scholarly journals Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Masayuki Takeda ◽  
Keiichi Inoue ◽  
Hidemi Oyama ◽  
Katsuo Uchiyama ◽  
Kanako Yoshinari ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Simulation Analysis ◽  
Carcass Traits ◽  
Real Data ◽  
Reference Population ◽  
Weighting Factor ◽  
Breeding Value ◽  
Japanese Black Cattle ◽  
Simulation Results ◽  
Estimated Breeding Value

Abstract Background Size of reference population is a crucial factor affecting the accuracy of prediction of the genomic estimated breeding value (GEBV). There are few studies in beef cattle that have compared accuracies achieved using real data to that achieved with simulated data and deterministic predictions. Thus, extent to which traits of interest affect accuracy of genomic prediction in Japanese Black cattle remains obscure. This study aimed to explore the size of reference population for expected accuracy of genomic prediction for simulated and carcass traits in Japanese Black cattle using a large amount of samples. Results A simulation analysis showed that heritability and size of reference population substantially impacted the accuracy of GEBV, whereas the number of quantitative trait loci did not. The estimated numbers of independent chromosome segments (Me) and the related weighting factor (w) derived from simulation results and a maximum likelihood (ML) approach were 1900–3900 and 1, respectively. The expected accuracy for trait with heritability of 0.1–0.5 fitted well with empirical values when the reference population comprised > 5000 animals. The heritability for carcass traits was estimated to be 0.29–0.41 and the accuracy of GEBVs was relatively consistent with simulation results. When the reference population comprised 7000–11,000 animals, the accuracy of GEBV for carcass traits can range 0.73–0.79, which is comparable to estimated breeding value obtained in the progeny test. Conclusion Our simulation analysis demonstrated that the expected accuracy of GEBV for a polygenic trait with low-to-moderate heritability could be practical in Japanese Black cattle population. For carcass traits, a total of 7000–11,000 animals can be a sufficient size of reference population for genomic prediction.

Genomic prediction of weight and wool traits in a multi-breed sheep population

2014 ◽  
Vol 54 (5) ◽  
pp. 544 ◽  
Author(s):  
N. Moghaddar ◽  
A. A. Swan ◽  
J. H. J. van der Werf
Keyword(s):  
Genomic Prediction ◽  
Pearson Correlation ◽  
Reference Population ◽  
Breeding Value ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Sheep Population ◽  
Australian Sheep ◽  
Estimated Breeding Value ◽  
Breed Sheep

The objective of this study was to predict the accuracy of genomic prediction for 26 traits, including weight, muscle, fat, and wool quantity and quality traits, in Australian sheep based on a large, multi-breed reference population. The reference population consisted of two research flocks, with the main breeds being Merino, Border Leicester (BL), Poll Dorset (PD), and White Suffolk (WS). The genomic estimated breeding value (GEBV) was based on GBLUP (genomic best linear unbiased prediction), applying a genomic relationship matrix calculated from the 50K Ovine SNP chip marker genotypes. The accuracy of GEBV was evaluated as the Pearson correlation coefficient between GEBV and accurate estimated breeding value based on progeny records in a set of genotyped industry animals. The accuracies of weight traits were relatively low to moderate in PD and WS breeds (0.11–0.27) and moderate to relatively high in BL and Merino (0.25–0.63). The accuracy of muscle and fat traits was moderate to relatively high across all breeds (between 0.21 and 0.55). The accuracy of GEBV of yearling and adult wool traits in Merino was, on average, high (0.33–0.75). The results showed the accuracy of genomic prediction depends on trait heritability and the effective size of the reference population, whereas the observed GEBV accuracies were more related to the breed proportions in the multi-breed reference population. No extra gain in within-breed GEBV accuracy was observed based on across breed information. More investigations are required to determine the precise effect of across-breed information on within-breed genomic prediction.

scholarly journals Effect of errors in pedigree on the accuracy of estimated breeding value for carcass traits in Korean Hanwoo cattle

