Literature DB >> 12073555

Use of randomization testing to detect multiple epistatic QTLs.

Orjan Carlborg1, Leif Andersson.   

Abstract

Here, we describe a randomization testing strategy for mapping interacting quantitative trait loci (QTLs). In a forward selection strategy, non-interacting QTLs and simultaneously mapped interacting QTL pairs are added to a total genetic model. Simultaneous mapping of epistatic QTLs increases the power of the mapping strategy by allowing detection of interacting QTL pairs where none of the QTL can be detected by their marginal additive and dominance effects. Randomization testing is used to derive empirical significance thresholds for every model selection step in the procedure. A simulation study was used to evaluate the statistical properties of the proposed randomization tests and for which types of epistasis simultaneous mapping of epistatic QTLs adds power. Least squares regression was used for QTL parameter estimation but any other QTL mapping method can be used. A genetic algorithm was used to search for interacting QTL pairs, which makes the proposed strategy feasible for single processor computers. We believe that this method will facilitate the evaluation of the importance at epistatic interaction among QTLs controlling multifactorial traits and disorders.

Mesh:

Year:  2002        PMID: 12073555     DOI: 10.1017/s001667230200558x

Source DB:  PubMed          Journal:  Genet Res        ISSN: 0016-6723            Impact factor:   1.588


  20 in total

1.  Model selection in binary trait locus mapping.

Authors:  Cynthia J Coffman; R W Doerge; Katy L Simonsen; Krista M Nichols; Christine K Duarte; Russell D Wolfinger; Lauren M McIntyre
Journal:  Genetics       Date:  2005-04-16       Impact factor: 4.562

2.  Simultaneous mapping of epistatic QTL in DU6i x DBA/2 mice.

Authors:  Orjan Carlborg; Gudrun A Brockmann; Chris S Haley
Journal:  Mamm Genome       Date:  2005-07       Impact factor: 2.957

Review 3.  Regression-based quantitative trait loci mapping: robust, efficient and effective.

Authors:  Sara A Knott
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2005-07-29       Impact factor: 6.237

4.  On locating multiple interacting quantitative trait loci in intercross designs.

Authors:  Andreas Baierl; Małgorzata Bogdan; Florian Frommlet; Andreas Futschik
Journal:  Genetics       Date:  2006-04-19       Impact factor: 4.562

5.  Mapping quantitative trait loci by an extension of the Haley-Knott regression method using estimating equations.

Authors:  Bjarke Feenstra; Ib M Skovgaard; Karl W Broman
Journal:  Genetics       Date:  2006-05-15       Impact factor: 4.562

6.  Statistical epistasis is a generic feature of gene regulatory networks.

Authors:  Arne B Gjuvsland; Ben J Hayes; Stig W Omholt; Orjan Carlborg
Journal:  Genetics       Date:  2006-10-08       Impact factor: 4.562

7.  Locating multiple interacting quantitative trait Loci using rank-based model selection.

Authors:  Małgorzata Zak; Andreas Baierl; Małgorzata Bogdan; Andreas Futschik
Journal:  Genetics       Date:  2007-05-16       Impact factor: 4.562

8.  Bayesian multiple quantitative trait loci mapping for complex traits using markers of the entire genome.

Authors:  Hanwen Huang; Chevonne D Eversley; David W Threadgill; Fei Zou
Journal:  Genetics       Date:  2007-05-04       Impact factor: 4.562

9.  Swift block-updating EM and pseudo-EM procedures for Bayesian shrinkage analysis of quantitative trait loci.

Authors:  Crispin M Mutshinda; Mikko J Sillanpää
Journal:  Theor Appl Genet       Date:  2012-07-24       Impact factor: 5.699

10.  A general model for multilocus epistatic interactions in case-control studies.

Authors:  Zhong Wang; Tian Liu; Zhenwu Lin; John Hegarty; Walter A Koltun; Rongling Wu
Journal:  PLoS One       Date:  2010-08-18       Impact factor: 3.240

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