scholarly journals Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle

Animals ◽  
2022 ◽  
Vol 12 (2) ◽  
pp. 136
Author(s):  
Menghua Zhang ◽  
Hanpeng Luo ◽  
Lei Xu ◽  
Yuangang Shi ◽  
Jinghang Zhou ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Bayesian Methods ◽  
Breeding Value ◽  
Production Traits ◽  
Milk Production Traits ◽  
Linear Unbiased Prediction ◽  
Brown Cattle ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Better Than

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.

scholarly journals The effectiveness of genomic selection for milk production traits of Holstein dairy cattle

2020 ◽  
Vol 33 (3) ◽  
pp. 382-389 ◽  
Author(s):  
Yun-Mi Lee ◽  
Chang-Gwon Dang ◽  
Mohammad Z. Alam ◽  
You-Sam Kim ◽  
Kwang-Hyeon Cho ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Milk Production ◽  
Genetic Gain ◽  
Milk Fat ◽  
Breeding Value ◽  
Production Traits ◽  
Milk Production Traits ◽  
Data Set ◽  
Selection For ◽  
Estimated Breeding Value

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population.Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction.Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L<sub>SB</sub>) and that for the production of cows (L<sub>SC</sub>) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L<sub>DB</sub>) and cows (L<sub>DC</sub>) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records.Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

A method for reducing inbreeding with Best Linear Unbiased Prediction

1993 ◽  
Vol 1993 ◽  
pp. 33-33
Author(s):  
B Grundy ◽  
WG Hill
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Linear Unbiased Prediction ◽  
Genetic Merit ◽  
Selection Decisions ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Commercial Environment ◽  
Unbiased Prediction

An optimum way of selecting animals is through a prediction of their genetic merit (estimated breeding value, EBV), which can be achieved using a best linear unbiased predictor (BLUP) (Henderson, 1975). Selection decisions in a commercial environment, however, are rarely made solely on genetic merit but also on additional factors, an important example of which is to limit the accumulation of inbreeding. Comparison of rates of inbreeding under BLUP for a range of hentabilities highlights a trend of increasing inbreeding with decreasing heritability. It is therefore proposed that selection using a heritability which is artificially raised would yield lower rates of inbreeding than would otherwise be the case.

scholarly journals Effect of DGAT1, BTN1A1, OLR1, and STAT1 genes on milk production and reproduction traits in the Czech Fleckvieh breed

2014 ◽  
Vol 59 (No. 2) ◽  
pp. 45-53 ◽  
Author(s):  
J. Rychtářová ◽  
Z. Sztankóová ◽  
J. Kyselová ◽  
V. Zink ◽  
M. Štípková ◽  
...  
Keyword(s):  
Milk Production ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Breeding Value ◽  
Production Traits ◽  
Milk Production Traits ◽  
Heterogeneous Effects ◽  
Reproduction Traits ◽  
Estimated Breeding Value ◽  
The Impact

The impact of polymorphism of the diacylglycerol acyltransferase (DGAT1), butyrophilin (BTN1A1), oxidized low-density lipoprotein receptor (OLR1), and signal transducer and activator of transcription 1 (STAT1) genes on milk production and reproduction traits in 419 Czech Fleckvieh cows was examined using polymerase chain reaction and restriction fragment length polymorphism. The loci DGAT1 and BTN1A1 were observed simultaneously to affect milk production, estimated breeding value of milk production traits, as well as reproduction parameters. Significant differences were found also between genotypes of the STAT1 loci in relation to estimated breeding value of milk production traits. Similar findings in pure dairy breeds suggest that heterogeneous effects of the observed loci can be explained by different genetic backgrounds in various breed populations selected to achieve different commercial goals. Thus, it is necessary to determine variability and influence of a molecular marker in a specific population when considering its inclusion into a breeding programme. &nbsp;

Heterogeneity of variance between herds for milk production traits

1991 ◽  
Vol 1991 ◽  
pp. 28-28
Author(s):  
P.M. Visscher
Keyword(s):  
Prediction Method ◽  
Parameter Estimates ◽  
Production Traits ◽  
Milk Production Traits ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Heterogeneity Of Variance ◽  
Abundant Evidence ◽  
Best Linear Unbiased ◽  
Fat Yield

One assumption made by most users of the BLUP (Best Linear Unbiased Prediction) method to predict breeding values, is that variances are homogeneous over herds or herd-year-seasons (HYS). In dairy cattle there is abundant evidence, however, of heterogeneity of variance across herds or herd groups (see e.g. Hill et al. 1983 and Brotherstone and Hill, 1986, for U.K. studies). The effect of ignoring heterogenous variances between herds on prediction of breeding values for bulls may be small when using a sire model, if sires were tested across many herd-variance groups. Loss in efficiency may be larger when sires are tested in few herds, or, for cows, when the genetic evaluation is for bulls and cows simultaneously (with an animal model (AM)). The aim of this study was to calculate individual herd parameter estimates to investigate heterogeneity of within herd variance in the U.K. dairy population. The investigated trait was fat yield and the estimations were carried out using a REML (Restricted Maximum Likelihood; Patterson and Thompson, 1971) program written by Karin Meyer (Meyer, 1989).

scholarly journals Genomic selection through single-step genomic best linear unbiased prediction improves the accuracy of evaluation in Hanwoo cattle

2020 ◽  
Vol 33 (10) ◽  
pp. 1544-1557
Author(s):  
Mi Na Park ◽  
Mahboob Alam ◽  
Sidong Kim ◽  
Byoungho Park ◽  
Seung Hwan Lee ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Population Level ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Breeding Value ◽  
Eye Muscle ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Increase In Accuracy ◽  
Unbiased Prediction

Objective: Genomic selection (GS) is becoming popular in animals’ genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method.Methods: A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson’s correlation of genomic estimated breeding value (GEBV) with EBV of all animals (rM1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (rM2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls).Results: The rM1 estimates in the study were between 0.90 and 0.96 among five traits. The rM1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average rM2 estimates were much smaller than rM1 (pedBLUP, 0.40 to0.44; ssGBLUP, 0.41 to 0.45) at population level. However, rM2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of rM2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the rM2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based rM2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%).Conclusion: A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo provenbull evaluation program.

scholarly journals The Accuracy of Genomic Selection in Norwegian Red Cattle Assessed by Cross-Validation

Genetics ◽  
2009 ◽  
Vol 183 (3) ◽  
pp. 1119-1126 ◽  
Author(s):  
Tu Luan ◽  
John A. Woolliams ◽  
Sigbjørn Lien ◽  
Matthew Kent ◽  
Morten Svendsen ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Cross Validation ◽  
Strong Relationship ◽  
Breeding Value ◽  
Production Traits ◽  
Phenotypic Data ◽  
Mixture Model Approach ◽  
High Heritability ◽  
Best Linear Unbiased ◽  
Similar Accuracy

Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative traits through the use of dense markers covering the whole genome. For a successful application of GS, accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991 SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield, protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more records will be needed.

A simulation study of open nucleus and closed nucleus breeding systems in a sheep population

1995 ◽  
Vol 60 (1) ◽  
pp. 117-124 ◽  
Author(s):  
J. A. Roden
Keyword(s):  
Genetic Gain ◽  
Live Weight ◽  
Breeding Systems ◽  
Breeding Value ◽  
Linear Unbiased Prediction ◽  
Sheep Population ◽  
Best Linear Unbiased ◽  
Nucleus Breeding ◽  
Open Nucleus ◽  
Selection Of

AbstractStochastic simulation was used to compare the results of alternative breeding systems in a sheep population divided into 10 flocks of 120 ewes. The breeding systems compared were selection within closed flocks (CF), a closed nucleus system (CNS), an open nucleus system (ONS) and open nucleus systems with the selection of nucleus replacements being restricted to either nucleus born males (ONSRm) or nucleus born females (ONSRf). Selection was for a best linear unbiased prediction of breeding value for lamb live weight which had a heritability of 0·17. The open nucleus breeding systems (ONS, ONSRm, ONSRf) resulted in higher rates of genetic gain, more predictable selection responses and lower rates of inbreeding than either the closed nucleus system (CNS) or selection within closed flocks (CF). Initial genetic differences between flocks resulted in higher rates of genetic gain in the nucleus breeding systems due to the use of between flock genetic variance. In the ONS system up to 25% of nucleus sires and approximately 50% of nucleus dams were born in base flocks. Nevertheless if selection of either nucleus sires or dams was restricted to nucleus born animals there was very little change in genetic gain or rate of inbreeding.

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.

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