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.

Genome-wide association study of Fusarium ear rot disease in the U.S.A. maize inbred line collection.

BACKGROUND: Resistance to Fusarium ear rot of maize is a quantitative and complex trait. Marker-trait associations to date have had small additive effects and were inconsistent between previous studies, likely due to the combined effects of genetic heterogeneity and low power of detection of many small effect variants. The complexity of inheritance of resistance hinders the use marker-assisted selection for ear rot resistance. RESULTS: We conducted a genome-wide association study (GWAS) for Fusarium ear rot resistance in a panel of 1687 diverse inbred lines from the USDA maize gene bank with 200,978 SNPs while controlling for background genetic relationships with a mixed model and identified seven single nucleotide polymorphisms (SNPs) in six genes associated with disease resistance in either the complete inbred panel (1687 lines with highly unbalanced phenotype data) or in a filtered inbred panel (734 lines with balanced phenotype data). Different sets of SNPs were detected as associated in the two different data sets. The alleles conferring greater disease resistance at all seven SNPs were rare overall (below 16%) and always higher in allele frequency in tropical maize than in temperate dent maize. Resampling analysis of the complete data set identified one robust SNP association detected as significant at a stringent p-value in 94% of data sets, each representing a random sample of 80% of the lines. All associated SNPs were in exons, but none of the genes had predicted functions with an obvious relationship to resistance to fungal infection. CONCLUSIONS: GWAS in a very diverse maize collection identified seven SNP variants each associated with between 1% and 3% of trait variation. Because of their small effects, the value of selection on these SNPs for improving resistance to Fusarium ear rot is limited. Selection to combine these resistance alleles combined with genomic selection to improve the polygenic background resistance might be fruitful. The genes associated with resistance provide candidate gene targets for further study of the biological pathways involved in this complex disease resistance.

Dominance Effects and Functional Enrichments Improve Prediction of Agronomic Traits in Hybrid Maize.

Single-cross hybrids have been critical to the improvement of maize (Zea mays L.), but the characterization of their genetic architectures remains challenging. Previous studies of hybrid maize have shown the contribution of within-locus complementation effects (dominance) and their differential importance across functional classes of loci. However, they have generally considered panels of limited genetic diversity, and have shown little benefit from genomic prediction based on dominance or functional enrichments. This study investigates the relevance of dominance and functional classes of variants in genomic models for agronomic traits in diverse populations of hybrid maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the U.S. (1106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1640 hybrids). We investigated three agronomic traits: days to silking (DTS), plant height (PH), and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results suggest that dominance and gene annotations improve genomic prediction across diverse populations in hybrid maize.

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.

Comprehensive genotyping of the USA national maize inbred seed bank.

BACKGROUND: Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world. RESULTS: The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits. CONCLUSIONS: The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity.