| Eyespot (caused by Aureobasidium zeae (Narita and Hiratsuka) Dingley (syn. Kabatiella zeae Narita and Hiratsuka) is a fungal leaf disease of maize (Zea mays L.) that can cause significant grain yield reductions. Genetic resistance to eyespot exists in maize, and it is the most recommended method of disease control. The objectives of this study were to identify quantitative trait loci (QTL) for eyespot resistance and to determine the relationship between eyespot resistance and days to flowering. Two segregating populations (F2:3 families) were evaluated across two North Dakota locations in 2004 and 2005. Single nucleotide polymorphism markers and composite interval mapping were utilized for the QTL analyses. A total of 22 QTLs were discovered for eyespot resistance across all maize chromosomes except chromosome 9. The presence of significant QTLs over four environments and two populations was consistent. Early and late ratings detected different QTLs for eyespot resistance. A significant negative correlation was found between eyespot ratings and days to flowering (DTF), but the correlation coefficients were always low (from -0.21 to -0.47). Few QTLs for DTF appear to correspond to QTLs for eyespot resistance. The QTLs on chromosome 8 had the largest genetic effects and explained the largest amount of the phenotypic variation for both traits. Another study determined the effects of marker distribution, the percentage of genome coverage, and population size on the power of QTL identification for eyespot resistance. Two groups of eight genetic maps each were developed using random SNP markers in order to reach 90 and 95% genome coverage, respectively. Percentage of genome coverage, average distance among markers, marker distribution, and QTL identification power were determined. Also, eight sets of 50, 100, and 200 F2:3 families were randomly selected and the QTL identification power, genetic effects, and coefficient of determination compared against the results obtained using F2:3 297 families. The distribution of random makers was always uneven; the expected percentage of genome coverage was never reached; and consequently, the power of QTL identification reduced. When using fewer than 200 families, the power of QTL identification was severely reduced and the estimates of QTL effects inflated. |
