Arabidopsis 2010: Functional Genomics of Quantitative Traits: Expression Level Polymorphisms of QTLs Affecting Disease Resistance Pathways in Arabidopsis

University Of California-Davis, Davis CA

Investigators

Abstract

Arabidopsis 2010: Functional Genomics of Quantitative Traits. Quantitative differences in the expression of genes involved in disease resistance responses will be investigated using a functional genomics approach that involves a novel application of quantitative trait locus (QTL) analysis to microarray data. Regulatory QTLs controlling natural variation in induced gene expression patterns (i.e., expression level polymorphisms, ELPs) through QTL analysis of microarray data for ELPs from genetically segregating populations will be identified. Dissection of regulatory networks using genetic analysis of natural allelic variation will provide an efficient method for searching for regulatory loci at the systems biology level and avoids unnatural traumatic perturbations to gene regulation that are caused by extreme mutations. QTL dissection of natural variation is complementary to mutant analysis as it is likely to reveal different aspects of the regulatory network controlling disease resistance than mutant analysis because qualitatively inherited resistance genes do not account for all the aspects of complex pathways. This project aims to: 1) develop integrated molecular and statistical approaches for the dissection of quantitatively inherited traits, 2) determine if expression level polymorphisms involved in the variation of disease resistance pathways in Arabidopsis thaliana are due to regulatory QTLs, structural QTLs, or both, and 3) characterize individual genes at the molecular level that encode the regulatory QTLs. This will be accomplished by surveying accessions for natural variation in ELPs in response to induction of defense related pathways by a salicylic acid analog (dichloroisonicotinic acid) and jasmonic acid using Affymetrix chips for the preliminary global screen and spotted microarrays to confirm reproducible ELPs. Recombinant inbred lines derived from crosses between polymorphic accessions will then be phenotyped for ELPs using targeted DNA microarrays designed with novel applications of statistical methods. QTLs associated with ELPs will be mapped by employing an innovative application of established QTL mapping methodologies, including composite interval mapping and permutation thresholds. This approach will allow the identification of regulatory QTLs, a subset of which will be cloned using a combination of candidate gene and extreme allele approaches. Ultimately, these approaches will enable massively parallel QTL analysis for mRNA, protein and metabolite levels relative to the whole plant phenotypes. The data generated from this project will be available at (www.niblrrs.ucdavis.edu) and (www.genomics.purdue.edu). This project will also provide multidisciplinary training at the interface of quantitative and molecular genetics, statistics, and genomics for postdoctoral researchers, graduate students, undergraduate students, and high school students.