Campus Units

Animal Science

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2-8-2020

Journal or Book Title

Livestock Science

DOI

10.1016/j.livsci.2020.103970

Abstract

The aims of this study were 1) estimate heritability and variance components for sow survival traits using random regression model and 2) to identify the best model when conducting a sow survival genetic evaluation for Thailand commercial farms by comparing RRM with different covariance functions (Legendre polynomial and linear spline). A total of 11,595 and 11,336 sows from Landrace and Large White sows, respectively, were used to compare random regression models. The model using a second to third order Legendre polynomial for additive genetic effects and second to fourth order Legendre polynomial for permanent environmental effects (LG22, LG23, LG33 and LG34) and linear splines 3 to 4 knots for additive genetic effects and permanent environmental effects (SPL33 and SPL44) were used for analyses of genetic parameters. Bayesian interference using Gibbs sampling was used to estimate all covariance components. The model that included Legendre polynomial functions LG22 provided the lowest the deviance information criterion (DIC), provide the best fit for both the Landrace and Large White datasets. The heritability estimates for sow survival obtained with LG22 (the best fit model) ranged from 0.12 to 0.15 and 0.14 to 0.18 for Landrace and Large White sows, respectively. The genetic correlation among sow survival obtained with LG22 (the best fit model) ranged from 0.27 to 0.99, 0.43 to 0.99 for Landrace and Large White sows, respectively. Results from this study indicate that RRM could be used for genetic evaluation of sow survival.

Comments

This is a manuscript of an article published as Plaengkaeo, Suppasit, Monchai Duangjinda, Wuttigrai Boonkum, Kenneth J. Stalder, and John W. Mabry. "Genetic evaluation of sow survival in Thailand commercial farms using random regression models." Livestock Science (2020). doi: 10.1016/j.livsci.2020.103970. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier B.V.

Language

en

File Format

application/pdf

Available for download on Monday, February 08, 2021

Published Version

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