Literature DB >> 19763936

A systematic strategy for the discovery of candidate genes responsible for phenotypic variation.

Paul Fisher1, Harry Noyes, Stephen Kemp, Robert Stevens, Andrew Brass.   

Abstract

It is increasingly common to combine genome-wide expression data with quantitative trait mapping data to aid in the search for sequence polymorphisms responsible for phenotypic variation. By joining these complex but different data types at the level of the biological pathway, we can take advantage of existing biological knowledge to systematically identify possible mechanisms of genotype-phenotype interaction. With the development of web services and workflows, this process can be made rapid and systematic. Our methodology was applied to a use case of resistance to African trypanosomiasis in mice. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://www.myexperiment.org/users/43/workflows .

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Year:  2009        PMID: 19763936     DOI: 10.1007/978-1-60761-247-6_18

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  3 in total

1.  Association genetics in Solanum tuberosum provides new insights into potato tuber bruising and enzymatic tissue discoloration.

Authors:  Claude Urbany; Benjamin Stich; Lysann Schmidt; Ludwig Simon; Hergen Berding; Holger Junghans; Karl-Heinz Niehoff; Alexander Braun; Eckhard Tacke; Hans-Rheinhardt Hofferbert; Jens Lübeck; Josef Strahwald; Christiane Gebhardt
Journal:  BMC Genomics       Date:  2011-01-05       Impact factor: 3.969

2.  The severity of retinal pathology in homozygous Crb1rd8/rd8 mice is dependent on additional genetic factors.

Authors:  Ulrich F O Luhmann; Livia S Carvalho; Sophia-Martha Kleine Holthaus; Jill A Cowing; Simon Greenaway; Colin J Chu; Philipp Herrmann; Alexander J Smith; Peter M G Munro; Paul Potter; James W B Bainbridge; Robin R Ali
Journal:  Hum Mol Genet       Date:  2014-08-21       Impact factor: 6.150

3.  Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.

Authors:  Steve O'Hagan; Joshua Knowles; Douglas B Kell
Journal:  PLoS One       Date:  2012-11-21       Impact factor: 3.240

  3 in total

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