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Abstract
In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. Although successful, a disadvantage of this method is low mapping resolution, as often several hundred candidate genes fall within the confidence interval for each locus. Methods have been developed to narrow these loci by combining the data from the different crosses, but they rely on the accurate mapping of the QTL and the treatment of the data in a consistent manner. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identified novel QTL and QTL that were mapped to the wrong part of chromosomes. Our new HDL QTL map allows for reliable combining of QTL data and candidate gene analysis, which we demonstrate by identifying Grin3a and Etv6, as candidate genes for QTL on chromosomes 4 and 6, respectively. In addition, we were able to narrow a QTL on Chr 19 to five candidates.
Highlights
In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma high-density lipoprotein (HDL) cholesterol levels
By using bioinformatics techniques, such as haplotyping, which leverages these comapping QTL clusters, we previously showed that a single nucleotide polymorphism (SNP) in Apoa2 is the most likely causative single-nucleotide polymorphism (SNP) underlying the QTL mapped to distal Chr 1 in all crosses where the two strains were polymorphic for this SNP [17]
Careful examination of the QTL mapped to distal Chr 1 in this study showed three clusters of comapping QTL: one cluster of five QTL centered at 70 cM, one cluster of nine QTL centered at 85 cM. The Apoa2 gene (80 cM), and one cluster of four QTL centered at 85 cM
Summary
In the past 15 years, the quantitative trait locus (QTL) mapping approach has been applied to crosses between different inbred mouse strains to identify genetic loci associated with plasma HDL cholesterol levels. We collected 23 raw datasets used for the mapping of previously published HDL QTL and reanalyzed the data from each cross using a consistent method and the latest mouse genetic map. By utilizing this approach, we identified novel QTL and QTL that were mapped to the wrong part of chromosomes. In the past few years, several methods have been developed that combine accumulated data from the different crosses, allowing for narrowing of the CI for QTL and reducing candidate gene lists [10].
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