Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP

Andre Garcia (1) , Ignacio Aguilar (2) , Andres Legarra (3) , Shogo Tsuruta (1) , Ignacy Misztal (1) , Daniela Lourenco (1)

1 USG - University System of Georgia
2 INIA - Instituto Nacional de Investigación Agropecuaria
3 GenPhySE - Génétique Physiologie et Systèmes d'Elevage

Abstract Background Although single-step GBLUP (ssGBLUP) is an animal model, SNP effects can be backsolved from genomic estimated breeding values (GEBV). Predicted SNP effects allow to compute indirect prediction (IP) per individual as the sum of the SNP effects multiplied by its gene content, which is helpful when the number of genotyped animals is large, for genotyped animals not in the official evaluations, and when interim evaluations are needed. Typically, IP are obtained for new batches of genotyped individuals, all of them young and without phenotypes. Individual (theoretical) accuracies for IP are rarely reported, but they are nevertheless of interest. Our first objective was to present equations to compute individual accuracy of IP, based on prediction error covariance (PEC) of SNP effects, and in turn, are obtained from PEC of GEBV in ssGBLUP. The second objective was to test the algorithm for proven and young (APY) in PEC computations. With large datasets, it is impossible to handle the full PEC matrix, thus the third objective was to examine the minimum number of genotyped animals needed in PEC computations to achieve IP accuracies that are equivalent to GEBV accuracies. Results Correlations between GEBV and IP for the validation animals using SNP effects from ssGBLUP evaluations were ≥ 0.99. When all available genotyped animals were used for PEC computations, correlations between GEBV and IP accuracy were ≥ 0.99. In addition, IP accuracies were compatible with GEBV accuracies either with direct inversion of the genomic relationship matrix ( G ) or using the algorithm for proven and young (APY) to obtain the inverse of G . As the number of genotyped animals included in the PEC computations decreased from around 55,000 to 15,000, correlations were still ≥ 0.96, but IP accuracies were biased downwards. Conclusions Theoretical accuracy of indirect prediction can be successfully obtained by computing SNP PEC out of GEBV PEC from ssGBLUP equations using direct or APY G inverse. It is possible to reduce the number of genotyped animals in PEC computations, but accuracies may be underestimated. Further research is needed to approximate SNP PEC from ssGBLUP to limit the computational requirements with many genotyped animals.

Domaines: Modélisation et simulation; Biotechnologies; Biologie moléculaire

Andre Garcia, Ignacio Aguilar, Andres Legarra, Shogo Tsuruta, Ignacy Misztal, et al.. Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP. Genetics Selection Evolution, 2022, 54 (1), 11 p. ⟨10.1186/s12711-022-00752-4⟩. ⟨hal-03800048⟩