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Selection of Genes and Single Nucleotide Polymorphisms for Fine Mapping Starting From a Broad Linkage Region

Published online by Cambridge University Press:  21 February 2012

An Windelinckx
Affiliation:
Research Center for Exercise and Health, Department of Biomedical Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium.
Robert Vlietinck
Affiliation:
Clinical Genetics Section, Department of Human Genetics, Faculty of Medicine, Katholieke Universiteit Leuven, Leuven, Belgium.
Jeroen Aerssens
Affiliation:
Department of Translational Medical Research, Tibotec bvba, Generaal De Witte Laan, Mechelen, Belgium.
Gaston Beunen
Affiliation:
Research Center for Exercise and Health, Department of Biomedical Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium.
Martine A. I. Thomis*
Affiliation:
Research Center for Exercise and Health, Department of Biomedical Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium. Martine.Thomis@faber.kuleuven.be
*
*Address for correspondence: Martine Thomis, Research Center for Exercise and Health, Department of Biomedical Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium.

Abstract

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Fine mapping of linkage peaks is one of the great challenges facing researchers who try to identify genes and genetic variants responsible for the variation in a certain trait or complex disease. Once the trait is linked to a certain chromosomal region, most studies use a candidate gene approach followed by a selection of polymorphisms within these genes, either based on their possibility to be functional, or based on the linkage disequilibrium between adjacent markers. For both candidate gene selection and SNP selection, several approaches have been described, and different software tools are available. However, mastering all these information sources and choosing between the different approaches can be difficult and time-consuming. Therefore, this article lists several of these in silico procedures, and the authors describe an empirical two-step fine mapping approach, in which candidate genes are prioritized using a bioinformatics approach (ENDEAVOUR), and the top genes are chosen for further SNP selection with a linkage disequilibrium based method (Tagger). The authors present the different actions that were applied within this approach on two previously identified linkage regions for muscle strength. This resulted in the selection of 331 polymorphisms located in 112 different candidate genes out of an initial set of 23,300 SNPs.

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