Utilizing Genomic Selection for Wheat Population Development and Improvement

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.

Smart breeding driven by advances in sequencing technology

“…For example, GBS is used in wheat breeding due to its low cost. 52 Similarly, GBS was used to evaluate grain yield, maturity, plant height and seed weight in soybeans. 53 These earlier studies suggest both Hyper-seq and GBS as simplified resequencing technologies; however, Hyper-seq is less expensive and has a wider distribution of markers than GBS, indicating its great application potential in GS-based breeding.…”

“…In addition to SNP microarrays, GBS is an outstanding technology widely used for genotyping in recent years. For example, GBS is used in wheat breeding due to its low cost 52 . Similarly, GBS was used to evaluate grain yield, maturity, plant height and seed weight in soybeans 53 .…”

Approaching 25 years of progress towards Fusarium head blight resistance in southern soft red winter wheat (Triticum aestivum L.)

“…This approach can be complemented with the use of DHs to rapidly generate a training population for a larger population of single-seed descent. The greatest improvements in yearly genetic gain may come from using genomic predictions to recycle families back into the crossing block at an earlier generation, with minimal within-family selection, effectively partitioning programmes into population improvement and cultivar development components (Merrick et al, 2022). Understanding the ability of current FHB training population approaches to make accurate selections at early generations, and if necessary, altering training population structure or developing alternative models, will be necessary to make the next jump forward and reduce overall cycle time.…”

Ridge regression and deep learning models for genomewide selection of complex traits in New Mexican chile peppers

“…While various BLUP-based and Bayesian models have been widely used to predict complex traits in crops, they only model the additive component of the variance [23,24]. Machine learning and deep learning-based models can include the non-genetic effects with appropriate parameterization where the whole genetic merit can be predicted without the need of portioning the effects [25].…”