Submitted to: NCR-167 Corn Breeding Committee Meeting
Publication Type: Abstract only
Publication Acceptance Date: 12/15/2002
Publication Date: N/A
Citation: Interpretive Summary:
Technical Abstract: The Germplasm Enhancement of Maize (GEM) Project is a cooperative effort between the USDA-ARS and public and industry researchers. The goal is to introgress exotic germplasm into elite U.S. Cornbelt inbred backgrounds to enhance genetic gain for yield, disease and pest resistance, and value-added traits. The Latin American Maize Project (LAMP) previously screened over 12,000 accessions and prioritized 268 as having maximum potential to enhance U.S. germplasm. Breeding crosses were made between these and the cooperators' elite proprietary inbreds. Selection and subsequent testing have produced a number of promising or competitive inbreds. The objectives of this study are (i) to estimate the proportion of the genetic composition coming from the racially diverse, exotic accession donors in 10 sets of the top 10 S2 GEM lines and (ii) to identify regions of the genome where segregation is distorted toward or away from exotic donor alleles. These may highlight regions that are key to fitness. The lines were chosen based on the ratio of mean yield and moisture to remove biases due to lateness. The lines are being genotyped with SSR and MITE (Miniature Inverted-repeat Transposable Elements) markers to determine allelic contribution. The 10 top lines per cross were selected to exclude mediocre genotypes, yet provide a reasonably powerful frequency test. A likelihood-ratio goodness of fit test (G-test) detects deviations from Hardy-Weinberg equilibrium. Large contiguous regions from one parent reveal either essential linkage blocks or lack of recombination. Independent evidence on the fitness contribution at highly skewed loci will be demonstrated with the inclusion of several different populations involving the same key exotic accession, Cuba 164. Finally, the most inferior lines from several pedigrees are being genotyped to verify that strong associations are not due to random sampling.