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Key Message: In this paper, we determine the genetic architecture controlling leaf flecking in maize and investigate its relationship to disease resistance and the defense response. Flecking is defined as a mild, often environmentally dependent lesion phenotype observed on the leaves of several commonly used maize inbred lines. Anecdotal evidence suggests a link between flecking and enhanced broad-spectrum disease resistance. Neither the genetic basis underlying flecking nor its possible relationship to disease resistance has been systematically evaluated. The commonly used maize inbred Mo17 has a mild flecking phenotype. The IBM-advanced intercross mapping population, derived from a cross between Mo17 and another commonly used inbred B73, has been used for mapping a number of traits in maize including several related to disease resistance. In this study, flecking was assessed in the IBM population over 6 environments. Several quantitative trait loci for flecking were identified, with the strongest one located on chromosome 6. Low but moderately significant correlations were observed between stronger flecking and higher disease resistance with respect to two diseases, southern leaf blight and northern leaf blight and between stronger flecking and a stronger defense response.

Quantitative trait locus (QTL) mapping in plants dates to the 1980s (Stuber et al. Crop Sci 27: 639-648, 1987; Paterson et al. Nature 335: 721-726, 1988), but earlier studies were often hindered by the expense and time required to identify large numbers of polymorphic genetic markers that differentiated the parental genotypes and then to genotype them on large segregating mapping populations. High-throughput sequencing has provided an efficient means to discover single nucleotide polymorphisms (SNPs) that can then be assayed rapidly on large populations with array-based techniques (Gupta et al. Heredity 101: 5-18, 2008). Alternatively, high-throughput sequencing methods such as restriction site-associated DNA sequencing (RAD-Seq) (Davey et al. Nat Rev Genet 12: 499-510, 2011; Baird et al. PloS ONE 3: e3376, 2008) and genotyping-by-sequencing (GBS) (Elshire et al. PLoS One 6: 2011; Glaubitz et al. PLoS One 9: e90346, 2014) can be used to identify and genotype polymorphic markers directly. Linkage disequilibrium (LD) between markers and causal variants is needed to detect QTL. The earliest QTL mapping methods used backcross and F2 generations of crosses between inbred lines, which have high levels of linkage disequilibrium (dependent entirely on the recombination frequency between chromosomal positions), to ensure that QTL would have sufficiently high linkage disequilibrium with one or more markers on sparse genetic linkage maps. The downside of this approach is that resolution of QTL positions is poor. The sequencing technology revolution, by facilitating genotyping of vastly more markers than was previously feasible, has allowed researchers to map QTL in situations of lower linkage disequilibrium, and consequently, at higher resolution. We provide a review of methods to identify QTL with higher precision than was previously possible. We discuss modifications of the traditional biparental mapping population that provide higher resolution of QTL positions, QTL fine-mapping procedures, and genome-wide association studies, all of which are greatly facilitated by high-throughput sequencing methods. Each of these procedures has many variants, and consequently many details to consider; we focus our chapter on the consequences of practical decisions that researchers make when designing QTL mapping studies and when analyzing the resulting data. The ultimate goal of many of these studies is to resolve a QTL to its causal sequence variation.

Background: A previous study reported a comprehensive quantitative trait locus (QTL) and genome wide association study (GWAS) of southern leaf blight (SLB) resistance in the maize Nested Association Mapping (NAM) panel. Since that time, the genomic resources available for such analyses have improved substantially. An updated NAM genetic linkage map has a nearly six-fold greater marker density than the previous map and the combined SNPs and read-depth variants (RDVs) from maize HapMaps 1 and 2 provided 28.5 M genomic variants for association analysis, 17 fold more than HapMap 1. In addition, phenotypic values of the NAM RILs were re-estimated to account for environment-specific flowering time covariates and a small proportion of lines were dropped due to genotypic data quality problems. Comparisons of original and updated QTL and GWAS results confound the effects of linkage map density, GWAS marker density, population sample size, and phenotype estimates. Therefore, we evaluated the effects of changing each of these parameters individually and in combination to determine their relative impact on marker-trait associations in original and updated analyses., Results: Of the four parameters varied, map density caused the largest changes in QTL and GWAS results. The updated QTL model had better cross-validation prediction accuracy than the previous model. Whereas joint linkage QTL positions were relatively stable to input changes, the residual values derived from those QTL models (used as inputs to GWAS) were more sensitive, resulting in substantial differences between GWAS results. The updated NAM GWAS identified several candidate genes consistent with previous QTL fine-mapping results., Conclusions: The highly polygenic nature of resistance to SLB complicates the identification of causal genes. Joint linkage QTL are relatively stable to perturbations of data inputs, but their resolution is generally on the order of tens or more Mbp. GWAS associations have higher resolution, but lower power due to stringent thresholds designed to minimize false positive associations, resulting in variability of detection across studies. The updated higher density linkage map improves QTL estimation and, along with a much denser SNP HapMap, greatly increases the likelihood of detecting SNPs in linkage with causal variants. We recommend use of the updated genetic resources and results but emphasize the limited repeatability of small-effect associations.

The intermated B73 x Mo17 (IBM) population, an advanced intercross recombinant inbred line population derived from a cross between the maize lines B73 (susceptible) and Mo17 (resistant), was evaluated in four environments for resistance to southern leaf blight (SLB) disease caused by Cochliobolus heterostrophus race O. Two environments were artificially inoculated, while two were not inoculated and consequently had substantially lower disease pressure. Four common SLB resistance quantitative trait loci (QTL) were identified in all environments, two in bin 3.04 and one each in bins 1.10 and 8.02/3. There was no significant correlation between disease resistance and days to anthesis. A direct comparison was made between SLB QTL detected in two populations, independently derived from the same parental cross: the IBM advanced intercross population and a conventional recombinant inbred line population. Several QTL for SLB resistance were detected in both populations, with the IBM providing between 5 and, in one case, 50 times greater mapping resolution.