Use of molecular technologies for the advancement of animal breeding: genomic selection in dairy cattle populations in Australia, Ireland and New Zealand

2013 ◽  
Vol 53 (9) ◽  
pp. 869 ◽  
Author(s):  
Richard J. Spelman ◽  
Ben J. Hayes ◽  
Donagh P. Berry
Keyword(s):  
New Zealand ◽  
Dairy Cattle ◽  
Evaluation System ◽  
Test Results ◽  
Genomic Information ◽  
Breeding Programs ◽  
Breeding Values ◽  
Genetic Evaluations ◽  
Initial Results ◽  
Genomic Predictions

The New Zealand, Australian and Irish dairy industries have used genomic information to enhance their genetic evaluations over the last 2–4 years. The improvement in the accuracy obtained from including genomic information on thousands of animals in the national evaluation system has revolutionised the dairy breeding programs in the three countries. The genomically enhanced breeding values (GEBV) of young bulls are more reliable than breeding values based on parent average, thus allowing the young bulls to be reliably selected and used in the national herd. Traditionally, the use of young bulls was limited and bulls were not used extensively until they were 5 years old when the more reliable progeny test results became available. Using young sires, as opposed to progeny-tested sires, in the breeding program dramatically reduces the generation interval, thereby facilitating an increase in the rate of genetic gain by 40–50%. Young sires have been marketed on their GEBV in the three countries over the last 2–4 years. Initial results show that the genomic estimates were overestimated in both New Zealand and Ireland. Adjustments have since been introduced into their respective national evaluations to reduce the bias. A bias adjustment has been included in the Australian evaluation since it began; however, official genomic evaluations have not been in place as long as in New Zealand and Ireland, so there has been less opportunity to validate if the correction accounts for all bias. Sequencing of the dairy cattle population has commenced in an effort to further improve the genomic predictions and also to detect causative mutations that underlie traits of economic performance.

scholarly journals Integration of genomic information into beef cattle and sheep genetic evaluations in Australia

2012 ◽  
Vol 52 (3) ◽  
pp. 126 ◽  
Author(s):  
Andrew A. Swan ◽  
David J. Johnston ◽  
Daniel J. Brown ◽  
Bruce Tier ◽  
Hans-U. Graser
Keyword(s):  
Beef Cattle ◽  
Covariance Matrices ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Genetic Covariance ◽  
Breeding Values ◽  
Snp Panels ◽  
Genetic Evaluations ◽  
Genomic Predictions ◽  
Cattle And Sheep

Genomic information has the potential to change the way beef cattle and sheep are selected and to substantially increase genetic gains. Ideally, genomic data will be used in combination with pedigree and phenotypic data to increase the accuracy of estimated breeding values (EBVs) and selection indexes. The first example of this in Australia was the integration of four markers for tenderness into beef cattle breeding values. Subsequently, the availability of high-density single nucleotide polymorphism (SNP) panels has made selection using genomic information possible, while at the same time creating significant challenges for genetic evaluation with regard to both data management and statistical modelling. Reference populations have been established in both the beef cattle and sheep industries, in which an extensive range of phenotypes have been collected and animals genotyped mainly using 50K SNP panels. From this information, genomic predictions of breeding value have been developed, albeit with varying levels of accuracy. These predictions have been incorporated into routine genetic evaluations using three approaches and trial results are now available to breeders. In the first, genomic predictions have been included in genetic evaluation models as additional traits. The challenges with this method have been the construction of consistent genetic covariance matrices, and a significant increase in computing time. The second approach has been to use a selection index procedure to blend genomic predictions with existing EBVs. This method has been shown to produce very similar results, and has the advantage of being simple to implement and fast to operate, although consistent genetic covariance matrices are still required. Third, in sheep a single-step analysis combining a genomic relationship matrix with a standard pedigree-based relationship matrix has been used to estimate breeding values for carcass and eating-quality traits. It is likely that this procedure or one similar will be incorporated into routine evaluations in the near future. While significant progress has been made in implementing methods of integrating genomic information in both beef and sheep evaluations in Australia, the major challenges for the future will be to continue to collect the phenotypes needed to derive accurate genomic predictions, and in managing much larger volumes of genomic data as the number of animals genotyped and the density of markers increase.

EVALUATION OF DAIRY CATTLE FOR FINAL CLASS USING ANIMAL MODELS

1988 ◽  
Vol 68 (3) ◽  
pp. 639-645 ◽  
Author(s):  
J. JAMROZIK ◽  
L. R. SCHAEFFER
Keyword(s):  
Animal Models ◽  
Dairy Cattle ◽  
Fixed Effects ◽  
Genetic Relationships ◽  
Residual Effects ◽  
Breeding Values ◽  
Stage Of Lactation ◽  
Key Words Animal ◽  
Estimated Breeding Values ◽  
Genetic Evaluations

