Short communication: Genomic selection in a crossbred cattle population using data from the Dairy Genetics East Africa Project

Brown, Alexandra R.; Ojango, Julie M.K.; Gibson, John P.; Coffey, M.P.; Okeyo, M; Mrode, Raphael

doi:10.3168/jds.2016-11083

Cited by 26 publications

(34 citation statements)

References 5 publications

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“…In addition, RRM are also more advantageous compared with P305 because selec-453 tion can be efficiently performed based on points of interest throughout the lactation curve. To overcome the limitations of P305 models, some studies have used the predicted P305 from traditional BLUP random regression genetic evaluation as a phenotype for 2-step GWS (Ding et al, 2013;Brown et al, 2016). The steps involved in the 2-step GWS approach are (1) estimation of breeding value based on traditional evaluation and its deregression and (2) estimation of direct genomic value (DGV) using deregressed EBV from step 1 as a phenotype for the genomic BLUP (GBLUP); subsequently, DGV can be combined with parent average (PA) into genomic EBV (GEBV).…”

Section: Introductionmentioning

confidence: 99%

Genomic prediction of lactation curves for milk, fat, protein, and somatic cell score in Holstein cattle

Oliveira

Brito

Silva

et al. 2019

Journal of Dairy Science

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Application of random regression models (RRM) in a 2-step genomic prediction might be a feasible way to select young animals based on the complete pattern of the lactation curve. In this context, the prediction reliability and bias of genomic estimated breeding value (GEBV) for milk, fat, and protein yields and somatic cell score over days in milk (DIM) using a 2-step genomic approach were investigated. In addition, the effect of including cows in the training and validation populations was investigated. Estimated breeding values for each DIM (from 5 to 305 d) from the first 3 lactations of Holstein animals were deregressed and used as pseudophenotypes in the second step. Individual additive genomic random regression coefficients for each trait were predicted using RRM and genomic best linear unbiased prediction and further used to derive GEBV for each DIM. Theoretical reliabilities of GEBV obtained by the RRM were slightly higher than theoretical reliabilities obtained by the accumulated yield up to 305 d (P305). However, validation reliabilities estimated for GEBV using P305 were higher than for GEBV using RRM. For all traits, higher theoretical and validation reliabilities were estimated when incorporating genomic information. Less biased GEBV estimates were found when using RRM compared with P305, and different validation reliability and bias patterns for GEBV over time were observed across traits and lactations. Including cows in the training population increased the theoretical reliabilities and bias of GEBV; nonetheless, the inclusion of cows in the validation population does not seem to affect the regression coefficients and the theoretical reliabilities. In summary, the use of RRM in 2-step genomic prediction produced fairly accurate GEBV over the entire lactation curve for all analyzed traits. Thus, selecting young animals based on the pattern of lactation curves seems to be a feasible alternative in genomic selection of Holstein cattle for milk production traits.

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Section: Introductionmentioning

confidence: 99%

Genomic prediction of lactation curves for milk, fat, protein, and somatic cell score in Holstein cattle

Oliveira

Brito

Silva

et al. 2019

Journal of Dairy Science

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“…In general, these reported studies on genomic prediction in dairy and beef cattle are characterized by small reference populations (500–3,000 animals, Table 1) and most validations are undertaken in test data sets created by either random or structured sampling from all genotyped animals. A few of these reference populations are a combination of both bulls and cows (Boison et al, 2017) but most are cows (Brown et al, 2016; Silva et al, 2016). This has implications in terms of the accuracy of genomic prediction, which has tended to be lower compared to those obtained in developed countries, given the limited information of the response variable when using cow records.…”

Section: Structure Of the Reference And Validation Populationsmentioning

confidence: 99%

“…However, in some countries like Brazil in Latin America, the existence of breed associations have resulted in the establishment of some degree of data and pedigree recording and genetic evaluation (Silva et al, 2016; Boison et al, 2017), but there is still the lack of breeding structures such as AI companies, to drive breed improvement programs. Therefore in the era of genomics, most genotyping activities in developing countries are undertaken by breed organizations or associations, such as in Brazil (Carvalheiro, 2014; Silva et al, 2016), or are a result of several development projects, such as the East Africa Dairy Development Project (Brown et al, 2016), and the African Dairy Genetic Gains Cattle project (https://www.ilri.org/node/40458). Consequently, the number of genotyped animals tend to be limited; are mostly females, and this has major influence on both the size and structure of the reference and validation populations.…”

