Comparison of repeatability and multiple trait threshold models for litter size in sheep using observed and simulated data in Bayesian analyses

W Mekkawy, R Roehe, RM Lewis, MH Davies, L Bunger, G Simm, W Haresign

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Bayesian analyses were used to estimate genetic parameters on 5580 records of litter size in the first four parities from 1758 Mule ewes. To examine the appropriateness of fitting repeatability (RM) or multiple trait threshold models (MTM) to litter size of different parities, both models were used to estimate genetic parameters on the observed data and were thereafter compared in a simulation study. Posterior means of the heritabilities of litter size in different parities using a MTM ranged from 0.12 to 0.18 and were higher than the heritability based on the RM (0.08). Posterior means of the genetic correlations between litter sizes of different parities were positive and ranged from 0.24 to 0.71. Data sets were simulated based on the same pedigree structure and genetic parameters of the Mule ewe population obtained from both models. The simulation showed that the relative loss in accuracy and increase in mean squared error (MSE) was substantially higher when using the RM, given that the parameters estimated from the observed data using the opposite model are the true parameters. In contrast, Bayesian information criterion (BIC) selected the RM as most appropriate model given the data because of substantial penalty for the higher number of parameters to be estimated in the MTM model. In conclusion, when the relative change in accuracy and MSE is of main interest for estimation of breeding values of litter size of different parities, the MTM is recommended for the given population. When reduction in risk of using the wrong model is the main aim, the BIC suggest that the RM is the most appropriate model.
Original languageEnglish
Pages (from-to)261 - 271
Number of pages11
JournalJournal of Animal Breeding and Genetics
Volume127
Issue number4
DOIs
Publication statusFirst published - 2010

Fingerprint

litter size
repeatability
parity (reproduction)
sheep
mules
ewes
heritability
breeding value
pedigree
genetic correlation

Bibliographical note

1020937
1023378

Keywords

  • Animal breeding
  • Bayesian analysis
  • Categorical trait
  • Heritability

Cite this

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title = "Comparison of repeatability and multiple trait threshold models for litter size in sheep using observed and simulated data in Bayesian analyses",
abstract = "Bayesian analyses were used to estimate genetic parameters on 5580 records of litter size in the first four parities from 1758 Mule ewes. To examine the appropriateness of fitting repeatability (RM) or multiple trait threshold models (MTM) to litter size of different parities, both models were used to estimate genetic parameters on the observed data and were thereafter compared in a simulation study. Posterior means of the heritabilities of litter size in different parities using a MTM ranged from 0.12 to 0.18 and were higher than the heritability based on the RM (0.08). Posterior means of the genetic correlations between litter sizes of different parities were positive and ranged from 0.24 to 0.71. Data sets were simulated based on the same pedigree structure and genetic parameters of the Mule ewe population obtained from both models. The simulation showed that the relative loss in accuracy and increase in mean squared error (MSE) was substantially higher when using the RM, given that the parameters estimated from the observed data using the opposite model are the true parameters. In contrast, Bayesian information criterion (BIC) selected the RM as most appropriate model given the data because of substantial penalty for the higher number of parameters to be estimated in the MTM model. In conclusion, when the relative change in accuracy and MSE is of main interest for estimation of breeding values of litter size of different parities, the MTM is recommended for the given population. When reduction in risk of using the wrong model is the main aim, the BIC suggest that the RM is the most appropriate model.",
keywords = "Animal breeding, Bayesian analysis, Categorical trait, Heritability",
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Comparison of repeatability and multiple trait threshold models for litter size in sheep using observed and simulated data in Bayesian analyses. / Mekkawy, W; Roehe, R; Lewis, RM; Davies, MH; Bunger, L; Simm, G; Haresign, W.

In: Journal of Animal Breeding and Genetics, Vol. 127, No. 4, 2010, p. 261 - 271.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Comparison of repeatability and multiple trait threshold models for litter size in sheep using observed and simulated data in Bayesian analyses

AU - Mekkawy, W

AU - Roehe, R

AU - Lewis, RM

AU - Davies, MH

AU - Bunger, L

AU - Simm, G

AU - Haresign, W

N1 - 1020937 1023378

PY - 2010

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N2 - Bayesian analyses were used to estimate genetic parameters on 5580 records of litter size in the first four parities from 1758 Mule ewes. To examine the appropriateness of fitting repeatability (RM) or multiple trait threshold models (MTM) to litter size of different parities, both models were used to estimate genetic parameters on the observed data and were thereafter compared in a simulation study. Posterior means of the heritabilities of litter size in different parities using a MTM ranged from 0.12 to 0.18 and were higher than the heritability based on the RM (0.08). Posterior means of the genetic correlations between litter sizes of different parities were positive and ranged from 0.24 to 0.71. Data sets were simulated based on the same pedigree structure and genetic parameters of the Mule ewe population obtained from both models. The simulation showed that the relative loss in accuracy and increase in mean squared error (MSE) was substantially higher when using the RM, given that the parameters estimated from the observed data using the opposite model are the true parameters. In contrast, Bayesian information criterion (BIC) selected the RM as most appropriate model given the data because of substantial penalty for the higher number of parameters to be estimated in the MTM model. In conclusion, when the relative change in accuracy and MSE is of main interest for estimation of breeding values of litter size of different parities, the MTM is recommended for the given population. When reduction in risk of using the wrong model is the main aim, the BIC suggest that the RM is the most appropriate model.

AB - Bayesian analyses were used to estimate genetic parameters on 5580 records of litter size in the first four parities from 1758 Mule ewes. To examine the appropriateness of fitting repeatability (RM) or multiple trait threshold models (MTM) to litter size of different parities, both models were used to estimate genetic parameters on the observed data and were thereafter compared in a simulation study. Posterior means of the heritabilities of litter size in different parities using a MTM ranged from 0.12 to 0.18 and were higher than the heritability based on the RM (0.08). Posterior means of the genetic correlations between litter sizes of different parities were positive and ranged from 0.24 to 0.71. Data sets were simulated based on the same pedigree structure and genetic parameters of the Mule ewe population obtained from both models. The simulation showed that the relative loss in accuracy and increase in mean squared error (MSE) was substantially higher when using the RM, given that the parameters estimated from the observed data using the opposite model are the true parameters. In contrast, Bayesian information criterion (BIC) selected the RM as most appropriate model given the data because of substantial penalty for the higher number of parameters to be estimated in the MTM model. In conclusion, when the relative change in accuracy and MSE is of main interest for estimation of breeding values of litter size of different parities, the MTM is recommended for the given population. When reduction in risk of using the wrong model is the main aim, the BIC suggest that the RM is the most appropriate model.

KW - Animal breeding

KW - Bayesian analysis

KW - Categorical trait

KW - Heritability

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DO - http://dx.doi.org/10.1111/j.1439-0388.2010.00852.x

M3 - Article

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EP - 271

JO - Journal of Animal Breeding and Genetics

JF - Journal of Animal Breeding and Genetics

SN - 0931-2668

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ER -