Enhancing the breeding gene pool of wheat using accessions in gene banks as demonstrated by the Watkins collection
摘要
This study demonstrates an approach to identify accessions carrying desirable alleles affecting traits of breeding interest, paving the way for the development of environmentally resilient cultivars.
AbstractRecent advances in genome technology have opened opportunities to explore old germplasm collections for accessions possessing traits valuable in cultivar development. This study analyzed genotypic and phenotypic data from the pre-Green Revolution Watkins collection, to find loci linked to climate adaptation and rust resistance (leaf, stem, and stripe) using genome scans and single-nucleotide polymorphism (SNP)-level fixation index (FST) analysis. Key signature regions were revealed on chromosomes 2A and 3B. Consistent with the past findings, a highly differentiated region was detected on 6B in the cultivar subset of the collection, highlighting this region a breeding target. Chromosome-based scans located a region previously associated with RVA peak viscosity and breakdown, spanning 12.5 Mb and 13 Mb on 7D, harboring 13 genes, including the most pleiotropic TRAESCS7D02G026700 (ET2/AP2/ERF). SNP-FST analysis noticeably differentiated several loci between spring and winter wheat, including 5A:582,550,290 linked to the Vrn-A1 gene. Likewise, SNP-level FST analysis between rust resistant and susceptible accessions detected loci associated with resistance such as 1B:670,137,479–670,543,997 linked to Lr46/Yr29, and 2B:763,926,560 in a region harboring the Yr7/Yr5/YrSP gene cluster. Significant phenotypic variation was observed between the resistant and susceptible alleles at the high-FST loci indicating the role of these loci in disease resistance. At all the top ten high-FST loci, eight and thirteen accessions carried the resistance alleles for leaf and stripe rust, respectively, suggesting these accessions as potential pre-breeding candidates. This study highlights how historic germplasm collections, combined with their genetic and environmental data, can enhance breeding diversity and identify pre-breeding materials for developing resilient cultivars.