Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 1, 2010
Publication Date: December 18, 2010
Citation: Kump, K.L., Holland, J.B., Jung, M.T., Wolters, P., Balint Kurti, P.J. 2010. Joint Analysis of Near Isogenic and Recombinant Inbred Line Populations Yields Precise Positional Estimates for QTL. The Plant Genome. 3:142-153. Interpretive Summary: Southern leaf blight (SLB) is an important pathogen of maize in hot humid areas of the world. We have identified a gene that confers a high level of quantitative (i.e. partial) resistance to SLB on chromosome 3 of maize. The cloning of genes with quantitative effects usually proceeds in two phases : an initial genome-wide mapping stage in which the approximate position of the gene is located within the genome and a subsequent fine-mapping stage in which the precise position of the gene is determined. Usually data generated during the first stage is not used in the second stage. Here we demonstrate that in the fine mapping of the SLB resistance gene on chromosome 3, this “first-stage” data can be effectively recycled to augment data generated in the second stage and improve estimates of gene location. Furthermore we have identified several candidate genes that may underlie the resistance effect. This method has potential broad applicability to the mapping and cloning of plant genes with quantitative effects.
Technical Abstract: Near isogenic lines (NILs) are typically constructed to fine-map quantitative trait loci (QTL). The data generated for the initial QTL mapping are usually ignored for fine-mapping purposes. However, combining already-available data from initial recombinant inbred line (RIL) studies with new data from NIL experiments would increase the number of recombination events sampled and lead to more precise position and effect estimates. Several QTL for resistance to southern leaf blight of maize, had been previously mapped in two RIL populations, each independently-derived from a cross between the maize lines B73 and Mo17. In each case the largest QTL was in bin 3.04. Here, two NIL pairs differing for the 3.04 QTL were derived from B73 and Mo17 and used to create two distinct F2:3 family populations which were assessed for SLB resistance. By accounting for the segregation of the other QTL in the original RIL data, we were able to combine this earlier data with the new genotypic and phenotypic data derived from the F2:3 families. This joint analysis yielded a narrower QTL confidence interval than could have been derived from analysis of any of the data sets alone. This is the first reported combined QTL analysis across discrete generations segregating for a common pair of alleles. Because data sets of this type are commonly produced, this approach is likely to prove widely applicable.