title: Selecting informative traits for multivariate quantitative trait locus mapping helps to gain optimal power.
abstract: A major consideration in multitrait analysis is which traits should be jointly analyzed. As a common strategy, multitrait analysis is performed either on pairs of traits or on all of traits. To fully exploit the power of multitrait analysis, we propose variable selection to choose a subset of informative traits for multitrait quantitative trait locus (QTL) mapping. The proposed method is very useful for achieving optimal statistical power for QTL identification and for disclosing the most relevant traits. It is also a practical strategy to effectively take advantage of multitrait analysis when the number of traits under consideration is too large, making the usual multivariate analysis of all traits challenging. We study the impact of selection bias and the usage of permutation tests in the context of variable selection and develop a powerful implementation procedure of variable selection for genome scanning. We demonstrate the proposed method and selection procedure in a backcross population, using both simulated and real data. The extension to other experimental mapping populations is straightforward.
authors: Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, Australian Capital Territory 0200, Australia.
authors: Riyan
authors: R
authors: Cheng
authors: Justin
authors: J
authors: Borevitz
authors: R W
authors: RW
authors: Doerge
mesh_terms: D017360:Arabidopsis / Q000235:genetics; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data; D016000:Cluster Analysis; D017343:Genes, Plant; D005819:Genetic Markers; D008957:Models, Genetic; D015999:Multivariate Analysis; D010641:Phenotype; D025341:Principal Component Analysis; D040641:Quantitative Trait Loci; D015983:Selection Bias
chemical_list: D005819:Genetic Markers
keywords: multitrait mapping; quantitative trait locus (QTL); statistical power; variable selection
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