Genomic selection in French dairy cattle

Genomic selection is implemented in French Holstein, Montbéliarde, and Normande breeds (70%, 16% and 12% of French dairy cows). A characteristic of the model for genomic evaluation is the use of haplotypes instead of single-nucleotide polymorphisms (SNPs), so as to maximise linkage disequilibrium between markers and quantitative trait loci (QTLs). For each trait, a QTL-BLUP model (i.e. a best linear unbiased prediction model including QTL random effects) includes 300–700 trait-dependent chromosomal regions selected either by linkage disequilibrium and linkage analysis or by elastic net. This model requires an important effort to phase genotypes, detect QTLs, select SNPs, but was found to be the most efficient one among all tested ones. QTLs are defined within breed and many of them were found to be breed specific. Reference populations include 1800 and 1400 bulls in Montbéliarde and Normande breeds. In Holstein, the very large reference population of 18 300 bulls originates from the EuroGenomics consortium. Since 2008, ~65 000 animals have been genotyped for selection by Labogena with the 50k chip. Bulls genomic estimated breeding values (GEBVs) were made official in June 2009. In 2010, the market share of the young bulls reached 30% and is expected to increase rapidly. Advertising actions have been undertaken to recommend a time-restricted use of young bulls with a limited number of doses. In January 2011, genomic selection was opened to all farmers for females. Current developments focus on the extension of the method to a multi-breed context, to use all reference populations simultaneously in genomic evaluation.

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Indirect predictions with a large number of genotyped animals using the algorithm for proven and young

Journal of Animal Science ◽

10.1093/jas/skaa154 ◽

2020 ◽

Vol 98 (6) ◽

Cited By ~ 1

Author(s):

Andre L S Garcia ◽

Yutaka Masuda ◽

Shogo Tsuruta ◽

Stephen Miller ◽

Ignacy Misztal ◽

...

Keyword(s):

Random Sample ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Nucleotide Polymorphisms ◽

Linear Unbiased Prediction ◽

Single Nucleotide ◽

Large Numbers ◽

Minimum Number ◽

Best Linear Unbiased ◽

Estimated Breeding Values

Abstract Reliable single-nucleotide polymorphisms (SNP) effects from genomic best linear unbiased prediction BLUP (GBLUP) and single-step GBLUP (ssGBLUP) are needed to calculate indirect predictions (IP) for young genotyped animals and animals not included in official evaluations. Obtaining reliable SNP effects and IP requires a minimum number of animals and when a large number of genotyped animals are available, the algorithm for proven and young (APY) may be needed. Thus, the objectives of this study were to evaluate IP with an increasingly larger number of genotyped animals and to determine the minimum number of animals needed to compute reliable SNP effects and IP. Genotypes and phenotypes for birth weight, weaning weight, and postweaning gain were provided by the American Angus Association. The number of animals with phenotypes was more than 3.8 million. Genotyped animals were assigned to three cumulative year-classes: born until 2013 (N = 114,937), born until 2014 (N = 183,847), and born until 2015 (N = 280,506). A three-trait model was fitted using the APY algorithm with 19,021 core animals under two scenarios: 1) core 2013 (random sample of animals born until 2013) used for all year-classes and 2) core 2014 (random sample of animals born until 2014) used for year-class 2014 and core 2015 (random sample of animals born until 2015) used for year-class 2015. GBLUP used phenotypes from genotyped animals only, whereas ssGBLUP used all available phenotypes. SNP effects were predicted using genomic estimated breeding values (GEBV) from either all genotyped animals or only core animals. The correlations between GEBV from GBLUP and IP obtained using SNP effects from core 2013 were ≥0.99 for animals born in 2013 but as low as 0.07 for animals born in 2014 and 2015. Conversely, the correlations between GEBV from ssGBLUP and IP were ≥0.99 for animals born in all years. IP predictive abilities computed with GEBV from ssGBLUP and SNP predictions based on only core animals were as high as those based on all genotyped animals. The correlations between GEBV and IP from ssGBLUP were ≥0.76, ≥0.90, and ≥0.98 when SNP effects were computed using 2k, 5k, and 15k core animals. Suitable IP based on GEBV from GBLUP can be obtained when SNP predictions are based on an appropriate number of core animals, but a considerable decline in IP accuracy can occur in subsequent years. Conversely, IP from ssGBLUP based on large numbers of phenotypes from non-genotyped animals have persistent accuracy over time.

