Abstract #300973

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JSM 2003 Abstract #300973
Activity Number: 37
Type: Invited
Date/Time: Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #300973
Title: Dimension Reduction for Mapping mRNA Abundance as Quantitative Traits
Author(s): Brian S. Yandell*+
Companies: University of Wisconsin, Madison
Address: Dept. of Statistics, Madison, WI, 53706-1613,
Keywords: QTL ; gene mapping ; microarray gene expression ; mRNA ; hierarchical clustering ; principal components
Abstract:

Sophisticated gene mapping of mRNA abundance from microarray analysis can yield quantitative trait loci (QTL) that seem to control expression. Simply mapping of thousands of mRNA traits in a typical microarray experiment is computationally intensive and subject to high variance. In addition, some benefits emerge by using correlation among mRNA traits. We present two methods to reduce dimensionality for mapping gene expression--a blind method, principal components, and a sighted method, hierarchical clustering, seeded by disease relevant traits--to define new supertraits. We validate the principle by mapping the expression of metabolism genes in a population of F2-ob/ob mice between BTBR and C57BL/6J. We find that lipogenic and gluconeogenic mRNAs, known targets of insulin action, closely associate with insulin. Multiple interval mapping and Bayesian interval mapping reveal significant linkages to chromosome regions associated with Type 2 diabetes in this same mouse population. We show in addition that principal component analysis effectively reduces dimensions for mapping phenotypes comprised of mRNA abundance.

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