R/qtlDesign: inbred line cross experimental design
An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calcula...
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| Published in | Mammalian genome Vol. 18; no. 2; pp. 87 - 93 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
New York : Springer-Verlag
01.02.2007
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0938-8990 1432-1777 |
| DOI | 10.1007/s00335-006-0090-y |
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| Abstract | An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at http://www.biostat.ucsf.edu/sen/software.html. |
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| AbstractList | An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at http://www.biostat.ucsf.edu/sen/software.html.[PUBLICATION ABSTRACT] An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at http://www.biostat.ucsf.edu/sen/software.html. An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at http://www.biostat.ucsf.edu/sen/software.html . An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at http://www.biostat.ucsf.edu/sen/software.html .An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at http://www.biostat.ucsf.edu/sen/software.html . |
| Author | Churchill, Gary A Broman, Karl W Satagopan, Jaya M Sen, Śaunak |
| AuthorAffiliation | 4 The Jackson Laboratory, Bar Harbor, Maine 04609, USA 1 Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California 94143, USA 3 Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205, USA 2 Department of Epidemiology and Biostatistics, Memorial Sloan Ketterning Cancer Center, New York, New York 10021, USA |
| AuthorAffiliation_xml | – name: 3 Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205, USA – name: 2 Department of Epidemiology and Biostatistics, Memorial Sloan Ketterning Cancer Center, New York, New York 10021, USA – name: 1 Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California 94143, USA – name: 4 The Jackson Laboratory, Bar Harbor, Maine 04609, USA |
| Author_xml | – sequence: 1 fullname: Sen, Śaunak – sequence: 2 fullname: Satagopan, Jaya M – sequence: 3 fullname: Broman, Karl W – sequence: 4 fullname: Churchill, Gary A |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/17347894$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1038/ng0198-19 10.1038/nrg1576 10.1093/genetics/151.1.373 10.1023/A:1021404714631 10.1093/bioinformatics/btg112 10.1080/10618600.1996.10474713 10.1093/bioinformatics/19.1.149 10.1534/genetics.104.038612 10.1007/978-1-4899-2887-0 10.1007/978-1-4899-4441-2 |
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| SubjectTerms | Animals Animals, Inbred Strains - genetics backcrossing Chromosome Mapping computer software Crosses, Genetic experimental design Genetic Variation genome genotyping inbred lines loci mammals phenotype Probability progeny Quantitative Trait Loci variance |
| Title | R/qtlDesign: inbred line cross experimental design |
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