Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle
ABSTRACT: The aim of this research was to evaluate the dimensional reduction of additive direct genetic covariance matrices in genetic evaluations of growth traits (range 100-730 days) in Simmental cattle using principal components, as well as to estimate (co)variance components and genetic parameters. Principal component analyses were conducted for five different models-one full and four reduced-rank models. Models were compared using Akaike information (AIC) and Bayesian information (BIC) criteria. Variance components and genetic parameters were estimated by restricted maximum likelihood (REML). The AIC and BIC values were similar among models. This indicated that parsimonious models could be used in genetic evaluations in Simmental cattle. The first principal component explained more than 96% of total variance in both models. Heritability estimates were higher for advanced ages and varied from 0.05 (100 days) to 0.30 (730 days). Genetic correlation estimates were similar in both models regardless of magnitude and number of principal components. The first principal component was sufficient to explain almost all genetic variance. Furthermore, genetic parameter similarities and lower computational requirements allowed for parsimonious models in genetic evaluations of growth traits in Simmental cattle.
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Universidade Federal de Santa Maria
2016
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oai:scielo:S0103-847820160009016562016-08-12Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattleMota,Rodrigo ReisCosta,Edson ViníciusLopes,Paulo SávioNascimento,MoysesSilva,Luciano Pinheiro daSilva,Fabyano Fonseca eMarques,Luiz Fernando Aarão computational demand genetic parameters heritability ABSTRACT: The aim of this research was to evaluate the dimensional reduction of additive direct genetic covariance matrices in genetic evaluations of growth traits (range 100-730 days) in Simmental cattle using principal components, as well as to estimate (co)variance components and genetic parameters. Principal component analyses were conducted for five different models-one full and four reduced-rank models. Models were compared using Akaike information (AIC) and Bayesian information (BIC) criteria. Variance components and genetic parameters were estimated by restricted maximum likelihood (REML). The AIC and BIC values were similar among models. This indicated that parsimonious models could be used in genetic evaluations in Simmental cattle. The first principal component explained more than 96% of total variance in both models. Heritability estimates were higher for advanced ages and varied from 0.05 (100 days) to 0.30 (730 days). Genetic correlation estimates were similar in both models regardless of magnitude and number of principal components. The first principal component was sufficient to explain almost all genetic variance. Furthermore, genetic parameter similarities and lower computational requirements allowed for parsimonious models in genetic evaluations of growth traits in Simmental cattle.info:eu-repo/semantics/openAccessUniversidade Federal de Santa MariaCiência Rural v.46 n.9 20162016-09-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782016000901656en10.1590/0103-8478cr20150927 |
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Mota,Rodrigo Reis Costa,Edson Vinícius Lopes,Paulo Sávio Nascimento,Moyses Silva,Luciano Pinheiro da Silva,Fabyano Fonseca e Marques,Luiz Fernando Aarão |
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Mota,Rodrigo Reis Costa,Edson Vinícius Lopes,Paulo Sávio Nascimento,Moyses Silva,Luciano Pinheiro da Silva,Fabyano Fonseca e Marques,Luiz Fernando Aarão Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle |
author_facet |
Mota,Rodrigo Reis Costa,Edson Vinícius Lopes,Paulo Sávio Nascimento,Moyses Silva,Luciano Pinheiro da Silva,Fabyano Fonseca e Marques,Luiz Fernando Aarão |
author_sort |
Mota,Rodrigo Reis |
title |
Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle |
title_short |
Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle |
title_full |
Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle |
title_fullStr |
Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle |
title_full_unstemmed |
Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle |
title_sort |
multi-trait analysis of growth traits: fitting reduced rank models using principal components for simmental beef cattle |
description |
ABSTRACT: The aim of this research was to evaluate the dimensional reduction of additive direct genetic covariance matrices in genetic evaluations of growth traits (range 100-730 days) in Simmental cattle using principal components, as well as to estimate (co)variance components and genetic parameters. Principal component analyses were conducted for five different models-one full and four reduced-rank models. Models were compared using Akaike information (AIC) and Bayesian information (BIC) criteria. Variance components and genetic parameters were estimated by restricted maximum likelihood (REML). The AIC and BIC values were similar among models. This indicated that parsimonious models could be used in genetic evaluations in Simmental cattle. The first principal component explained more than 96% of total variance in both models. Heritability estimates were higher for advanced ages and varied from 0.05 (100 days) to 0.30 (730 days). Genetic correlation estimates were similar in both models regardless of magnitude and number of principal components. The first principal component was sufficient to explain almost all genetic variance. Furthermore, genetic parameter similarities and lower computational requirements allowed for parsimonious models in genetic evaluations of growth traits in Simmental cattle. |
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Universidade Federal de Santa Maria |
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2016 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782016000901656 |
work_keys_str_mv |
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