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Analyzed the data: AB. Contributed reagents/materials/analysis tools: KKO. Wrote the paper: AB RvK ARN KKO.

The authors have declared that no competing interests exist.

Genetic factors are important determinants of overweight. We examined whether there are differential effect sizes depending on children's body composition.

We analysed data of n = 4,837 children recorded in the Avon Longitudinal Study of Parents and Children (ALSPAC), applying quantile regression with sex- and age-specific standard deviation scores (SDS) of body mass index (BMI) or with body fat mass index and fat-free mass index at 9 years as outcome variables and an “obesity-risk-allele score” based on eight genetic variants known to be associated with childhood BMI as the explanatory variable.

The quantile regression coefficients increased with increasing child's BMI-SDS and fat mass index percentiles, indicating larger effects of the genetic factors at higher percentiles. While the associations with BMI-SDS were of similar size in medium and high BMI quantiles (40th percentile and above), effect sizes with fat mass index increased over the whole fat mass index distribution. For example, the fat mass index of a normal-weight (50th percentile) child was increased by 0.13 kg/m^{2} (95% confidence interval (CI): 0.09, 0.16) per additional allele, compared to 0.24 kg/m^{2} per allele (95% CI: 0.15, 0.32) in children at the 90th percentile. The genetic associations with fat-free mass index were weaker and the quantile regression effects less pronounced than those on fat mass index.

Genetic risk factors for childhood overweight appear to have greater effects on fatter children. Interaction of known genetic factors with environmental or unknown genetic factors might provide a potential explanation of these findings.

Increasing prevalence of childhood overweight has been reported worldwide

Recent genome-wide association (GWA) studies allowed identifying several genetic factors associated with childhood and adult obesity, such as variants of the FTO and MC4R genes

Similarly, shifts in mean BMI have been observed for environmental factors which, upon more detailed scrutiny, were mainly caused by shifts in the upper tail of the distribution

We therefore hypothesized that effect sizes of genetic risk factors for overweight might be stronger in children with high compared to children with normal or low BMI or fat mass. In order to answer this question, we assessed BMI and fat mass dependent associations of genetic risk factors for childhood obesity by quantile regression.

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a longitudinal birth cohort study of the determinants of development, health, and disease during childhood and beyond and has been described in more detail elsewhere

Childhood weight and height was measured annually between ages 7 and 11 y at dedicated ALSPAC Focus clinics by a trained research team. Height was measured to the nearest 0.1 cm using a Leicester Height Measure (Holtain Crosswell, Dyfed) and weight while wearing underwear was measured to the nearest 0.1 kg using Tanita electronic scales. Fat mass and fat-free mass was assessed (only) at the 9-year-old research clinic visit (at which 7,725 of the children were seen) by whole body dual energy X-ray absorptiometry (DXA) (Prodigy scanner, Lunar Radiation Corp, Madison, Wisconsin, US).

We calculated BMI as weight/height^{2} (kg/m^{2}). To adjust for sex and age, we transformed the observed BMI values to sex- and age-specific standard deviation scores (SDS) established by the World Health Organisation (WHO, available at: ^{2})

Genotype information was available for 7,333 children with respect to six GWA-obesity variants previously reported to show association with BMI or obesity in children

We restricted our analyses to singleton white Europeans plus one randomly selected child from each mother for whom more than one child had entered the study (n = 7,146 children). In total, the dataset contained n = 4,837 observations with full information on both BMI at 9 years and the obesity-risk-allele score, n = 4,613 of which had also measurements of fat mass and fat-free mass recorded.

Quantile regression is a statistical approach of modelling different sample percentiles (‘quantiles’) of an outcome variable by a number of explanatory variables ^{th} percentile (0.90 quantile) - and, like linear regression, uses all available data, irrespective of the percentile modelled. In both cases, regression coefficients quantify potential effects on the specific parameter (mean or quantile) of the outcome distribution on a population level. This means that linear regression coefficients for a binary risk factor can be interpreted as difference of the mean value of the outcome distribution between subjects exposed and not exposed. Similarly, quantile regression coefficients for a binary risk factor represent the difference of the respective quantile in the estimated outcome distribution in subjects exposed vs. not exposed (irrespectively of how many exposed and not exposed subjects lie above or below the respective quantile). In summary, quantile regression leads to more comprehensive results, because it allows to assess any part of the outcome distribution.

We calculated separate quantile regression models with either BMI-SDS, fat mass index or fat-free mass index as outcome variable and the obesity-risk-allele score as explanatory variable, assessing the 0.03, 0.1, 0.2,…, 0.9, and 0.97 quantiles of the respective outcome variable. We adjusted models with fat mass index and fat-free mass index as an outcome for sex, age and height. Models for BMI-SDS were initially not adjusted for sex and age, since BMI-SDS was defined based on sex- and age-specific transformations. In a sensitivity analysis, we examined whether adjustment for sex and age changed the results from the models for BMI-SDS.

We calculated 95% confidence intervals (CIs) for quantile regression effect estimates using bootstrap methods

All calculations were carried out with the statistical software R 2.6.2 (

The children analysed had a mean BMI of 17.6 kg/m^{2}, a mean BMI-SDS of 0.35 and a mean fat mass index of 4.3 kg/m^{2} at 9 years of age (^{2}), BMI-SDS (0.40) and fat mass index (4.4 kg/m^{2}).

Variable | Mean (SD)/n (%) |

Children's BMI [kg/m^{2}] |
17.6 (2.8) |

Children's BMI standard deviation score (SDS) | 0.35 (1.14) |

Fat mass index [kg/m^{2}] |
4.3 (2.4) |

Fat-free mass index [kg/m^{2}] |
12.6 (1.0) |

Age [y] | 9.9 (0.3) |

Girls | n = 2,450 (50.7%) |

Overweight/obese children |
n = 993 (20.5%) |

Obese children |
n = 191 (3.9%) |

The linear regression estimates indicated a mean increase of 0.08 units (95% confidence interval (CI): 0.07, 0.10) in BMI-SDS and of 0.13 kg/m^{2} (95% CI: 0.09, 0.16) in fat mass index per allele increase in the obesity-risk-allele score (^{th} percentile) regression. The respective quantile regression coefficients were positive for any BMI-SDS or fat mass index percentile. This indicates that the obesity-risk-allele score was associated with shifts to higher values in any category of both BMI-SDS and fat mass index.

Outcome variable | LR | 0.03p | 0.10p | 0.20p | 0.30p | 0.40p | 0.50p | 0.60p | 0.70p | 0.80p | 0.90p | 0.97p |

BMI-SDS | 0.08 | 0.04 | 0.06 | 0.06 | 0.07 | 0.09 | 0.10 | 0.10 | 0.10 | 0.10 | 0.09 | 0.09 |

[0.07, 0.10] | [0.00, 0.08] |
[0.03, 0.08] | [0.04, 0.08] | [0.05, 0.10] | [0.07, 0.11] | [0.07, 0.12] | [0.08, 0.12] | [0.07, 0.13] | [0.06, 0.13] | [0.06, 0.13] | [0.05, 0.13] | |

Fat mass index | 0.13 | 0.04 | 0.04 | 0.05 | 0.07 | 0.10 | 0.13 | 0.17 | 0.17 | 0.19 | 0.24 | 0.38 |

[0.09, 0.16] | [0.01, 0.06] | [0.02, 0.06] | [0.03, 0.07] | [0.05, 0.10] | [0.07, 0.13] | [0.10, 0.17] | [0.12, 0.21] | [0.11, 0.22] | [0.12, 0.25] | [0.15, 0.32] | [0.26, 0.49] | |

Fat-free mass index | 0.03 | 0.00 | 0.01 | 0.03 | 0.03 | 0.02 | 0.02 | 0.03 | 0.04 | 0.03 | 0.03 | 0.07 |

[0.01, 0.04] | [-0.03, 0.02] | [-0.01, 0.03] | [0.01, 0.05] | [0.01, 0.04] | [0.01, 0.04] | [0.01, 0.04] | [0.01, 0.05] | [0.02, 0.06] | [0.01, 0.05] | [0.01, 0.06] | [0.03, 0.12] |

However, the estimated effects increased by child's BMI-SDS and fat mass index percentiles, pointing to an additional shift of the upper percentiles. While the associations were of similar size in medium and high BMI-SDS quantiles (40^{th} percentile and above), increasing effect sizes over the whole distribution were observed with respect to children's fat mass index (^{th} percentile) child was increased by 0.13 kg/m^{2} (95% CI: 0.09, 0.16) per additional allele of the obesity-risk-allele score. This risk factor was associated with an average difference of 0.24 (95% CI: 0.15, 0.32) kg/m^{2} in children at the 90^{th} percentile and of 0.38 (95% CI: 0.26, 0.49) units at the 97^{th} percentile per allele. The effects on fat-free mass index were weaker compared to those on fat mass index, and there was no clear pattern of increasing effect sizes by fat-free mass index percentiles. The sensitivity analyses for BMI-SDS with adjustment for sex and age yielded virtually identical results compared to those without adjustment (data not shown).

The dots represent specific FMI percentiles (0.03 percentile, 0.1 to 0.9 deciles, and 0.97 percentile) in the quantile regression model with adjustment for sex, age and height and are connected by dashes to visualize trends by outcome percentiles. The grey horizontal lines represent the linear regression coefficients and their respective confidence intervals (dashed).

Our analyses showed evidence of weight status dependent effects of genetic risk factors for overweight on body composition: In general, the obesity-risk-allele score was associated with an increase in any part of the BMI and fat mass distributions. However, particularly with respect to body fat mass, the effect size was directly modified by the percentile of the outcome variable. These results suggest that genetic risk factors influence body composition not only continuously over the whole distribution, but also to a stronger extent in heavier children. An additional implication of our findings might lie in risk prediction. If gene-environment, and gene-gene, interactions have such marked effects on fat mass as suggested by our findings, it is therefore possible that genetic variants and environmental determinants might have far stronger predictive abilities for obesity among children who are already overweight, or those with obese parents.

The mechanisms by which certain genetic variants contribute to higher body mass are still largely unclear. We hypothesize that our findings reflect one of the following two potential mechanisms: Firstly, hitherto unknown obesity risk genes could interact with, or modify, the effects of the genetic factors examined in this study. However, as yet there is little evidence for marked gene-gene interactions between known disease-related variants. Interestingly, one study reported differential associations of the FTO gene for Whites and African-Americans with respect to obesity

Another potential explanation is that genetic risk factors for overweight may cause an increased susceptibility to certain environmental obesity risk factors. The presence of gene-environment interactions could also explain the similar quantile regression patterns found for environmental risk factors of overweight in previous studies

A particular strength of our study is the appliance of quantile regression which offers a more comprehensive approach than linear regression. While linear regression focuses on shifts of the mean which may be caused by a true shift of the mean with a shift of the entire distribution or a shift in the upper tail or lower tail only, quantile regression allows differentiating shifts in different parts of the distribution. Therefore, this approach enabled us to reveal an additional shift of the upper percentiles of BMI-SDS and fat mass in children, additionally to the previous shown mean shift of these two outcomes

A potential limitation of our study might consist in the high drop-out rate (44.7%) between enrolment and 9-year visit. Reasons for loss to follow-up were that children were no longer eligible, did not respond to the invitation letter for the 9-year visit, refused or failed to attend the visit. However, it is difficult to imagine why nonparticipation might account for different effects of genotype data on different parts of the body composition distribution.

Selection bias due to children with missing genotype data should not be a major issue with respect to our analyses, since there were no substantial differences in BMI or fat mass index at 9 years between responders and non-responders.

Potential intercorrelation in close family members is another important issue in studies assessing effects of genetic predispositions. We had therefore restricted our analyses to one child per mother in the dataset.

In conclusion, for genetic risk factors of childhood overweight, stronger associations in children with higher levels of BMI and fat mass were observed. Interaction between genetic and environmental risk factors might provide a potential explanation of these findings.

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The UK Medical Research Council, the Wellcome Trust and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors, and AB and RvK will serve as guarantors for the contents of this paper.