A QUANTITATIVE GENOMIC APPROACH TO QTL MAPPING
Theodore J. Morgan, KSU Division of Biology

PROJECT SUMMARY
The majority of traits of interest to the fields of medicine, agriculture, and evolutionary biology are expressed in a continuous manner, rather than displaying discrete phenotypic categories. These phenotypes are ultimately produced by the interplay between a complex genomic system and the influence of environmental factors on a trait. In the past, the analysis of quantitative phenotypes has been limited to the statistical description of the observed phenotypic variation. During the last two decades, advances in the development of molecular genetic technologies and experimental designs allow the localization of the causal variants controlling natural quantitative trait variation, while accomplishing this goal at very high resolution. However, relatively few cases exist where this level of detail has been described.

Given the strong foundation of quantitative genetic theory and the recent emergence of high-throughput genomic technologies the ability to rapidly genetically dissect complex phenotypes with the precision discussed above should be obtainable. In this proposal we will merge classical quantitative and population genetic theory with whole-genome microarray technology to develop a rapid method for identifying the genomic regions underlying complex trait phenotypes in Drosophila. Specifically during this project we will: 1) Develop methodology to genomically map the quantitative trait loci (QTL) underlying the complex trait of cold tolerance in Drosophila. Our strategy is to use high-density tiling microarrays to identify polymorphisms in a panel of replicated artificial selection lines. Quantitative genetic theory predicts that selection on quantitative traits will result in significant associations among markers around the selected sites, a phenomenon known as linkage disequilibrium (LD). By searching for the LD in the selected lines, we will identify the genomic regions (QTL) under positive selection between sets of lines. 2) Confirm the validity of the QTL search method developed in project #1 via direct sequence and molecular evolution analysis of the positively selected genomic regions. 3) Determine the causal targets of selection (i.e., the individual genes) within these genomic regions via a combination of deficiency and mutant analysis. When developed and validated, our methodologies will allow researchers to quickly identify the genomic regions relevant to a specific phenotype and rapidly direct their efforts on these QTL rather than spending time and resources laboriously searching for the QTL using conventional methods. The methodologies proposed for this project will therefore revolutionize the speed and ease of QTL mapping, and can easily be expanded to non-model systems via the use of mass parallel sequencing technologies.