Obesitysusceptibility loci and the tails of the pediatric BMI distribution.  
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PMID: 23408508 Owner: NLM Status: MEDLINE 
Abstract/OtherAbstract:

OBJECTIVE: To determine whether previously identified adult obesity susceptibility loci were associated uniformly with childhood BMI across the BMI distribution. DESIGN AND METHODS: Children were recruited through the Children's Hospital of Philadelphia (n = 7,225). Associations between the following loci and BMI were assessed using quantile regression: FTO (rs3751812), MC4R (rs12970134), TMEM18 (rs2867125), BDNF (rs6265), TNNI3K (rs1514175), NRXN3 (rs10146997), SEC16B (rs10913469), and GNPDA2 (rs13130484). BMI zscore (age and gender adjusted) was modeled as the dependent variable, and genotype risk score (sum of risk alleles carried at the 8 loci) was modeled as the independent variable. RESULTS: Each additional increase in genotype risk score was associated with an increase in BMI zscore at the 5th, 15th, 25th, 50th, 75th, 85th, and 95th BMI zscore percentiles by 0.04 (±0.02, P = 0.08), 0.07 (±0.01, P = 9.58 × 10(7) ), 0.07 (±0.01, P = 1.10 × 10(8) ), 0.09 (±0.01, P = 3.13 × 10(22) ), 0.11 (±0.01, P = 1.35 × 10(25) ), 0.11 (±0.01, P = 1.98 × 10(20) ), and 0.06 (±0.01, P = 2.44 × 10(6) ), respectively. Each additional increase in genotype risk score was associated with an increase in mean BMI zscore by 0.08 (±0.01, P = 4.27 × 10(20) ). CONCLUSION: Obesity risk alleles were more strongly associated with increases in BMI zscore at the upper tail compared to the lower tail of the distribution. 
Authors:

Jonathan A Mitchell; Hakon Hakonarson; Timothy R Rebbeck; Struan F A Grant 
Publication Detail:

Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, NonU.S. Gov't 
Journal Detail:

Title: Obesity (Silver Spring, Md.) Volume: 21 ISSN: 1930739X ISO Abbreviation: Obesity (Silver Spring) Publication Date: 2013 Jun 
Date Detail:

Created Date: 20130805 Completed Date: 20140228 Revised Date: 20140408 
Medline Journal Info:

Nlm Unique ID: 101264860 Medline TA: Obesity (Silver Spring) Country: United States 
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Languages: eng Pagination: 125660 Citation Subset: IM 
Copyright Information:

Copyright © 2013 The Obesity Society. 
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MeSH Terms  
Descriptor/Qualifier:

Adolescent Alleles Body Mass Index* BrainDerived Neurotrophic Factor / genetics, metabolism Child Child, Preschool DNABinding Proteins / genetics, metabolism European Continental Ancestry Group / genetics GeneEnvironment Interaction Genetic Loci* Genetic Predisposition to Disease* Humans Linear Models Logistic Models Nerve Tissue Proteins / genetics, metabolism Obesity / genetics* Philadelphia Polymorphism, Single Nucleotide Proteins / genetics, metabolism 
Grant Support  
ID/Acronym/Agency:

DP1 OD006445/OD/NIH HHS; F32 CA162847/CA/NCI NIH HHS; F32CA162847/CA/NCI NIH HHS; P30 ES013508/ES/NIEHS NIH HHS; R01 HD056465/HD/NICHD NIH HHS; R01 HD056465/HD/NICHD NIH HHS; U01 HG006830/HG/NHGRI NIH HHS 
Chemical  
Reg. No./Substance:

0/BrainDerived Neurotrophic Factor; 0/DNABinding Proteins; 0/FTO protein, human; 0/Nerve Tissue Proteins; 0/Proteins; 0/RGPR protein, human; 0/brainderived neurotrophic factor, human; 0/neurexin IIIalpha 
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Full Text  
Journal Information Journal ID (nlmjournalid): 101264860 Journal ID (pubmedjrid): 32902 Journal ID (nlmta): Obesity (Silver Spring) Journal ID (isoabbrev): Obesity (Silver Spring) ISSN: 19307381 ISSN: 1930739X 
Article Information Download PDF nihmssubmitted publication date: Day: 17 Month: 1 Year: 2013 Print publication date: Month: 6 Year: 2013 pmcrelease publication date: Day: 01 Month: 12 Year: 2013 Volume: 21 Issue: 6 First Page: 1256 Last Page: 1260 PubMed Id: 23408508 ID: 3661695 DOI: 10.1002/oby.20319 ID: NIHMS433108 
Obesitysusceptibility loci and the tails of the pediatric BMI distribution  
Jonathan A. Mitchell1  
Hakon Hakonarson234  
Timothy R. Rebbeck1  
Struan F.A. Grant234  
1Center for Genetics and Complex Traits, Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia 

