Enhancing Genetic Gain in Maize with Tropical Germplasm, QTL Mapping, and Spatial Methodologies

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Date

2008-05-16

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Abstract

Advance-cycle breeding is restricting the germplasm base for U.S. maize (Zea Mays L.). Many breeding programs devote efforts to adapt diverse germplasm to U.S. growing conditions, but few are participating in continual enhancement. Incorporating tropical germplasm into U.S. breeding pools could broaden the maize germplasm base, while concomitantly providing favorable alleles for yield and disease resistance. Knowing the genomic regions, or quantitative trait loci (QTL), for disease resistance can enhance gain by permitting selection on marker genotypes in the absence of disease expression. In addition, accounting for spatial variability can improve the precision of experiments and aid breeders in line advancement decisions and QTL mapping. Recombinant inbred (RI) lines were derived from a cross between NC300, a temperate-adapted, all-tropical line, and B104, a Stiff-Stalk-synthetic line. The RI lines were topcrossed to the tester FR615.FR697 (a C103 sister line cross). Resistance QTL for Southern Rust (rust) (Puccinia polysora) were mapped in the topcrosses, while Gray Leaf Spot (GLS) (Cercospora zeae-maydis) QTL were mapped in both the RI lines and topcross populations. A major resistance gene for rust was identified on the short-arm of chromosome 10, while ten GLS QTL mapped to chromosomes 1, 2, 3, 4, 8, and 10. Similar markers on chromosome 1 and 8 flanked three GLS and flowering time QTL pairs, and the resistance alleles were associated with increased flowering time. No flowering time regions co-localized with rust-resistance loci. The major rust-resistance gene and three GLS QTL corresponded to regions mapped in prior populations. The tropical parental allele, NC300, increased resistance at three of these four loci. Extensively haplotyping germplasm at these four consensus regions could aid in forward breeding strategies to efficiently integrate resistance packages into U.S. maize breeding populations. Spatial analyses, such as trend and trend analysis with correlated errors models, can improve precision of genotype means estimates. These analyses often reduce the phenotypic variance among family means, and in doing so, increase the response to selection. A dynamic SAS program, entitled SPATIALPRO, was developed to implement spatial analytical techniques. The program constructs and optimizes several spatial models for each trait and single-environment-trial combination, and chooses a preferred model based on a specified criterion. Results from the preferred model are outputted into SAS data sets. A long term breeding effort was initiated in 1975 to adapt and subsequently enhance tropical germplasm. Founder germplasm included seven double-cross-tropical hybrids. Based on the poor per se performance of the first and second-cycle lines, at least five cycles of S1 recurrent selection (RS) for grain yield has been practiced on two populations derived from these lines. Cycles per se and cycle-topcrosses to LH132.LH51 were grown in separate yield trials to estimate responses to selection. In both instances, grain yield increased linearly across the cycles of selection for each population, but the yield responses across the cycle-topcrosses are approximately half the average annual gains of commercial breeding activities in the U.S. Corn Belt. To determine the current range in combining ability, ninety-six S1 families were sampled from the latest cycles of each population and topcrossed to LH132.LH51. Three topcross families did not differ significantly in yield from the commercial check hybrid average. Variance components estimated from the topcross families suggest that S1 topcross RS is more promising in maintaining relevancy, and appears to be a more favorable method of enhancement, as resources are devoted to families with superior combining ability.

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Keywords

germplasm enhancement, maize, gain

Citation

Degree

PhD

Discipline

Crop Science

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