Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction

Abstract

Reference populations for genomic selection usually involve selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with individuals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is, Best Linear Unbiased Prediction of breeding values using pedigree-based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single-Step approach (SSGBLUP) using both. For a scenario with no-selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all individuals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices. Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single-Step approach to obtain accurate and unbiased prediction of GEBV. K E Y W O R D Sgenomic selection, GWAS, prediction bias, selective genotyping, single-step GBLUP How to cite this article: Gowane GR, Lee SH, Clark S, Moghaddar N, Al-Mamun HA, van der Werf JHJ. Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction.

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

“…In many breeding programs, genotyping is limited to phenotypically superior animals, referred to as selective genotyping of top animals. Such selective genotyping leads to biased predictions of genomic breeding values (GEBV) when genomic-based best linear unbiased prediction (GBLUP) is used (Gowane et al, 2019;Wang et al, 2020). For example, Wang et al (2020) showed that the use of a combined matrix (Christensen and Lund, 2010;Aguilar et al, 2011) of pedigree and genomic relationships in a single-step GBLUP (ssGBLUP) prediction resulted in the upward bias of GEBV and overestimation of variance components when only a proportion of top individuals were genotyped.…”

“…Gowane et al (2019) also showed that with selective genotyping of top animals, the use of a genomic relationship matrix in a GBLUP prediction led to biased GEBV, but the use of a combined relationship matrix (Christensen and Lund, 2010;Aguilar et al, 2011) constructed from pedigree and genomic information resulted in the unbiased prediction of GEBV. Compared to random genotyping or selective genotyping of phenotypically contrasting animals, selective genotyping of top animals in a reference population for training genomic selection models less accurately predicted GEBV (Boligon et al, 2012;Gowane et al, 2019). 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.…”

“…Compared to random genotyping or selective genotyping of phenotypically contrasting animals, selective genotyping of top animals in a reference population for training genomic selection models less accurately predicted GEBV (Boligon et al, 2012;Gowane et al, 2019). 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).…”

“…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. However, the difference in correlations between different genotyping strategies may not be consistent with the difference in realized genetic gains, because other factors -such as the intensity of selection, prediction bias, and changes in variance due to selection -may affect these gains.…”

“…However, the difference in correlations between different genotyping strategies may not be consistent with the difference in realized genetic gains, because other factors -such as the intensity of selection, prediction bias, and changes in variance due to selection -may affect these gains. These studies (Patry and Ducrocq, 2011;Vitezica et al, 2011;Boligon et al, 2012;Gowane et al, 2019) did not account for selection, and the effects of bias on genetic gains in breeding programs were not investigated for different genotyping strategies. Therefore, in a comparison of genotyping strategies, gains in genetic merits in a breeding program may be a better measure of assessment.…”