Optimizing Training Population Size and Genotyping Strategy for Genomic Prediction Using Association Study Results and Pedigree Information. a Case of Study in Advanced Wheat Breeding Lines

Wheat breeding programs generate a large amount of variation which cannot be completely explored because of limited phenotyping throughput. Genomic prediction (GP) was proposed as a new tool which provides breeding value estimations without the need of phenotyping all the material produced but only a subset of it, named training population (TP). However, genotyping of all the accessions under analysis is needed. Optimizing TP dimension and genotyping strategy is therefore pivotal to implement GP in commercial breeding schemes. Here, we explored the optimum TP size and we integrated pedigree records and genome wide association studies (GWAS) results to optimize the genotyping strategy.

A total of 988 advanced wheat breeding lines were genotyped with the Illumina 15K SNP wheat chip and phenotyped across several years and locations for yield, lodging, and starch content. Cross-validations using the largest possible TP size and all the SNPs available after editing (~11k), yielded predictive abilities (r_GP) ranging between 0.5-0.6. In order to explore the TP size, r_GP were computed using progressively smaller TP. These exercises showed that TP of around 700 lines were enough to yield the highest observed r_GP. Moreover, r_GP were calculated by randomly reducing the SNP number. This showed that around 1K markers were enough to reach the highest observed r_GP. GWAS was used to identify markers associated with the traits analyzed. A GWAS-based selection of SNPs resulted in increased r_GP when compared with random selection and few hundreds SNPs where sufficient to obtain the highest observed r_GP. For each of these scenarios advantages of adding the pedigree information were shown.

Our results indicate that moderate TP sizes are sufficient to yield high r_GP and that pedigree information and GWAS results can be used to greatly optimize the genotyping strategy.