Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat

Abstract

With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled1 breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the ‘lme4’ R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the ‘STPGA’ R package. Third, for each TP, phenotypic values and SNP data were incorporated into the ‘rrBLUP’ mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.

Designing optimal training sets for genomic prediction using adversarial validation with probit regression

“…The AVG method only in few cases was better than the conventional method, while in the CD and PEV we observed that these methods in any case were superior in terms of prediction accuracy than the conventional method; this lack of efficiency of the CD and PEV methods was also reported by Berro et al (2019) for optimization strategies to choose individuals from the training population with higher predictive ability for a specific testing set. Also, regarding the CD method, Ola- and PEV methods when the testing set is not defined a priori and after selecting the optimal training the remaining individuals constitutes the testing set (Ballén-Taborda et al, 2022;Rincent et al, 2017Rincent et al, , 2012.…”

“…Consequently, they strongly advise against employing the CD procedure in practical applications and instead propose utilizing all available individuals as the training set. However, some other studies have shown a gain in prediction accuracy by implementing CD and PEV methods when the testing set is not defined a priori and after selecting the optimal training the remaining individuals constitutes the testing set (Ballén‐Taborda et al, 2022; Rincent et al, 2017, 2012).…”

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

“…Historical data for FHB partial-resistance QTL were used to assess potential yield drag for several moderate effect QTL across three major southern SRWW production environments (Ballén-Taborda et al, 2022). The three resistance QTL selected were Qfhb.…”

Understanding and improving resistance to Hessian fly (Mayetiola destructor) in US wheat using genetic mapping and molecular techniques

“…A multi‐environment study across the southeastern United States used a QTL‐by‐environment approach to correlate yield with various resistance genes across three major wheat‐growing regions. H13 , which is used widely in this region, was in ∼11% of the 1104 breeding lines in the study and had a positive effect on grain yield (Ballen et al., 2022). This collaborative breeding group successfully used genomic prediction for multiple traits, including Hessian fly resistance, on a large group of wheat genotypes and breeding lines (Ballen et al., 2022).…”

“…H13 , which is used widely in this region, was in ∼11% of the 1104 breeding lines in the study and had a positive effect on grain yield (Ballen et al., 2022). This collaborative breeding group successfully used genomic prediction for multiple traits, including Hessian fly resistance, on a large group of wheat genotypes and breeding lines (Ballen et al., 2022).…”