ArticleGenetics, selection, evolution : GSE2020
Spatial modelling improves genetic evaluation in smallholder breeding programs.
Article in Genetics, selection, evolution : GSE, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Spatial modelling improves genomic evaluation in Tanzanian smallholder admixed dairy cattle.Genetics, selection, evolution : GSE · 2026Article
- Genotype-by-environment interaction with high-dimensional environmental data: an example in pigs.Genetics, selection, evolution : GSE · 2025Article
- The genetic structure and diversity of smallholder dairy cattle in Rwanda.BMC genomic data · 2025Article
- Evaluation of crossbreeding strategies for improved adaptation and productivity in African smallholder cattle farms.Genetics, selection, evolution : GSE · 2025Article
- On the ability of the LR method to detect bias when there is pedigree misspecification and lack of connectedness.Genetics, selection, evolution : GSE · 2024Article
- SIMplyBee: an R package to simulate honeybee populations and breeding programs.Genetics, selection, evolution : GSE · 2023Article
- Livestock phenomics and genetic evaluation approaches in Africa: current state and future perspectives.Frontiers in genetics · 2023Article
- Use of Milk Infrared Spectral Data as Environmental Covariates in Genomic Prediction Models for Production Traits in Canadian Holstein.Animals : an open access journal from MDPI · 2022Article
- Genomic evaluation of milk yield in a smallholder crossbred dairy production system in India.Genetics, selection, evolution : GSE · 2021Article
- GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle.Animals : an open access journal from MDPI · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundBreeders and geneticists use statistical models 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 environments. However, separating the genetic and environmental effects in smallholder systems is challenging due to small herd sizes and weak genetic connectedness across herds. We hypothesised that accounting for spatial relationships between nearby herds can improve genetic evaluation in smallholder systems. Furthermore, geographically referenced environmental covariates are increasingly available and could model underlying sources of spatial relationships. The objective of this study was therefore, to evaluate the potential of spatial modelling to improve genetic evaluation in dairy cattle smallholder systems.
methodsWe performed simulations and real dairy cattle data analysis to test our hypothesis. We modelled environmental variation by estimating herd and spatial effects. Herd effects were considered independent, whereas spatial effects had distance-based covariance between herds. We compared these models using pedigree or genomic data.
resultsThe results show that in smallholder systems (i) standard models do not separate genetic and environmental effects accurately, (ii) spatial modelling increases the accuracy of genetic evaluation for phenotyped and non-phenotyped animals, (iii) environmental covariates do not substantially improve the accuracy of genetic evaluation beyond simple distance-based relationships between herds, (iv) the benefit of spatial modelling was largest when separating the genetic and environmental effects was challenging, and (v) spatial modelling was beneficial when using either pedigree or genomic data.
conclusionsWe have demonstrated the potential of spatial modelling to improve genetic evaluation in smallholder systems. This improvement is driven by establishing environmental connectedness between herds, which enhances separation of genetic and environmental effects. We suggest routine spatial modelling in genetic evaluations, particularly for smallholder systems. Spatial modelling could also have a major impact in studies of human and wild populations.
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