Strategies for comparing and combining different genetic and genomic evaluations: A review

Vandenplas, Jérémie; Gengler, Nicolas

doi:10.1016/j.livsci.2015.09.012

Cited by 14 publications

(19 citation statements)

References 41 publications

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“…In general, there is a high variability of REL EBV between animals in dairy cattle populations, especially between bulls and cows. Thus, considering this variability may yield more reliable predictions (Vandenplas & Gengler, ). To verify whether the performance of the deregression methods is influenced for this variability, all analyses were carried out considering no weight (first subscenario), and considering the ERC of an animal, excluding the PA information (ERC (Ind‐PA) ), as weight for the inverse of residual variance matrix (second subscenario).…”

Section: Methodsmentioning

confidence: 99%

Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle

Oliveira

Silva

Brito

et al. 2018

J Animal Breeding Genetics

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We aimed to investigate the performance of three deregression methods (VanRaden, VR; Wiggans, WG; and Garrick, GR) of cows' and bulls' breeding values to be used as pseudophenotypes in the genomic evaluation of test-day dairy production traits. Three scenarios were considered within each deregression method: (i) including only animals with reliability of estimated breeding value (REL ) higher than the average of parent reliability (REL ) in the training and validation populations; (ii) including only animals with REL higher than 0.50 in the training and REL higher than REL in the validation population; and (iii) including only animals with REL higher than 0.50 in both training and validation populations. Individual random regression coefficients of lactation curves were predicted using the genomic best linear unbiased prediction (GBLUP), considering either unweighted or weighted residual variances based on effective records contributions. In summary, VR and WG deregression methods seemed more appropriate for genomic prediction of test-day traits without need for weighting in the genomic analysis, unless large differences in REL between training population animals exist.

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Section: Methodsmentioning

confidence: 99%

Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle

Oliveira

Silva

Brito

et al. 2018

J Animal Breeding Genetics

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“…Further, 346 some phenotyping resources could be diverted to genotyping to maximize return on investment. A 347 comparable level of accuracy can be also achieved with international reference populations (Jorjani, 348 2012;Špehar et al, 2013) or a combination of national and international reference populations 349 (Vandenplas and Gengler, 2015;Vandenplas et al, 2017;Vandenplas et al, 2018). When this level 350 of accuracy is combined with a reduced generation interval, small populations can achieve 351 substantially larger genetic gains than with progeny testing.…”

Section: Genetic Gain With Genomic Truncation Selection 326mentioning

confidence: 98%

Efficient use of genomic information for sustainable genetic improvement in small cattle populations

Obšteter

Jenko

Hickey

2019

Preprint

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17This paper compares genetic gain, genetic variation, and the efficiency of converting variation into 18 gain under different genomic selection scenarios with truncation or optimum contribution selection 19 in a small dairy population by simulation. Breeding programs have to maximize genetic gain but 20 also ensure sustainability by maintaining genetic variation. Numerous studies showed that genomic 21 selection increases genetic gain. Although genomic selection is a well-established method, small 22 populations still struggle with choosing the most sustainable strategy to adopt this type of selection. 23We developed a simulator of a dairy population and simulated a model after the Slovenian Brown 24Swiss population with ~10,500 cows. We compared different truncation selection scenarios by 25 varying i) the method of sire selection and their use on cows or bull-dams, and ii) selection intensity 26 and the number of years a sire is in use. Furthermore, we compared different optimum contribution 27 selection scenarios with optimization of sire selection and their usage. We compared the scenarios 28 in terms of genetic gain, selection accuracy, generation interval, genetic and genic variance, the rate 29 of coancestry, effective population size, and the conversion efficiency. The results show that early 30 use of genomically tested sires increased genetic gain compared to progeny testing as expected from 31 changes in selection accuracy and generation interval. A faster turnover of sires from year to year 32 and higher intensity increased the genetic gain even further but increased the loss of genetic 33 variation per year. While maximizing intensity gave the lowest conversion efficiency, a faster turn-34 over of sires gave an intermediate conversion efficiency. The largest conversion efficiency was 35 achieved with the simultaneous use of genomically and progeny tested sires that were used over 36 several years. Compared to truncation selection optimizing sire selection and their usage increased 37 the conversion efficiency by either achieving comparable genetic gain for a smaller loss of genetic 38 variation or achieving higher genetic gain for a comparable loss of genetic variation. Our results 39 will help breeding organizations to implement sustainable genomic selection. 40Key words: small population, sustainability, genomic selection, optimum contribution selection 41 progeny testing with genomic pre-selection prior to progeny testing or direct genomic selection for 59 widespread use without progeny testing (de Roos et al., 2011;Lillehammer et al., 2011; Pryce et al., 60 2010). These studies reported up to 30% increase in genetic gain with the genomic pre-selection and 61 up to 195% increase with the direct genomic selection. Thomasen et al. (2014) deterministically 62 evaluated hybrid schemes that use both progeny and young genomically tested sires in populations 63 of different size. They concluded that genomic selection gives higher genetic gain than conventional 64 progeny testing irrespective of popu...

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“…The small reference populations indicate the need for across regional genomic prediction systems where this is possible with data pooled across nearby countries especially in sub-Sahara Africa, where dairy systems tend to be similar. Several procedures and approaches for combining data across breeding programs or countries have been developed and these range from post-evaluation blending procedures, application of appropriate linear models, or Bayesian methods (Vandenplas and Gengler, 2015; Vandenplas et al, 2018). Mrode et al (2018) analyzed pooled data for milk yield from crossbred cattle in Kenya and Tanzania.…”

Section: Structure Of the Reference And Validation Populationsmentioning

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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Strategies for comparing and combining different genetic and genomic evaluations: A review

Cited by 14 publications

References 41 publications

Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle

Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle

Efficient use of genomic information for sustainable genetic improvement in small cattle populations

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

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