Among maize (Zea maize L.) breeders, there is a heightened awareness of the necessity for both maintaining genetic diversity for crop improvement and improving the quality of genetic resource management. Restriction fragment length polymorphisms (RFLPs) and isozymes can serve as genetic markers for estimating divergence or diversity; however, the limited number of polymorphic isozyme loci available and the labor intensive and time consuming nature of RFLPs make their use for this purpose prohibitive. Simple sequence repeats (SSRs), when resolved using agarose gels, may be a viable and costeffective alternative to RFLPs and isozymes. Ninety‐four elite maize inbred lines, representative of the genetic diversity among lines derived from the Corn Belt Dent and Southern Dent maize races, were assayed for polymorphism at 70 SSR marker loci using agarose gels. The 365 alleles identified served as raw data for estimating genetic similarities among these lines. The patterns of genetic divergence revealed by the SSR polymorphisms were consistent with known pedigrees. A cluster analysis placed the inbred lines in nine clusters that correspond to major heterotic groups or market classes for North American maize. A unique fingerprint for each inbred line could be obtained from as few as five SSR loci. The utility of polymerase chain reaction (PCR)‐based markers such as SSRs for measuring genetic diversity, for assigning lines to heterotic groups and for genetic fingerprinting equals or exceeds that of RFLP markers, a property that may prove a valuable asset for a maize breeding program.

Published studies focused on characterizing the allelopathy-based weed suppression by rye cover crop mulch have provided varying and inconsistent estimates of weed suppression. Studies were initiated to examine several factors that could influence the weed suppressiveness of rye: kill date, cultivar, and soil fertility. Ten cultivars of rye were planted with four rates of nitrogen fertilization, and tissue from each of these treatment combinations was harvested three times during the growing season. Concentrations of a known rye allelochemical DIBOA (2,4-dihydroxy-1,4-(2H)benzoxazine-3-one) were quantified from the harvested rye tissue using high performance liquid chromatography (HPLC). Phytotoxicity observed from aqueous extracts of the harvested rye tissue correlated with the levels of DIBOA recovered in harvested tissue. The amount of DIBOA in rye tissue varied depending on harvest date and rye cultivar, but was generally lower with all cultivars when rye was harvested later in the season. However, the late maturing variety 'Wheeler' retained greater concentrations of DIBOA in comparison to other rye cultivars when harvested later in the season. The decline in DIBOA concentrations as rye matures, and the fact that many rye cultivars mature at different rates may help explain why estimates of weed suppression from allelopathic agents in rye have varied so widely in the literature.

A physically anchored consensus map is foundational to modern genomics research; however, construction of such a map in oat (Avena sativa L., 2n = 6x = 42) has been hindered by the size and complexity of the genome, the scarcity of robust molecular markers, and the lack of aneuploid stocks. Resources developed in this study include a modified SNP discovery method for complex genomes, a diverse set of oat SNP markers, and a novel chromosome-deficient SNP anchoring strategy. These resources were applied to build the first complete, physically-anchored consensus map of hexaploid oat. Approximately 11,000 high-confidence in silico SNPs were discovered based on nine million inter-varietal sequence reads of genomic and cDNA origin. GoldenGate genotyping of 3,072 SNP assays yielded 1,311 robust markers, of which 985 were mapped in 390 recombinant-inbred lines from six bi-parental mapping populations ranging in size from 49 to 97 progeny. The consensus map included 985 SNPs and 68 previously-published markers, resolving 21 linkage groups with a total map distance of 1,838.8 cM. Consensus linkage groups were assigned to 21 chromosomes using SNP deletion analysis of chromosome-deficient monosomic hybrid stocks. Alignments with sequenced genomes of rice and Brachypodium provide evidence for extensive conservation of genomic regions, and renewed encouragement for orthology-based genomic discovery in this important hexaploid species. These results also provide a framework for high-resolution genetic analysis in oat, and a model for marker development and map construction in other species with complex genomes and limited resources.

Key message The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Abstract Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat ( Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici) . Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes. Electronic supplementary material The online version of this article (10.1007/s00122-019-03276-6) contains supplementary material, which is available to authorized users.

Six hundred thirty five oat (Avena sativa L.) lines and 4561 single-nucleotide polymorphism (SNP) loci were used to evaluate population structure, linkage disequilibrium (LD), and genotypephenotype association with heading date. The first five principal components (PCs) accounted for 25.3% of genetic variation. Neither the eigenvalues of the first 25 PCs nor the cross-validation errors from K = 1 to 20 model-based analyses suggested a structured population. However, the PC and K = 2 model-based analyses supported clustering of lines on spring oat vs. southern United States origin, accounting for 16% of genetic variation (p < 0.0001). Single-locus F-statistic (F ST ) in the highest 1% of the distribution suggested linkage groups that may be differentiated between the two population subgroups. Population structure and kinship-corrected LD of r 2 = 0.10 was observed at an average pairwise distance of 0.44 cM (0.71 and 2.64 cM within spring and southern oat, respectively). On most linkage groups LD decay was slower within southern lines than within the spring lines. A notable exception was found on linkage group Mrg28, where LD decay was substantially slower in the spring subpopulation. It is speculated that this may be caused by a heterogeneous translocation event on this chromosome. Association with heading date was most consistent across location-years on linkage groups Mrg02, Mrg12, Mrg13, and Mrg24. Core Ideas• An oat association-mapping panel contributed by active breeding programs worldwide.• Characterized population structure and found subdivisions related to adaptation• Characterized genome-wide and chromosomespecific linkage disequilibrium• Performed association-mapping and post hoc modeling of heading date• Found several consistently associated QTL