2Center for Applied Genomics, Abramson Research Center, Children's Hospital of Philadelphia, Philadelphia 

3Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA 

4Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA 

Correspondence: Corresponding Author: Jonathan Mitchell, 423 Guardian Drive, 222 Blockley Hall, Philadelphia, PA 19104 jmitch@mail.med.upenn.edu Phone: 856 392 9626 Fax: 215 573 1050 
Since 2007, genomewide association studies (GWAS) have identified adult obesitysusceptibility loci, and some of those loci are associated with childhood obesity (^{1}^{}^{4}). Linear regression and logistic regression were used in those studies, and body mass index (BMI) was used as a measure of obesity (^{1}^{}^{4}). The former regression approach determined if risk alleles were associated with mean BMI, whereas the latter regression approach determined if risk alleles increased the likelihood of being classified as obese (^{5}). A limitation of modeling the mean BMI is that the associations at the upper and lower tails of the distribution are not distinguished, and the upper tail of the BMI distribution is of primary interest when studying childhood obesity. Categorizing children as obese recognizes the importance of the upper tail of the BMI distribution; however, such categorization of a continuous variable reduces statistical power; and considers individuals in proximity, but on opposite sides of the category cutoff, as being very different, when in reality they are very similar (^{6}).
In contrast to linear regression and logistic regression, quantile regression allows for the study of predictors across the entire BMI distribution, without having to categorize, and may provide additional insight into the relationship between obesitysusceptibility loci and BMI (^{7}). To the best of our knowledge only a single study in the UK has used quantile regression to study obesitysusceptibility loci across the childhood BMI distribution (^{8}). In that study each additional risk allele carried was associated with increases in BMI, and the associations were stronger at the upper tail, compared to the lower tail, of the BMI distribution (^{8}). The purpose of our study was to determine if previously identified adult obesitysusceptibility loci were uniformly associated with BMI across the BMI distribution, in a large sample of U.S. children and adolescents.
Participants were recruited through the Children's Hospital of Philadelphia network between 2006 and 2010 (n=7225). All participants were of European ancestry, unrelated, and aged between 2 and 18 years old (^{3}). Parental informed consent was given for each participant, and the Institutional Review Board of the Children's Hospital of Pennsylvania approved the study.
The participant's height (m) and weight (kg) were measured and BMI was calculated (kg/m^{2}). BMI's were converted to age and gender specific zscores(^{9}). Participants with a BMI zscore of ≤ 3 or ≥3 were excluded from the study as this may reflect measurement error, or a Mendelian cause of extreme obesity in the case a ≥3 zscore (n=265).
DNA was extracted from blood samples and highthroughput genotyping was performed at the Center for Applied Genomics at the Children's Hospital of Philadelphia, using Illumina Infinium™ II HumanHap550 BeadChip (^{4}). All genotyped SNPs had call rates >95%, minor allele frequencies >1%, and did not deviate from Hardy Weinberg equilibrium.
Based on the linear and logistic regression analyses in the two previous studies involving our cohort of children, associations between the following adult obesitysusceptibility loci and BMI were observed: FTO (rs3751812), MC4R (rs12970134), TMEM18 (rs2867125), BDNF (rs6265), TNNI3K (rs1514175), NRXN3 (rs10146997), SEC16B (rs10913469), and GNPDA2 (rs13130484)(^{3}, ^{4}). In the present study these SNPs were selected for reanalysis using quantile regression.
Quantile regression was used to address the aims of the study (^{7}, ^{8}). The coefficients at the 5^{th}, 15^{th}, 25^{th}, 50^{th}, 75^{th}, 85^{th}, and 95^{th} BMI percentiles are presented. Each SNP was biallelic and was coded 0, 1, or 2 to represent the number of risk alleles carried. A genotype risk score was created by summing the number of risk alleles carried at the 8 obesitysusceptibility loci. The coefficients at each BMI percentile are interpreted as the change in BMI zscore for each additional risk allele carried. The 95% confidence intervals and standard errors (SE) were calculated based on 500 bootstrap samples. All analyses were performed using the simultaneous quantile regression command in Stata 12.1 (StataCorp LP, College Station, TX)(^{10}).
