F,
    18. Potoczna N,
    19. Hercberg S,
    20. Le Stunff C,
    21. Bougneres P,
    22. Kovacs P,
    23. Marre M,
    24. Balkau B,
    25. Cauchi S,
    26. Chevre JC,
    27. Froguel P
    . Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet 2007;39:724–726
    OpenUrlCrossRefPubMedWeb of Science
  24. ↵
    1. Hardy R,
    2. Wills AK,
    3. Wong A,
    4. Elks CE,
    5. Wareham NJ,
    6. Loos RJ,
    7. Kuh D,
    8. Ong KK
    . Life course variations in the associations between FTO and MC4R gene variants and body size. Hum Mol Genet 2010;19:545–552
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Liem ET,
    2. Vonk JM,
    3. Sauer PJ,
    4. van der Steege G,
    5. Oosterom E,
    6. Stolk RP,
    7. Snieder H
    . Influence of common variants near INSIG2, in FTO, and near MC4R genes on overweight and the metabolic profile in adolescence: the TRAILS (TRacking Adolescents' Individual Lives Survey) Study. Am J Clin Nutr 2010;91:321–328
    OpenUrlAbstract/FREE Full Text
  26. ↵
    1. Liu G,
    2. Zhu H,
    3. Lagou V,
    4. Gutin B,
    5. Barbeau P,
    6. Treiber FA,
    7. Dong Y,
    8. Snieder H
    . Common variants near melanocortin 4 receptor are associated with general and visceral adiposity in European- and African-American youth. J Pediatr 2010;156:598–605
    OpenUrlCrossRefPubMed
  27. ↵
    1. Liu G,
    2. Zhu H,
    3. Lagou V,
    4. Gutin B,
    5. Stallmann-Jorgensen IS,
    6. Treiber FA,
    7. Dong Y,
    8. Snieder H
    . FTO variant rs9939609 is associated with body mass index and waist circumference, but not with energy intake or physical activity in European- and African-American youth. BMC Med Genet 2010;11:57
    OpenUrlCrossRefPubMed
  28. ↵
    1. Zhao J,
    2. Bradfield JP,
    3. Li M,
    4. Wang K,
    5. Zhang H,
    6. Kim CE,
    7. Annaiah K,
    8. Glessner JT,
    9. Thomas K,
    10. Garris M,
    11. Frackelton EC,
    12. Otieno FG,
    13. Shaner JL,
    14. Smith RM,
    15. Chiavacci RM,
    16. Berkowitz RI,
    17. Hakonarson H,
    18. Grant SF
    . 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
    OpenUrlCrossRefPubMed
  29. ↵
    1. Andreasen CH,
    2. Stender-Petersen KL,
    3. Mogensen MS,
    4. Torekov SS,
    5. Wegner L,
    6. Andersen G,
    7. Nielsen AL,
    8. Albrechtsen A,
    9. Borch-Johnsen K,
    10. Rasmussen SS,
    11. Clausen JO,
    12. Sandbaek A,
    13. Lauritzen T,
    14. Hansen L,
    15. Jorgensen T,
    16. Pedersen O,
    17. Hansen T
    . Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes 2008;57:95–101
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Mustelin L,
    2. Silventoinen K,
    3. Pietilainen K,
    4. Rissanen A,
    5. Kaprio J
    . Physical activity reduces the influence of genetic effects on BMI and waist circumference: a study in young adult twins. Int J Obes (Lond) 2009;33:29–36
    OpenUrlCrossRefPubMed
  31. ↵
    1. Rampersaud E,
    2. Mitchell BD,
    3. Pollin TI,
    4. Fu M,
    5. Shen H,
    6. O'Connell JR,
    7. Ducharme JL,
    8. Hines S,
    9. Sack P,
    10. Naglieri R,
    11. Shuldiner AR,
    12. Snitker S
    . Physical activity and the association of common FTO gene variants with body mass index and obesity. Arch Intern Med 2008;168:1791–1797
    OpenUrlCrossRefPubMedWeb of Science
  32. ↵
    1. Riddoch C,
    2. Edwards D,
    3. Page A,
    4. Froberg K,
    5. Andersen A,
    6. Wedderkopp N,
    7. Brage S,
    8. Cooper A,
    9. Sardinha L,
    10. Harro M,
    11. Klasson Heggebro L,
    12. van Mechelen W,
    13. Boreham C,
    14. Ekelund U,
    15. Andersen LB
