The impact of selective genotyping on the response to selection using single-step genomic best linear unbiased prediction

Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions

“…In addition, bias may lead to estimates of genetic trends that are higher or lower than the true rate of genetic gain. Gowane et al (2019) and Howard et al (2018) found that the unbiased prediction of GEBV in a breeding program with selective genotyping could be obtained with ssGBLUP. ssGBLUP uses phenotypes of non-genotyped animals and combined pedigree and genomic information to construct a relationship matrix between individuals.…”

“…According to Gowane et al (2019), genotyping of phenotypically contrasting animals (selective genotyping of top and bottom animals) for selection candidates is superior to selective genotyping of top animals. The undesirable consequences of selectively genotyping top animals have been addressed extensively (VanRaden et al, 2009;Patry and Ducrocq, 2011;Vitezica et al, 2011;Boligon et al, 2012;Jiménez-Montero et al, 2012;Chu et al, 2019;Gowane et al, 2019;Wang et al, 2020), but the superiority of this selective strategy for breeding programs has been shown only by Howard et al (2018). In addition, some simulation studies (Patry and Ducrocq, 2011;Vitezica et al, 2011;Boligon et al, 2012;Gowane et al, 2019) have used the correlation between true breeding values (TBV) and GEBV to compare different genotyping strategies.…”

“…Comparisons of genotyping strategies have focused on species other than rainbow trout (Boligon et al, 2012;Howard et al, 2018;Gowane et al, 2019;Wang et al, 2020). In two studies (Boligon et al, 2012;Gowane et al, 2019), different genotyping strategies based on phenotypes were used with individuals in the reference population, but the animals in the validation population did not have phenotypes, and thus, random genotyping was used with these validation individuals.…”

The impact of reducing the frequency of animals genotyped at higher density on imputation and prediction accuracies using ssGBLUP1

“…To generate linkage disequilibrium levels across the genome that are similar to a cattle population the "Ne100_Scen1" option within Geno-Diver was utilized when generating sequence data in the founder population. The pattern of linkage disequilibrium decay shown by Howard et al (2018) is representative of the pattern from the current simulation. After generating sequence data for a founder population containing 100 male and 500 female individuals, 450 quantitative trait loci (QTL) and 9,000 neutral markers, spread equally across the 3 chromosomes, were generated to construct a MD marker panel.…”

“…The application of single-step genomic BLUP (ssGBLUP) has been the method of choice for many organizations across multiple species (Cardoso et al, 2015;Lourenco et al, 2015b;Campos et al, 2018) due to the increase in accuracies of genomic breeding values compared with multiple-step approaches. Simulation studies have compared different methods and addressed genotyping strategies for genomic selection with ssGBLUP (Lourenco et al, 2013;Howard et al, 2018;Piccoli et al, 2018) and imputation strategies using different densities of SNP panels in sheep (Raoul et al, 2017) and dairy cattle (Daetwyler et al, 2011;VanRaden et al, 2011). However, strategies including re-genotyping are largely unexplored.…”