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Maximizing genetic gain for the sire line of a crossbreeding scheme utilizing both purebred and crossbred information

Animal Science ◽

10.1017/s135772980000970x ◽

1998 ◽

Vol 66 (2) ◽

pp. 529-542 ◽

Cited By ~ 45

Author(s):

P. Bijma ◽

J. A. M. van Arendonk

Keyword(s):

Linkage Disequilibrium ◽

Selection Index ◽

Optimum Number ◽

Linear Unbiased Prediction ◽

Index Procedure ◽

Breeding Goal ◽

Breeding Schemes ◽

Best Linear Unbiased ◽

Estimated Breeding Values ◽

Selection Intensities

AbstractA selection index procedure which utilizes both, purebred and crossbred information was developed for the sire line of a three-path crossbreeding scheme in pigs, to predict response to best linear unbiased prediction (BLUP) selection with an animal model. Purebred and crossbred performance were treated as correlated traits. The breeding goal was crossbred performance but methods can be applied to other goals. A hierarchical mating structure was used. Sires were mated to purebred dams to generate replacements and to F^ from the dam line to generate fattening pigs. Generations were discrete, inbreeding was ignored. The selection index included purebred and crossbred phenotypic information of the current generation and estimated breeding values for purebred and crossbred performance of parents and mates of parents from the previous generation. Reduction of genetic variance due to linkage disequilibrium and reduction of selection intensity due to finite population size and due to correlated index values was accounted for. Selection was undertaken until asymptotic responses were reached. The index was used to optimize the number of selected parents per generation and the number of offspring tested per litter, and to make inferences on the value of crossbred information when the breeding goal was crossbred performance. It was optimal to test a maximum number of offspring per litter, mainly due to increased female selection intensities. Maximum response reductions due to linkage disequilibrium and correlated index values were 32% and 29% respectively. Correcting for correlated index values changed ranking of breeding schemes. Benefit of crossbred information was largest when the genetic correlation between purebred and crossbred performance was low. Due to high correlations between index values in that case, the optimum number of selected sires increased considerably when crossbred information was included.

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Short Communication: Investigation of the feasibility of genomic selection in Icelandic Cattle

Journal of Animal Science ◽

10.1093/jas/skab139 ◽

2021 ◽

Author(s):

Egill Gautason ◽

Goutam Sahana ◽

Guosheng Su ◽

Baldur Helgi Benjamínsson ◽

Guðmundur Jóhannesson ◽

...

Keyword(s):

Genomic Selection ◽

Cattle Breed ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Breeding Scheme ◽

Linear Unbiased Prediction ◽

Percentage Points ◽

Best Linear Unbiased ◽

Estimated Breeding Values ◽

Unbiased Prediction

Abstract Icelandic Cattle is a local dairy cattle breed in Iceland. With about 26,000 breeding females, it is by far the largest among the indigenous Nordic cattle breeds. The objective of this study was to investigate the feasibility of genomic selection in Icelandic Cattle. Pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) were compared. Accuracy, bias, and dispersion of estimated breeding values (EBV) for milk yield (MY), fat yield (FY), protein yield (PY), and somatic cell score (SCS) were estimated in a cross validation-based design. Accuracy (r) was estimated by the correlation between EBV and corrected phenotype in a validation set. The accuracy (r) of predictions using ssGBLUP increased by 13, 23, 19 and 20 percentage points for MY, FY, PY, and SCS for genotyped animals, compared to PBLUP. The accuracy of non-genotyped animals was not improved for MY and PY, but increased by 0.9 and 3.5 percentage points for FY and SCS. We used the linear regression (LR) method to quantify relative improvements in accuracy, bias (∆), and dispersion (b) of EBV. Using the LR method, the relative improvements in accuracy of validation from PBLUP to ssGBLUP were 43%, 60%, 50%, and 48% for genotyped animals for MY, FY, PY, and SCS. Single-step GBLUP EBV were less underestimated (∆), and less over-dispersed (b) than PBLUP EBV for FY and PY. Pedigree-based BLUP EBV were close to unbiased for MY and SCS. Single-step GBLUP underestimated MY EBV but overestimated SCS EBV. Based on the average accuracy of 0.45 for ssGBLUP EBV obtained in this study, selection intensities according to the breeding scheme of Icelandic Cattle, and assuming a generation interval of 2.0 years for sires of bulls, sires of dams and dams of bulls, genetic gain in Icelandic Cattle could be increased by about 50% relative to the current breeding scheme.

