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Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model

Problem statement:Genetic correlations among traits refer to the extent of relatedness among them due to genetic causes. Estimating genetic correlations for quantitative traits is tedious if done manually. Approach:However, the use of the computer software SAS, applying mixed-model analysis of varia...

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Main Authors: Kashiani, Pedram, Saleh, Ghizan
Format: Article
Language:English
Published: Science Publications 2010
Online Access:http://psasir.upm.edu.my/id/eprint/14569/1/ajabssp.2010.309.314.pdf
http://psasir.upm.edu.my/id/eprint/14569/
http://www.thescipub.com/abstract/10.3844/ajabssp.2010.309.314
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spelling my.upm.eprints.145692017-11-21T02:02:12Z http://psasir.upm.edu.my/id/eprint/14569/ Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model Kashiani, Pedram Saleh, Ghizan Problem statement:Genetic correlations among traits refer to the extent of relatedness among them due to genetic causes. Estimating genetic correlations for quantitative traits is tedious if done manually. Approach:However, the use of the computer software SAS, applying mixed-model analysis of variance has facilitated many recent studies in evolutionary quantitative genetics. Results:In this two-way statistical model, the variance component corresponding to the random statement is the covariance associated with a level of the random factor across levels of the fix factor. Therefore, the SAS model has a natural application for estimating genetic correlations among traits measured. Correlation studies were undertaken for 10 yield-related traits on a series of near-homozygous sweet corn inbred lines obtained from various tropical source populations. The SAS program was used to estimate genetic correlation coefficients among traits observed, where effects of blocks were considered fixed while effects of inbred lines as random. The "ASYCOV" was added to the "PROC MIXED" statement in order to produce the variance-covariance matrix of variance components. The "TYPE = UN" option requested in "RANDOM" statement resulted in an unstructured covariance matrix for each inbred line being estimated, while the "G" and "GCORR" options produced genetic variance-covariance matrix and genetic correlation matrix between traits, respectively. Results showed that there was no significant difference between genetic correlations estimated by SAS MIXED model and those estimated by manual calculation. Conclusion/Recommendations: This indicated that SAS has the natural capability to estimate genetic correlations among traits measured, as opposed to manual methods employing quantitative genetics equations. Science Publications 2010 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/14569/1/ajabssp.2010.309.314.pdf Kashiani, Pedram and Saleh, Ghizan (2010) Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model. American Journal of Agricultural and Biological Sciences, 5 (3). pp. 309-314. ISSN 1557-4989; ESSN: 1557-4997 http://www.thescipub.com/abstract/10.3844/ajabssp.2010.309.314 10.3844/ajabssp.2010.309.314
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Problem statement:Genetic correlations among traits refer to the extent of relatedness among them due to genetic causes. Estimating genetic correlations for quantitative traits is tedious if done manually. Approach:However, the use of the computer software SAS, applying mixed-model analysis of variance has facilitated many recent studies in evolutionary quantitative genetics. Results:In this two-way statistical model, the variance component corresponding to the random statement is the covariance associated with a level of the random factor across levels of the fix factor. Therefore, the SAS model has a natural application for estimating genetic correlations among traits measured. Correlation studies were undertaken for 10 yield-related traits on a series of near-homozygous sweet corn inbred lines obtained from various tropical source populations. The SAS program was used to estimate genetic correlation coefficients among traits observed, where effects of blocks were considered fixed while effects of inbred lines as random. The "ASYCOV" was added to the "PROC MIXED" statement in order to produce the variance-covariance matrix of variance components. The "TYPE = UN" option requested in "RANDOM" statement resulted in an unstructured covariance matrix for each inbred line being estimated, while the "G" and "GCORR" options produced genetic variance-covariance matrix and genetic correlation matrix between traits, respectively. Results showed that there was no significant difference between genetic correlations estimated by SAS MIXED model and those estimated by manual calculation. Conclusion/Recommendations: This indicated that SAS has the natural capability to estimate genetic correlations among traits measured, as opposed to manual methods employing quantitative genetics equations.
format Article
author Kashiani, Pedram
Saleh, Ghizan
spellingShingle Kashiani, Pedram
Saleh, Ghizan
Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model
author_facet Kashiani, Pedram
Saleh, Ghizan
author_sort Kashiani, Pedram
title Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model
title_short Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model
title_full Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model
title_fullStr Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model
title_full_unstemmed Estimation of genetic correlations on sweet corn inbred lines using SAS mixed model
title_sort estimation of genetic correlations on sweet corn inbred lines using sas mixed model
publisher Science Publications
publishDate 2010
url http://psasir.upm.edu.my/id/eprint/14569/1/ajabssp.2010.309.314.pdf
http://psasir.upm.edu.my/id/eprint/14569/
http://www.thescipub.com/abstract/10.3844/ajabssp.2010.309.314
_version_ 1643825671648051200
score 13.211869

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