Correlation Trait Loci (CTL) mapping: phenotype network inference subject to genotype

Danny Arends; Yang Li; Gudrun A. Brockmann; Ritsert C. Jansen; Robert W. Williams; Pjotr Prins

doi:10.21105/joss.00087

Correlation Trait Loci (CTL) mapping: phenotype network inference subject to genotype

Danny Arends, Yang Li, Gudrun A. Brockmann, Ritsert C. Jansen, Robert W. Williams, Pjotr Prins

Applied Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

The CTLmapping repository contains an implementation of the Correlation Trait Loci (CTL) algorithm first presented in (Danny Arends 2014). CTL mapping allows geneticists to pursue network inference by discovering genetic loci associated with correlation difference between phenotypes.
CTL mapping is complementary to the proven quantitative trait locus (QTL) mapping method which maps/associates each separately observed phenotype against genotype. CTL mapping, in contrast, associates correlation differences observed between two phenotypes at a time, subject to the genotype. In other words, QTL mapping treats phenotypes independently while CTL mapping connects phenotypes.

Original language	English
Pages (from-to)	87-87
Number of pages	1
Journal	The Journal of Open Source Software
Volume	1
Issue number	6
DOIs	https://doi.org/10.21105/joss.00087
Publication status	Published - 30 Oct 2016

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Cite this

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title = "Correlation Trait Loci (CTL) mapping: phenotype network inference subject to genotype",

abstract = "The CTLmapping repository contains an implementation of the Correlation Trait Loci (CTL) algorithm first presented in (Danny Arends 2014). CTL mapping allows geneticists to pursue network inference by discovering genetic loci associated with correlation difference between phenotypes.CTL mapping is complementary to the proven quantitative trait locus (QTL) mapping method which maps/associates each separately observed phenotype against genotype. CTL mapping, in contrast, associates correlation differences observed between two phenotypes at a time, subject to the genotype. In other words, QTL mapping treats phenotypes independently while CTL mapping connects phenotypes.",

author = "Danny Arends and Yang Li and Brockmann, {Gudrun A.} and Jansen, {Ritsert C.} and Williams, {Robert W.} and Pjotr Prins",

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AU - Arends, Danny

AU - Li, Yang

AU - Brockmann, Gudrun A.

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AU - Williams, Robert W.

AU - Prins, Pjotr

PY - 2016/10/30

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N2 - The CTLmapping repository contains an implementation of the Correlation Trait Loci (CTL) algorithm first presented in (Danny Arends 2014). CTL mapping allows geneticists to pursue network inference by discovering genetic loci associated with correlation difference between phenotypes.CTL mapping is complementary to the proven quantitative trait locus (QTL) mapping method which maps/associates each separately observed phenotype against genotype. CTL mapping, in contrast, associates correlation differences observed between two phenotypes at a time, subject to the genotype. In other words, QTL mapping treats phenotypes independently while CTL mapping connects phenotypes.

AB - The CTLmapping repository contains an implementation of the Correlation Trait Loci (CTL) algorithm first presented in (Danny Arends 2014). CTL mapping allows geneticists to pursue network inference by discovering genetic loci associated with correlation difference between phenotypes.CTL mapping is complementary to the proven quantitative trait locus (QTL) mapping method which maps/associates each separately observed phenotype against genotype. CTL mapping, in contrast, associates correlation differences observed between two phenotypes at a time, subject to the genotype. In other words, QTL mapping treats phenotypes independently while CTL mapping connects phenotypes.

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Correlation Trait Loci (CTL) mapping: phenotype network inference subject to genotype

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