Genetic evaluation to design a reference cow population for the Holstein breed in Tunisia: a first step toward genomic selection

List of abbreviationsGSGenomic SelectionBLUPBest Linear Unbiased PredictionEBVsEstimated Breeding ValuesEGVsEstimated genetic ValuesGEBVsGenomic Estimated Breeding ValuesSNPsSingle Nucleotide polymorphismsGxEGenotype-by-environment interactionsGxEGenotype-by-environment interactionsGxGGene-by-gene interactionsGxGxEGene-by-gene-by-environment interactionsuTUnivariate single environment one-step modeluEUnivariate multi environment one-step modelMTMulti-trait single environment one-step modelMEMultivariate single trait multi environment modelAbstractBackgroundGenomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for long cycle crops like cassava. To practically implement GS in cassava breeding, it is useful to evaluate different GS models and to develop suitable models for an optimized breeding pipeline.MethodsWe compared prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for single environment genetic evaluation (Scenario 1) while for multi-environment evaluation accounting for genotype-by-environment interaction (Scenario 2) we compared accuracies from a univariate (uE) and a multivariate (ME) multi-environment mixed model. We used sixteen years of data for six target cassava traits for these analyses. All models for Scenario 1 and Scenario 2 were based on the one-step approach. A 5-fold cross validation scheme with 10-repeat cycles were used to assess model prediction accuracies.ResultsIn Scenario 1, the MT models had higher prediction accuracies than the uT models for most traits and locations analyzed amounting to 32 percent better prediction accuracy on average. However for Scenario 2, we observed that the ME model had on average (across all locations and traits) 12 percent better predictive power than the uE model.ConclusionWe recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

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Relevance of genomic evaluation for egg quality traits in layers

10.1101/704742 ◽

2019 ◽

Author(s):

David Picard Druet ◽

Amandine Varenne ◽

Florian Herry ◽

Frédéric Hérault ◽

Sophie Allais ◽

...

Keyword(s):

Dairy Cattle ◽

Genomic Selection ◽

Egg Quality ◽

Pure Line ◽

Genetic Evaluation ◽

Evaluation Methodology ◽

Quality Traits ◽

Genomic Evaluation ◽

Cattle Breeding ◽

Dairy Cattle Breeding

AbstractBackgroundGenomic evaluation, based on thousands of genetic markers, has become the standard evaluation methodology in dairy cattle breeding programs over the past few years. Despite the many differences between dairy cattle breeding and poultry breeding, genomic selection seems very promising for the avian sector, and studies are currently being conducted to optimize avian selection schemes. In this optimization perspective, one of the key parameters is to properly predict the accuracy of genomic evaluation in pure line layers.MethodsBoth genetic evaluation and genomic evaluation were performed on three candidate populations (male and female), using different sizes of phenotypic records on five egg quality traits and at two different ages. The methodologies used were BLUP & ssGBLUP, and variance-covariance matrices were estimated through REML. To estimate evaluation accuracy, the LR method was implemented. Four statistics were used to assess the relative accuracy of the estimated breeding values of candidates, their bias and dispersion, as well as the differences between genetic evaluation and genomic evaluation.ResultsIt was observed that genomic evaluation, whether performed on males or females, always proved more accurate than genetic evaluation. The gain was higher when phenotypic information was narrowed and an augmentation of the size of the reference population led to an increase in accuracy prediction, for what regards genomic evaluation. By taking into account the increase of selection intensity and the decrease of the generation interval induced by genomic selection, the expected annual genetic gain would be higher with ancestry-based genomic evaluation of male candidates than with genetic evaluation based on collaterals. This advantage of genomic selection over genetic selection requires to be studied in more details for female candidates.ConclusionsIn conclusion, in the population studied, genomic evaluation for egg quality traits of breeding birds at birth seems a promising strategy, at least for what regards males selection.

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52 Genomic selection in the beef industry: Current achievements and future directions

Journal of Animal Science ◽

10.1093/jas/skz258.110 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 54-55

Author(s):

Daniel W Moser ◽

Stephen P Miller ◽

Kelli J Retallick ◽

Duc Lu ◽

Larry A Kuehn

Keyword(s):

Beef Cattle ◽

Genomic Selection ◽

Large Scale ◽

Genetic Evaluation ◽

Fiscal Year ◽

Genomic Testing ◽

Routine Practice ◽

The Past ◽

Angus Cattle ◽

Genetic Evaluations

Abstract In the past decade, genomic testing of beef cattle has evolved from applications in research to a routine practice for many beef cattle seedstock breeders. Testing for lethal genetic conditions or parentage was many breeders’ first experience with genomic testing. While the American Angus Association (AAA) began utilizing 384 SNP genotypes in genetic evaluations in 2009, the adoption of genotyping with higher density (~50,000 SNP) arrays by AAA in 2010 launched large-scale genotyping of Angus cattle for genetic evaluation. AAA transitioned from semi-annual to weekly genetic evaluations in 2010, and cost of genotyping decreased from $139 per animal in 2011, to $37 in 2017. In fiscal year 2018, AAA members genotyped over 160,000 animals for genetic evaluation, and as of April 2019, the AAA and Canadian Angus Association joint genetic evaluation includes over 635,000 genotyped animals. Now genotyping arrays with Angus-specific SNP content are used. The primary benefit to Angus breeders has been increased accuracy of genetic prediction for young animals, especially for traits with limited phenotypic information such as carcass traits, feed intake and mature cow size. Future benefits from genotyping include identification and selection against embryonic lethal alleles, better characterization of inbreeding, and selection tools for additional traits relevant to or measured in unique environments. Electronic sensors and other novel approaches may yield previously unmeasurable phenotypes for health and efficiency traits, which can be extended to wider populations for selection using genomics. New techniques such as DNA pooling and genotyping by sequencing may reduce costs enabling widespread testing in commercial cow-calf and cattle feeding enterprises. The application of genomic selection has clearly been a significant advancement in genetic selection in Angus cattle in the past ten years. This early adoption will expedite subsequent genomic tools at an increasing rate and will foster innovation.

