Spatial modelling improves genetic evaluation in smallholder breeding programs

Abstract

Breeders and geneticists use statistical models for genetic evaluation of animals to separate genetic and environmental effects on phenotype. A common way to separate these effects is to model a descriptor of an environment, a contemporary group or herd, and account for genetic relationship between animals across the environments. However, separating the genetic and environmental effects in smallholder systems is challenging due to small herd sizes and weak genetic connectedness across herds. Our hypothesis was that accounting for spatial relationships between nearby herds can improve genetic evaluation in smallholder systems. Further, geographically referenced environmental covariates are increasingly available and could be used to model underlying sources of the spatial relationships. The objective of this study was therefore to evaluate the potential of spatial modelling to improve genetic evaluation in smallholder systems. We focus solely on dairy cattle smallholder systems. We performed simulations and real dairy cattle data analysis to test our hypothesis. We used a range of models to account for environmental variation by estimating herd and spatial effects. We compared these models using pedigree or genomic data. The results show that in smallholder systems (i) standard models are not able to separate genetic and environmental effects, (ii) spatial modelling increases accuracy of genetic evaluation for phenotyped and non-phenotyped animals, (iii) environmental covariates do not substantially improve accuracy of genetic evaluation beyond simple distance-driven spatial relationships between herds, (iv) the benefit of spatial modelling was the largest when the genetic and environmental effects were hard to separate and (v) spatial modelling was beneficial when using either pedigree or genomic data. We have demonstrated the potential of spatial modelling to improve genetic evaluation in smallholder systems. This improvement is driven by establishing environmental connectedness between herds that enhances separation of the genetic and environmental effects. We suggest routine spatial modelling in genetic evaluations, particularly for smallholder systems. Spatial modelling could also have major impact in studies of human and wild populations.