Optimizing Genomic Selection for a Sorghum Breeding Program in Haiti: A Simulation Study

Abstract

Young breeding programs in developing countries, like the Chibas sorghum breeding program in Haiti, face the challenge of increasing genetic gain with limited resources. Implementing genomic selection (GS) could increase genetic gain, but optimization of GS is needed to account for these programs’ unique challenges and advantages. Here, we used simulations to identify conditions under which genomic-assisted recurrent selection (GARS) would be more effective than phenotypic recurrent selection (PRS) in small new breeding programs. We compared genetic gain, cost per unit gain, genetic variance, and prediction accuracy of GARS (two or three cycles per year) vs. PRS (one cycle per year) assuming various breeding population sizes and trait genetic architectures. For oligogenic architecture, the maximum relative genetic gain advantage of GARS over PRS was 12–88%, which was observed only during the first few cycles. For the polygenic architecture, GARS provided maximum relative genetic gain advantage of 26–165%, and was always superior to PRS. Average prediction accuracy declines substantially after several cycles of selection, suggesting the prediction models should be updated regularly. Updating prediction models every year increased the genetic gain by up to 33–39% compared to no-update scenarios. For small populations and oligogenic traits, cost per unit gain was lower in PRS than GARS. However, with larger populations and polygenic traits cost per unit gain was up to 67% lower in GARS than PRS. Collectively, the simulations suggest that GARS could increase the genetic gain in small young breeding programs by accelerating the breeding cycles and enabling evaluation of larger populations.

Impact of sorghum racial structure and diversity on genomic prediction of grain yield components

“…However, studies investigating prospects and applications of genomic prediction in sorghum are limited. A few studies have been reported for biomass traits in diversity panels (Fernandes et al., 2018; Yu et al., 2016), GY in pedigreed male inbred lines (Hunt et al., 2018), and a simulation study investigating prospects in a small sorghum breeding program (Muleta et al., 2019). Although clearly defined heterotic pools do not exist in sorghum as they do in maize, races have long been exploited by sorghum breeding programs for hybrid production.…”

“…Our study suggests maintaining a genetically diverse training population that includes a mixed or intermediate race might boost prediction accuracy when training population size is constrained. This strategy might be beneficial for young and small breeding programs where breeders have limited resource to construct an individual training population for different breeding populations (Muleta et al., 2019). Furthermore, new phenotypic data from diverse lines when added into the training population can allow for maintenance and increase in the frequency of advantageous minor alleles in the gene pool.…”

Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection

“…In plant breeding, the potential of GS was first evaluated in corn (Zea mays L.) using simulations [98]. A range of simulation studies in different crop species such as wheat [92], barley [99], rice [100], and sorghum [101] have shown that implementing GS could result in a significant increase in genetic gain. However, only limited reports are available in crops on the realized genetic gain that were achieved as an outcome of implementing GS.…”

Genome-Wide Association Studies and Genomic Selection in Pearl Millet: Advances and Prospects

“…Genomic (or genome-wide) selection (GS) is a promising strategy that has huge potential to explore and increase the genetic gain per selection in a breeding scheme per unit timeline and, thus, speed and efficacy in breeding programs (Spindel et al, 2015). GS has proven to be an economical and viable alternative to marker-assisted selection (MAS) and phenotypic selection (PS) for quantitative traits and accelerated crop improvement programs in cereals and several other crops (Heffner et al, 2009;Zhong et al, 2009;Crossa et al, 2010;Ornella et al, 2012;Poland et al, 2012;Spindel et al, 2015;Muleta et al, 2019). By developing efficient training population (having both genotypic and phenotypic data) designs, it predicts the genomic estimated breeding values (GEBV) of the testing population (having only genotypic data) by utilizing genome-wide high throughput DNA markers that are in linkage disequilibrium (LD) with QTL, and predicted GEBVs are used for selection (Meuwissen et al, 2001).…”