A Real-Time PCR Differentiating Pantoea stewartii subsp. stewartii From P. stewartii subsp. indologenes in Corn Seed.

Stewart's wilt of corn caused by the bacterium Pantoea stewartii subsp. stewartii is a seed-borne disease of major phytosanitary importance. Many countries have imposed restrictions on corn seed imports from regions where the disease occurs to prevent the potential introduction of the pathogen. Current laboratory testing methods (enzyme-linked immunosorbent assay [ELISA] and polymerase chain reaction [PCR]) cannot readily distinguish P. stewartii subsp. stewartii from the closely related subspecies Pantoea stewartii subsp. indologenes. However, P. stewartii subsp. indologenes, a nonpathogen on corn, is occasionally found on corn seed as part of the resident bacterial population and can yield false positive test results. A real-time PCR targeting the cpsAB intergenic sequence was developed to specifically detect P. stewartii subsp. stewartii from corn seeds and distinguish it from P. stewartii subsp. indologenes. The assay successfully detected P. stewartii subsp. stewartii from corn seed, and P. stewartii subsp. indologenes-contaminated seed lots, which previously yielded false positives by ELISA and published PCR methods, were negative. The absence of P. stewartii subsp. stewartii and the presence of P. stewartii subsp. indologenes in this seed were confirmed by size differentiation of the cpsAB amplicons in a conventional PCR. By distinguishing the two subspecies, the assays described would avoid false positive results and help prevent unnecessary restrictions on international movement of corn seed.

Brassinosteroid and gibberellin control of seedling traits in maize (Zea mays L.).

In this study, we established two doubled haploid (DH) libraries with a total of 207 DH lines. We applied BR and GA inhibitors to all DH lines at seedling stage and measured seedling BR and GA inhibitor responses. Moreover, we evaluated field traits for each DH line (untreated). We conducted genome-wide association studies (GWAS) with 62,049 genome wide SNPs to explore the genetic control of seedling traits by BR and GA. In addition, we correlate seedling stage hormone inhibitor response with field traits. Large variation for BR and GA inhibitor response and field traits was observed across these DH lines. Seedling stage BR and GA inhibitor response was significantly correlate with yield and flowering time. Using three different GWAS approaches to balance false positive/negatives, multiple SNPs were discovered to be significantly associated with BR/GA inhibitor responses with some localized within gene models. SNPs from gene model GRMZM2G013391 were associated with GA inhibitor response across all three GWAS models. This gene is expressed in roots and shoots and was shown to regulate GA signaling. These results show that BRs and GAs have a great impact for controlling seedling growth. Gene models from GWAS results could be targets for seeding traits improvement.

The genetic architecture of maize height.

Height is one of the most heritable and easily measured traits in maize (Zea mays L.). Given a pedigree or estimates of the genomic identity-by-state among related plants, height is also accurately predictable. But, mapping alleles explaining natural variation in maize height remains a formidable challenge. To address this challenge, we measured the plant height, ear height, flowering time, and node counts of plants grown in >64,500 plots across 13 environments. These plots contained >7300 inbreds representing most publically available maize inbreds in the United States and families of the maize Nested Association Mapping (NAM) panel. Joint-linkage mapping of quantitative trait loci (QTL), fine mapping in near isogenic lines (NILs), genome-wide association studies (GWAS), and genomic best linear unbiased prediction (GBLUP) were performed. The heritability of maize height was estimated to be >90%. Mapping NAM family-nested QTL revealed the largest explained 2.1 ± 0.9% of height variation. The effects of two tropical alleles at this QTL were independently validated by fine mapping in NIL families. Several significant associations found by GWAS colocalized with established height loci, including brassinosteroid-deficient dwarf1, dwarf plant1, and semi-dwarf2. GBLUP explained >80% of height variation in the panels and outperformed bootstrap aggregation of family-nested QTL models in evaluations of prediction accuracy. These results revealed maize height was under strong genetic control and had a highly polygenic genetic architecture. They also showed that multiple models of genetic architecture differing in polygenicity and effect sizes can plausibly explain a population's variation in maize height, but they may vary in predictive efficacy.