2020 ◽  
Vol 33 (7) ◽  
pp. 1057-1067 ◽  
Author(s):  
Chiemela Peter Nwogwugwu ◽  
Yeongkuk Kim ◽  
Yun Ji Chung ◽  
Sung Bong Jang ◽  
Seung Hee Roh ◽  
...  
Keyword(s):  
Genetic Gain ◽  
Carcass Traits ◽  
Error Rates ◽  
Breeding Value ◽  
Marbling Score ◽  
Muscle Area ◽  
Data Set ◽  
Eye Muscle ◽  
Estimated Breeding Value ◽  
Hanwoo Cattle

Objective: This study evaluated the effect of pedigree errors (PEs) on the accuracy of estimated breeding value (EBV) and genetic gain for carcass traits in Korean Hanwoo cattle.Methods: The raw data set was based on the pedigree records of Korean Hanwoo cattle. The animals’ information was obtained using Hanwoo registration records from Korean animal improvement association database. The record comprised of 46,704 animals, where the number of the sires used was 1,298 and the dams were 38,366 animals. The traits considered were carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS). Errors were introduced in the pedigree dataset through randomly assigning sires to all progenies. The error rates substituted were 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation was performed to produce a population of 1,650 animals from the pedigree data. A restricted maximum likelihood based animal model was applied to estimate the EBV, accuracy of the EBV, expected genetic gain, variance components, and heritability (h2) estimates for carcass traits. Correlation of the simulated data under PEs was also estimated using Pearson’s method.Results: The results showed that the carcass traits per slaughter year were not consistent. The average CWT, EMA, BFT, and MS were 342.60 kg, 78.76 cm<sup>2, 8.63 mm, and 3.31, respectively. When errors were introduced in the pedigree, the accuracy of EBV, genetic gain and h2 of carcass traits was reduced in this study. In addition, the correlation of the simulation was slightly affected under PEs.Conclusion: This study reveals the effect of PEs on the accuracy of EBV and genetic parameters for carcass traits, which provides valuable information for further study in Korean Hanwoo cattle.

Estimation of variance and genomic prediction using genotypes imputed from low-density marker subsets for carcass traits in Japanese black cattle

2015 ◽  
Vol 87 (9) ◽  
pp. 1106-1113 ◽  
Author(s):  
Shinichiro Ogawa ◽  
Hirokazu Matsuda ◽  
Yukio Taniguchi ◽  
Toshio Watanabe ◽  
Yoshikazu Sugimoto ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Carcass Traits ◽  
Low Density ◽  
Japanese Black Cattle ◽  
Estimation Of Variance ◽  
Density Marker

Genomic prediction for carcass traits in Japanese Black cattle using single nucleotide polymorphism markers of different densities

2017 ◽  
Vol 57 (8) ◽  
pp. 1631 ◽  
Author(s):  
Shinichiro Ogawa ◽  
Hirokazu Matsuda ◽  
Yukio Taniguchi ◽  
Toshio Watanabe ◽  
Yuki Kitamura ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Genomic Prediction ◽  
Carcass Traits ◽  
Snp Markers ◽  
Carcass Weight ◽  
Nucleotide Polymorphism ◽  
Japanese Black Cattle ◽  
Marbling Score ◽  
Single Nucleotide ◽  
Breeding Values

Genomic prediction (GP) of breeding values using single nucleotide polymorphism (SNP) markers can be conducted even when pedigree information is unavailable, providing phenotypes are known and marker data are provided. While use of high-density SNP markers is desirable for accurate GP, lower-density SNPs can perform well in some situations. In the present study, GP was performed for carcass weight and marbling score in Japanese Black cattle using SNP markers of varying densities. The 1791 fattened steers with phenotypic data and 189 having predicted breeding values provided by the official genetic evaluation using pedigree data were treated as the training and validation populations respectively. Genotype data on 565837 autosomal SNPs were available and SNPs were selected to provide different equally spaced SNP subsets of lower densities. Genomic estimated breeding values (GEBVs) were obtained using genomic best linear unbiased prediction incorporating one of two types of genomic relationship matrices (G matrices). The GP accuracy assessed as the correlation between the GEBVs and the corrected records divided by the square root of estimated heritability was around 0.85 for carcass weight and 0.60 for marbling score when using 565837 SNPs. The type of G matrix used gave no substantial difference in the results at a given SNP density for traits examined. Around 80% of the GP accuracy was retained when the SNP density was decreased to 1/1000 of that of all available SNPs. These results indicate that even when a SNP panel of a lower density is used, GP may be beneficial to the pre-selection for the carcass traits in Japanese Black young breeding animals.

