Nested association mapping (NAM) offers power to resolve complex, quantitative traits to their causal loci. The maize NAM population, consisting of 5,000 recombinant inbred lines (RILs) from 25 families representing the global diversity of maize, was evaluated for resistance to southern leaf blight (SLB) disease. Joint-linkage analysis identified 32 quantitative trait loci (QTLs) with predominantly small, additive effects on SLB resistance. Genome-wide association tests of maize HapMap SNPs were conducted by imputing founder SNP genotypes onto the NAM RILs. SNPs both within and outside of QTL intervals were associated with variation for SLB resistance. Many of these SNPs were within or near sequences homologous to genes previously shown to be involved in plant disease resistance. Limited linkage disequilibrium was observed around some SNPs associated with SLB resistance, indicating that the maize NAM population enables high-resolution mapping of some genome regions.

To capture diverse alleles at a set of loci associated with disease resistance in maize, heterogeneous inbred family (HIF) analysis was applied for targeted QTL mapping and near-isogenic line (NIL) development. Tropical maize lines CML52 and DK888 were chosen as donors of alleles based on their known resistance to multiple diseases. Chromosomal regions ("bins"; n = 39) associated with multiple disease resistance (MDR) were targeted based on a consensus map of disease QTLs in maize. We generated HIFs segregating for the targeted loci but isogenic at ~97% of the genome. To test the hypothesis that CML52 and DK888 alleles at MDR hotspots condition broad-spectrum resistance, HIFs and derived NILs were tested for resistance to northern leaf blight (NLB), southern leaf blight (SLB), gray leaf spot (GLS), anthracnose leaf blight (ALB), anthracnose stalk rot (ASR), common rust, common smut, and Stewart's wilt. Four NLB QTLs, two ASR QTLs, and one Stewart's wilt QTL were identified. In parallel, a population of 196 recombinant inbred lines (RILs) derived from B73 × CML52 was evaluated for resistance to NLB, GLS, SLB, and ASR. The QTLs mapped (four for NLB, five for SLB, two for GLS, and two for ASR) mostly corresponded to those found using the NILs. Combining HIF- and RIL-based analyses, we discovered two disease QTLs at which CML52 alleles were favorable for more than one disease. A QTL in bin 1.06-1.07 conferred resistance to NLB and Stewart's wilt, and a QTL in 6.05 conferred resistance to NLB and ASR.

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 F 2:3 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 F 2:3 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 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.

In this study we examined the effects of two quantitative trait loci (QTL) for southern leaf blight (SLB) resistance on several agronomic traits including disease resistance and yield. B73–3B and B73–6A are two near‐isogenic lines (NILs) in the background of the maize (Zea mays L.) inbred B73, each carrying one introgression (called 3B and 6A respectively) encompassing a QTL for SLB resistance. Sets of isohybrid triplets were developed by crossing B73, B73–3B, and B73–6A to several inbred lines. A subset of these triplets for which the B73–3B and/or B73–6A hybrid was significantly more SLB resistant than the B73 check hybrid was selected and assessed in multi‐environment yield trials with and without disease. In the presence of SLB, 3B was associated with an approximately 3% yield increase over B73. 6A was associated with a yield advantage in the presence of SLB in specific pedigrees where the 6A resistance phenotype was highly expressed. Results suggested that both introgressions might confer a yield cost in the absence of SLB, but only introgression 6A was associated with a statistically significant reduction. We present evidence to suggest that the yield cost is associated with the resistance phenotype rather than with linkage drag.