Abstract
Quantitative trait loci (QTL) mapping is a popular statistical method that is often used in agricultural applications to identify genomic regions associated with phenotypic traits of interest. In its most common form, a QTL analysis tests one phenotypic trait at a time using a variety of research hypotheses that depend on the application. When multiple traits are available, there are considerable benefits to analyzing subsets of biologically related traits in a multipletrait QTL mapping framework. Determining the most informative subset(s) of traits is the critical challenge that we address in this work. We present our approach, as well as simulations that demonstrate the performance. We also discuss an application of our approach as applied to an Arabidopsis thaliana data set.
Keywords
quantitative trait loci (QTL), sparse principal component analysis (sparse PCA), variable selection, ionomic phenotype mapping
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Achberger, Tilman; Fleet, James C.; Salt, David E.; and Doerge, R. W.
(2010).
"INTRODUCTION TO SELECTING SUBSETS OF TRAITS FOR QUANTITATIVE TRAIT LOCI ANALYSIS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1071
INTRODUCTION TO SELECTING SUBSETS OF TRAITS FOR QUANTITATIVE TRAIT LOCI ANALYSIS
Quantitative trait loci (QTL) mapping is a popular statistical method that is often used in agricultural applications to identify genomic regions associated with phenotypic traits of interest. In its most common form, a QTL analysis tests one phenotypic trait at a time using a variety of research hypotheses that depend on the application. When multiple traits are available, there are considerable benefits to analyzing subsets of biologically related traits in a multipletrait QTL mapping framework. Determining the most informative subset(s) of traits is the critical challenge that we address in this work. We present our approach, as well as simulations that demonstrate the performance. We also discuss an application of our approach as applied to an Arabidopsis thaliana data set.
