Genomics for Ruminants in Developing Countries: From Principles to Practice

Ducrocq, Vincent; Laloë, Dénis; Swaminathan, Marimuthu; Rognon, Xavier; Tixier‐Boichard, Michèle; Zerjal, Tatiana

doi:10.3389/fgene.2018.00251

Cited by 33 publications

(32 citation statements)

References 23 publications

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“…For sheep, goat, pig and cattle the genotyping cost, via Beadchip of around 50K SNP markers, is 67 USD/individual on average, depending on the species and the manufacturer. (ii) High throughput genotyping techniques generate huge data files, the analyses of which require a powerful computing cluster and associated computer knowledge [44]. (iii) Finally, it should be stressed that breeds are still largely managed in the traditional way in developing countries.…”

Section: Resultsmentioning

confidence: 99%

Inference of Breed Structure in Farm Animals: Empirical Comparison between SNP and Microsatellite Performance

Laoun

Harkat

Lafri

et al. 2020

Genes

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Knowledge of population structure is essential to improve the management and conservation of farm animal genetic resources. Microsatellites, which have long been popular for this type of analysis, are more and more neglected in favor of whole-genome single nucleotide polymorphism (SNP) chips that are now available for the main farmed animal species. In this study, we compared genetic patterns derived from microsatellites to that inferred by SNPs, considering three pairs of datasets of sheep and cattle. Population genetic differentiation analyses (Fixation index, FST), as well as STRUCTURE analyses showed a very strong consistency between the two types of markers. Microsatellites gave pictures that were largely concordant with SNPs, although less accurate. The best concordance was found in the most complex dataset, which included 17 French sheep breeds (with a Pearson correlation coefficient of 0.95 considering the 136 values of pairwise FST, obtained with both types of markers). The use of microsatellites reduces the cost and the related analyses do not require specific computer equipment (i.e., information technology (IT) infrastructure able to provide adequate computing and storage capacity). Therefore, this tool may still be a very appropriate solution to evaluate, in a first stage, the general state of livestock at national scales. At a time when local breeds are disappearing at an alarming rate, it is urgent to improve our knowledge of them, in particular by promoting tools accessible to the greatest number.

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Section: Resultsmentioning

confidence: 99%

Inference of Breed Structure in Farm Animals: Empirical Comparison between SNP and Microsatellite Performance

Laoun

Harkat

Lafri

et al. 2020

Genes

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“…The variance of bull EBVs for 305-day milk yield in Australia and Sri Lanka were 73,532 kg 2 EBVs found in Sri Lanka were smaller than Australia.…”

Section: Estimated Breeding Valuesmentioning

confidence: 79%

Genetic parameters for milk yield in imported Jersey and Jersey-Friesian cows using daily milk records in Sri Lanka

Samaraweera

Boerner

Cyril

et al. 2020

Asian-Australas J Anim Sci

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Objective: Estimate genetic parameters for milk yield traits using daily milk yield records from parlour data generated in an intensively managed commercial dairy farm with Jersey and Jersey-Friesian cows in Sri Lanka. Methods: Genetic parameters were estimated for first and second lactation predicted and realized 305-day milk yield using univariate animal models. Genetic parameters were also estimated for total milk yield for each 30day intervals of the first lactation using univariate animal models and for daily milk yield using random regression models fitting second-order Legendre polynomials and assuming heterogeneous residual variances. Breeding values for predicted 305-day milk yield were estimated using an animal model. Results: For the first lactation, the heritability of predicted 305-day milk yield in Jersey cows (0.08 ± 0.03) was higher than that of Jersey-Friesian cows (0.02 ± 0.01). The second lactation heritability estimates were similar to that of first lactation. The repeatability of the daily milk records was 0.28 ± 0.01 and the heritability ranged from 0.002 ± 0.05 to 0.19 ± 0.02 depending on day of milk. Pearson product-moment correlations between the bull EBVs in Australia and bull EBVs in Sri Lanka for 305-day milk yield were 0.39 in Jersey cows and-0.35 in Jersey-Friesian cows. Conclusion: The heritabilities estimated for milk yield in Jersey and Jersey-Friesian cows in Sri Lanka were low, and were associated with low additive genetic variances for the traits. Sire differences in Australia were not expressed in the tropical low-country of Sri Lanka. Therefore, genetic progress achieved by importing genetic material from Australia can be expected to be slow. This emphasizes the need for a within-country evaluation of bulls to produce locally adapted dairy cows.

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“…The cost of genotyping of the animals is still high (US$ 100 per sample) by Indian standards. However, efforts are now being made for genotyping using an indigenously developed SNP chip for cattle (Nayee et al, ) and development of a female reference population of Bos indicus and cross‐breds, recording phenotypes using smartphones, or other cheap avenues at low cost in small herds (Ducrocq et al, ). This will help to increase the pace of genetic improvement for the dairy industry in the near future.…”

Section: Opportunities For Better Breedingmentioning

confidence: 99%