Practical Applications of the Bioinformatics Toolbox for Narrowing Quantitative Trait Loci

Abstract

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.

Dissection of Genetic Factors Modulating Fetal Growth in Cattle Indicates a Substantial Role of the Non-SMC Condensin I Complex, Subunit G (NCAPG) Gene

“…Thus, if indeed there is a species-spanning homologous locus affecting mammalian bone growth, IBSP as well as secreted phosphoprotein 1 (SPP1, also known as osteopontin) and matrix extracellular phosphoglycoprotein (MEPE), all well described for their effects on bone metabolism (Beck et al 2000;Nampei et al 2004;Karadag and Fisher 2006), but located centromeric of IBSP on BTA6, should be excluded as candidate genes for this locus. Our results demonstrate that comparative QTL information originating from different species can help to refine the genetic background of a complex trait in the target species as suggested by Burgess-Herbert et al (2008).…”

Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus

“…More than half of the 386 minor QTL were also repeatedly identified 2-10 times. Cloning such QTL via fine mapping may be unlikely, but the application of bioinformatics (such as meta-analysis, comparative genomics, and haplotype association mapping) and/or transcriptomics to narrow QTL/QTG (quantitative trait genes) without fine mapping seems promising (Price 2006;Burgess-Herbert et al 2008;Norton et al 2008). Recently, four minor QTL for disease resistance in rice, each explaining ,5% of the phenotypic variation, have been isolated successfully by a strategy involving candidate genes that integrated expression profiling, bioinformatics, and functional complementation analysis (Hu et al 2008).…”

Refined Mapping of the Renal Failure Rf-3 Quantitative Trait Locus

“…29 -31 We used comparative genomics to both validate the utility of cloning this gene by position and further reduce the interval. This strategy has been used by several groups to reduce complex disease QTL intervals in the mouse and human for atherosclerosis, [32][33][34][35] hypertension, 32,36 and renal disease. 26 Sheehan et al 26 compared mouse and human homologous renal function QTL to narrow the candidate region, Albq5.…”