Identification of novel loci associated with maturity and yield traits in early maturity soybean plant introduction lines.

BACKGROUND: To continue to meet the increasing demands of soybean worldwide, it is crucial to identify key genes regulating flowering and maturity to expand the cultivated regions into short season areas. Although four soybean genes have been successfully utilized in early maturity breeding programs, new genes governing maturity are continuously being identified suggesting that there remains as yet undiscovered loci governing agronomic traits of interest. The objective of this study was to identify novel loci and genes involved in a diverse set of early soybean maturity using genome-wide association (GWA) analyses to identify loci governing days to maturity (DTM), flowering (DTF) and pod filling (DTPF), as well as yield and 100 seed weight in Canadian environments. To do so, soybean plant introduction lines varying significantly for maturity, but classified as early varieties, were used. Plants were phenotyped for the five agronomic traits for five site-years and GWA approaches used to identify candidate loci and genes affecting each trait. RESULTS: Genotyping using genotyping-by-sequencing and microarray methods identified 67,594 single nucleotide polymorphisms, of which 31,283 had a linkage disequilibrium < 1 and minor allele frequency > 0.05 and were used for GWA analyses. A total of 9, 6, 4, 5 and 2 loci were detected for GWA analyses for DTM, DTF, DTPF, 100 seed weight and yield, respectively. Regions of interest, including a region surrounding the E1 gene for flowering and maturity, and several novel loci, were identified, with several loci having pleiotropic effects. Novel loci affecting maturity were identified on chromosomes five and 13 and reduced maturity by 7.2 and 3.3 days, respectively. Novel loci for maturity and flowering contained genes orthologous to known Arabidopsis flowering genes, while loci affecting yield and 100 seed weight contained genes known to cause dwarfism. CONCLUSIONS: This study demonstrated substantial variation in soybean agronomic traits of interest, including maturity and flowering dates as well as yield, and the utility of GWA analyses in identifying novel genetic factors underlying important agronomic traits. The loci and candidate genes identified serve as promising targets for future studies examining the mechanisms underlying the related soybean traits.

A Systematic Gene-Centric Approach to Define Haplotypes and Identify Alleles on the Basis of Dense Single Nucleotide Polymorphism Datasets.

CORE IDEAS: A gene-centric approach for haplotype definition was developed and implemented in R. The tool allows for allelic characterization at given loci in germplasm collections. Allelic status at four maturity genes is predicted on the basis of marker genotyping data. Assessing the allelic diversity within a germplasm collection and identifying individuals carrying favorable alleles is challenging. Advances in high-throughput technologies allow the genotyping of many individuals for thousands of markers but bridging the gap between single nucleotide polymorphisms (SNPs) and relevant alleles remains difficult. We developed a systematic approach that defines haplotypes from large SNP catalogs that aims to identify haplotypes that can be equated to alleles at given genes. Unlike haplotype visualization tools, our approach selects SNP markers that flank a gene and define haplotypes that correspond to this gene's alleles. We tested this approach on four known soybean [Glycine max (L.) Merr.] maturity genes (E1, GmGia, GmPhyA3, and GmPhyA2) in a collection of 67 lines and two genotypic datasets [a SNP array and genotyping-by-sequencing (GBS)]. For E1, GmGia, and GmPhyA3, we identified SNP haplotypes such that the allele found at these genes could be accurately predicted from the haplotype in 97.3% of the cases. For these genes, of the 12 known alleles in the collection, 10 and 8 could be correctly predicted from the haplotypes found with the SNP array and GBS datasets, with success rates of 98 and 97% for all allele-line combinations, respectively. The approach proved equally successful for data derived from a SNP array and GBS. However, in the case of GmPhyA2, a lack of markers in the genomic region prevented the identification of alleles, regardless of the dataset. We demonstrate the feasibility and reproducibility of our approach and identify limits to its applicability.

A molecular marker map in 'Kanota' x 'Ogle' hexaploid oat (Avena spp.) enhanced by additional markers and a robust framework.

Molecular mapping of cultivated oats was conducted to update the previous reference map constructed using a recombinant inbred (RI) population derived from Avena byzantina C. Koch cv. Kanota x Avena sativa L. cv. Ogle. In the current work, 607 new markers were scored, many on a larger set of RI lines (133 vs. 71) than previously reported. A robust, updated framework map was developed to resolve linkage associations among 286 markers. The remaining 880 markers were placed individually within the most likely framework interval using chi2 tests. This molecular framework incorporates and builds on previous studies, including physical mapping and linkage mapping in additional oat populations. The resulting map provides a common tool for use by oat researchers concerned with structural genomics, functional genomics, and molecular breeding.