For the SNPs at SEC16B, TMEM18, GNPDA2, BDNF, NRXN3, FTO, and MC4R no associations were observed with BMI at the 5^{th} BMI percentile (Table 1). The SNP at FTO was associated with an increase in BMI at the 15^{th} BMI percentile (β=0.10, SE ±0.04), and the association gained in strength towards the 85^{th} BMI percentile (β=0.19, SE ±0.03) (Table 1). A similar pattern of increasing association from the 15^{th} to the 85^{th} BMI percentile was observed for the SNPs at SEC16B, GNPDA2, BDNF, and NRXN3 (Table 1). Relatively constant associations were observed between the SNPs at TMEM18 and MC4R between the 15^{th} and 85^{th} BMI percentiles (Table 1). For the SNP at TNNIK3, associations were observed with BMI at the 5^{th} BMI percentile and between the 50^{th} and 75^{th} BMI percentiles (Table 1). The overall genotype score was not associated with BMI at the 5^{th} BMI percentile, but was associated with BMI at all other percentiles, with the association gaining in strength from the 15^{th} to the 85^{th} BMI percentile (Table 1). At all the loci (except GNPDA2) the strength of the associations weakened towards the null between the 85^{th} and 95^{th} BMI percentile; only associations between the SNPs at FTO and GNPDA2, and the genotype risk score remained at the 95^{th} BMI percentile (Table 1). To help interpret the findings in Table 1, visual representation of BMI zscore distributions by rs3751812 genotype (FTO) are presented in Supplementary Figure 1. The proportion of overweight/obesity was 9.5% higher among the homozygotes for the risk allele at rs3751812 (FTO), compared to homozygotes for the nonrisk allele at rs3751812 (FTO) (Supplementary Figure 1).
Comparisons between linear and quantile regression findings are presented in Figure 1. Based on the point estimates, the linear regression findings tended to overestimate the strength of the association at the lower tail of the BMI distribution (<50^{th} BMI percentile), and underestimate the strength of the association at the upper tail of the BMI distribution (> 50^{th} BMI percentile), especially for the SNPs at SEC16B, GNPDA2, BDNF, NRXN3, and FTO, and for the genotype risk score (Figure 1). Postestimation tests found that the 85^{th} percentile point estimate was greater than the 15^{th} percentile point estimate for the overall score (0.04, SE ±0.02, p=0.017); and for the FTO (0.09, SE ±0.04, p=0.03) and GNPDA2 SNPs (0.09, SE ±0.04, p=0.05).
Compared to linear regression findings, we found that SNPs at SEC16B, GNPDA2, BDNF, NRXN3, and FTO were more strongly associated with childhood BMI at the upper tail of the BMI distribution, and more weakly associated with childhood BMI at the lower tail of the BMI distribution. These findings complement those reported in a study of children (^{8}), and in a study of adults (^{11}). Collectively, these data demonstrate that modeling the mean BMI may have underestimated the strength of the association between obesitysusceptibility loci in the context of obesity.
We hypothesize that the nonuniform associations observed across the BMI distribution may be explained by geneenvironment interactions. For example, those at the lower tail of the BMI distribution may be more physically active, or consume fewer calories, compared to those at the upper tail of the BMI distribution, thereby modifying the associations. In support of this hypothesis, there is evidence that more physical activity attenuates the association between FTO and BMI in children (^{12}^{}^{14}). However, not all studies support this modifying effect in children (^{15}), and there is little evidence that caloric intakes modify the association between FTO and childhood obesity (^{16}). Importantly, these studies modeled the mean BMI, or BMI categories, and it would be of interest to determine if geneenvironment interactions are uniform across the BMI distribution. It is a limitation that no environmental exposure data are available in our cohort of children to directly test for geneenvironment interactions across the BMI distribution. This modeling approach, coupled with large sample sizes and valid environmental measures, could advance the study of childhood obesity geneenvironment interactions.
An interesting observation was the decreasing strength of the association between the obesitysusceptibility loci and childhood BMI from the 85^{th} to the 95^{th} BMI percentiles. This pattern of association may be due to the biological limitations of increasing BMI greatly beyond the 95^{th} percentile, and so finding the strongest association at the 95^{th} BMI percentile would not be expected. We observed associations for FTO, GNPDA2 and the genotype risk score at the 95^{th} BMI percentile, and a larger sample size would likely detect associations at the 95^{th} BMI percentile for the other loci. The standard errors and 95% confidence intervals were narrower at the upper tail of the BMI distribution compared to the lower tail of the BMI distribution for all the loci, supporting the consensus that a larger sample size could detect associations at the 95^{th} BMI percentile.
In conclusion, we found that previously identified adult obesitysusceptibility loci were more strongly associated with childhood BMI at the upper tail of the BMI distribution. Geneenvironment interactions may explain the nonuniform associations across the BMI distribution, and quantile regression could be used to better understand geneenvironment interactions in relation to childhood obesity.
Notes
FN1Disclosure
No conflicts of interest to declare.
We thank all participating subjects and families, and research staff who provided expert assistance with genotyping, data collection and data management. The study is supported in part by a Research Development Award from the Cotswold Foundation (HH and SG) and National Institutes of Health R01 HD056465 (SG).
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