1a. Objectives (from AD-416):
Using DNA markers, determine the genetic relatedness among 35 major ancestral lines of U.S. soybean varieties and 54 exotic accessions used as parent in our breeding program. Identify the genomic differences between the gene pool of U.S. soybean varieties and the alternative gene pool that we have created from an independent set of exotic soybean lines. Determine the changes in the two gene pools that have occurred as a result of selection for yield. Identify genomes regions that are associated with changes that have increased yield and determine which regions are the same and which are different between the two gene pools.
1b. Approach (from AD-416):
All annual accessions in the USDA Soybean Germplasm Collection are being characterized with 50,000 single nucleotide polymorphisms (SNPs). This will provide the most extensive genetic characterization of any germplasm collection in the world. From these data we will track genomic changes that have occurred through 80 years of soybean breeding in the U.S. For over 30 years we have been developing high yielding experimental lines derived from more than 40 soybean introductions that have not been used in commercial soybean breeding. The highest yielding experimental lines have yielded significantly more than the best public cultivars when widely tested over years. These lines and the progenitor lines tracing back to the original introductions will be characterized with the same 50,000 SNP markers. Genetic comparison will be made between the two sets of ancestral lines and genomic changes made through 30 years of selection within this alternative gene pool will be compared with the changes made in the commercially used gene pool.
3. Progress Report:
Data on over 40,000 SNP markers from the nearly 100 ancestral lines of U.S. soybean breeding and from approximately 50 exotic accessions that have contributed to high yielding experimental lines in our breeding programs have been obtained. This data set is being used to compare the genetic relationships between these two sets of lines and develop procedures that will be used to compare other larger sets of lines using similar data.