Description
Identifying the genes underlying quantitative trait loci (QTL) for disease has proven difficult, mainly due to the low resolution of the approach and the complex genetics involved. However, recent advances in bioinformatics and the availability of genetic resources now make it possible to narrow the genetic intervals and test candidate genes. In addition to identifying the causative genes, defining the pathways that are affected by these QTL is of major importance as it can give us insight into the disease process and provide evidence to support candidate genes. In this study we mapped three significant and one suggestive QTL on Chromosomes (Chrs) 1, 4, 15, and 17, respectively, for increased albumin excretion (measured as albumin-to-creatinine ratio) in a cross between the MRL/MpJ and SM/J mouse inbred strains. By combining data from several sources and by utilizing gene expression data, we identified Tlr12 as a likely candidate for the Chr 4 QTL. Through the mapping of 33,881 transcripts measured by microarray on kidney RNA from each of the 173 male F2 animals, we identified several downstream pathways associated with these QTL. Among these were the glycan degradation, leukocyte migration, and antigen presenting pathways. We demonstrate that by combining data from multiple sources, we can identify not only genes that are likely to be causal candidates for QTL, but also the pathways through which these genes act to alter phenotypes. This combined approach provides valuable insights into the causes and consequences of renal disease.