Abstract
Wheat (Triticum aestivum L.) breeding programs can take over a decade to release a new variety. However, new methods of selection, such as genomic selection (GS), must be integrated to decrease the time it takes to release new varieties to meet the demand of a growing population. The implementation of GS into breeding programs is still being explored, with many studies showing its potential to change wheat breeding through achieving higher genetic gain. In this review, we explore the integration of GS for a wheat breeding program by redesigning the traditional breeding pipeline to implement GS. We propose implementing a two-part breeding strategy by differentiating between population improvement and product development. The implementation of GS in the product development pipeline can be integrated into most stages and can predict within and across breeding cycles. Additionally, we explore optimizing the population improvement strategy through GS recurrent selection schemes to reduce crossing cycle time and significantly increase genetic gain. The recurrent selection schemes can be optimized for parental selection, maintenance of genetic variation, and optimal cross-prediction. Overall, we outline the ability to increase the genetic gain of a breeding program by implementing GS and a two-part breeding strategy.
Highlights
One modern breeding approach, genomic selection (GS), is posed to increase genetic gains and reduce cycle time for complex agronomic traits such as grain yield and disease resistance [5,6]
Due to the decreasing costs of genotyping, GS is starting to be practically cheaper than phenotypic selection (PS), which allows for the use of genomic estimated breeding values (GEBVs) in lieu of phenotypic data
The two-part breeding program was proposed to differentiate between population improvement (PI) and product development (PD), which showed up to 2.47 times the genetic gain than a traditional breeding program with no GS [54]
Summary
Genomic selection (GS), is posed to increase genetic gains and reduce cycle time for complex agronomic traits such as grain yield and disease resistance [5,6]. GS reduced genetic variance, which indicated that maintaining genetic variance needs to be accounted for in a GS breeding pipeline Both PS and marker-assisted selection (MAS) are effective for increasing genetic gain for highly heritable traits [8]. In order to implement GS and replace PS, breeding programs have to account for selecting on GEBVs instead of phenotypic values, the time it takes for genotyping and DNA extraction, and the use of doubled-haploids (DHs) in rapidcycle recurrent GS schemes. We explored the integration of GS for a wheat breeding program by redesigning the traditional breeding pipeline to differentiate between PD and PI while optimizing a two-part strategy based on the components of the breeder’s equation
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