    . The European Youth Heart Study: cardiovascular disease risk factors in children: rationale, aims, study design, and validation of methods. J Phys Act Health 2005;2:115–129
    OpenUrl
  33. ↵
    Council of Europe. The Eurofit Test Battery. Strasburg, France, Council of Europe, 1988
  34. ↵
    1. Cole T,
    2. Bellizzi M,
    3. Flegal K,
    4. Dietz W
    . Establishing a standard definition for child overweight and obesity worldwide: international survey. Br Med J 2000;320:1240–1243
    OpenUrlAbstract/FREE Full Text
  35. ↵
    1. Lohman T,
    2. Roche A,
    3. Martorell R
    . Anthropometric Standardization Reference Manual. Champaign, IL, Human Kinetics Books, 1991, p. 1–96
  36. ↵
    1. Tanner J
    . Growth at Adolescence. Oxford, Blackwell, 1962
  37. ↵
    1. Ekelund U,
    2. Sardinha LB,
    3. Anderssen SA,
    4. Harro M,
    5. Franks PW,
    6. Brage S,
    7. Cooper AR,
    8. Andersen LB,
    9. Riddoch C,
    10. Froberg K
    . Associations between objectively assessed physical activity and indicators of body fatness in 9- to 10-y-old European children: a population-based study from 4 distinct regions in Europe (the European Youth Heart Study). Am J Clin Nutr 2004;80:584–590
    OpenUrlAbstract/FREE Full Text
  38. ↵
    1. Ekelund U,
    2. Sjostrom M,
    3. Yngve A,
    4. Poortvliet E,
    5. Nilsson A,
    6. Froberg K,
    7. Wedderkopp N,
    8. Westerterp K
    . Physical activity assessed by activity monitor and doubly labeled water in children. Med Sci Sports Exerc 2001;33:275–281
    OpenUrlCrossRefPubMedWeb of Science
  39. ↵
    1. Li S,
    2. Zhao JH,
    3. Luan J,
    4. Luben RN,
    5. Rodwell SA,
    6. Khaw KT,
    7. Ong KK,
    8. Wareham NJ,
    9. Loos RJ
    . Cumulative effects and predictive value of common obesity-susceptibility variants identified by genome-wide association studies. Am J Clin Nutr 2010;91:184–190
    OpenUrlAbstract/FREE Full Text
  40. ↵
    1. Janssens AC,
    2. Moonesinghe R,
    3. Yang Q,
    4. Steyerberg EW,
    5. van Duijn CM,
    6. Khoury MJ
    . The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases. Genet Med 2007;9:528–535
    OpenUrlPubMedWeb of Science
  41. ↵
    1. Bauer F,
    2. Elbers CC,
    3. Adan RA,
    4. Loos RJ,
    5. Onland-Moret NC,
    6. Grobbee DE,
    7. van Vliet-Ostaptchouk JV,
    8. Wijmenga C,
    9. van der Schouw YT
    . Obesity genes identified in genome-wide association studies are associated with adiposity measures and potentially with nutrient-specific food preference. Am J Clin Nutr 2009;90:951–959
    OpenUrlAbstract/FREE Full Text
  42. ↵
    1. Pischon T,
    2. Boeing H,
    3. Hoffmann K,
    4. Bergmann M,
    5. Schulze MB,
    6. Overvad K,
    7. van der Schouw YT,
    8. Spencer E,
    9. Moons KG,
    10. Tjonneland A,
    11. Halkjaer J,
    12. Jensen MK,
    13. Stegger J,
    14. Clavel-Chapelon F,
    15. Boutron-Ruault MC,
    16. Chajes V,
    17. Linseisen J,
    18. Kaaks R,
    19. Trichopoulou A,
    20. Trichopoulos D,
    21. Bamia C,
    22. Sieri S,
    23. Palli D,
    24. Tumino R,
    25. Vineis P,
    26. Panico S,
    27. Peeters PH,
    28. May AM,
    29. Bueno-de-Mesquita HB,
    30. van Duijnhoven FJ,
    31. Hallmans G,
    32. Weinehall L,
    33. Manjer J,
    34. Hedblad B,
    35. Lund E,
    36. Agudo A,
    37. Arriola L,
    38. Barricarte A,
    39. Navarro C,
    40. Martinez C,
    41. Quiros JR,
    42. Key T,
    43. Bingham S,
    44. Khaw KT,
    45. Boffetta P,
    46. Jenab M,
    47. Ferrari P,
    48. Riboli E
    . General and abdominal adiposity and risk of death in Europe. N Engl J Med 2008;359:2105–2120
    OpenUrlCrossRefPubMedWeb of Science
  43. ↵
