Genomic Selection in Small populations

Julius Mugambe

2022-02-04

The accuracy of Genomic Predictions (GP) in small populations especially in developing countries and breeding companies is constrained by; terms of the structure of the reference and validation populations, response variables, genomic prediction models, validation methods.

For numerically small breeds, setting up a large reference population for GP is hard and thus to overcome the lack of reference data the following strategies can be employed (Schöpke and Swalve (2016); Gholami et al. (2021); Mrode et al. (2019));

  • The use of combined reference populations from different breeds, different countries, or different research populations.
  • Including female information into the reference population.
  • Imputation of un-genotyped animals.
  • Collaboration between developing (with small reference populations) and developed countries is important in implementing genomic breeding technologies in the former, especial in dairy cattle, where there has been a large importation of bulls. It is likely that most of these bulls have been genotyped in the developed countries and willingness to share genotypes and some other relevant performance data will help in enlarging the reference population and hence the accuracy of genomic predictions in developing countries.

Additionally, the use of a multi-breed GRM model (MBMG) makes it possible to use information from numerically large breeds to improve prediction accuracy of numerically small breeds. The superiority of MBMG is mainly due to its ability to use information on pre-selected markers, to explain the remaining genetic variance and weigh information from a different breed by the genetic correlation between the breeds (Raymond et al. (2018)).

Currently, the statistical approaches that provide the highest accuracy for genomic evaluation of small populations involve the use of two types of models and they include:
1. The single-step genomic best linear unbiased prediction models
2. The single-step Bayesian models

The validation of genomic evaluations using the single-step methods is done using the LR method (Legarra and Reverter (2018)).

References

Gholami, Mahmood, Valentin Wimmer, Carolina Sansaloni, Cesar Petroli, Sarah J. Hearne, Giovanny Covarrubias-Pazaran, Stefan Rensing, et al. 2021. “A Comparison of the Adoption of Genomic Selection Across Different Breeding Institutions.” Frontiers in Plant Science 12. https://www.frontiersin.org/article/10.3389/fpls.2021.728567.
Legarra, Andres, and Antonio Reverter. 2018. “Semi-Parametric Estimates of Population Accuracy and Bias of Predictions of Breeding Values and Future Phenotypes Using the LR Method.” Genetics Selection Evolution 50 (1): 53. https://doi.org/10.1186/s12711-018-0426-6.
Mrode, Raphael, Julie M. K Ojango, A. M. Okeyo, and Joram M. Mwacharo. 2019. “Genomic Selection and Use of Molecular Tools in Breeding Programs for Indigenous and Crossbred Cattle in Developing Countries: Current Status and Future Prospects.” Frontiers in Genetics 9. https://www.frontiersin.org/article/10.3389/fgene.2018.00694.
Raymond, Biaty, Aniek C. Bouwman, Yvonne C. J. Wientjes, Chris Schrooten, Jeanine Houwing-Duistermaat, and Roel F. Veerkamp. 2018. “Genomic Prediction for Numerically Small Breeds, Using Models with Pre-Selected and Differentially Weighted Markers.” Genetics Selection Evolution 50 (1): 49. https://doi.org/10.1186/s12711-018-0419-5.
Schöpke, K., and H. H. Swalve. 2016. “Review: Opportunities and Challenges for Small Populations of Dairy Cattle in the Era of Genomics.” Animal 10 (6): 1050–60. https://doi.org/10.1017/S1751731116000410.