78 Evaluation of the genomic estimated breeding value of carcass traits in blastocyst-stage embryos derived from Japanese Black cattle

2019 ◽  
Vol 31 (1) ◽  
pp. 164
Author(s):  
T. Fujii ◽  
A. Naito ◽  
H. Hirayama ◽  
M. Kashima ◽  
S. Kageyama ◽  
...  
Keyword(s):  
Pregnancy Rate ◽  
Evaluation System ◽  
Genetic Improvement ◽  
Carcass Traits ◽  
Blastocyst Stage ◽  
Carcass Weight ◽  
Breeding Value ◽  
Japanese Black Cattle ◽  
Marbling Score ◽  
Genomic Evaluation

Genomic selection based on a high-throughput microarray for genotyping single nucleotide polymorphism (SNP) is expected to accelerate genetic improvement in cattle. Recently, a genomic evaluation system for carcass traits, such as carcass weight and marbling score, is being established in Japanese Black cattle. To further increase genetic improvement efficiency in this breed, establishing a genomic evaluation system for pre-implantation embryos before embryo transfer (ET) is required. Here, we examined the correlation between genomic estimated breeding value (GEBV) of carcass traits calculated from embryonic (blastocyst) biopsy cells and from a corresponding calf produced by ET (Experiment 1); we also evaluated the pregnancy rate following ET of GEBV-evaluated blastocysts (GEBV blastocysts) preserved by vitrification (Experiment 2). In total, 16 Japanese Black dams and cryopreserved semen from 6 Japanese Black sires were used for producing in vivo blastocysts (Day 7-8). In Experiment 1, four blastocysts (IETS code 1) were divided into biopsy cells (15-20 cells) and biopsied embryos using a micromanipulator equipped with a micro blade. Biopsy cells were processed for DNA extraction and whole-genome amplification. Freshly biopsied embryos were transferred to recipient cows, and DNA was extracted from the blood or ear cells of the resulting 4 calves. Then SNP genotyping was performed using Illumina bovine LD BeadChip (Illumina, San Diego, CA, USA). The GEBV of 6 carcass traits (carcass weight, ribeye area, rib thickness, subcutaneous fat thickness, estimated yield percent, and marbling score) were calculated using phenotypic and genotypic data from 4,311 Japanese Black steers, and these were compared between biopsy cells and the corresponding calf. In Experiment 2, 134 blastocysts (IETS code 1 and 2) in total were biopsied (10-20 cells), and the biopsied embryos were vitrified by the cryotop method. Biopsy cells were processed for SNP genotyping as in Experiment 1, and the samples in which the call rate was more than 85% were used for GEBV calculation. Based on GEBV records, 24 vitrified GEBV blastocysts were warmed, cultured for 3 to 5h, and 22 GEBV blastocysts that survived (re-expanded) post-culture were transferred to recipient cows. Pregnancy in these cows was diagnosed using ultrasonography during Day 55 to 60 of gestation. In Experiment 1, the SNP call rates of the biopsy cells and corresponding calf were 98.5 to 99.3% and 99.7 to 99.8%, respectively. The GEBV of 6 carcass traits from biopsy cells and from the corresponding calf had almost the same values. In Experiment 2, the SNP call rates of the biopsy cells were ranged from 26.1 to 99.3%. The GEBV of 6 carcass traits varied among full-sib embryos. The pregnancy rate following ET of vitrified GEBV blastocysts was 40.9% (9/22). These results suggest the possible application of a genomic evaluation system for carcass traits at the blastocyst stage in Japanese Black cattle. Further large-scale assessment of pregnancy rates following ET of cryopreserved GEBV blastocysts is required for practical application of the evaluation system.

