Identification of quantitative trait loci controlling resistance to gray leaf spot disease in maize

Abstract

Breeding maize for gray leaf spot (GLS) resistance has been hindered by the quantitative nature of the inheritance of GLS resistance and by the limitations of selection under less than optimumal disease pressure. In order to identify the quantitative trait loci (QTLs) controlling GLS resistance, a cross was made between B73 (susceptible) and Va14 (resistant) to generate a large F2 population. Six GLS disease assessments were made throughout the disease season for over 1000 F2 plants in 1989, and for 600 F2-derived F3 lines replicated in two blocks in 1990. RFLP analysis for78 marker loci representing all ten maize chromosomes was conducted in 239 F2 individuals including those with the extreme GLS disease phenotypes. The GLS disease scores of the three field evaluations, each averaged over six ratings, were separately used for the interval mapping in order to determine the consistency of the QTL effects. The heavy GLS disease pressure, meticulous disease ratings, and large population size of this study afforded us the sensitivity for detecting QTL effects. QTLs located on three chromosomes (1, 4, and 8) had large effects on GLS resistance, each explaining 35.0-56.0%, 8.8-14.3%, and 7.7-11.0% of the variance, respectively. These three QTL effects were remarkably consistent across three disease evaluations over 2 years and two generations. Smaller QTL effects were also found on chromosomes 2 and 5, but the chromosome-5 effect might be a false positive because it was not repeatable even in the same location. The chromosome-1 QTLs had the largest effect or highest R(2) reported for any quantitative trait to-date. Except for the chromosome-4 gene, which was from the susceptible parent B73, the resistance alleles at all QTL were derived from Va14. The resistance QTLs on chromosomes 1 and 2 appear to have additive effects, but those on chromosomes 4 and 8 are dominant and recessive, respectively. Significant interaction between the QTLs on chromosomes 1 and 4 was detected in all three evaluations. Cumulatively, the four QTLs identified in this study explained 44, 60, and 68% of the variance in F2, and in F3 replications 1 and 2, respectively.

Mapping QTL conferring resistance in maize to gray leaf spot disease caused by Cercospora zeina

“…The QTL with the allele associated with GLS resistance derived from CML444 in bin 4.08 was identified in four environments as well as the joint GLS QTL (Table 2, Additional file 1). Both inbred line VP31 and its progenitor VO163Y have a QTL in this bin [7,24], which also overlap with QTL reported by Bubeck et al [21] and Saghai Maroof et al [22]. The GLS QTL with the SC Malawi allele associated with resistance in bin 7.02/7.03 overlaps with QTL from four studies [3,21,23,26] (Additional file 1).…”

“…Detection of QTL for GLS resistance has been reported to be variable from site to site and season to season [21]. For example, Saghai Maroof et al [22] identified three QTL that were consistent in F2 and F3 generations and seasons, but another two QTL that were not. The use of a RIL population in our study allowed testing in multiple environments, which gave confidence in the detected QTL.…”

“…For example, the hotspot on chromosome one has been reported from populations with diverse origins [3,22,23,30], including a Mo17 allele (bin 1.05) [25]. This may represent a cluster of R genes or alleles.…”

“…V0613Y and ADENT were developed in different breeding programmes on different continents [21,29]. Interestingly, B73 carries a susceptible allele in this bin region in the ADENT X B73rhm population, whereas it confers a resistant allele in the Val14 X B73 population [21,22]. …”

“…We wished to identify sources of resistance to GLS caused by C. zeina in germplasm that had been developed under subtropical conditions in Africa, which could be taken up relatively quickly in local breeding programmes or deployed in hybrid combinations [16]. RIL populations have the advantage of being “immortal” so replicated trials can be planted in multiple sites and seasons, which mitigates the problem that GLS QTL identification can be variable from season to season due to differences in environmental conditions that affect disease severity [21,22]. …”