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PROC QTL—A SAS Procedure for Mapping Quantitative Trait Loci
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 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.
- University of California, Riverside United States
Article Subject
Article Subject
2113
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- 0345205
- Directorate for Biological Sciences | Division of Biological Infrastructure
- University of California, Riverside United States
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.