Title: "Trait Selection for Quantitative Trait Loci Mapping via Sparse Principal Component Analysis"
Speaker: Tilman Achberger, Department of Statistics, Purdue University
Place: LILLY G126; November 16, 2010, Tuesday, 4:30pm


Identifying genetic determinants of complex traits (e.g., yield, disease resistance, etc.) is a fundamental challenge in genetics research. Historically, a powerful statistical procedure called quantitative trait loci (QTL) mapping has been used to investigate experimental populations for the purpose of finding genomic regions associated with phenotypes or quantitative traits. When multiple traits are available, there are considerable benefits to analyzing subsets of biologically related traits in a multiple-trait QTL mapping framework. Unfortunately, prior knowledge of which traits are biologically related is often incomplete or missing all-together. In such cases, there is a need for efficient statistical procedures that select groups of potentially related traits. In this presentation, a novel application of sparse principal component analysis is proposed to simultaneously estimate the unobserved latent variables associated with the co-variation in the quantitative traits, and select groups of traits for subsequent use in multiple-trait QTL mapping procedures. Simulation studies are presented to demonstrate the performance of the proposed procedures, and an application to an Arabidopsis thaliana recombinant inbred line population is discussed.

Associated Reading:

Buescher E, Achberger T, Amusan I, Giannini A, Ochsenfeld C, et al. (2010) Natural Genetic Variation in Selected Populations of Arabidopsis thaliana Is Associated with Ionomic Differences. PLoS ONE 5(6): e11081.

Shen H, Huang J. Z. (2008) Sparse Principal Component Analysis via Regularized Low Rank Matrix Approximation. Journal of Multivariate Analysis 99(6): 1015 - 1034.

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