Estimated breeding values for final class of 364 868 Canadian Holstein Friesian cows and 10 186 bulls from three different animal models were compared. FIRST lactation, first classifications were described by a model with fixed effects of herd-round-classifier, linear and quadratic effects of age at calving and stage of lactation at classification, and random effects of additive genetic effects of cows, and residual effects. All additive genetic relationships among animals were included. A second model used the LATEST classification on each cow rather than the first and these observations were pre-adjusted for age and stage. The third model used ALL classifications on each cow, and these were also pre-adjusted for age and stage effects. Correlations among estimated breeding values between methods ranged from 0.92 to 0.95. Estimated breeding values from LATEST were most highly correlated to sire proofs from the currently official sire model which also used the latest classification of each cow. Correlations of estimated breeding values between sires and their sons showed that results from LATEST were more accurate than results from the other two models. A model similar to that for LATEST is proposed for official genetic evaluations for conformation in the Canadian Holstein population. Key words: Animal model, conformation, dairy cattle

scholarly journals Genome-wide association study and low-cost genomic predictions for growth and fillet yield in Nile tilapia (Oreochromis niloticus)

2019 ◽  
Author(s):  
Grazyella M. Yoshida ◽  
Jean P. Lhorente ◽  
Katharina Correa ◽  
Jose Soto ◽  
Diego Salas ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Nile Tilapia ◽  
Cost Effective ◽  
Genotype Imputation ◽  
Genome Wide Association ◽  
Genomic Information ◽  
Breeding Programs ◽  
Genome Wide ◽  
Fillet Yield ◽  
Genomic Predictions

ABSTRACTFillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in tilapia breeding programs.

A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population

2018 ◽  
Vol 98 (3) ◽  
pp. 565-575 ◽  
Author(s):  
Mario L. Piccoli ◽  
Luiz F. Brito ◽  
José Braccini ◽  
Fernanda V. Brito ◽  
Fernando F. Cardoso ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Production Systems ◽  
Simulated Data ◽  
Single Step ◽  
Breeding Values ◽  
True Breeding ◽  
Blending Method ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
Genomic Predictions

The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.

scholarly journals Developing a genetic evaluation system for milk traits in Russian black and white dairy cattle

2018 ◽  
Vol 27 (2) ◽  
Author(s):  
Andrei A. Kudinov ◽  
Jarmo Juga ◽  
Esa A. Mäntysaari ◽  
Ismo Strandén ◽  
Ekaterina I. Saksa ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Evaluation System ◽  
Linear Models ◽  
Developed Countries ◽  
Comparison Method ◽  
Leningrad Region ◽  
Mixed Linear Models ◽  
Milk Traits ◽  
Breeding Values ◽  
Genetic Trends

Mixed linear models have been applied for predicting breeding values of dairy cattle in most of the developed countries since the 1980s. However, the Russian Federation is still using the old contemporary comparison method. The objective of our study was to develop a best linear unbiased prediction (BLUP) for an animal model of breeding values for the Leningrad region. We tested both a first-lactation model (FLM) and a multi-lactation repeatability model (MLM). The data included milk records of 206 114 cows from 49 herds. Estimated heritabilities from FLM were 0.24, 0.20, and 0.20 for milk, protein, and fat yields, respectively, and 0.18, 0.19, and 0.20 from MLM. Repeatabilities were 0.34 for milk yield and 0.31 for both fat and protein yields. Genetic trends were similar for both models (FLM vs MLM): 59 vs 56 kg year-1 for milk, 1.90 vs 1.84 kg year-1 for fat, and 1.67 vs 1.62 kg year-1 for protein yield during 2000–2016. Based on the difference between the genetic trends in FLM and MLM, the applied BLUP method passed the validation method I by Interbull.

scholarly journals Estimation of (co)variance components across breeds by a test-day model adapted to New Zealand dairy cattle

2005 ◽  
Vol 2005 ◽  
pp. 114-114
Author(s):  
S. Vanderick ◽  
B. Harris ◽  
P. Mayeres ◽  
A. Gillon ◽  
C. Croquet ◽  
...  
Keyword(s):  
New Zealand ◽  
Dairy Cattle ◽  
Variance Components ◽  
Dairy Farmers ◽  
Breeding Values ◽  
Dairy Heifers ◽  
Breed Differences ◽  
Genetic Contributions

In New Zealand, crossbreeding is largely used by dairy farmers. Currently an important proportion of cows are crossbreds, mostly Holstein-Friesians (HF) x Jersey (JE). Crossbred bulls are currently being progeny tested in New Zealand. Actually, more than one third of the replacement dairy heifers are crossbred animals (Montgomerie, 2002). However currently available methods to model genetic contributions of purebreds to crossbreds take breed differences only partly into account and therefore do not permit an optimal use of crossbred data. The first objective of our study was to allow the modelling of different additive breeding values according to parental breeds to define overall additive breeding values as a function of breed composition.

scholarly journals Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Megan Scholtens ◽  
Nicolas Lopez-Villalobos ◽  
Klaus Lehnert ◽  
Russell Snell ◽  
Dorian Garrick ◽  
...  
Keyword(s):  
New Zealand ◽  
Somatic Cell ◽  
Milk Fat ◽  
Single Step ◽  
Somatic Cell Score ◽  
Dairy Goat ◽  
Nucleotide Polymorphisms ◽  
Genomic Information ◽  
Breeding Values ◽  
Goat Population

