Biology subjects

Mrode, R.

Publications and source records attributed to Mrode, R..

3 recordsLinked to original sources

bioRxiv2025

Spatial modelling improves genomic evaluation in Tanzanian smallholder admixed dairy cattle

BackgroundSmallholder 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. ResultsWe 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 Matern 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. ConclusionsThis 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.

genomics↗

bioRxiv2024

Genetic estimates and genome-wide association studies of antibody response in Tanzanian dairy cattle

Identifying the genetic determinants of host defence against infectious pathogens is central to enhancing disease resilience and therapeutic efficacy in livestock. Here we have taken a genome-wide association approach to identify genetic variants associated with the presence of serological markers for important infectious diseases affecting dairy cattle in smallholder farms. Assessing 668,911 single-nucleotide polymorphisms in 1977 crossbreed cattle sampled from six regions of Tanzania, we identified high levels of interregional admixture and European introgression which may increase infectious disease susceptibility relative to indigenous breeds. Heritability estimates ranged from 0.03 (SE {+/-} 0.06) to 0.44 (SE {+/-} 0.07) depending on the pathogen assayed. Preliminary genome scans revealed several loci associated with seropositivity to the viral diseases Rift Valley fever and bovine viral diarrhoea, the protozoan parasites Neospora caninum and Toxoplasma gondii, and the bacterial pathogens Brucella sp, Leptospira hardjo and Coxiella burnetti. The associated loci mapped to genes involved in immune defence, tumour suppression, neurological processes, and cell exocytosis. We discuss future work to clarify the cellular pathways contributing to general and taxon-specific infection responses and to advance selective breeding and therapeutic target designs.

genetics↗

bioRxiv2019

Genomic data enables genetic evaluation using data recorded on LMIC smallholder dairy farms

BackgroundGenetic evaluation is a central component of a breeding program. 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 create strong genetic connectedness that enables the genetic and environmental effects of an individual animals phenotype to be accurately separated. In contrast to this, herds are neither large nor have strong genetic connectedness in smallholder dairy production systems of many low to middle-income countries (LMIC). This limits genetic evaluation, and furthermore, the pedigree information needed for traditional genetic evaluation is typically unavailable. Genomic information keeps track of shared haplotypes rather than shared relatives. This information could capture and strengthen genetic connectedness between herds and through this may enable genetic evaluations for LMIC 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.\n\nResultsThe results from this study show: (i) the genetic evaluation of phenotyped cows using genomic information had higher accuracy compared to pedigree information across all breeding designs; (ii) the genetic evaluation of phenotyped cows with genomic information and modelling herd as a random effect had higher or equal accuracy compared to modelling herd as a fixed effect; (iii) the genetic evaluation of phenotyped cows from breeding designs with strong genetic connectedness had higher accuracy compared to breeding designs with weaker genetic connectedness; (iv) genomic prediction of young bulls was possible using marker estimates from the genetic evaluations of their phenotyped dams. For example, the accuracy of genomic prediction of young bulls from an average herd size of 1 (=1.58) was 0.40 under a breeding design with 1,000 sires mated per generation and a training set of 8,000 phenotyped and genotyped cows.\n\nConclusionsThis study demonstrates the potential of genomic information to be an enabling technology in LMIC smallholder dairy production systems by facilitating genetic evaluations with in-situ records collected from farms with herd sizes of four cows or less. Across a range of breeding designs, genomic data enabled accurate genetic evaluation of phenotyped cows and genomic prediction of young bulls using data sets that contained small herds with weak genetic connections. The use of smallholder dairy data in genetic evaluations would enable the establishment of breeding programs to improve in-situ germplasm and, if required, would enable the importation of the most suitable external germplasm. This could be individually tailored for each target environment. Together this would increase the productivity, profitability and sustainability of LMIC smallholder dairy production systems. However, data collection, including genomic data, is expensive and business models will need to be carefully constructed so that the costs are sustainably offset.

genetics↗