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Dairy genetic improvement through artificial insemination, performance recording and genetic evaluation

Canadian Journal of Animal Science ◽

10.4141/a03-023 ◽

2003 ◽

Vol 83 (3) ◽

pp. 385-392 ◽

Cited By ~ 3

Author(s):

B. J. Van Doormaal ◽

G. J. Kistemaker

Keyword(s):

Dairy Cattle ◽

Artificial Insemination ◽

Genetic Improvement ◽

Genetic Evaluation ◽

Production Traits ◽

Genetic Progress ◽

Genetic Gains ◽

Holstein Breed ◽

The Past ◽

Genetic Evaluations

Artificial insemination (AI) of dairy cattle in Canada was started more than half a century ago and today it is estimated that at least 75% of all dairy cattle nationally are bred using this common reproductive technology. A Best Linear Unbiased Prediction sire model for estimating genetic evaluations for production traits was introduced in 1975. The combination of extensive use of AI with genetic evaluations for bulls and cows has resulted in significant phenotypic and genetic gains over the past 20 yr. In the Holstein breed, mature equivalent yields have increased by an average of 200 kg milk, 7.0 kg fat and 6.3 kg protein per year since 1980. Genetically, the relative emphasis realized for production traits versus overall type during the past 5 yr has followed the 60:40 breeding goal represented in the Lifetime Profit Index, which has increased at an average rate of 0.28 standard units per year. Examination of the generation interval in the Canadian Holstein breed, associated with each of the four pathways for genetic improvement, indicates a 46% increase in the rate of annual genetic gain today compared to 20 yr ago. The increased accuracy and intensity of selection associated with the use of AI and genetic evaluations have also contributed to the rates of phenotypic and genetic progress achieved over the years. In the future , AI will continue to be a critical component of the genetic gains possible in dairy cattle breeding but it will be complemented by other reproductive technologies aimed at further reducing generation intervals and increasing the accuracy and selection of intensity, especially on the female side. Key words: Dairy cattle, artificial insemination, genetic progress, genetic evaluation

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Will models of genetic evaluation and genomic selection ‘converge’?

Journal of Animal Breeding and Genetics ◽

10.1111/jbg.12184 ◽

2015 ◽

Vol 132 (5) ◽

pp. 339-339

Author(s):

R.J.C. Cantet

Keyword(s):

Genomic Selection ◽

Genetic Evaluation

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Genomic selection in dairy cattle simulated populations

Journal of Dairy Research ◽

10.1017/s0022029918000304 ◽

2018 ◽

Vol 85 (2) ◽

pp. 125-132

Author(s):

Leonardo de Oliveira Seno ◽

Diego Gomes Freire Guidolin ◽

Rusbel Raul Aspilcueta-Borquis ◽

Guilherme Batista do Nascimento ◽

Thiago Bruno Ribeiro da Silva ◽

...

Keyword(s):

Dairy Cattle ◽

Genomic Selection ◽

Milk Yield ◽

Genetic Gain ◽

Random Mating ◽

Allelic Frequency ◽

Genetic Evaluation ◽

Phenotypic Data ◽

Generation Structure ◽

Frequency Changes

Genomic selection is arguably the most promising tool for improving genetic gain in domestic animals to emerge in the last few decades, but is an expensive process. The aim of this study was to evaluate the economic impact related to the implementation of genomic selection in a simulated dairy cattle population. The software QMSim was used to simulate genomic and phenotypic data. The simulated genome contained 30 chromosomes with 100 cm each, 1666 SNPs markers equally spread and 266 QTLs randomly designated for each chromosome. The numbers of markers and QTLs were designated according to information available from Animal QTL (http://www.animalgenome.org/QTLdb) and Bovine QTL (http://bovineqtl.tamu.edu/). The allelic frequency changes were assigned in a gamma distribution with alpha parameters equal to 0·4. Recurrent mutation rates of 1·0e−4 were assumed to apply to markers and QTLs. A historic population of 1000 individuals was generated and the total number of animals was reduced gradually along 850 generations until we obtained a number of 200 animals in the last generation, characterizing a bottleneck effect. Progenies were created along generations from random mating of the male and female gametes, assuming the same proportion of both genders. Than the population was extended for another 150 generations until we obtained 17 000 animals, with only 320 male individuals in the last generation. After this period a 25 year of selection was simulated taking into account a trait limited by sex with heritability of 0·30 (i.e. milk yield), one progeny/cow/year and variance equal to 1·0. Annually, 320 bulls were mated with 16 000 dams, assuming a replacement rate of 60 and 40% for males and females, respectively. Selection and discard criteria were based in four strategies to obtain the EBVs assuming as breeding objective to maximize milk yield. The progeny replaced the discarded animals creating an overlapping generation structure. The selection strategies were: RS is selection based on random values; PS is selection based on phenotypic values; Blup is selection based on EBVs estimated by BLUP; and GEBV is selection based on genomic estimated breeding values in one step, using high (GBlup) and low (GBlupi) density panels. Results indicated that the genetic evaluation using the aid of genomic information could provide better genetic gain rates in dairy cattle breeding programs as well as reduce the average inbreeding coefficient in the population. The economic viability indicators showed that only Blup and GBlup/GBlupi strategies, the ones that used milk control and genetic evaluation were economic viable, considering a discount rate of 6·32% per year.

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