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

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.

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

BMC Genomics ◽

10.1186/s12864-021-08121-z ◽

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.

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Optimizing genomic prediction using low‐density marker panels for streptococcosis resistance in red tilapia ( Oreochromis spp.)

Animal Genetics ◽

10.1111/age.13114 ◽

2021 ◽

Author(s):

S. Sukhavachana ◽

P. Tongyoo ◽

A. Luengnaruemitchai ◽

S. Poompuang

Keyword(s):

Genomic Prediction ◽

Low Density ◽

Red Tilapia ◽

Density Marker

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Accuracy of genomic prediction for seed oil concentration in high oleic soybean populations using a low‐density marker panel

Crop Science ◽

10.1002/csc2.20607 ◽

2021 ◽

Author(s):

Joel Hemingway ◽

Steve R. Schnebly ◽

Istvan Rajcan

Keyword(s):

Genomic Prediction ◽

Seed Oil ◽

Low Density ◽

Marker Panel ◽

High Oleic ◽

Oil Concentration ◽

Density Marker

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Restricted Maximum Likelihood Estimation of Heritabilities for Carcass Traits in the Base and Current Populations of Japanese Black Cattle

Nihon Chikusan Gakkaiho ◽

10.2508/chikusan.65.720 ◽

1994 ◽

Vol 65 (8) ◽

pp. 720-725 ◽

Cited By ~ 2

Author(s):

Kazuyuki MORIYA ◽

Taiji DOHOGO ◽

Yoshiyuki SASAKI

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Carcass Traits ◽

Likelihood Estimation ◽

Restricted Maximum Likelihood ◽

Japanese Black Cattle ◽

Restricted Maximum Likelihood Estimation

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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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