Can genomics enable genetic evaluations with phenotypes recorded on smallholder dairy farms?
Owen Powell1, R. Chris Gaynor1, Janez Jenko1, Gregor Gorjanc1, Okeyo Mwai2, Raphael Mrode2,3 & John M. Hickey1
1The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Research Centre, Midlothian EH25 9RG, UK; 2ILRI, International Livestock Research Institute; 3Scotland's Rural College (SRUC), Peter Wilson Building, Kings Buildings, West Mains Road, Edinburgh, EH9 3JG
Background: Genetic evaluation is a central component of genetic improvement programs. In advanced economies, most genetic evaluations depend on large quantities of data that are recorded on commercial farms. Large herd sizes and widespread use of artificial insemination enable the genetic and environmental components of an individual animal's phenotype to be accurately separated. In contrast to this, herds are neither large nor have high genetic connectedness in smallholder farming systems, such as in East Africa. This limits genetic evaluation with pedigree information. Genomic information keeps track of shared haplotypes rather than animals. This information could capture and strengthen connectedness between herds and through this may enable genetic evaluations based on phenotypes recorded on smallholder dairy farms. The objective of this study was to use simulation to quantify the power of genomic information to enable genetic evaluation under such conditions.
Results: The results from this study show; (i) GBLUP produced higher accuracies than PBLUP at all population sizes and herd sizes, (ii) Models with herd fitted as a random effect produced equal or higher accuracies than the model with herd fitted as a fixed effect across all herd size scenarios, (iii) At low levels of genetic connectedness, with four offspring per sire and one to two animals per herd, GBLUP produced EBV accuracies greater than 0.5. Generally, a decrease in the number of sires mated per generation showed consistently higher accuracies compared to when more sires were used.
Conclusions: This study has demonstrated the potential of genomic information to be an enabling technology in smallholder dairy economies by facilitating genetic evaluations with records collected from farms with herd sizes of four cows or less. The inclusion of smallholder dairy data in genetic evaluations could provide increases in local and national milk production, in regions such as East Africa, with downstream impacts upon wider societal, nutritional and economic outcomes.