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PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci
Statistical analysis system (SAS) is the most comprehensive statistical analysis software package in the world. It offers data analysis for almost all experiments under various statistical models. Each analysis is performed using a particular subroutine, called a procedure (PROC). For example, PROC...
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Formato: | Texto |
Lenguaje: | English |
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Hindawi Publishing Corporation
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796467/ https://www.ncbi.nlm.nih.gov/pubmed/20037656 http://dx.doi.org/10.1155/2009/141234 |
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author | Hu, Zhiqiu Xu, Shizhong |
author_facet | Hu, Zhiqiu Xu, Shizhong |
author_sort | Hu, Zhiqiu |
collection | PubMed |
description | Statistical analysis system (SAS) is the most comprehensive statistical analysis software package in the world. It offers data analysis for almost all experiments under various statistical models. Each analysis is performed using a particular subroutine, called a procedure (PROC). For example, PROC ANOVA performs analysis of variances. PROC QTL is a user-defined SAS procedure for mapping quantitative trait loci (QTL). It allows users to perform QTL mapping for continuous and discrete traits within the SAS platform. Users of PROC QTL are able to take advantage of all existing features offered by the general SAS software, for example, data management and graphical treatment. The current version of PROC QTL can perform QTL mapping for all line crossing experiments using maximum likelihood (ML), least square (LS), iteratively reweighted least square (IRLS), Fisher scoring (FISHER), Bayesian (BAYES), and empirical Bayes (EBAYES) methods. |
format | Text |
id | pubmed-2796467 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-27964672009-12-23 PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci Hu, Zhiqiu Xu, Shizhong Int J Plant Genomics Resource Review Statistical analysis system (SAS) is the most comprehensive statistical analysis software package in the world. It offers data analysis for almost all experiments under various statistical models. Each analysis is performed using a particular subroutine, called a procedure (PROC). For example, PROC ANOVA performs analysis of variances. PROC QTL is a user-defined SAS procedure for mapping quantitative trait loci (QTL). It allows users to perform QTL mapping for continuous and discrete traits within the SAS platform. Users of PROC QTL are able to take advantage of all existing features offered by the general SAS software, for example, data management and graphical treatment. The current version of PROC QTL can perform QTL mapping for all line crossing experiments using maximum likelihood (ML), least square (LS), iteratively reweighted least square (IRLS), Fisher scoring (FISHER), Bayesian (BAYES), and empirical Bayes (EBAYES) methods. Hindawi Publishing Corporation 2009 2009-12-08 /pmc/articles/PMC2796467/ /pubmed/20037656 http://dx.doi.org/10.1155/2009/141234 Text en Copyright © 2009 Z. Hu and S. Xu. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Resource Review Hu, Zhiqiu Xu, Shizhong PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci |
title | PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci |
title_full | PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci |
title_fullStr | PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci |
title_full_unstemmed | PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci |
title_short | PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci |
title_sort | proc qtl—a sas procedure for mapping quantitative trait loci |
topic | Resource Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796467/ https://www.ncbi.nlm.nih.gov/pubmed/20037656 http://dx.doi.org/10.1155/2009/141234 |
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