Epidemiological studies show that even small increases in high-density lipoprotein (HDL) cholesterol afford considerable protection against heart disease. Through quantitative trait locus (QTL) mapping in both mice and humans, many genomic regions have been found that contain genes affecting HDL levels. Combining data from our own laboratory with that from the literature shows that most HDL QTLs have been discovered many times, that the recently reported QTLs simply confirm previously reported ones, and that mouse and human QTLs are found in homologous chromosomal regions, suggesting that the same genes determine HDL in these species. We estimate that between 23 and 30 genes account for most population variation in HDL concentrations. Identifying these genes would increase our understanding of what factors cause an increase in HDL levels and may also provide therapeutic targets. Using mouse models, we aim to identify the genes underlying five HDL QTLs in the mouse chosen for further analysis because they are found in homologous regions of the human and mouse genomes; they have been found multiple times in different mouse crosses; and their effects are large enough to work with. For each QTL, we will test the hypothesis that increased HDL protects against atherosclerosis using congenic strains moving the allele causing increased HDL into the B6 genetic background. Using bioinformatics resources, we will narrow each QTL by combining statistical data from several crosses, comparing mouse and human homologies, applying haplotype analysis, and comparing sequence among mouse strains. Using genetic resources, we will further narrow each QTL with recombinant inbred strains, advanced intercross lines, and overlapping congenics. Candidate genes will be tested for expression and sequence differences; highly probable candidates will be proven by transgenic, knockout, and other technologies. Finally, we will build a public database of the raw QTL data that we and others have generated so that new techniques of analysis, such as determining the 95% confidence interval of a QTL, finding gene interactions, and analyzing combined cross data can be applied to published data. ? ?
Showing the most recent 10 out of 27 publications
