On the association of common and rare genetic variation influencing body mass index: a combined SNP and CNV analysis
1 Virginia Institute for Psychiatric and Behavioral Genetics, Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Biotech I, 800 E. Leigh Street, Richmond, VA 23298-0126, USA
2 Virginia Institute for Psychiatric and Behavioral Genetics, Department of Human and Molecular Genetics, Massey Cancer Center, School of Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA
3 Department of Psychiatry, Washington University, St. Louis, MO 63105, USA
4 Department of Psychiatry, University of Iowa, Iowa City, IA 52240, USA
5 Department of Psychiatry, School of Medicine, University of Connecticut, Farmington, CT 06030, USA
6 Institute of Psychiatric Research, Department of Psychiatry, School of Medicine, Indiana University, Indianapolis, IN 46226, USA
7 Department of Biochemistry and Molecular Biology, School of Medicine, Indiana University, Indianapolis, IN 46226, USA
8 Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA
9 Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, School of Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA
BMC Genomics 2014, 15:368 doi:10.1186/1471-2164-15-368Published: 14 May 2014
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.
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).
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.