Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Ethics Statement
2.2. Population and Phenotypes
2.3. Genotype Data
2.4. Statistical Model
2.4.1. BLUP
2.4.2. GBLUP
2.5. Cross-Validation
3. Results
3.1. Descriptive Statistics and Estimates of Genetic Parameters
3.2. The Accuracy of Genomic Prediction with the BLUP and GBLUP Methods
3.3. Breeding Values of Various Traits Predicted with BLUP and GBLUP for Birds with Low or High SRBC and H/L
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Trait 1 | N-Obs 2 | Average | SD 3 | Min Value | Max Value |
---|---|---|---|---|---|
Ab-AIV | 455 | 9.479 | 1.558 | 5.000 | 12.000 |
Ab-NDV | 384 | 5.505 | 1.262 | 3.000 | 9.000 |
SRBC | 519 | 7.788 | 1.191 | 4.000 | 10.000 |
H/L | 519 | 0.438 | 0.294 | 0.024 | 1.579 |
IgG | 466 | 542.855 | 219.233 | 144.670 | 1754.730 |
SII | 519 | 0.150 | 0.037 | 0.059 | 0.303 |
TII | 514 | 0.477 | 0.174 | 0.043 | 1.045 |
TW | 515 | 7.217 | 2.786 | 0.460 | 17.610 |
SW | 519 | 2.269 | 0.622 | 0.800 | 4.360 |
Trait 1 | σa 2 | σe 3 | h 4 | |||
---|---|---|---|---|---|---|
Component | Std. Error | Component | Std. Error | Estimate | Std. Error | |
Ab-AIV | 0.662 | 0.261 | 1.764 | 0.245 | 0.273 | 0.101 |
Ab-NDV | 0 | 0 | 1.593 | 0.115 | 0 | 0 |
SRBC | 0.263 | 0.128 | 1.157 | 0.131 | 0.185 | 0.087 |
H/L | 0.022 | 0.009 | 0.064 | 0.008 | 0.265 | 0.100 |
IgG | 0.006 | 0 | 9409.678 | 617.111 | 0 | 0 |
SII | 0.001 | 0 | 0.001 | 0 | 0.631 | 0.110 |
TII | 0.009 | 0.003 | 0.022 | 0.003 | 0.281 | 0.098 |
TW | 1.546 | 0.722 | 6.233 | 0.726 | 0.199 | 0.089 |
SW | 0.223 | 0.052 | 0.166 | 0.038 | 0.573 | 0.109 |
Trait 1 | σg 2 | σe 3 | h 4 | |||
---|---|---|---|---|---|---|
Component | Std. Error | Component | Std. Error | Estimate | Std. Error | |
Ab-AIV | 0.644 | 0.224 | 1.774 | 0.210 | 0.266 | 0.086 |
Ab-NDV | 0 | 0 | 1.593 | 0.115 | 0 | 0 |
SRBC | 0.082 | 0.087 | 1.337 | 0.116 | 0.058 | 0.061 |
H/L | 0.011 | 0.006 | 0.075 | 0.007 | 0.128 | 0.065 |
IgG | 0.003 | 0 | 9409.681 | 617.111 | 0 | 0 |
SII | 0.001 | 0 | 0.001 | 0 | 0.472 | 0.083 |
TII | 0.011 | 0.003 | 0.020 | 0.002 | 0.353 | 0.086 |
TW | 2.332 | 0.701 | 5.434 | 0.619 | 0.300 | 0.082 |
SW | 0.158 | 0.036 | 0.223 | 0.028 | 0.415 | 0.080 |
Trait 1 | SRBC | H/L | ||||
---|---|---|---|---|---|---|
Predictive Ability | Prediction Accuracy | Unbiasedness | Predictive Ability | Prediction Accuracy | Unbiasedness | |
BLUP | 0.132 ± 0.0242 2 | 0.307 ± 0.056 | 1.000 ± 0.001 | 0.185 ± 0.030 | 0.326 ± 0.528 | 0.999 ± 0.006 |
GBLUP | 0.052 ± 0.027 | 0.216 ± 0.112 | 1.000 ± 0.000 | 0.119 ± 0.025 | 0.333 ± 0.069 | 0.999 ± 0.004 |
P-value 3 | 1.082 × 10 −28 | 1.082 × 10 −28 | 9.282 × 10 −01 | 2.309 × 10 −20 | 2.309 × 10 −20 | 9.164 × 10 −01 |
