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Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace
Gregor Gorjanc1,2, Janez Jenko3,4, Sarah J Hearne5
1Biotechnical Faculty, University of Ljubljana, 1000, Ljubljana, Slovenia. gregor.gorjanc@bf.uni-lj.si.
BMC Genomics
|January 7, 2016
Summary
To enhance maize breeding, pre-breeding programs should start directly from landraces, not testcrosses. Genomic selection effectively utilizes diverse genetic resources for improved germplasm, even with limited data recording.
Area of Science:
- Plant Breeding
- Genetics
- Agricultural Science
Background:
- Elite maize germplasm exhibits limited genetic diversity, posing challenges for breeding against new threats.
- Landrace diversity is crucial for overcoming genetic limitations in elite maize varieties.
- The Seeds of Discovery (SeeD) initiative aims to leverage landrace variation for maize improvement.
Purpose of the Study:
- To evaluate designs for initiating a maize pre-breeding program within the SeeD initiative.
- To assess methods for harnessing polygenic variation from landraces using genomic selection.
- To provide decision support for optimizing pre-breeding program design through stochastic simulation.
Main Methods:
- Stochastic simulation was used to evaluate various pre-breeding program designs.
- Factors assessed included initiation approach (landraces, doubled haploids, testcrosses), genetic parameters, and logistical constraints.
- Genomic prediction accuracy was analyzed across different design factors.
Main Results:
- Initiating pre-breeding programs directly from landraces is recommended over testcrosses.
- Testcross initiation can lead to rapid reconstruction of the elite donor genome.
- Genomic selection demonstrates significant power for pre-breeding programs with high genetic diversity and limited data recording resources.
Conclusions:
- Provides guidelines for the SeeD initiative and similar programs on initiating pre-breeding efforts.
- Emphasizes harnessing polygenic variation from landraces for robust maize breeding.
- Optimizes pre-breeding strategies through decision trees summarizing design factor effects.
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