2022 article
Genomic prediction for the Germplasm Enhancement of Maize project
Rogers, A. R., Bian, Y., Krakowsky, M., Peters, D., Turnbull, C., Nelson, P., & Holland, J. B. (2022, October 24). PLANT GENOME.
MeSH headings : Zea mays / genetics; Plant Breeding; Genomics; Alleles; Edible Grain / genetics
TL;DR:
Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly, so using GS models trained within program should be able to more effectively deliver on its mission to broaden the genetic diversity of the U.S. maize crop.
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Source: Web Of Science
Added: November 7, 2022
AbstractThe Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public‐ and private‐sector maize (Zea mays L.) breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects progeny lines with high topcross yield potential from crosses between elite temperate lines and exotic parents. The GEM project has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multienvironment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM project may contribute to increases in the rate of genetic gain. Here we evaluated the prediction ability of GS models trained on 6 yr of topcross evaluations from the two GEM programs in Raleigh, NC, and Ames, IA, documenting prediction abilities ranging from 0.36 to 0.75 for grain yield and from 0.78 to 0.96 for grain moisture when models were cross‐validated within program and heterotic group. Predicted genetic gain from GS ranged from 0.95 to 2.58 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly. Using GS models trained within program, the GEM programs should be able to more effectively deliver on its mission to broaden the genetic base of U.S. germplasm.