Combining QTL Data for HDL Cholesterol Levels from Two Different Species Leads to Smaller Confidence Intervals

QTL analysis detects regions of a genome that are linked to a complex trait. Once a QTL is detected, the region is narrowed via positional cloning in the hope of determining the underlying candidate gene – methods used include creating congenic strains, comparative genomics and gene expression analysis. Combined cross analysis may also be used for species such as the mouse if the QTL is detected in multiple crosses. This process involves the recoding of QTL data on a per-chromosome basis with the genotype recoded based on high- and low-allele status. The data are then combined and analyzed; a successful analysis results in a narrowed and more significant QTL. Using parallel methods, we demonstrate that it is possible to narrow a QTL by combining data from two different species, the rat and the mouse. We combined standardized HDL phenotype values and genotype data for the rat and mouse using information from one rat cross and two mouse crosses. We successfully combined data within homologous regions from rat chromosome 6 onto mouse chromosome 12 and from rat chromosome 10 onto mouse chromosome 11. The combinations and analyses resulted in QTL with smaller confidence intervals and increased LOD scores. The numbers of candidate genes encompassed by the QTL on mouse chromosomes 11 and 12 were reduced from 1343 to 761 genes and 613 to 304 genes, respectively. This is the first time that QTL data from different species were successfully combined; this method promises to be a useful tool for narrowing QTL intervals.