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Meta-Analysis
. 2013 Sep 1;22(17):3597-607.
doi: 10.1093/hmg/ddt205. Epub 2013 May 12.

Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course

Collaborators, Affiliations
Meta-Analysis

Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course

Mariaelisa Graff et al. Hum Mol Genet. .

Abstract

Genetic loci for body mass index (BMI) in adolescence and young adulthood, a period of high risk for weight gain, are understudied, yet may yield important insight into the etiology of obesity and early intervention. To identify novel genetic loci and examine the influence of known loci on BMI during this critical time period in late adolescence and early adulthood, we performed a two-stage meta-analysis using 14 genome-wide association studies in populations of European ancestry with data on BMI between ages 16 and 25 in up to 29 880 individuals. We identified seven independent loci (P < 5.0 × 10⁻⁸) near FTO (P = 3.72 × 10⁻²³), TMEM18 (P = 3.24 × 10⁻¹⁷), MC4R (P = 4.41 × 10⁻¹⁷), TNNI3K (P = 4.32 × 10⁻¹¹), SEC16B (P = 6.24 × 10⁻⁹), GNPDA2 (P = 1.11 × 10⁻⁸) and POMC (P = 4.94 × 10⁻⁸) as well as a potential secondary signal at the POMC locus (rs2118404, P = 2.4 × 10⁻⁵ after conditioning on the established single-nucleotide polymorphism at this locus) in adolescents and young adults. To evaluate the impact of the established genetic loci on BMI at these young ages, we examined differences between the effect sizes of 32 published BMI loci in European adult populations (aged 18-90) and those observed in our adolescent and young adult meta-analysis. Four loci (near PRKD1, TNNI3K, SEC16B and CADM2) had larger effects and one locus (near SH2B1) had a smaller effect on BMI during adolescence and young adulthood compared with older adults (P < 0.05). These results suggest that genetic loci for BMI can vary in their effects across the life course, underlying the importance of evaluating BMI at different ages.

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Figures

Figure 1.
Figure 1.
Potential secondary signal at POMC contributing to BMI in young adults and adolescents. (A) Plot with unconditioned results illustrating the most significant SNPs (rs2118404 and rs1561288) and the previously reported SNP (rs713586) in the unconditional analysis. Rs1561288 was the most significant SNP in this region in the primary analysis, but in the subset of studies used for the conditional analysis, rs2118404 (which has r2 = 1 with rs1561288) was the most significant SNP. (B) Plot highlighting relevant SNPs after conditioning on previous reported locus, rs713586.

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