Analyzing the Economic Effectiveness of Genomic Selection Relative to Conventional Breeding Approaches

Comparing the economic efficiency of alternative strategies for breeding requires to compare the genetic gain obtained with breeding schemes that represent the same total investment. In this chapter, we present a generic method to assess this economic efficiency for alternative breeding schemes. After presenting the baseline framework and the necessity of comparing breeding schemes with equivalent total investment, we propose one illustrative example on wheat breeding. In this application, we compare the use of conventional breeding and genomic selection. With this example, we explain the requirements and the different steps to implement this method. At last, we discuss several extensions of the baseline model.

Economical optimization of a breeding scheme by selective phenotyping of the calibration set in a multi-trait context: application to bread making quality

Trait-assisted genomic prediction approach is a way to improve genetic gain by cost unit, by reducing budget allocated to phenotyping or by increasing the program's size for the same budget. This study compares different strategies of genomic prediction to optimize resource allocation in breeding schemes by using information from cheaper correlated traits to predict a more expensive trait of interest. We used bread wheat baking score (BMS) calculated for French registration as a case study. To conduct this project, 398 lines from a public breeding program were genotyped and phenotyped for BMS and correlated traits in 11 locations in France between 2000 and 2016. Single-trait (ST), multi-trait (MT) and trait-assisted (TA) strategies were compared in terms of predictive ability and cost. In MT and TA strategies, information from dough strength (W), a cheaper trait correlated with BMS (r = 0.45), was evaluated in the training population or in both the training and the validation sets, respectively. TA models allowed to reduce the budget allocated to phenotyping by up to 65% while maintaining the predictive ability of BMS. TA models also improved the predictive ability of BMS compared to ST models for a fixed budget (maximum gain: + 0.14 in cross-validation and + 0.21 in forward prediction). We also demonstrated that the budget can be further reduced by approximately one fourth while maintaining the same predictive ability by reducing the number of phenotypic records to estimate BMS adjusted means. In addition, we showed that the choice of the lines to be phenotyped can be optimized to minimize cost or maximize predictive ability. To do so, we extended the mean of the generalized coefficient of determination (CD_mean) criterion to the multi-trait context (CD_multi).

SNP markers for early identification of high molecular weight glutenin subunits (HMW-GSs) in bread wheat

A set of eight SNP markers was developed to facilitate the early selection of HMW-GS alleles in breeding programmes. In bread wheat (Triticum aestivum), the high molecular weight glutenin subunits (HMW-GSs) are the most important determinants of technological quality. Known to be very diverse, HMW-GSs are encoded by the tightly linked genes Glu-1-1 and Glu-1-2. Alleles that improve the quality of dough have been identified. Up to now, sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) of grain proteins is the most widely used for their identification. To facilitate the early selection of HMW-GS alleles in breeding programmes, we developed DNA-based molecular markers. For each accession of a core collection (n = 364 lines) representative of worldwide bread wheat diversity, HMW-GSs were characterized by both genotyping and SDS-PAGE. Based on electrophoresis, we observed at least 8, 22 and 9 different alleles at the Glu-A1, Glu-B1 and Glu-D1 loci, respectively, including new variants. We designed a set of 17 single-nucleotide polymorphism (SNP) markers that were representative of the most frequent SDS-PAGE alleles at each locus. At Glu-A1 and Glu-D1, two and three marker-based haplotypes, respectively, captured the diversity of the SDS-PAGE alleles rather well. Discrepancies were found mainly for the Glu-B1 locus. However, statistical tests revealed that two markers at each Glu-B1 gene and their corresponding haplotypes were more significantly associated with the rheological properties of the dough than were the relevant SDS-PAGE alleles. To conclude, this study demonstrates that the SNP markers developed provide additional information on HMW-GS diversity. Two markers at Glu-A1, four at Glu-B1 and two at Glu-D1 constitute a useful toolbox for breeding wheat to improve end-use value.