Section: Introductionmentioning

confidence: 99%

Genomic Selection and Use of Molecular Tools in Breeding Programs for Indigenous and Crossbred Cattle in Developing Countries: Current Status and Future Prospects

et al. 2019

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Genomic selection (GS) has resulted in rapid rates of genetic gains especially in dairy cattle in developed countries resulting in a higher proportion of genomically proven young bulls being used in breeding. This success has been undergirded by well-established conventional genetic evaluation systems. Here, the status of GS in terms of the structure of the reference and validation populations, response variables, genomic prediction models, validation methods, and imputation efficiency in breeding programs of developing countries, where smallholder systems predominate and the basic components for conventional breeding are mostly lacking is examined. Also, the application of genomic tools and identification of genome-wide signatures of selection is reviewed. The studies on genomic prediction in developing countries are mostly in dairy and beef cattle usually with small reference populations (500–3,000 animals) and are mostly cows. The input variables tended to be pre-corrected phenotypic records and the small reference populations has made implementation of various Bayesian methods feasible in addition to GBLUP. Multi-trait single-step has been used to incorporate genomic information from foreign bulls, thus GS in developing countries would benefit from collaborations with developed countries, as many dairy sires used are from developed countries where they may have been genotyped and phenotyped. Cross validation approaches have been implemented in most studies resulting in accuracies of 0.20–0.60. Genotyping animals with a mixture of HD and LD chips, followed by imputation to the HD have been implemented with imputation accuracies of 0.74–0.99 reported. This increases the prospects of reducing genotyping costs and hence the cost-effectiveness of GS. Next-generation sequencing and associated technologies have allowed the determination of breed composition, parent verification, genome diversity, and genome-wide selection sweeps. This information can be incorporated into breeding programs aiming to utilize GS. Cost-effective GS in beef cattle in developing countries may involve usage of reproductive technologies (AI and in-vitro fertilization) to efficiently propagate superior genetics from the genomics pipeline. For dairy cattle, sexed semen of genomically proven young bulls could substantially improve profitability thus increase prospects of small holder farmers buying-in into genomic breeding programs.

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“…Such a concept was tested on a real data set of 1,013 dairy cows in Kenya, which exhibited various degrees of crossbreeding with exotic breeds ( Brown et al, 2016 ). A principal component analysis based on SNP data showed that individuals could be clustered in three groups according to the proportion of exotic breeds, with a reference and a validation data set for each group.…”

Section: Part 1: Current Contribution Of Genomic Information To Animamentioning

confidence: 99%

Genomics for Ruminants in Developing Countries: From Principles to Practice

et al. 2018

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Using genomic information, local ruminant populations can be better characterized and compared to selected ones. Genetic relationships between animals can be established even without systematic pedigree recording, provided a budget is available for genotyping. Genomic selection (GS) can rely on a subset of the total population and does not require a costly national infrastructure, e.g., based on progeny testing. Yet, the use of genomic tools for animal breeding in developing countries is still limited. We identify three main reasons for this: (i) the instruments for cheap recording of phenotypes and data management are still limiting. (ii) many developing countries are recurrently exposed to unfavorable conditions (heat, diseases, poor nutrition) requiring special attention to fitness traits, (iii) a high level of expertise in quantitative genetics, modeling, and data manipulation is needed to perform genomic analyses. Yet, the potential outcomes go much beyond genetic improvements and can improve the resilience of the whole farming system. They include a better management of genetic diversity of local populations, a more balanced genetic progress and the possibility to unravel the genetic basis of adaptation of local breeds through whole genome approaches. A GS program being developed by BAIF, a large Indian NGO, is analyzed as a pilot case. It relies on the creation of a female reference population of Bos indicus and crossbreds, recorded with modern technology (e.g., smartphones) to collect performances at low cost in tiny herds on production and fertility. Finally, recommendations for the implementation of GS in developing countries are proposed.

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Short communication: Genomic selection in a crossbred cattle population using data from the Dairy Genetics East Africa Project

Cited by 26 publications

References 5 publications

Genomic prediction of lactation curves for milk, fat, protein, and somatic cell score in Holstein cattle

Genomic prediction of lactation curves for milk, fat, protein, and somatic cell score in Holstein cattle

Genomic Selection and Use of Molecular Tools in Breeding Programs for Indigenous and Crossbred Cattle in Developing Countries: Current Status and Future Prospects

Genomics for Ruminants in Developing Countries: From Principles to Practice

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