Selection on genomic breeding values (GBVs) is now readily available for ranking candidates in improvement schemes. Our objective was to quantify benefits in terms of accuracy of prediction from including genomic information in the single-trait estimation of breeding values (BVs) for a New Zealand mixed breed dairy goat herd. The dataset comprised phenotypic and pedigree records of 839 does. The phenotypes comprised estimates of 305-day lactation yields of milk, fat, and protein and average somatic cell score from the 2016 production season. Only 388 of the goats were genotyped with a Caprine 50K SNP chip and 41,981 of the single nucleotide polymorphisms (SNPs) passed quality control. Pedigree-based best linear unbiased prediction (PBLUP) was used to obtain across-breed breeding values (EBVs), whereas a single-step BayesC model (ssBC) was used to estimate across-breed GBVs. The average prediction accuracies ranged from 0.20 to 0.22 for EBVs and 0.34 to 0.43 for GBVs. Accuracies of GBVs were up to 103% greater than EBVs. Breed effects were more reliably estimated in the ssBC model compared with the PBLUP model. The greatest benefit of genomic prediction was for individuals with no pedigree or phenotypic records. Including genomic information improved the prediction accuracy of BVs compared with the current pedigree-based BLUP method currently implemented in the New Zealand dairy goat population.

scholarly journals Investigation of reliability of genomic predictions in the admixed Nordic Red dairy cattle

2012 ◽  
pp. 1-5
Author(s):  
Mahlako L. Makgahlela ◽  
E. A. Mäntysaari ◽  
I. Strandén ◽  
M. Koivula ◽  
U.S. Nielsen ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Specific Model ◽  
Snp Markers ◽  
Homogeneous Population ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Specific Effects ◽  
Estimated Breeding Values ◽  
Genomic Predictions ◽  
Uniform Population

The success of genomic selection (GS) in small breeds which are likely to have admixed structures has been minimal. This is because accuracy of GS depends on the extent of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL) and LD depends on the genetic structure of the population and marker density. In the current study, we evaluate reliability of genomic predictions in young unproven bulls, when interactions between marker effects and breed of origin are accounted for in the Nordic Red dairy cattle (RDC). The population structure of the RDC is admixed. Data consisted of animal breed proportions calculated from the full pedigree, deregressed proofs (DRP) of published estimated breeding values (EBV) for yield traits and genotypic data for 37,595 SNP markers. Direct genomic breeding values (DGV) were estimated using 2 models, one accounting for breed-specific effects and other assuming uniform population. Validation reliabilities were calculated as the squared correlation between DRP and DGV (r2DRP, DGV), corrected by the mean reliability ofDRP. Using the breed-specific model increased the reliability of DGV by 2% and 3% for milk and protein, respectively, when compared to homogeneous population GBLUP model. The exception was for fat, where there was no gain in reliability. Estimated validation reliabilities were low for milk (0.32) and protein (0.32) and slightly higher (0.42) for fat.

scholarly journals 384 Genetic and Genomic Analysis in Livestock with Increasing Datasets

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 137-138
Author(s):  
Daniela Lourenco ◽  
Shogo Tsuruta ◽  
Yutaka Masuda ◽  
Ignacy Misztal
Keyword(s):  
Evaluation System ◽  
Sequence Data ◽  
Genomic Analysis ◽  
Animal Breeding ◽  
Whole Genome Sequence ◽  
Limiting Factor ◽  
Genomic Information ◽  
Estimated Breeding Values ◽  
Genetic Evaluations ◽  
Breeding And Genetics

Abstract In animal breeding and genetics, statistical methods have been used for several decades to identify animals with the best genetic potential. One of the foundations for computing accurate estimated breeding values (EBV) is the amount of data that is used in the evaluation system — as the more data points one animal has, the more accurate its EBV is going to be. However, the animal breeding and genetics field periodically faces a big data paradox, where efficient methods have to be developed to handle the amount of data collected over time, given the computing capacity becomes the limiting factor. For instance, running genetic evaluations based on phenotypes and pedigree for a million animals was impossible in 1970. Methods and algorithms evolved to a point where using data for millions of animals was not a problem, until genomic information became available. After the development of single nucleotide polymorphism (SNP) chips for livestock in 2008, genomic information started being used in addition to phenotypes and pedigree to further improve accuracy of EBV. However, each animal is genotyped for around 50,000 SNP, which makes this data dense and difficult to work with. Over 790,000 Angus and 3.4 million Holstein cattle have been genotyped in the US as of April 2020. As the amount of new data considerably increases every week, most of the genomic evaluations are done on a weekly basis. Given that data have to be processed, the computation of EBV cannot take more than four days, which can be challenging depending on the model. In this talk we will discuss the challenges and solutions for successful genomic evaluations in large livestock populations. Finally, perspectives on the use of whole-genome sequence data and high-throughput phenotypes in genomic analysis will be summarized.

Sign in / Sign up

Export Citation Format

Share Document