332 Indirect predictions based on SNP effects from GBLUP with increasing number of genotyped animals

Abstract

Abstract With the increasing number of genotyped animals, the algorithm for proven and young (APY) can be used to compute the inverse of the genomic relationship matrix (G-1apy) in genomic BLUP (GBLUP) and single-step GBLUP (ssGBLUP). This algorithm also allows the use of all genotyped animals to calculate SNP effects from genomic EBV (GEBV), which can then be used to obtain indirect predictions (IP) for interim evaluations, or as genomic prediction for animals not included in official evaluations. The objective of the study was to evaluate the quality of IP from GBLUP with increasing number of genotyped animals. Birth weight, weaning weight and post-wearing gain phenotypes and genotypes were provided by the American Angus Association. Phenotypes and genotypes were divided in 3 scenarios based on birth year: genotyped animals born up to 2013 (114,937), 2014 (183,847) and 2015 (280,506). A 3-trait model was fit and GBLUP with APY was used to calculate GEBV and SNP effects. To calculate G-1apy, 19,021 core animals were randomly sampled from animals born up to 2013. Core animals remained the same, whereas the number of non-core animals increased as more genotyped animals were added. Additional analyses had updated core animals for each scenario. SNP effects were also calculated based on G-1apy and G-1 only for core animals (G-1core). IP were computed for all animals in each scenario by multiplying the centered genotypes by the SNP effects. To access the quality of IP, correlation between IP and GEBV was calculated. The Correlations were greater than 0.99 for all traits in all scenarios. Despite the increase of non-core animals in APY, GEBV were successfully retrieved from SNP effects using IP. When SNP effects were calculated based on G-1core, updating the core animals as the number of genotyped animals increase seems to be the best choice.