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qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software.

Nelson RM, Shen X, Carlborg O - BMC Res Notes (2011)

Bottom Line: qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools.Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL. qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets.This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, SE-75007 Uppsala, Sweden. Ronnie.Nelson@slu.se.

ABSTRACT

Background: qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl.

Findings: Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL.

Conclusion: qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

No MeSH data available.


Related in: MedlinePlus

The graph was obtained by using outbred line cross data (domesticated and wild chicken intercross genotypic data with simulated phenotype), calculating genotype probabilities with the triM algorithm from the qtl.outbred interface and importing it directly to R/qtl where the genome scans were performed. LOD scores for Haley-Knott regression [6] for (a) single-QTL genome scan and (b) two-QTL genome scan are reported. LOD scores are indicated on the colour scale where, numbers to the left correspond to the upper triangle indicating two-locus epistasis and values to the right correspond to the lower triangle indicating the significance for a test of two versus one QTL.
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Figure 1: The graph was obtained by using outbred line cross data (domesticated and wild chicken intercross genotypic data with simulated phenotype), calculating genotype probabilities with the triM algorithm from the qtl.outbred interface and importing it directly to R/qtl where the genome scans were performed. LOD scores for Haley-Knott regression [6] for (a) single-QTL genome scan and (b) two-QTL genome scan are reported. LOD scores are indicated on the colour scale where, numbers to the left correspond to the upper triangle indicating two-locus epistasis and values to the right correspond to the lower triangle indicating the significance for a test of two versus one QTL.

Mentions: qtl.outbred has been extensively tested. Firstly, we established that the triM algorithm produce exactly the same genotype probabilities as R/qtl when inbred line cross data are used (i.e. line crosses of inbred mouse strains). Secondly, we used genotypic data from an outbred line cross between domesticated and wild chickens with a simulated phenotype. Genotype probabilities were calculated with the triM algorithm using qtl.outbred to interface it with R/qtl. The single- and two-QTL genome scan for this dataset is illustrated in Figure 1. The identified peaks correspond to where the QTL were simulated. Lastly, we calculated QTL genotype probabilities for the simulated chicken intercross using GridQTL. These genotype probabilities were imported in R/qtl, using the qtl.outbred interface, and the conducted QTL scan gave similar results to those reported in Figure 1.


qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software.

Nelson RM, Shen X, Carlborg O - BMC Res Notes (2011)

The graph was obtained by using outbred line cross data (domesticated and wild chicken intercross genotypic data with simulated phenotype), calculating genotype probabilities with the triM algorithm from the qtl.outbred interface and importing it directly to R/qtl where the genome scans were performed. LOD scores for Haley-Knott regression [6] for (a) single-QTL genome scan and (b) two-QTL genome scan are reported. LOD scores are indicated on the colour scale where, numbers to the left correspond to the upper triangle indicating two-locus epistasis and values to the right correspond to the lower triangle indicating the significance for a test of two versus one QTL.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117720&req=5

Figure 1: The graph was obtained by using outbred line cross data (domesticated and wild chicken intercross genotypic data with simulated phenotype), calculating genotype probabilities with the triM algorithm from the qtl.outbred interface and importing it directly to R/qtl where the genome scans were performed. LOD scores for Haley-Knott regression [6] for (a) single-QTL genome scan and (b) two-QTL genome scan are reported. LOD scores are indicated on the colour scale where, numbers to the left correspond to the upper triangle indicating two-locus epistasis and values to the right correspond to the lower triangle indicating the significance for a test of two versus one QTL.
Mentions: qtl.outbred has been extensively tested. Firstly, we established that the triM algorithm produce exactly the same genotype probabilities as R/qtl when inbred line cross data are used (i.e. line crosses of inbred mouse strains). Secondly, we used genotypic data from an outbred line cross between domesticated and wild chickens with a simulated phenotype. Genotype probabilities were calculated with the triM algorithm using qtl.outbred to interface it with R/qtl. The single- and two-QTL genome scan for this dataset is illustrated in Figure 1. The identified peaks correspond to where the QTL were simulated. Lastly, we calculated QTL genotype probabilities for the simulated chicken intercross using GridQTL. These genotype probabilities were imported in R/qtl, using the qtl.outbred interface, and the conducted QTL scan gave similar results to those reported in Figure 1.

Bottom Line: qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools.Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL. qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets.This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Breeding and Genetics, Swedish University of Agricultural Sciences, Box 7023, SE-75007 Uppsala, Sweden. Ronnie.Nelson@slu.se.

ABSTRACT

Background: qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl.

Findings: Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL.

Conclusion: qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

No MeSH data available.


Related in: MedlinePlus