    1. Freedman DS,
    2. Serdula MK,
    3. Srinivasan SR,
    4. Berenson GS
    . Relation of circumferences and skinfold thicknesses to lipid and insulin concentrations in children and adolescents: the Bogalusa Heart Study. Am J Clin Nutr 1999;69:308–317
    OpenUrlAbstract/FREE Full Text
  44. ↵
    1. Steinberg GR,
    2. Kemp BE,
    3. Watt MJ
    . Adipocyte triglyceride lipase expression in human obesity. Am J Physiol Endocrinol Metab 2007;293:E958–E964
    OpenUrlAbstract/FREE Full Text
  45. ↵
    1. Mattocks C,
    2. Ness A,
    3. Leary S,
    4. Tilling K,
    5. Blair SN,
    6. Shield J,
    7. Deere K,
    8. Saunders J,
    9. Kirkby J,
    10. Smith GD,
    11. Wells J,
    12. Wareham N,
    13. Reilly J,
    14. Riddoch C
    . Use of accelerometers in a large field-based study of children: protocols, design issues, and effects on precision. J Phys Act Health 2008;5(Suppl. 1):S98–S111
    OpenUrlPubMed
  46. ↵
    1. van Sluijs EM,
    2. Skidmore PM,
    3. Mwanza K,
    4. Jones AP,
    5. Callaghan AM,
    6. Ekelund U,
    7. Harrison F,
    8. Harvey I,
    9. Panter J,
    10. Wareham NJ,
    11. Cassidy A,
    12. Griffin SJ
    . Physical activity and dietary behaviour in a population-based sample of British 10-year old children: the SPEEDY study (Sport, Physical activity and Eating behaviour: environmental Determinants in Young people). BMC Public Health 2008;8:388
    OpenUrlCrossRefPubMed
  47. ↵
    1. Hawkins MS,
    2. Storti KL,
    3. Richardson CR,
    4. King WC,
    5. Strath SJ,
    6. Holleman RG,
    7. Kriska AM
    . Objectively measured physical activity of USA adults by sex, age, and racial/ethnic groups: a cross-sectional study. Int J Behav Nutr Phys Act 2009;6:31
    OpenUrlCrossRefPubMed
  48. ↵
    1. Jacobi D,
    2. Charles MA,
    3. Tafflet M,
    4. Lommez A,
    5. Borys JM,
    6. Oppert JM
    . Relationships of self-reported physical activity domains with accelerometry recordings in French adults. Eur J Epidemiol 2009;24:171–179
    OpenUrlCrossRefPubMed
  49. ↵
    1. Chambers JC,
    2. Elliott P,
    3. Zabaneh D,
    4. Zhang W,
    5. Li Y,
    6. Froguel P,
    7. Balding D,
    8. Scott J,
    9. Kooner JS
    . Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet 2008;40:716–718
    OpenUrlCrossRefPubMedWeb of Science
  50. ↵
    1. Gerken T,
    2. Girard CA,
    3. Tung YC,
    4. Webby CJ,
    5. Saudek V,
    6. Hewitson KS,
    7. Yeo GS,
    8. McDonough MA,
    9. Cunliffe S,
    10. McNeill LA,
    11. Galvanovskis J,
    12. Rorsman P,
    13. Robins P,
    14. Prieur X,
    15. Coll AP,
    16. Ma M,
    17. Jovanovic Z,
    18. Farooqi IS,
    19. Sedgwick B,
    20. Barroso I,
    21. Lindahl T,
    22. Ponting CP,
    23. Ashcroft FM,
    24. O'Rahilly S,
    25. Schofield CJ
    . The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science 2007;318:1469–1472
    OpenUrlAbstract/FREE Full Text
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Genetic Susceptibility to Obesity and Related Traits in Childhood and Adolescence
Marcel den Hoed, Ulf Ekelund, Søren Brage, Anders Grontved, Jing Hua Zhao, Stephen J. Sharp, Ken K. Ong, Nicholas J. Wareham, Ruth J.F. Loos
Diabetes Nov 2010, 59 (11) 2980-2988; DOI: 10.2337/db10-0370

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Genetic Susceptibility to Obesity and Related Traits in Childhood and Adolescence
Marcel den Hoed, Ulf Ekelund, Søren Brage, Anders Grontved, Jing Hua Zhao, Stephen J. Sharp, Ken K. Ong, Nicholas J. Wareham, Ruth J.F. Loos
Diabetes Nov 2010, 59 (11) 2980-2988; DOI: 10.2337/db10-0370
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