Evaluation of Genome-Enabled Prediction for Carcass Primal Cut Yields Using Single-Step Genomic Best Linear Unbiased Prediction in Hanwoo Cattle

There is a growing interest worldwide in genetically selecting high-value cut carcass weights, which allows for increased profitability in the beef cattle industry. Primal cut yields have been proposed as a potential indicator of cutability and overall carcass merit, and it is worthwhile to assess the prediction accuracies of genomic selection for these traits. This study was performed to compare the prediction accuracy obtained from a conventional pedigree-based BLUP (PBLUP) and a single-step genomic BLUP (ssGBLUP) method for 10 primal cut traits—bottom round, brisket, chuck, flank, rib, shank, sirloin, striploin, tenderloin, and top round—in Hanwoo cattle with the estimators of the linear regression method. The dataset comprised 3467 phenotypic observations for the studied traits and 3745 genotyped individuals with 43,987 single-nucleotide polymorphisms. In the partial dataset, the accuracies ranged from 0.22 to 0.30 and from 0.37 to 0.54 as evaluated using the PBLUP and ssGBLUP models, respectively. The accuracies of PBLUP and ssGBLUP with the whole dataset varied from 0.45 to 0.75 (average 0.62) and from 0.52 to 0.83 (average 0.71), respectively. The results demonstrate that ssGBLUP performed better than PBLUP averaged over the 10 traits, in terms of prediction accuracy, regardless of considering a partial or whole dataset. Moreover, ssGBLUP generally showed less biased prediction and a value of dispersion closer to 1 than PBLUP across the studied traits. Thus, the ssGBLUP seems to be more suitable for improving the accuracy of predictions for primal cut yields, which can be considered a starting point in future genomic evaluation for these traits in Hanwoo breeding practice.

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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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Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction

Journal of Animal Science ◽

10.1093/jas/skaa374 ◽

2020 ◽

Vol 98 (12) ◽

Author(s):

Ignacy Misztal ◽

Shogo Tsuruta ◽

Ivan Pocrnic ◽

Daniela Lourenco

Keyword(s):

Prediction Accuracy ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Relationship Matrix ◽

Linear Unbiased Prediction ◽

Breeding Values ◽

Best Linear Unbiased ◽

The Impact ◽

Genomic Predictions ◽

Unbiased Prediction

Abstract Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to create the inverse of the genomic relationship matrix (GRM) are reduced by inverting only a portion of that matrix for core animals. However, using different core sets of the same size causes fluctuations in genomic estimated breeding values (GEBVs) up to one additive standard deviation without affecting prediction accuracy. About 2% of the variation in the GRM is noise. In the recursion formula for APY, the error term modeling the noise is different for every set of core animals, creating changes in breeding values. While average changes are small, and correlations between breeding values estimated with different core animals are close to 1.0, based on the normal distribution theory, outliers can be several times bigger than the average. Tests included commercial datasets from beef and dairy cattle and from pigs. Beyond a certain number of core animals, the prediction accuracy did not improve, but fluctuations decreased with more animals. Fluctuations were much smaller than the possible changes based on prediction error variance. GEBVs change over time even for animals with no new data as genomic relationships ties all the genotyped animals, causing reranking of top animals. In contrast, changes in nongenomic models without new data are small. Also, GEBV can change due to details in the model, such as redefinition of contemporary groups or unknown parent groups. In particular, increasing the fraction of blending of the GRM with a pedigree relationship matrix from 5% to 20% caused changes in GEBV up to 0.45 SD, with a correlation of GEBV > 0.99. Fluctuations in genomic predictions are part of genomic evaluation models and are also present without the APY algorithm when genomic evaluations are computed with updated data. The best approach to reduce the impact of fluctuations in genomic evaluations is to make selection decisions not on individual animals with limited individual accuracy but on groups of animals with high average accuracy.

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Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle

BMC Genetics ◽

10.1186/s12863-020-00928-1 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Masoumeh Naserkheil ◽

Deuk Hwan Lee ◽

Hossein Mehrban

Keyword(s):

Prediction Accuracy ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Farm Animals ◽

Body Measurement ◽

Data Set ◽

Linear Unbiased Prediction ◽

Body Measurement Traits ◽

Best Linear Unbiased ◽

Unbiased Prediction

Abstract Background Recently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations. Results The accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method. Conclusions Our findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.

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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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Correction: Weighted Genomic Best Linear Unbiased Prediction for Carcass Traits in Hanwoo Cattle. Genes 2019, 10, 1019

Genes ◽

10.3390/genes11091013 ◽

2020 ◽

Vol 11 (9) ◽

pp. 1013

Author(s):

Bryan Irvine Lopez ◽

Seung-Hwan Lee ◽

Jong-Eun Park ◽

Dong-Hyun Shin ◽

Jae-Don Oh ◽

...

Keyword(s):

Carcass Traits ◽

Best Linear Unbiased Prediction ◽

Linear Unbiased Prediction ◽

Best Linear Unbiased ◽

Hanwoo Cattle ◽

Unbiased Prediction

The authors wish to make the following corrections to this paper [...]

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GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study

Scientific Reports ◽

10.1038/s41598-021-85203-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Mahdi Akbarzadeh ◽

Saeid Rasekhi Dehkordi ◽

Mahmoud Amiri Roudbar ◽

Mehdi Sargolzaei ◽

Kamran Guity ◽

...

Keyword(s):

Lipid Profile ◽

Genetic Study ◽

Prediction Accuracy ◽

Significant Snps ◽

Snp Markers ◽

Linear Unbiased Prediction ◽

Genetic Prediction ◽

Heritability Estimation ◽

Precise Prediction ◽

Best Linear Unbiased

AbstractIn recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs’ subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.

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Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Genes ◽

10.3390/genes12020266 ◽

2021 ◽

Vol 12 (2) ◽

pp. 266

Author(s):

Hossein Mehrban ◽

Masoumeh Naserkheil ◽

Deuk Hwan Lee ◽

Chungil Cho ◽

Taejeong Choi ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Beef Cattle ◽

Genomic Prediction ◽

Quantitative Trait ◽

Carcass Traits ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Linear Unbiased Prediction ◽

Single Nucleotide ◽

Best Linear Unbiased

The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

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Genomic selection in French dairy cattle

Animal Production Science ◽

10.1071/an11119 ◽

2012 ◽

Vol 52 (3) ◽

pp. 115 ◽

Cited By ~ 63

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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