Identification of domestication-related loci associated with flowering time and seed size in soybean with the RAD-seq genotyping method

Flowering time and seed size are traits related to domestication. However, identification of domestication-related loci/genes of controlling the traits in soybean is rarely reported. In this study, we identified a total of 48 domestication-related loci based on RAD-seq genotyping of a natural population comprising 286 accessions. Among these, four on chromosome 12 and additional two on chromosomes 11 and 15 were associated with flowering time, and four on chromosomes 11 and 16 were associated with seed size. Of the five genes associated with flowering time and the three genes associated with seed size, three genes Glyma11g18720, Glyma11g15480 and Glyma15g35080 were homologous to Arabidopsis genes, additional five genes were found for the first time to be associated with these two traits. Glyma11g18720 and Glyma05g28130 were co-expressed with five genes homologous to flowering time genes in Arabidopsis, and Glyma11g15480 was co-expressed with 24 genes homologous to seed development genes in Arabidopsis. This study indicates that integration of population divergence analysis, genome-wide association study and expression analysis is an efficient approach to identify candidate domestication-related genes.

High Resolution Melt (HRM) analysis is an efficient tool to genotype EMS mutants in complex crop genomes

Background: Targeted Induced Loci Lesions IN Genomes (TILLING) is increasingly being used to generate and identify mutations in target genes of crop genomes. TILLING populations of several thousand lines have been generated in a number of crop species including Brassica rapa. Genetic analysis of mutants identified by TILLING requires an efficient, high-throughput and cost effective genotyping method to track the mutations through numerous generations. High resolution melt (HRM) analysis has been used in a number of systems to identify single nucleotide polymorphisms (SNPs) and insertion/deletions (IN/DELs) enabling the genotyping of different types of samples. HRM is ideally suited to high-throughput genotyping of multiple TILLING mutants in complex crop genomes. To date it has been used to identify mutants and genotype single mutations. The aim of this study was to determine if HRM can facilitate downstream analysis of multiple mutant lines identified by TILLING in order to characterise allelic series of EMS induced mutations in target genes across a number of generations in complex crop genomes. Results: We demonstrate that HRM can be used to genotype allelic series of mutations in two genes, BraA.CAX1a and BraA.MET1.a in Brassica rapa. We analysed 12 mutations in BraA.CAX1.a and five in BraA.MET1.a over two generations including a back-cross to the wild-type. Using a commercially available HRM kit and the Lightscanner™ system we were able to detect mutations in heterozygous and homozygous states for both genes. Conclusions: Using HRM genotyping on TILLING derived mutants, it is possible to generate an allelic series of mutations within multiple target genes rapidly. Lines suitable for phenotypic analysis can be isolated approximately 8-9 months (3 generations) from receiving M3 seed of Brassica rapa from the RevGenUK TILLING service.

Evaluation of the use of high-density SNP genotyping to implement UPOV Model 2 for DUS testing in barley

Developments in high-throughput genotyping provide an opportunity to explore the application of marker technology in distinctness, uniformity and stability (DUS) testing of new varieties. We have used a large set of molecular markers to assess the feasibility of a UPOV Model 2 approach: “Calibration of threshold levels for molecular characteristics against the minimum distance in traditional characteristics”. We have examined 431 winter and spring barley varieties, with data from UK DUS trials comprising 28 characteristics, together with genotype data from 3072 SNP markers. Inter varietal distances were calculated and we found higher correlations between molecular and morphological distances than have been previously reported. When varieties were grouped by kinship, phenotypic and genotypic distances of these groups correlated well. We estimated the minimum marker numbers required and showed there was a ceiling after which the correlations do not improve. To investigate the possibility of breaking through this ceiling, we attempted genomic prediction of phenotypes from genotypes and higher correlations were achieved. We tested distinctness decisions made using either morphological or genotypic distances and found poor correspondence between each method.