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. 2014 May 14;15(1):368.
doi: 10.1186/1471-2164-15-368.

On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis

Roseann E Peterson  1 , Hermine H Maes, Peng Lin, John R Kramer, Victor M Hesselbrock, Lance O Bauer, John I Nurnberger Jr, Howard J Edenberg, Danielle M Dick, Bradley T Webb
Affiliations

On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis

Roseann E Peterson et al. BMC Genomics. .

Abstract

Background: As the architecture of complex traits incorporates a widening spectrum of genetic variation, analyses integrating common and rare variation are needed. Body mass index (BMI) represents a model trait, since common variation shows robust association but accounts for a fraction of the heritability. A combined analysis of single nucleotide polymorphisms (SNP) and copy number variation (CNV) was performed using 1850 European and 498 African-Americans from the Study of Addiction: Genetics and Environment. Genetic risk sum scores (GRSS) were constructed using 32 BMI-validated SNPs and aggregate-risk methods were compared: count versus weighted and proxy versus imputation.

Results: The weighted SNP-GRSS constructed from imputed probabilities of risk alleles performed best and was highly associated with BMI (p=4.3×10(-16)) accounting for 3% of the phenotypic variance. In addition to BMI-validated SNPs, common and rare BMI/obesity-associated CNVs were identified from the literature. Of the 84 CNVs previously reported, only 21-kilobase deletions on 16p12.3 showed evidence for association with BMI (p=0.003, frequency=16.9%), with two CNVs nominally associated with class II obesity, 1p36.1 duplications (OR=3.1, p=0.009, frequency 1.2%) and 5q13.2 deletions (OR=1.5, p=0.048, frequency 7.7%). All other CNVs, individually and in aggregate, were not associated with BMI or obesity. The combined model, including covariates, SNP-GRSS, and 16p12.3 deletion accounted for 11.5% of phenotypic variance in BMI (3.2% from genetic effects). Models significantly predicted obesity classification with maximum discriminative ability for morbid-obesity (p=3.15×10(-18)).

Conclusion: Results show that incorporating validated effect sizes and allelic probabilities improve prediction algorithms. Although rare-CNVs did not account for significant phenotypic variation, results provide a framework for integrated analyses.

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Figures

Figure 1
Figure 1
Distribution of BMI-risk alleles by ancestry. Note: BMI = body mass index kg/m2.
See this image and copyright information in PMC

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