Predicting quantitative trait levels by modeling SNP interaction.

Abstract

Predicting phenotype from genotype is difficult when the phenotype is affected by a gene with numerous weakly penetrant alleles that differ only in the pattern of their single nucleotide polymorphisms (SNPs). While it is probable that SNP interactions affect phenotype, to our knowledge no one has determined the most effective way of evaluating whether SNPs interact and of modeling the interaction. Therefore, to explore this issue, we investigate here three methods of modeling SNP interaction using data from Genetic Analysis Workshop 12. Since major gene 5 (MG5) has sequence information and explains 37% of the variation in quantitative trait 5 (Q5), we focus on using SNPs within MG5 to predict Q5 among individuals who married into the pedigree. As a preliminary screening step, we reduced the number of SNPs from 269 to 34 based on their association with Q5. In our first models we assumed that SNPs affected Q5 in a simple additive manner. These models explained 34% and 15% of the variation in Q5 in women and men, respectively. Our second model was a linear model, which used individual SNPs and simple interaction terms as predictors. These models explained 36% and 16% of the variation in Q5 levels for women and men, respectively. Our last model was a "hit"-based model which was motivated by the hypothesis that disequilibrium between SNPs may reflect the fact that SNPs affect phenotype by acting in concert with other SNPs within their "disequilibrium set." Thus, the number of hits within the disequilibrium sets were used as predictors. These models explained 35% and 19% of the variation in Q5 for women and men, respectively. Our results suggest that phenotype can be predicted from complex patterns of weakly penetrant SNPs using relatively simple models. We concluded that SNP interaction either was not included in the simulation model, or had only a weak impact on Q5 levels.