Genotyping strategies for single-step genomic predictions in simulated sheep populations under different scenarios of pedigree error types

Authors: ROCHA, ARTUR - Purdue University; GLORIA, LEONARDO - Genus; ARAUJO, ANDRE - Acufast; WEN, HUI - Purdue University; Wilson, Carrie; Freking, Bradley; Murphy Jr, Thomas; Burke, Joan; LEWIS, RONALD - University Of Nebraska; BRITO, LUIZ - Purdue University

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/20/2025
Publication Date: 11/9/2025
Citation: Rocha, A.O., Gloria, L.S., Araujo, A.C., Wen, H., Wilson, C.S., Freking, B.A., Murphy Jr, T.W., Burke, J.M., Lewis, R.M., Brito, L.F. 2025. Genotyping strategies for single-step genomic predictions in simulated sheep populations under different scenarios of pedigree error types. Frontiers in Genetics. 16. Article 1697103. https://doi.org/10.3389/fgene.2025.1697103.
DOI: https://doi.org/10.3389/fgene.2025.1697103

Interpretive Summary: Genomic predictions provide more accurate estimated breeding values (EBV) in younger animals, enabling greater rates of genetic gains over time. However, sheep reference populations are still small and if the animals included in the reference populations are not chosen carefully, genomic predictions may be biased. This study used simulation to compare genotyping strategies for 1) proportion of animals genotyped with a 50K SNP array and 2) pedigree errors, which were either misidentified sires or missing information. Compared to random genotyping, selective genotyping captured less genomic diversity, limiting the effectiveness of the reference population. These findings highlight the importance of genotyping strategies when implementing genomic selection in sheep and the usefulness of genomic information for minimizing the impact of pedigree errors.

Technical Abstract: Genomic predictions provide more accurate estimated breeding values (EBV) in younger animals, enabling greater rates of genetic gains over time. However, sheep reference populations are still small and if the animals included in the reference populations are not chosen carefully, genomic predictions may be biased. In this context, this study compared genotyping strategies varying in the proportion of animals genotyped (using a 50K SNP panel) and the extent of pedigree errors (misidentified sires or missing information) on the true and estimated accuracy, bias, and dispersion of genomically-enhanced estimated breeding values (GEBV). We simulated a composite sheep population mimicking the formation and flock structure of Katahdin sheep using the AlphaSimR package. Sixteen flocks with an effective population size of 103 were simulated for two traits with heritabilities of 0.35 and 0.10. Breeding values were predicted with Best Linear Unbiased Prediction (BLUP) and Single-step Genomic BLUP (ssGBLUP). Scenarios included combinations of 0–100% males or females genotyped, 0–20% pedigree errors, and three genotyping strategies (random, highest EBV, or highest phenotypic values). The final population (18,717 animals) was divided into training and validation sets for calculating validation statistics of GEBV. Genomic prediction accuracy significantly improved with random genotyping, outperforming phenotype and EBV-based strategies by up to 19%. Pedigree errors reduced GEBV accuracy while increasing bias and dispersion. Missing pedigree information impacted results more than misidentified sires. Increasing the proportion of animals genotyped improved GEBV prediction metrics, with random genotyping yielding higher accuracies, lower biases, and dispersion closer to 1 (desirable). Prioritizing the genotyping of males up to 10% before incorporating females enhanced the accuracy of GEBV. Genomic information mitigated some pedigree error effects. The ssGBLUP method was robust to pedigree errors and outperformed BLUP with a complete pedigree. However, selective genotyping increased GEBV bias and dispersion, and reduced prediction accuracy. Compared to random genotyping, selective genotyping captured less genomic diversity, limiting the effectiveness of the reference population. Similar conclusions were obtained for both trait heritability levels. These findings highlight the importance of genotyping strategies when implementing genomic selection in sheep and the usefulness of genomic information for minimizing the impact of pedigree errors.