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Genetic Evaluation: Difference between revisions
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===BLUP=== | ===BLUP=== | ||
===ssGBLUP (Suggested writer: Daniela Lourenco)=== | ===ssGBLUP (Suggested writer: Daniela Lourenco)=== | ||
===Single-step Hybrid Marker Effects Models (Suggested writer: Bruce Golden)=== | ===[[Single-step Hybrid Marker Effects Models]] (Suggested writer: Bruce Golden)=== | ||
Marker effects models<ref>Fernando, R. L., H. Cheng, B. L. Golden, and D. J. Garrick. 2016. Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals. Genet. Sol and Evol. 46:96 DOI: 10.1186/s12711-016-0273-2.</ref><ref>Fernando RL. Genetic evaluation and selection using genotypic, phenotypic and pedigree information. In: Proceedings of the 6th World Congress on Genetics Applied to Livestock Production: 11–16 January 1998. vol. 26. Armidale; 1998. pp. 329–36.</ref><ref>Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–1829. [PMC free article] [PubMed]</ref> (MEM) explicitly include random effects for genomic markers in the model. In typical genetic evaluations using MEM the large majority of animals involved have not been genotyped. However, when related to genotyped animals, non-genotyped animals' marker effects can be predicted by imputation of their genotypes from their genotyped relatives by regression. In the form of the MEM currently used in national cattle evaluations using MEM, this imputation is not explicit. This form is called the "hybrid model" in Fernando, et al. (2016), but is also commonly referred to as the super hybrid model. | Marker effects models<ref>Fernando, R. L., H. Cheng, B. L. Golden, and D. J. Garrick. 2016. Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals. Genet. Sol and Evol. 46:96 DOI: 10.1186/s12711-016-0273-2.</ref><ref>Fernando RL. Genetic evaluation and selection using genotypic, phenotypic and pedigree information. In: Proceedings of the 6th World Congress on Genetics Applied to Livestock Production: 11–16 January 1998. vol. 26. Armidale; 1998. pp. 329–36.</ref><ref>Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–1829. [PMC free article] [PubMed]</ref> (MEM) explicitly include random effects for genomic markers in the model. In typical genetic evaluations using MEM the large majority of animals involved have not been genotyped. However, when related to genotyped animals, non-genotyped animals' marker effects can be predicted by imputation of their genotypes from their genotyped relatives by regression. In the form of the MEM currently used in national cattle evaluations using MEM, this imputation is not explicit. This form is called the "hybrid model" in Fernando, et al. (2016), but is also commonly referred to as the super hybrid model. | ||
Revision as of 17:14, 23 January 2019
EPD
Utility (compared to actual/adjusted phenotypes, ratios, disjoined marker scores, etc.) (Suggested writer: Megan Rolf)
Basic Models
BLUP
ssGBLUP (Suggested writer: Daniela Lourenco)
Single-step Hybrid Marker Effects Models (Suggested writer: Bruce Golden)
Marker effects models[1][2][3] (MEM) explicitly include random effects for genomic markers in the model. In typical genetic evaluations using MEM the large majority of animals involved have not been genotyped. However, when related to genotyped animals, non-genotyped animals' marker effects can be predicted by imputation of their genotypes from their genotyped relatives by regression. In the form of the MEM currently used in national cattle evaluations using MEM, this imputation is not explicit. This form is called the "hybrid model" in Fernando, et al. (2016), but is also commonly referred to as the super hybrid model.
Because of this imputation of genotypes for non-genotyped animals, the super hybrid MEM includes an effect for marker effects plus residual imputation errors for non-genotyped animals. This effect is often called the residual polygenic effect (RPE).
Current marker effects fit in the MEM do not account for all the genetic variation. Therefore, in the MEM implemented for genetic evaluations an extra polygenic effect (EPE) is often included. The EPE is fit as a tradition PBLUP with genetic covariance between animals described by the numerator relationship matrix.
The final EPD for genotyped animals is calculated as,
Interim Calculations
Bias
(in)complete reporting / contemporary groups / preferential treatment (Suggested writer: Bob Weaber
Accuracy (Suggested writer: Matt Spangler)
meaning of accuracy
what impacts accuracy
different definitions of accuracy (true, BIF, reliability)
Variance components (Suggested writer: Steve Kachman)
Impact on EPD, accuracy, genetic gain (Suggested writer: Steve Kachman)
Heterogeneous variance
Connectivity (Suggested writer: Ron Lewis)
Measures of (Suggested writer: Ron Lewis)
==Impact on GE== (Suggested Writer: Ron Lewis)
Current GE
How each breed (organization) is modeling each trait (Suggested writers: Steve Miller, Lauren Hyde, AHA)
- ↑ Fernando, R. L., H. Cheng, B. L. Golden, and D. J. Garrick. 2016. Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals. Genet. Sol and Evol. 46:96 DOI: 10.1186/s12711-016-0273-2.
- ↑ Fernando RL. Genetic evaluation and selection using genotypic, phenotypic and pedigree information. In: Proceedings of the 6th World Congress on Genetics Applied to Livestock Production: 11–16 January 1998. vol. 26. Armidale; 1998. pp. 329–36.
- ↑ Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819–1829. [PMC free article] [PubMed]
