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

Authors

Roseann Peterson, Virginia Commonwealth UniversityFollow
Hermine Maes, Virginia Commonwealth UniversityFollow
Peng Lin, Washington University
John R. Kramer, University of Iowa
Victor M. Hesselbrock, University of Connecticut
Lance O. Bauer, University of Connecticut
John I. Nurnberger, Indiana University
Howard J. Edenberg, Indiana University
Danielle Dick, Virginia Commonwealth UniversityFollow
Bradley T. Webb, Virginia Commonwealth UniversityFollow

Document Type

Article

Original Publication Date

2014

Journal/Book/Conference Title

BMC Genomics

Volume

15

DOI of Original Publication

10.1186/1471-2164-15-368

Comments

Originally published at http://dx.doi.org/10.1186/1471-2164-15-368

Date of Submission

August 2014

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.

Rights

© 2014 Peterson et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

Is Part Of

VCU Psychiatry Publications