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LASSO with cross-validation for genomic selection

Genetics Research ◽

10.1017/s0016672309990334 ◽

2009 ◽

Vol 91 (6) ◽

pp. 427-436 ◽

Cited By ~ 94

Author(s):

M. GRAZIANO USAI ◽

MIKE E. GODDARD ◽

BEN J. HAYES

Keyword(s):

Genomic Selection ◽

Cross Validation ◽

Simulated Data ◽

Reference Population ◽

Good Alternative ◽

Data Sets ◽

Nucleotide Polymorphisms ◽

Data Set ◽

Estimated Breeding Values ◽

Selection Operator

SummaryWe used a least absolute shrinkage and selection operator (LASSO) approach to estimate marker effects for genomic selection. The least angle regression (LARS) algorithm and cross-validation were used to define the best subset of markers to include in the model. The LASSO–LARS approach was tested on two data sets: a simulated data set with 5865 individuals and 6000 Single Nucleotide Polymorphisms (SNPs); and a mouse data set with 1885 individuals genotyped for 10 656 SNPs and phenotyped for a number of quantitative traits. In the simulated data, three approaches were used to split the reference population into training and validation subsets for cross-validation: random splitting across the whole population; random sampling of validation set from the last generation only, either within or across families. The highest accuracy was obtained by random splitting across the whole population. The accuracy of genomic estimated breeding values (GEBVs) in the candidate population obtained by LASSO–LARS was 0·89 with 156 explanatory SNPs. This value was higher than those obtained by Best Linear Unbiased Prediction (BLUP) and a Bayesian method (BayesA), which were 0·75 and 0·84, respectively. In the mouse data, 1600 individuals were randomly allocated to the reference population. The GEBVs for the remaining 285 individuals estimated by LASSO–LARS were more accurate than those obtained by BLUP and BayesA for weight at six weeks and slightly lower for growth rate and body length. It was concluded that LASSO–LARS approach is a good alternative method to estimate marker effects for genomic selection, particularly when the cost of genotyping can be reduced by using a limited subset of markers.

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Accuracies of direct genomic breeding values for birth and weaning weights of registered Charolais cattle in Mexico

Animal Production Science ◽

10.1071/an18363 ◽

2020 ◽

Vol 60 (6) ◽

pp. 772

Author(s):

Francisco J. Jahuey-Martínez ◽

Gaspar M. Parra-Bracamonte ◽

Dorian J. Garrick ◽

Nicolás López-Villalobos ◽

Juan C. Martínez-González ◽

...

Keyword(s):

Genomic Prediction ◽

Reference Population ◽

Single Step ◽

Bayesian Regression ◽

Nucleotide Polymorphisms ◽

Linear Unbiased Prediction ◽

Genomic Breeding ◽

Breeding Values ◽

Charolais Cattle ◽

Best Linear Unbiased

Context Genomic prediction is now routinely used in many livestock species to rank individuals based on genomic breeding values (GEBV). Aims This study reports the first assessment aimed to evaluate the accuracy of direct GEBV for birth (BW) and weaning (WW) weights of registered Charolais cattle in Mexico. Methods The population assessed included 823 animals genotyped with an array of 77000 single nucleotide polymorphisms. Genomic prediction used genomic best linear unbiased prediction (GBLUP), Bayes C (BC), and single-step Bayesian regression (SSBR) methods in comparison with a pedigree-based BLUP method. Key results Our results show that the genomic prediction methods provided low and similar accuracies to BLUP. The prediction accuracy of GBLUP and BC were identical at 0.31 for BW and 0.29 for WW, similar to BLUP. Prediction accuracies of SSBR for BW and WW were up to 4% higher than those by BLUP. Conclusions Genomic prediction is feasible under current conditions, and provides a slight improvement using SSBR. Implications Some limitations on reference population size and structure were identified and need to be addressed to obtain more accurate predictions in liveweight traits under the prevalent cattle breeding conditions of Mexico.

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Effect of using different number and type of records from different generations as reference population on the accuracy of genomic evaluation

Archives Animal Breeding ◽

10.7482/0003-9438-56-068 ◽

2013 ◽

Vol 56 (1) ◽

pp. 684-690

Author(s):

A. Boustan ◽

A. Nejati-Javaremi ◽

M. M. Shahrbabak ◽

M. Saatchi

Keyword(s):

Finite Population ◽

Random Mating ◽

Reference Population ◽

Breeding Value ◽

Nucleotide Polymorphisms ◽

Genomic Evaluation ◽

Single Nucleotide ◽

A Genome ◽

Estimated Breeding Values ◽

Marker Distance

Abstract. One important question about genomic evaluation is how distance between generations of individuals in reference population and selection candidates, would affect the accuracy of genomic estimated breeding value of selection candidates. There were two schemes in the present study. In first scheme, for each individual a genome consisting 30 chromosomes each with 100 equally spaced single nucleotide polymorphisms (SNPs) and in second scheme a genome consisting 3 chromosomes each with 1000 equally spaced SNPs was simulated. To generate enough linkage disequilibrium between loci, random mating for 50 generations was done in a finite population. In generation 51, population size was expanded to 250 individuals. This structure was continued until generation 55. Individuals in generation 55 were juvenile and did not have phenotypic records and were selection candidates. Heritability was assumed to be 0.3. Our results showed using information from more distant generations would decrease accuracy of genomic estimated breeding values of selection candidates but in scheme in which marker distance was 1 centimorgan, increasing generation number between reference population and selection candidates would decrease accuracy more than scheme in which marker distance was 0.1 centimorgan. According to our results using EBVs of reference population instead of phenotypic records would increase accuracy extremely.

