Accuracy of genomic prediction for seed oil concentration in high‐oleic soybean populations using a low‐density marker panel

Abstract

Insoybean [Glycine max (L.) Merr.], seed oil concentration is a complex quantitative trait, and genomic selection (GS) has been shown to be a valuable tool for performing selection on such traits. The objectives of this study were to evaluate multiple GS models for seed oil concentration using a low-density marker panel in four biparental soybean populations and to assess predictive ability of the models using six unique training populations (TPs). Individuals were grown as BC 1 F 4 :F 5 progeny rows in 2014. Genomic estimated breeding values (GEBVs) were calculated for each genotype within a population using genomic best linear unbiased predictor (GBLUP), BayesA, and BayesB models in a biparental specific context. In 2015, 60 individuals from each population were randomly selected and grown at six locations with two replications each to generate a "true" phenotypic value for each genotype. Prediction accuracies for each estimation set were generated by correlating the GEBVs with the "true" phenotypic value. Across all populations, the GS prediction accuracy was greatest using GBLUP; however, no GS prediction model showed a significant advantage in accuracy over the phenotypic values. Generally, TPs consisting of more individuals had higher prediction accuracies; however, variations were observed across populations and models. The results show that GS in a biparental context and with low marker densities can be a valuable tool for breeders focused on making gains for oil concentration; however, consideration must be given as to how to apply these methods to each situation.

Six decades of soybean breeding in Ontario, Canada: a tradition of innovation

“…Therefore, GS can be more useful in the case of the homogenous population, which is equal to ∼F 5 in soybean breeding programs. Multiple GS models were evaluated for seed oil concentration in four biparental soybean populations, and results confirmed the efficiency of using GS in breeding programs to increase the speed of oil improvement in soybean (Hemingway et al 2021). One of the recent projects supported by Genome Canada and Genome Quebec is "Development and implementation of a toolkit for genomics-assisted breeding in soybean", which is mainly focused on adapting GS in major soybean breeding programs in Canada, including Ontario.…”

Implementation of genomic selection in public-sector plant breeding programs: Current status and opportunities

“…No major differences in selection accuracy were found for yield, protein, and oil content but selections using GS can be made prior to harvest, improving the efficiency in comparison to PS. Similarly, no major differences in selection accuracy between GS and PS for seed oil concentration were observed by Hemingway et al (2021) with biparental context-specific GS targeting the progeny row stage in four biparental soybean populations (BC 1 F 4:5 lines) using a low-density marker panel.…”

Soybean genetics, genomics, and breeding for improving nutritional value and reducing antinutritional traits in food and feed

“…(2001) proposed the concept of genomic prediction for genomic selection, it was successfully implemented in an animal breeding program for complex quantitative traits (Schaeffer et al., 2006) and subsequently utilized in a plant breeding program (Massman et al., 2013). To date, genomic selection has been successfully applied in soybean breeding programs for key traits, including seed oil and protein (Hemingway et al., 2021; Jarquin et al., 2016; Stewart‐Brown et al., 2019), grain yield (Bhat et al., 2022; Ravelombola et al., 2021; Stewart‐Brown et al., 2019), agronomic traits (Ma et al., 2016; Zhang et al., 2016), and disease resistance (Bao et al., 2014; de Azevedo Peixoto et al., 2017; Shi et al., 2022). Further nutritional component applications of genomic prediction in soybean will be discussed in later sections.…”

“…The 11S protein is a hexamer of 360 kDa. The six subunits are composed of acidic (A1a, A1b, A2, A3, A4, and A5) and basic (B1a, B1b, B2, B3, and B4) polypeptides linked by disulfide bonds (Bradley et al., 1975; Li & Zhang, 2011; Ma et al., 2010, 2016; Zhang et al., 2021). Multiple genes have been reported to encode for the 11S subunits, and these genes have been designated as Group 1: Gy1 (A1aB2), Gy2 (A2B1a), and Gy3 (A1bB1b); Group 2: Gy4 (A5A4B3) and Gy5 (A3B4); Group 3: two pseudogenes (gy6 and gy8); and Gy7 (polypeptide groups not assigned) (Beilinson et al., 2002; Boehm et al., 2018; Fischer & Goldberg, 1982; Li & Zhang, 2011; Nielsen et al., 1989; Scallon et al., 1985).…”

“…The 7S is a trimer of α, αʹ, and β subunits that is approximately 180 kDa (Ma et al., 2016; Thanh & Shibasaki, 1978; Zhang, Du et al., 2021). A total of 15 genes (CG1–CG15) have been reported to encode the 7S subunits (Harada et al., 1989; Singh et al., 2015; Yoshino et al., 2002; Zhang et al., 2021).…”