On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study

• Published in: PLoS genetics Vol. 6; no. 6; p. e1000981
• Main Authors: Paré, Guillaume, Cook, Nancy R., Ridker, Paul M., Chasman, Daniel I.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2010; Public Library of Science (PLoS)
• Subjects: Body mass index; C-reactive protein; Charitable foundations; Design; Diabetes and Endocrinology/Obesity; Female; Genetic aspects; Genetic Association Studies - methods; Genetics; Genetics and Genomics/Bioinformatics; Genetics and Genomics/Complex Traits; Genome, Human; Genomes; Genotype; Genotype & phenotype; Health aspects; Humans; Hypothesis testing; Inequality; Insulin; Insulin resistance; Intercellular Adhesion Molecule-1 - blood; Intercellular Adhesion Molecule-1 - genetics; Methods; Obesity; Physiological aspects; Polymorphism, Single Nucleotide; Proteins; Quantitative Trait Loci; Single nucleotide polymorphisms; Smoking; Studies; Women; Canada; United States
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1000981

Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene-gene and gene-environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation.