Spatial modelling improves genomic evaluation in Tanzanian smallholder admixed dairy cattle

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cg.authorship.typesCGIAR and developing country institute
cg.authorship.typesCGIAR and advanced research institute
cg.contributor.affiliationUniversity of Edinburgh
cg.contributor.affiliationUniversity of South Africa
cg.contributor.affiliationScotland's Rural College
cg.contributor.affiliationInternational Livestock Research Institute
cg.contributor.affiliationTanzania Livestock Research Institute
cg.contributor.affiliationSokoine University of Agriculture
cg.contributor.affiliationUniversity of Ljubljana
cg.contributor.donorRoyal Society Newton International Fellowship
cg.contributor.donorRoslin Foundation
cg.contributor.donorBiotechnology and Biological Sciences Research Council, United Kingdom
cg.contributor.donorGates Foundation
cg.contributor.donorForeign, Commonwealth and Development Office, United Kingdom
cg.coverage.countryTanzania
cg.coverage.iso3166-alpha2TZ
cg.coverage.regionAfrica
cg.coverage.regionEastern Africa
cg.coverage.regionSouthern Africa
cg.creator.identifierRaphael Mrode: 0000-0003-1964-5653
cg.creator.identifierOjango J.M.K.: 0000-0003-0224-5370
cg.creator.identifierChinyere Ekine-Dzivenu: 0000-0002-8526-435X
cg.creator.identifierAlly Okeyo Mwai: 0000-0003-2379-7801
cg.creator.identifierAppolinaire Djikeng: 0000-0001-9271-3419
cg.howPublishedFormally Published
cg.identifier.dataurlhttps://github.com/HighlanderLab/ihouaga_adgg_spatial
cg.identifier.doihttps://doi.org/10.1186/s12711-025-01021-w
cg.isijournalISI Journal
cg.issn0999-193X
cg.issue1
cg.journalGenetics Selection Evolution
cg.number8
cg.reviewStatusPeer Review
cg.subject.ilriCATTLE
cg.subject.ilriDAIRYING
cg.subject.ilriGENETICS
cg.subject.impactAreaNutrition, health and food security
cg.subject.sdgSDG 2 - Zero hunger
cg.volume58
dc.contributor.authorHouaga, I.
dc.contributor.authorMrode, Raphael A.
dc.contributor.authorOjango, Julie M.K.
dc.contributor.authorEkine-Dzivenu, Chinyere C.
dc.contributor.authorOkeyo Mwai, Ally
dc.contributor.authorNziku, Z.
dc.contributor.authorNguluma, A.
dc.contributor.authorLavrenčič, E.
dc.contributor.authorLindgren, F.
dc.contributor.authorPocrnic, I.
dc.contributor.authorDjikeng, Appolinaire
dc.contributor.authorGorjanc, G.
dc.date.accessioned2026-01-24T13:28:50Z
dc.date.available2026-01-24T13:28:50Z
dc.identifier.urihttps://hdl.handle.net/10568/180583
dc.titleSpatial modelling improves genomic evaluation in Tanzanian smallholder admixed dairy cattleen
dcterms.abstractBackground: Smallholder dairy production systems in low-and middle-income countries are characterised by large phenotypic variance due to diverse environmental effects, farming practices, and crossbreeding. Furthermore, small herds, low genetic connectedness, and limited data recording challenge accurate separation of environmental and genetic effect in such settings, limiting genetic improvement. Here, we evaluated the impact of modelling spatial variation between herds to address these challenges and improve the accuracy of genomic evaluation for Tanzanian smallholder dairy cattle. Results: We analysed 19,375 test-day milk yield records of 1894 dairy cows from 1386 herds across four distinct geographical regions in Tanzania. The cows had 664,822 SNP marker genotypes after quality control and were highly admixed. We fitted a series of GBLUP models to evaluate the impact of modelling the herd effect and the spatial effect on. The herd effect was fitted as an independent random effect, while the spatial effect was fitted as a random effect with Euclidean distance-based Matérn covariance function. The models were compared based on: model fit; estimates of variance components and breeding values; correlations between the estimated contribution of breeding values, herd effect, and spatial effect to phenotype values; and the accuracy of phenotype prediction in cross-validation and forward validation. The results showed large differences in milk yield between and within regions, as well as significant variation due to the spatial effect, which were not fully captured by modelling the herd effect. The results also strongly indicate that a model with just the herd effect underestimated breeding values of animals in less favourable environments and overestimated breeding values of animals in more favourable environments. Conclusions: This study demonstrated the challenge of achieving accurate genomic evaluation in smallholder settings. By leveraging spatial modelling we maximised the use of available data and improved the separation of genetic and environmental effects. Further work is required to improve smallholder genetic evaluations by understanding environmental and genetic processes that drive the large phenotypic variance in African smallholder setting.en
dcterms.accessRightsOpen Access
dcterms.audienceAcademics
dcterms.audienceScientists
dcterms.available2026-01-21
dcterms.bibliographicCitationHouaga, I., Mrode, R., Ojango, J., Ekine-Dzivenu, C.C., Okeyo, M., Nziku, Z., Nguluma, A., Lavrenčič, E., Lindgren, F., Pocrnic, I., Djikeng, A. and Gorjanc, G. 2026. Spatial modelling improves genomic evaluation in Tanzanian smallholder admixed dairy cattle. Genetics Selection Evolution 58 (1): 8.
dcterms.issued2026-12
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherBioMed Central
dcterms.subjectcattle
dcterms.subjectdairying
dcterms.subjectgenomics
dcterms.typeJournal Article

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