title: Multiple loci mapping via model-free variable selection.
abstract: Despite recent flourish of proposals on variable selection, genome-wide multiple loci mapping remains to be challenging. The majority of existing variable selection methods impose a model, and often the homoscedastic linear model, prior to selection. However, the true association between the phenotypical trait and the genetic markers is rarely known a priori, and the presence of epistatic interactions makes the association more complex than a linear relation. Model-free variable selection offers a useful alternative in this context, but the fact that the number of markers p often far exceeds the number of experimental units n renders all the existing model-free solutions that require n > p inapplicable. In this article, we examine a number of model-free variable selection methods for small-n-large-p regressions in the context of genome-wide multiple loci mapping. We propose and advocate a multivariate group-wise adaptive penalization solution, which requires no model prespecification and thus works for complex trait-marker association, and handles one variable at a time so that works for n < p. Effectiveness of the new method is demonstrated through both intensive simulations and a comprehensive real data analysis across 6100 gene expression traits.
authors.affiliation: Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, USA. wsun@bios.unc.edu
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
authors.forename: Lexin
authors.initials: L
authors.lastname: Li
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D056426:Genetic Loci / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics*; D015999:Multivariate Analysis
chemical_list: D005819:Genetic Markers