Investigations on Pleiotropy for Growth Related Traits based on Bayesian Residuals by Principal Component Analyses using F2 Mice Dataset

Abstract

Admixture mapping could be used to detect inheritance patterns of complex traits associated with Single Nucleotide Polymorphisms. Genetic stratification in the population could be detected and corrected using Bayesian residuals in admixture mapping. Prediction and use of principal components may lead to detection of pleiotropic genes. Ancestral genotypes of founder populations were available for the F₂ mice for growth related traits at 8 weeks of age. Genotypes of founders could be used to predict ancestry informativeness of the markers. The objective of this study was to detect pleiotropic genes for growth related traits using Bayesian residuals and principal component analyses by F2 admixture model under various ancestry informative levels of markers. The model identified that SNPs from chromosomes 6, 10 and 11 potentially have (suggestive) pleiotropic effects. As was expected higher ancestry informative levels lead to higher test statistics in the admixture model. However none of the SNPs were found statistically significant. Since Bayesian models could be used with non-normal and/or small datasets; employing Bayesian residuals in the admixture model instead of (restricted) maximum likelihood residuals may lead to advantages in practical applications. Principal component analyses could be useful to detect pleiotropic genes.