Estimation of Genetic Parameters on Carcass Traits for Estimated Breeding Value in Hanwoo Cow

2020 ◽  
Vol 54 (6) ◽  
pp. 73-80
Author(s):  
Ji-Hyun Son ◽  
◽  
Yang-Mo Koo ◽  
Yeoung-Ho Jeoung ◽  
Dae-Hyeop Cha ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Carcass Traits ◽  
Breeding Value ◽  
Estimated Breeding Value ◽  
Estimation Of Genetic Parameters

scholarly journals Upgrading breeding value estimation in beef cattle

2018 ◽  
pp. 451-458
Author(s):  
Ferenc Szabó ◽  
Márton Szűcs ◽  
Károly Tempfli ◽  
Berry Donagh
Keyword(s):  
Beef Cattle ◽  
Genomic Prediction ◽  
Real Life ◽  
Reference Population ◽  
Accurate Estimate ◽  
Breeding Value ◽  
Cattle Breeding ◽  
Real Life Data ◽  
Value Estimation ◽  
Genomic Predictions

This paper gives a summary of the possibility for applying genomic information for breeding value estimation in beef cattle breeding. This process is called genomic prediction and is now widely used in dairy cattle globally as well as in some beef and sheep populations. The advantage of genomic prediction is a more accurate estimate of the genetic merit of an individual at a young age thereby facilitating greater annual genetic gain, predominantly through shorter generation intervals. Genomic predictions are more advantageous for sex-linked (e.g., milk yield), low heritability (e.g., fertility) and difficult-to-measure (e.g., feed intake) traits. The larger the reference population, on average, the more accurate the genomic predictions; additionally, the closer genetically the reference population is to the candidate population, the greater the accuracy of genomic predictions. Research is continuing on strategies to generate accurate genomic predictions using a reference population consisting of multiple breeds (and crossbred). Retrospective analysis of real-life data where genomic predictions have been operation for several years clearly shows a benefit of this technology.

scholarly journals Review: Optimizing genomic selection for crossbred performance by model improvement and data collection

2021 ◽  
Author(s):  
Pascal Duenk ◽  
Piter Bijma ◽  
Yvonne C J Wientjes ◽  
Mario P L Calus
Keyword(s):  
Genomic Prediction ◽  
Real Data ◽  
Reference Population ◽  
Additive Models ◽  
Future Research ◽  
Response To Selection ◽  
Dominance Model ◽  
Breeding Programs ◽  
Specific Effects ◽  
Breeding Goal

Abstract Breeding programs aiming to improve the performance of crossbreds may benefit from genomic prediction of crossbred (CB) performance for purebred (PB) selection candidates. In this review, we compared genomic prediction strategies that differed in (1) the genomic prediction model used, or (2) the data used in the reference population. We found 27 unique studies, two of which used deterministic simulation, 11 used stochastic simulation, and 14 real data. Differences in accuracy and response to selection between strategies depended on i) the value of the purebred crossbred genetic correlation (rpc), ii) the genetic distance between the parental lines, iii) the size of PB and CB reference populations, and iv) the relatedness of these reference populations to the selection candidates. In studies where a PB reference population was used, the use of a dominance model yielded accuracies that were equal to or higher than those of additive models. When rpc was lower than ~0.8, and was caused mainly by GxE, it was beneficial to create a reference population of PB animals that are tested in a CB environment. In general, the benefit of collecting CB information increased with decreasing rpc. For a given rpc, the benefit of collecting CB information increased with increasing size of the reference populations. Collecting CB information was not beneficial when rpc was higher than ~0.9, especially when the reference populations were small. Collecting only phenotypes of CB animals may slightly improve accuracy and response to selection, but requires that the pedigree is known. It is therefore advisable to genotype these CB animals as well. Finally, considering the breed-origin of alleles allows for modelling breed-specific effects in the CB, but this did not always lead to higher accuracies. Our review shows that the differences in accuracy and response to selection between strategies depend on several factors. One of the most important factors is rpc, and we therefore recommend to obtain accurate estimates of rpc of all breeding goal traits. Furthermore, knowledge about the importance of components of rpc (i.e., dominance, epistasis, and GxE) can help breeders to decide which model to use, and whether to collect data on animals in a CB environment. Future research should focus on the development of a tool that predicts accuracy and response to selection from scenario specific parameters.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

Sign in / Sign up

Export Citation Format

Share Document