The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Phenotype Data
2.2. Genotype Data
2.3. Variance Component Estimation
2.4. Prediction Accuracy
3. Results
3.1. Genetic Parameters of Wool Traits
3.2. Accuracy of EBV and GEBV Predictions
4. Discussion
4.1. Genetic Parameters of Wool Traits
4.2. Accuracy of EBV and GEBV Predictions
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Traits 1 | PBLUP | ssGBLUP |
---|---|---|
SC | −0.03 | −0.12 |
CRIM | −0.04 | 0.25 |
OIL | −0.14 | −0.12 |
BS | −0.14 | 0.04 |
FL | 0.02 | 0.05 |
GFW | −0.01 | 0.27 |
MFD | 0.00 | 0.04 |
CN | −0.19 | 0.17 |
BWPS | −0.14 | 0.00 |
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Trait 1 | Scale of Assessment | ||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
SC | poor | below average | average | above average | excellent |
CRIM | poor | below average | average | above average | excellent |
OIL | dark yellow | faint yellow | milk white | white | - |
BS | narrow | below average | average | above average | wide |
Traits 1 | Number | Minimum | Maximum | Average | SD | CV | |
---|---|---|---|---|---|---|---|
Subjectively-assessed wool traits | SC | 28390 | 1.00 | 5.00 | 2.78 | 0.84 | 0.30 |
CRIM | 19356 | 1.00 | 5.00 | 3.83 | 0.82 | 0.21 | |
OIL | 21173 | 1.00 | 4.00 | 3.36 | 0.78 | 0.23 | |
BS | 27110 | 1.00 | 5.00 | 2.80 | 1.22 | 0.44 | |
Objectively-measured traits | FL/cm | 28658 | 6.00 | 15.00 | 10.12 | 1.10 | 0.11 |
GFW/kg | 27379 | 1.00 | 9.00 | 3.98 | 0.94 | 0.24 | |
MFD/μm | 5140 | 12.77 | 28.13 | 18.41 | 2.28 | 0.12 | |
CN/per 2.5 cm | 4326 | 7.00 | 25.00 | 12.50 | 2.53 | 0.20 | |
BWPS/kg | 25676 | 20.00 | 78.00 | 34.30 | 5.47 | 0.16 |
PBLUP | (ssGBLUP) | |||||||
---|---|---|---|---|---|---|---|---|
Traits 1 | ||||||||
SC | 0.011 (0.002) | 0.014 (0.006) | 0.029 (0.014) | 0.201 (0.046) | 0.011 (0.002) | 0.014 (0.006) | 0.029 (0.013) | 0.204 (0.046) |
CRIM | 0.370 (0.340) | 1.523 (0.901) | 2.796 (1.86) | 0.079 (0.071) | 0.463 (0.376) | 1.619 (0.864) | 2.934 (1.761) | 0.107 (0.070) |
OIL | 0.196 (0.086) | 1.366 (0.077) | 1.245 (1.364) | 0.070 (0.037) | 0.210 (0.088) | 1.418 (0.786) | 1.297 (1.431) | 0.072 (0.039) |
BS | 0.183 (0.028) | 0.350 (0.153) | 0.593 (0.391) | 0.163 (0.049) | 0.179 (0.028) | 0.312 (0.153) | 0.597 (0.373) | 0.165 (0.049) |
FL | 0.302 (0.016) | 0.228 (0.029) | 0.579 (0.014) | 0.272 (0.016) | 0.308 (0.016) | 0.227 (0.029) | 0.578 (0.014) | 0.277 (0.016) |
GFW | 0.123 (0.008) | 0.245 (0.031) | 0.343 (0.007) | 0.173 (0.013) | 0.123 (0.008) | 0.246 (0.003) | 0.343 (0.007) | 0.173 (0.013) |
MFD | 0.827 (0.127) | 0.309 (0.080) | 2.408 (0.117) | 0.233 (0.035) | 1.024 (0.114) | 0.302 (0.076) | 2.201 (0.101) | 0.290 (0.031) |
CN | 0.506 (0.167) | 0.276 (0.083) | 4.880 (0.182) | 0.089 (0.029) | 0.859 (0.191) | 0.255 (0.078) | 4.542 (0.197) | 0.152 (0.032) |
BWPS | 5.767 (0.346) | 8.064 (1.072) | 12.189 (0.298) | 0.222 (0.015) | 5.790 (0.336) | 8.009 (1.052) | 12.166 (0.284) | 0.223 (0.015) |
Whole Population | Genotyped Subpopulation | |||||||
---|---|---|---|---|---|---|---|---|
Traits 1 | PBLUP | ssGBLUP | Δacc (%) | Correlation | PBLUP | ssGBLUP | Δacc (%) | Correlation |
SC | 0.532 | 0.539 | 0.699 | 0.855 ** | 0.569 | 0.602 | 3.274 | 0.769 ** |
CRIM | 0.367 | 0.433 | 6.638 | 0.767 ** | 0.430 | 0.507 | 7.712 | 0.664 ** |
OIL | 0.389 | 0.414 | 2.518 | 0.777 ** | 0.318 | 0.384 | 6.659 | 0.682 ** |
BS | 0.517 | 0.527 | 0.993 | 0.852 ** | 0.503 | 0.563 | 6.032 | 0.752 ** |
FL | 0.559 | 0.563 | 0.372 | 0.862 ** | 0.646 | 0.676 | 3.016 | 0.668 ** |
GFW | 0.517 | 0.523 | 0.595 | 0.854 ** | 0.436 | 0.553 | 11.736 | 0.705 ** |
MFD | 0.476 | 0.521 | 4.544 | 0.833 ** | 0.580 | 0.630 | 5.012 | 0.580 ** |
CN | 0.362 | 0.437 | 7.486 | 0.717 ** | 0.400 | 0.487 | 8.659 | 0.525 ** |
BWPS | 0.562 | 0.573 | 1.089 | 0.858 ** | 0.623 | 0.656 | 3.237 | 0.598 ** |
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Wei, C.; Luo, H.; Zhao, B.; Tian, K.; Huang, X.; Wang, Y.; Fu, X.; Tian, Y.; Di, J.; Xu, X.; et al. The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep. Animals 2020, 10, 569. https://doi.org/10.3390/ani10040569
Wei C, Luo H, Zhao B, Tian K, Huang X, Wang Y, Fu X, Tian Y, Di J, Xu X, et al. The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep. Animals. 2020; 10(4):569. https://doi.org/10.3390/ani10040569
Chicago/Turabian StyleWei, Chen, Hanpeng Luo, Bingru Zhao, Kechuan Tian, Xixia Huang, Yachun Wang, Xuefeng Fu, Yuezhen Tian, Jiang Di, Xinming Xu, and et al. 2020. "The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep" Animals 10, no. 4: 569. https://doi.org/10.3390/ani10040569
APA StyleWei, C., Luo, H., Zhao, B., Tian, K., Huang, X., Wang, Y., Fu, X., Tian, Y., Di, J., Xu, X., Wu, W., Tulafu, H., Yasen, M., Zhang, Y., & Zhao, W. (2020). The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep. Animals, 10(4), 569. https://doi.org/10.3390/ani10040569