A model selection approach for the identification of quantitative trait loci in experimental crosses

By Karl W. Broman, Terence P. Speed. 2002

198 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f4702fd95388eed7269e1da8c0377206dd37eb90

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In Genetics

In Plant Science

Citations in the corpus

A SIMPLE BAYESIAN APPROACH TO MULTIPLE CHANGE-POINTS (2011, methodology): “Earlier, Birgé and Massart (2001), Broman and Speed (2002), and Lavielle (2005) have used penalized likelihood methods that involve a shrinkage-type parameter to be chosen by the user, e.g., by cross validation.”
Genomic Selection for Crop Improvement (2017, unknown): “The challenge of QTL analysis is the selection of the appropriate statistical model to identify QTL and estimate their eff ects (Broman and Speed, 2002).”
Extended Bayesian information criteria for model selection with large model spaces (2008, methodology): “This phenomenon has been observed by Broman & Speed (2002), Siegmund (2004) and Bogdan et al. (2004), in their use of bic for quantitative trait loci mapping, and will also be shown later in this article.”
Inclusive composite interval mapping of quantitative trait genes. (2009, unknown): “A global search reveals epistatic interaction between QTL for early growth in the chicken.”
Selecting traits that explain species–environment relationships: a generalized linear mixed model approach (2013, unknown): “They are also an important ingredient of conservation planning and management (Carroll et al. 2001, Johnson et al. 2004, Raxworthy et al. 2003).”
A NOTE ON THE LASSO AND RELATED PROCEDURES IN MODEL SELECTION (2006, methodology): “See, for example, Rao and Wu (1989), Shao (1997) and Broman and Speed (2002) for some studies on the consistent variable selection problem. It is of interest to investigate the consistency properties of the Lasso and related methods in terms of variable selection as they are often used for this purp”
R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations (2018, unknown): “R/qtl1 includes a quite comprehensive set of QTL mapping methods, including multiple-QTL exploration and model selection (Broman and Speed 2002; Manichaikul et al. 2009; Arends et al. 2010), as well as extensive visualization and data diagnostics tools (Broman and Sen 2009).”
Genome-wide genetic association of complex traits in heterogeneous stock mice (2006, methodology): “The extensive repertoire of methods developed for whole-genome analysis of a classical intercros”