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

1998 ◽  
Vol 66 (2) ◽  
pp. 529-542 ◽  
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.

scholarly journals Genomic selection in French dairy cattle

2012 ◽  
Vol 52 (3) ◽  
pp. 115 ◽  
Author(s):  
D. Boichard ◽  
F. Guillaume ◽  
A. Baur ◽  
P. Croiseau ◽  
M. N. Rossignol ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Genomic Selection ◽  
Reference Population ◽  
Specific Reference ◽  
Nucleotide Polymorphisms ◽  
Genomic Evaluation ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Estimated Breeding Values ◽  
Restricted Use

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.

Calculation of response and variance reduction due to multi-stage and multiple trait selection

1994 ◽  
Vol 58 (1) ◽  
pp. 1-9 ◽  
Author(s):  
S. Andersen
Keyword(s):  
Variance Reduction ◽  
Selection Index ◽  
Breeding Value ◽  
Multiple Trait ◽  
Multi Stage ◽  
Breeding Schemes ◽  
Unselected Population ◽  
Best Linear Unbiased ◽  
Selection Intensities ◽  
Set Up

AbstractIn deterministic comparisons of breeding schemes it is necessary to take account of variance reduction due to selection. This can take place as multi-stage selection within generations and it takes place across generations when offspring of selected parents are selected. A standard way to deal with this is to set up selection index equations where the parameters are altered as a consequence of selection. It is shown that if the breeding schemes use a univariate or multivariate best linear unbiased prediction (BLUP) animal model for prediction of breeding values this procedure can be simplified. This is done by modelling the distribution of estimated breeding value (EBV) utilizing that changes in EBV of an individual are independent of selection. In the univariate case the variance reduction and the resulting genetic gain can be calculated from the selection intensities and the accuracies in the unselected population. An expression is given for the response in each generation when selection is started in a base population with complete pedigree. This shows that a limiting value is obtained within three to four generations. The asymptotic response for several traits is described in the case where selection is on multitrait BLUP.

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

2020 ◽  
Vol 98 (6) ◽  
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.

scholarly journals On Marker-Assisted Prediction of Genetic Value: Beyond the Ridge

Genetics ◽  
2003 ◽  
Vol 163 (1) ◽  
pp. 347-365 ◽  
Author(s):  
Daniel Gianola ◽  
Miguel Perez-Enciso ◽  
Miguel A Toro
Keyword(s):  
Genetic Variability ◽  
Multilevel Models ◽  
Selection Index ◽  
Spatial Covariance ◽  
Linear Unbiased Prediction ◽  
Lorenz Curves ◽  
Gini Coefficients ◽  
Best Linear Unbiased ◽  
Genetic Value ◽  
Dense Marker

Abstract Marked-assisted genetic improvement of agricultural species exploits statistical dependencies in the joint distribution of marker genotypes and quantitative traits. An issue is how molecular (e.g., dense marker maps) and phenotypic information (e.g., some measure of yield in plants) is to be used for predicting the genetic value of candidates for selection. Multiple regression, selection index techniques, best linear unbiased prediction, and ridge regression of phenotypes on marker genotypes have been suggested, as well as more elaborate methods. Here, phenotype-marker associations are modeled hierarchically via multilevel models including chromosomal effects, a spatial covariance of marked effects within chromosomes, background genetic variability, and family heterogeneity. Lorenz curves and Gini coefficients are suggested for assessing the inequality of the contribution of different marked effects to genetic variability. Classical and Bayesian methods are presented. The Bayesian approach includes a Markov chain Monte Carlo implementation. The generality and flexibility of the Bayesian method is illustrated when a Lorenz curve is to be inferred.

Genetic changes generated within the Katanning Merino Resource flocks

2006 ◽  
Vol 46 (7) ◽  
pp. 803 ◽  
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.