Model | Group 1 | Ab-AIV 2 | SRBC | Ab-NDV | H/L | IgG | SII | TII | TW | SW |
---|---|---|---|---|---|---|---|---|---|---|
BLUP | SRBCeff | 9.240 ± 1.964 | 9.567 ± 0.504 | 5.500 ± 1.303 | 0.446 ± 0.358 | 651.166 ± 330.626 | 0.140 ± 0.036 | 0.438 ± 0.152 | 6.541 ± 2.554 | 2.054 ± 0.585 |
GBLUP | SRBCeff | 9.346 ± 1.788 | 9.300 ± 0.651 | 5.500 ± 1.251 | 0.442 ± 0.358 | 545.036 ± 252.567 | 0.141 ± 0.029 | 0.450 ± 0.163 | 6.794 ± 0.714 | 2.103 ± 0.517 |
p-value 3 | 0.841 | 0.081 | 1.000 | 0.968 | 0.191 | 0.898 | 0.780 | 0.714 | 0.734 | |
BLUP | SRBCineff | 9.708 ± 1.301 | 5.533 ± 0.860 | 5.818 ± 1.468 | 0.611 ± 0.352 | 558.716 ± 296.872 | 0.151 ± 0.049 | 0.440 ± 0.117 | 6.757 ± 2.012 | 2.297 ± 0.793 |
GBLUP | SRBCineff | 9.870 ± 1.180 | 5.933 ± 1.048 | 5.789 ± 1.548 | 0.512 ± 0.240 | 591.997 ± 282.724 | 0.146 ± 0.047 | 0.466 ± 0.141 | 7.078 ± 2.287 | 2.201 ± 0.780 |
p-value | 0.659 | 0.112 | 0.952 | 0.208 | 0.693 | 0.678 | 0.440 | 0.573 | 0.639 |
Model | Group 1 | Ab-AIV 2 | SRBC | Ab-NDV | H/L | IgG | SII | TII | TW | SW |
---|---|---|---|---|---|---|---|---|---|---|
BLUP | H/Leff | 9.393 ± 1.792 | 7.600 ± 1.102 | 5.304 ± 1.185 | 0.134 ± 0.062 | 537.249 ± 207.421 | 0.145 ± 0.037 | 0.528 ± 0.142 | 7.614 ± 2.077 | 2.114 ± 0.582 |
GBLUP | H/Leff | 9.500 ± 1.679 | 7.733 ± 1.112 | 5.455 ± 1.101 | 0.153 ± 0.075 | 544.312 ± 226.187 | 0.137 ± 0.036 | 0.480 ± 0.181 | 6.904 ± 2.701 | 1.967 ± 0.618 |
p-value 3 | 0.822 | 0.643 | 0.662 | 0.296 | 0.905 | 0.408 | 0.266 | 0.264 | 0.347 | |
BLUP | H/Lineff | 9.818 ± 1.220 | 7.567 ± 1.194 | 5.750 ± 1.446 | 1.100 ± 0.340 | 585.091 ± 181.843 | 0.165 ± 0.046 | 0.479 ± 0.172 | 7.595 ± 2.954 | 2.616 ± 0.797 |
GBLUP | H/Lineff | 9.348 ± 1.613 | 7.367 ± 1.129 | 5.286 ± 1.309 | 0.949 ± 0.393 | 580.645 ± 238.276 | 0.167 ± 0.046 | 0.473 ± 0.195 | 7.518 ± 3.564 | 2.634 ± 0.856 |
p-value | 0.278 | 0.508 | 0.287 | 0.115 | 0.942 | 0.873 | 0.903 | 0.928 | 0.932 |
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Zhang, J.; Wang, J.; Li, Q.; Wang, Q.; Wen, J.; Zhao, G. Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens. Animals 2020, 10, 419. https://doi.org/10.3390/ani10030419
Zhang J, Wang J, Li Q, Wang Q, Wen J, Zhao G. Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens. Animals. 2020; 10(3):419. https://doi.org/10.3390/ani10030419
Chicago/Turabian StyleZhang, Jin, Jie Wang, Qinghe Li, Qiao Wang, Jie Wen, and Guiping Zhao. 2020. "Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens" Animals 10, no. 3: 419. https://doi.org/10.3390/ani10030419
APA StyleZhang, J., Wang, J., Li, Q., Wang, Q., Wen, J., & Zhao, G. (2020). Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens. Animals, 10(3), 419. https://doi.org/10.3390/ani10030419