Volume 13, Issue 38 (12-2022)
rap 2022, 13(38): 187-193 |
Back to browse issues page
University of Mohaghegh Ardabili, Faculty of Agriculture and Natural Resources, Department of Animal Science, Ardabil, Iran
Abstract: (1189 Views)
Extended Abstract
Introduction and Objective: Dominant genetic effects may have an important contribution to the total genetic diversity of quantitative and complex traits. However, genome-wide marker research to study genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of non-additive genetic effects have recently attracted much attention to combining these effects into genomic prediction models.
Material and Methods: A genome with 3 chromosomes of 100 cM each, with 200 QTL and 1000 biallelic markers on each chromosome were simulated.. Then the information related to records of pedigree, year of birth, weaning weight, offspring sex, percentage of twinning, carcass weight, carcass quality, age of first calving, wool density and other economic traits of Moghani sheep that were obtained through Jafarabad Moghani Breeding Center (during 1382 to 1393) formed the phenotypic matrix of the model. Additive and dominance genetic effects and accuracy of genomic prediction of 7 traits, including growth, carcass quality, wool and fertility were adopted through two linear models: (1) ) an additive effect model (MAG) and (2) a model that includes additive and dominance genetic effects (MADG). In addition, the 5-layer cross-validation method was used with R package “HIBLUP”to evaluate the GP capability in two different models.
Results: The results of estimating variance components for each trait show that carcass weight (0.617) and lambing percentage per ewe (0.578), a large part of the phenotypic variation is explained by dominance genetic effects. Cross-validation results showed that the MADG model, including additive and dominance genetic effects, has a clear advantage over the MAG model, which includes only additive genetic effects. that’s means, the model that includes dominant genetic effects improves the accuracy of genomic prediction.
Conclusion: The better performance (prediction accuracy) of the MADG model for some traits compared to the MAG model shows that dominance effects should be included in animal genetic evaluation models to improve the accuracy of predicting future phenotypes. MADG model can also be a useful tool for culling decision in farms, and the use of the entire genetic potential of progeny in mating programs may improve progeny performance.
Introduction and Objective: Dominant genetic effects may have an important contribution to the total genetic diversity of quantitative and complex traits. However, genome-wide marker research to study genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of non-additive genetic effects have recently attracted much attention to combining these effects into genomic prediction models.
Material and Methods: A genome with 3 chromosomes of 100 cM each, with 200 QTL and 1000 biallelic markers on each chromosome were simulated.. Then the information related to records of pedigree, year of birth, weaning weight, offspring sex, percentage of twinning, carcass weight, carcass quality, age of first calving, wool density and other economic traits of Moghani sheep that were obtained through Jafarabad Moghani Breeding Center (during 1382 to 1393) formed the phenotypic matrix of the model. Additive and dominance genetic effects and accuracy of genomic prediction of 7 traits, including growth, carcass quality, wool and fertility were adopted through two linear models: (1) ) an additive effect model (MAG) and (2) a model that includes additive and dominance genetic effects (MADG). In addition, the 5-layer cross-validation method was used with R package “HIBLUP”to evaluate the GP capability in two different models.
Results: The results of estimating variance components for each trait show that carcass weight (0.617) and lambing percentage per ewe (0.578), a large part of the phenotypic variation is explained by dominance genetic effects. Cross-validation results showed that the MADG model, including additive and dominance genetic effects, has a clear advantage over the MAG model, which includes only additive genetic effects. that’s means, the model that includes dominant genetic effects improves the accuracy of genomic prediction.
Conclusion: The better performance (prediction accuracy) of the MADG model for some traits compared to the MAG model shows that dominance effects should be included in animal genetic evaluation models to improve the accuracy of predicting future phenotypes. MADG model can also be a useful tool for culling decision in farms, and the use of the entire genetic potential of progeny in mating programs may improve progeny performance.
Type of Study: Research |
Subject:
ژنتیک و اصلاح نژاد دام
Received: 2022/10/4 | Revised: 2023/01/8 | Accepted: 2022/11/15 | Published: 2022/12/1
Received: 2022/10/4 | Revised: 2023/01/8 | Accepted: 2022/11/15 | Published: 2022/12/1
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |