Volume 12, Issue 4 (Autumn-In Press 2024)
Iran J Health Sci 2024, 12(4): 0-0 |
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Ethics code: 1473601
Clinical trials code: 1473601
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Ghazanfari A, Fayyaz Movaghar A. Genomic Selection Based on Bayesian Model Averaging with Skewed Error Distributed. Iran J Health Sci 2024; 12 (4)
URL: http://jhs.mazums.ac.ir/article-1-967-en.html
URL: http://jhs.mazums.ac.ir/article-1-967-en.html
Department of Statistics, School of Mathematical Sciences, University of Mazandaran, Babolsar, Iran , a_fayyaz@umz.ac.ir
Abstract: (124 Views)
Background and Purpose: Genomic selection (GS) is utilized as a means of selecting candidates in breeding programs for organisms. To this purpose, we fit a linear model to find the genetic variants related to some traits.
Materials and Methods: In most studies, the distribution of error terms is assumed to be normal. In the context of genomic selection applications, we suggest expanding the Bayesian whole genome to accommodate data with two skew distributions: skew normal and skew t. In this study, we apply BMA to linear regression models with skew normal and skew t distributions to determine the best subset of predictors. Two techniques, Occam’s window and MC3, are used to determine the “best" model and its uncertainty.
Results: In comparison, the Occam’s window method runs faster than MC^3. Regardless of computation time, the simulation study reveals that MC^3 method suggests the better choices for both linear models with errors of skew normal and skew t distribution.
Conclusion: Simulated and real data results show that the MC3,method performs better than Occam’s window method.
Materials and Methods: In most studies, the distribution of error terms is assumed to be normal. In the context of genomic selection applications, we suggest expanding the Bayesian whole genome to accommodate data with two skew distributions: skew normal and skew t. In this study, we apply BMA to linear regression models with skew normal and skew t distributions to determine the best subset of predictors. Two techniques, Occam’s window and MC3, are used to determine the “best" model and its uncertainty.
Results: In comparison, the Occam’s window method runs faster than MC^3. Regardless of computation time, the simulation study reveals that MC^3 method suggests the better choices for both linear models with errors of skew normal and skew t distribution.
Conclusion: Simulated and real data results show that the MC3,method performs better than Occam’s window method.
Type of Study: Original Article |
Subject:
Biostatistics
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