Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci.
Dissecting the genes involved in complex traits can be confounded by multiple factors, including extensive epistatic interactions among genes, the involvement of epigenetic regulators, and the variable expressivity of traits. Although quantitative trait locus (QTL) analysis has been a powerful tool for localizing the chromosomal regions underlying complex traits, systematically identifying the causal genes remains challenging. Here, through its application to plasma levels of high-density lipoprotein cholesterol (HDL) in mice, we demonstrate a strategy for narrowing QTL that utilizes comparative genomics and bioinformatics techniques. We show how QTL detected in multiple crosses are subjected to both combined cross analysis and haplotype block analysis; how QTL from one species are mapped to the concordant regions in another species; and how genomewide scans associating haplotype groups with their phenotypes can be used to prioritize the narrowed regions. Then we illustrate how these individual methods for narrowing QTL can be systematically integrated for mouse chromosomes 12 and 15, resulting in a significantly reduced number of candidate genes, often from hundreds to <10. Finally, we give an example of how additional bioinformatics resources can be combined with experiments to determine the most likely quantitative trait genes.
The Jackson Laboratory, Bar Harbor, Maine 04609, USA.
Sarah L
SL
Burgess-Herbert
Allison
A
Cox
Shirng-Wern
SW
Tsaih
Beverly
B
Paigen
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003433:Crosses, Genetic; D030541:Databases, Genetic; D006239:Haplotypes; D008075:Lipoproteins, HDL / Q000235:genetics; D051379:Mice; D040641:Quantitative Trait Loci
D008075:Lipoproteins, HDL