Location: Plant Science ResearchTitle: Genomic Prediction for the Germplasm Enhancement of Maize Project
|ROGERS, ANNA - Bayer Corporation|
|BIAN, YANG - Bayer Corporation|
|TURNBULL, CLINT - Bayer Corporation|
|NELSON, PAUL - Bayer Corporation|
|Holland, Jim - Jim|
Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/18/2022
Publication Date: 10/24/2022
Citation: Rogers, A., Bian, Y., Krakowsky, M.D., Peters, D.W., Turnbull, C., Nelson, P., Holland, J.B. 2022. Genomic Prediction for the Germplasm Enhancement of Maize Project. The Plant Genome. 15:4. https://doi.org/10.1002/tpg2.20267.
Interpretive Summary: The Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public and private sector maize breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects lines with high yield from crosses between elite temperate lines and lines developed in different countries. The GEM program has released hundreds of useful breeding lines based on traditional trait-driven selection. Modern genomics technologies can provide large numbers of genetic markers on these lines. We can use genetic marker information in concert with already existing yield trial data to developing genomic prediction models for the GEM program. Here we developed such models to predict grain yield and grain moisture (which measures how quickly different hybrids dry down their grain to facilitate early harvest and better grain storage) for GEM program lines. We demonstrated that the genomic predictions of lines would be at least as effective as direct hybrid yield trial performance for selecting the best yielding and lower moisture lines. This opens the potential to use genomic prediction to improve the genetic gains from selection in the GEM program.
Technical Abstract: The Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public and private sector maize 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 program has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multi-environment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM program may contribute to increases in the rate of genetic gain by 1) increased selection intensity through sampling more germplasm 2) improved selection accuracy compared to early generation topcross trials with limited replication, and 3) reduction of the breeding cycle time. Here we evaluated the prediction ability of GS models trained on five years of GEM topcross evaluations from the two GEM programs in Raleigh, NC and Ames, IA, documenting prediction abilities for grain yield ranging from 0.34 to 0.79 for grain yield and from 0.80 to 0.95 for grain moisture when models were cross-validated within program and heterotic group. Predicted genetic gain from genomic selection ranged from 0.98 to 2.03 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Using GS models trained within-program, each GEM program should be able to effectively expand the pool of selection candidates beyond current phenotyping capacity, contributing to long-term genetic gain.