NRI Award #2005-35300-15469
PI: Zhao-Bang Zeng
Departments of Statistics and Genetics, and Bioinformatics Research Center
North Carolina State University, Raleigh, NC 27695-7566
E-mail: zeng@stat.ncsu.edu
Telephone: 919-515-1942
Website: http://statgen.ncsu.edu/~zeng/

OBJECTIVES (abstract presented at 2005 ASHS)

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Establishing a causal relationship between genotypes and phenotypes is of fundamental importance to our understanding of the genetic basis of quantitative traits and many practical applications including plant breeding. This relationship is traditionally estimated by mapping quantitative trait loci (QTL) in a designed experiment using genome-wide molecular markers. Recently, gene expression microarrays have been applied to these mapping populations to localize and map QTL that regulate the expression of genes (eQTL) in a whole genome and also to estimate the genetic effect networks that depict the causal relationships from eQTL (and their candidate genes) to mRNA transcript levels and to quantitative traits.

Statistically, there are many challenges to fully analyze the genetic information from this massive amount of data. The data could include genome-wide molecular marker information, genome-wide gene expression information in targeted tissues, and quantitative trait measurements in a number of individuals in a designed experiment.

Build on our previous research on QTL mapping analysis, we will develop statistical methods and computer programs to perform eQTL mapping analysis. Specifically, we will:

• Develop an efficient strategy and analysis procedures for microarrays gene expression QTL mapping analysis. The procedures will be adapted from composite interval mapping (for eQTL identification) and multiple interval mapping (for estimating interaction of eQTL on each gene expression profile).
• Extend multiple trait multiple interval mapping (MT-MIM) to gene expression data to study the genetic basis of co-regulation of gene expressions.
• Develop statistical methods to associate gene expression data to quantitative trait data based on a multiple regression approach.
• Upgrade the functions, capability and interface of QTL Cartographer and Windows QTL Cartographer. Maintain the interaction with users.

This eQTL analysis will provide us much more detailed information about the basis of gene expression regulation in targeted tissues and the genetic effect networks that cascade information from expression QTL (eQTL) to mRNA transcripts to quantitative traits. The implementation of the methods in QTL Cartographer and Windows QTL Cartographer will facilitate the usage and rapid applications of the methods in a variety of eQTL mapping experiments for the scientific community.

Download powerpoint presentations at 2005 ASHS meeting