Practical breeding developments and future prospects

1990 ◽  
Vol 14 ◽  
pp. 13-22
Author(s):  
M. K. Curran
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Future Prospects ◽  
Population Changes ◽  
Linear Unbiased Prediction ◽  
Multiple Ovulation ◽  
Group Breeding ◽  
Breeding Schemes ◽  
Best Linear Unbiased ◽  
The Uk ◽  
Terminal Sire

AbstractThis paper is a review of practical sheep breeding improvement schemes and techniques in the UK. Recent breed population changes in each of the broad categories of hill, longwool/crossing, longwool ewe, terminal sire and shortwool ewe breeds are outlined. Current or planned improvement programmes are reported for Welsh Mountain, Beulah, Scottish Blackface, Border Leicester, Cambridge, Friesland, Romney, Texel, Suffolk, Lleyn and Merino breeds. The techniques of genetic improvement currently available are discussed including some costs and likely genetic gains; techniques include group breeding schemes, artificial insemination, multiple ovulation and embryo transplant, best linear unbiased prediction and transgenic methods. The application of these techniques and contribution they could make to future sheep improvement are assessed.

scholarly journals Artificial selection on phenotypically plastic traits

1999 ◽  
Vol 74 (3) ◽  
pp. 265-270 ◽  
Author(s):  
MARK KIRKPATRICK ◽  
THOMAS BATAILLON
Keyword(s):  
Artificial Selection ◽  
Mass Selection ◽  
Genetic Covariance ◽  
Linear Unbiased Prediction ◽  
Infinite Dimensional ◽  
Covariance Functions ◽  
Breeding Schemes ◽  
Best Linear Unbiased ◽  
Efficient Breeding ◽  
Selection Of

Many phenotypes respond physiologically or developmentally to continuously distributed environmental variables such as temperature and nutritional quality. Information about phenotypic plasticity can be used to improve the efficiency of artificial selection. Here we show that the quantitative genetic theory for ‘infinite-dimensional’ traits such as reaction norms provides a natural framework to accomplish this goal. It is expected to improve selection responses by making more efficient use of information about environmental effects than do conventional methods. The approach is illustrated by deriving an index for mass selection of a phenotypically plastic trait. We suggest that the same approach could be extended directly to more general and efficient breeding schemes, such as those based on general best linear unbiased prediction. Methods for estimating genetic covariance functions are reviewed.

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

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.

ACCURACY OF SELECTION INDEX AND BEST LINEAR UNBIASED PREDICTION FOR WITHIN-HERD SELECTION WITH ASSORTATIVE MATING OF BEEF CATTLE

1989 ◽  
Vol 69 (2) ◽  
pp. 315-322 ◽  
Author(s):  
J. A. B. ROBINSON ◽  
J. W. WILTON ◽  
L. R. SCHAEFFER
Keyword(s):  
Beef Cattle ◽  
Assortative Mating ◽  
Index System ◽  
Selection Index ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Net Worth ◽  
Best Linear Unbiased ◽  
Mating Scheme ◽  
Unbiased Prediction

A simulation of a selection and mating scheme for beef herds was conducted to compare the genetic progress achieved over 20 generations through evaluation of the animals by best linear unbiased prediction and by a selection index. For comparison, the same selection and mating scheme was applied to the herd using the true genetic values of each animal. Traits considered in the simulation were direct maternal genetic calving ease, birth weight, weaning weight and yearling weight. The analysis was replicated 100 times for each method of evaluation. In general, the best linear unbiased prediction system achieved greater genetic response than the selection index system. The BLUP system gave 18.7% better genetic improvement in total net worth than the selection index system. However, the selection index system gave only 42.7% and the BLUP system gave 50.6% of the response from selection on true net worth values. Keywords: Beef cattle, selection index, assortative mating.

A selection index for dairy cows

1983 ◽  
Vol 37 (3) ◽  
pp. 313-319 ◽  
Author(s):  
W. G. Hill ◽  
G. J. T. Swanson
Keyword(s):  
Dairy Cows ◽  
Selection Index ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Genetic Base ◽  
Sire Evaluation ◽  
Selection Indices ◽  
Best Linear Unbiased ◽  
The Uk ◽  
Method Of Evaluation

ABSTRACTThe rationale for a method of computing selection indices for dairy cows is outlined. It uses predicted transmitting abilities of sires from a best linear unbiased prediction or similar method of evaluation together with mean transmitting abilities (cow indices) of dams to estimate herd genetic levels. Indices for individual cows are computed using deviations from those of contemporaries in the herd of her sire's transmitting ability, her dam's index and her own production in each lactation.The method is being used in the UK, cow indices being expressed as transmitting abilities relative to the same fixed genetic base as for sire evaluation.

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