Search

Data generated for initial quantitative trait loci (QTL) mapping using recombinant inbred line (RIL) populations are usually ignored during subsequent fi ne-mapping using near-isogenic lines (NILs). Combining both datasets would increase the number of recombination events sampled and generate better position and effect estimates. Previously, several QTL for resistance to southern leaf blight of maize were mapped in two RIL populations, each independently derived from a cross between the lines B73 and Mo17. In each case the largest QTL was in bin 3.04. Here, two NIL pairs differing for this QTL were derived and used to create two distinct F2:3 family populations that were assessed for southern leaf blight (SLB) resistance. By accounting for segregation of the other QTL in the original RIL data, we were able to combine these data with the new genotypic and phenotypic data from the F2:3 families. Joint analysis yielded a narrower QTL support interval compared to that derived from analysis of any one of the data sets alone, resulting in the localization of the QTL to a less than 0.5 cM interval. Candidate genes identifi ed within this interval are discussed. This methodology allows combined QTL analysis in which data from RIL populations is combined with data derived from NIL populations segregating for the same pair of alleles. It improves mapping resolution over the conventional approach with virtually no additional resources. Because data sets of this type are commonly produced, this approach is likely to prove widely applicable. THE UTILITY of quantitative trait locus (QTL) mapping to identify specifi c genes aff ecting complex traits is limited by a lack of precision of QTL position estimates and biased estimates of their eff ects (Holland, 2007). Increasing the number of lines sampled, the number of markers genotyped, or number of replications grown will reduce these problems (Beavis, 1998). In addition, a diffi culty of QTL analysis is the simultaneous segregation of multiple QTL within a test population, resulting in reduced detection power, and infl ated eff ect estimates of those QTL detected, a problem that becomes severe in small population samples (Beavis, 1998; Schon et al., 2004). To study more precisely the eff ect and position of a specifi c QTL, a uniform genetic background, diff ering only for the target QTL, should be constructed to eliminate all other sources of genetic variation. Near-isogenic lines (NILs), pairs of lines that are identical except for a single genomic segment, are ideal for this purpose (Szalma et al., 2007; Tanksley, 1993). Near-isogenic lines can be derived through repeated backcrossing to a recurrent parent. Molecular Published in The Plant Genome 3:142–153. Published 18 Dec. 2010. doi: 10.3835/plantgenome2010.05.0011 © Crop Science Society of America 5585 Guilford Rd., Madison, WI 53711 USA An open-access publication All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. K.L. Kump, Dep. of Crop Science, North Carolina State Univ., Raleigh, NC 27695; J.B. Holland, USDA-ARS, Plant Science Research Unit and Dep. of Crop Science, North Carolina State Univ., Raleigh NC 27695; M.T. Jung and P. Wolters, DuPont Crop Genetics Research, Experimental Station, P.O. Box 80353, Wilmington, DE 19880; P.J. Balint-Kurti, USDA-ARS, Plant Science Research Unit and Dep. of Plant Pathology, North Carolina State Univ., Raleigh NC 27695. Received 27 May 2010. *Corresponding author (Peter.Balint-Kurti@ars.usda.gov). Abbreviations: AUDPC, area under disease progress curve; BLUP, best linear unbiased predictor; CTAB, cetyltrimethylammonium bromide; DTA, days to anthesis; HIF, heterogenous inbred family; HR, hypersensitive response; HST, host-selective toxin; IcM, IBM centiMorgan, IBM, intermated B73 × Mo17; LOD, log of odds; LRR, leucine-rich repeat; NBS, nucleotide binding site; NIL, near-isogenic line; PCR, polymerase chain reaction; QTL, quantitative trait locus/ loci; RIL, recombinant inbred line; SLB, southern leaf blight; SNP, single nucleotide polymorphism; SSR, simple sequence repeat; STWMD, standardized weighted mean disease; TBE, Tris-BorateEDTA; WMD, weighted mean disease.

The mixed linear model (MLM) is an advanced statistical technique applicable to many fields of science. The multivariate MLM can be used to model longitudinal data, such as repeated ratings of disease resistance taken across time. In this study, using an example data set from a multi-environment trial of northern leaf blight disease on 290 maize lines with diverse levels of resistance, multivariate MLM analysis was performed and its utility was examined. In the population and environments tested, genotypic effects were highly correlated across disease ratings and followed an autoregressive pattern of correlation decay. Because longitudinal data are often converted to the univariate measure of area under the disease progress curve (AUDPC), comparisons between univariate MLM analysis of AUDPC and multivariate MLM analysis of longitudinal data were made. Univariate analysis had the advantage of simplicity and reduced computational demand, whereas multivariate analysis enabled a comprehensive perspective on disease development, providing the opportunity for unique insights into disease resistance. To aid in the application of multivariate MLM analysis of longitudinal data on disease resistance, annotated program syntax for model fitting is provided for the software ASReml.