A Comparative Genomics Approach to the Identification of QTL Candidate Genes

Abstract

Despite rapid advances in sequencing technology, many commercially relevant species remain unsequenced, and many that are sequenced have very poorly annotated genomes. There is therefore still considerable interest in using comparative approaches to exploit information from well-characterised model organisms in order to better understand related species. This paper develops a statistical method for automating part of a comparative genomics bioinformatic pipeline for the identification of genes and genomic regions in a model organism associated with a QTL region in an unsequenced species. A non-parametric Bayesian statistical model is used for characterising the density of a large number of BLAST hits across a model species genome. The method is illustrated using a test problem demonstrating that markers associated with Bovine hemoglobin can be automatically mapped to a region of the human genome containing human hemoglobin genes. Consequently, by exploiting the (relatively) high quality of genome annotation for model organisms and humans it is possible to quickly identify candidate genes in those well-characterised genomes relevant to the quantitative trait of interest.