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Genomic best linear unbiased prediction method including imprinting effects for genomic evaluation

Genetics Selection Evolution ◽

10.1186/s12711-015-0091-y ◽

2015 ◽

Vol 47 (1) ◽

Cited By ~ 11

Author(s):

Motohide Nishio ◽

Masahiro Satoh

Keyword(s):

Prediction Method ◽

Best Linear Unbiased Prediction ◽

Genomic Evaluation ◽

Linear Unbiased Prediction ◽

Best Linear Unbiased ◽

Unbiased Prediction

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The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs

Journal of Animal Science and Biotechnology ◽

10.1186/s40104-020-00493-8 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Hailiang Song ◽

Qin Zhang ◽

Xiangdong Ding

Keyword(s):

Genomic Prediction ◽

Production Systems ◽

Genetic Correlations ◽

Reproductive Traits ◽

Reference Population ◽

Linear Unbiased Prediction ◽

Genotype By Environment ◽

Best Linear Unbiased ◽

Effect Estimation ◽

Two Populations

Abstract Background Different production systems and climates could lead to genotype-by-environment (G × E) interactions between populations, and the inclusion of G × E interactions is becoming essential in breeding decisions. The objective of this study was to investigate the performance of multi-trait models in genomic prediction in a limited number of environments with G × E interactions. Results In total, 2,688 and 1,384 individuals with growth and reproduction phenotypes, respectively, from two Yorkshire pig populations with similar genetic backgrounds were genotyped with the PorcineSNP80 panel. Single- and multi-trait models with genomic best linear unbiased prediction (GBLUP) and BayesC π were implemented to investigate their genomic prediction abilities with 20 replicates of five-fold cross-validation. Our results regarding between-environment genetic correlations of growth and reproductive traits (ranging from 0.618 to 0.723) indicated the existence of G × E interactions between these two Yorkshire pig populations. For single-trait models, genomic prediction with GBLUP was only 1.1% more accurate on average in the combined population than in single populations, and no significant improvements were obtained by BayesC π for most traits. In addition, single-trait models with either GBLUP or BayesC π produced greater bias for the combined population than for single populations. However, multi-trait models with GBLUP and BayesC π better accommodated G × E interactions, yielding 2.2% – 3.8% and 1.0% – 2.5% higher prediction accuracies for growth and reproductive traits, respectively, compared to those for single-trait models of single populations and the combined population. The multi-trait models also yielded lower bias and larger gains in the case of a small reference population. The smaller improvement in prediction accuracy and larger bias obtained by the single-trait models in the combined population was mainly due to the low consistency of linkage disequilibrium between the two populations, which also caused the BayesC π method to always produce the largest standard error in marker effect estimation for the combined population. Conclusions In conclusion, our findings confirmed that directly combining populations to enlarge the reference population is not efficient in improving the accuracy of genomic prediction in the presence of G × E interactions, while multi-trait models perform better in a limited number of environments with G × E interactions.

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Genetic changes generated within the Katanning Merino Resource flocks

Australian Journal of Experimental Agriculture ◽

10.1071/ea06007 ◽

2006 ◽

Vol 46 (7) ◽

pp. 803 ◽

Cited By ~ 7

Author(s):

J. C. Greeff ◽

G. Cox

Keyword(s):

Body Weight ◽

Phase 1 ◽

Fibre Diameter ◽

Linear Unbiased Prediction ◽

Genetic Changes ◽

Breeding Values ◽

Best Linear Unbiased ◽

Fleece Weight ◽

Estimated Breeding Values ◽

Genetic Trends

Genetic changes for clean fleece weight, fibre diameter and hogget body weight were determined in the Katanning Merino Resource flocks from 1982 to 2004. From 1982 to 1992 genetic trends are presented for individual studs that used mainly subjective classing selection methods (Phase 1) and the genetic trends from 1997 to 2004 demonstrate the genetic changes that can be achieved from using estimated breeding values calculated from best linear unbiased prediction (BLUP) mixed methodology (Phase 2). The results during the first phase show that very few genetic changes occurred in most studs, except for the 4 studs of the Performance Sheep Breeding strain which showed genetic increases in hogget body weight. The genetic trends show that some studs generated change towards their breeding objective, while others show no changes or changes in the opposite direction. In contrast, the use of BLUP estimated breeding values resulted in positive changes in clean fleece weight, fibre diameter and body weight in accordance with the defined breeding objectives.

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