Breeding and Genetics Symposium I: How genomic selection has changed livestock breeding
Early application of genomic selection relied on SNP estimation with phenotypes or de-regressed proofs (DRP). Chips of 50k SNP seemed sufficient. Estimated breeding value was an index with parent average and deduction to eliminate double counting. Use of SNP selection or weighting increased accuracy with small data sets but less or none with large data sets. Use of DRP with female information required ad-hoc modifications. As BLUP is biased by genomic selection, use of DRP under genomic selection required adjustments. Efforts to include potentially causative SNP derived from sequence analysis showed limited or no gain. The genomic selection was greatly simplified using single-step GBLUP (ssGBLUP) because the procedure automatically creates the index, can use any combination of male and female genotypes, and accounts for preselection. ssGBLUP requires careful scaling for compatibility between pedigree and genomic relationships to avoid biases especially under strong selection. Large data computations in ssGBLUP were solved by exploiting limited dimensionality of SNP due to limited effective population size. With such dimensionality ranging from 4k in chicken to about 15k in Holsteins, the inverse of GRM can be created directly (e.g., by the APY algorithm) in linear cost. Due to its simplicity and accuracy ssGBLUP is routinely used for genomic selection by major companies in chicken, pigs and beef. ssGBLUP can be used to derive SNP effects for indirect prediction, and for GWAS, including computations of the p-values. An alternative single-step called ssBR exists that uses SNP effects instead of GRM. As BLUP is affected by pre-selection, there is need for new validation procedures unaffected by selection, and for parameter estimation that accounts for all the genomic data used in selection. Another issue are reduced variances due to the Bulmer effect.