Project 1: The Mouse Brain Library Project 2: Internet Microscopy (iScope) Project 3: Neurocartographer and Segmentation of the MBL Project 4: The Neurogenetics Tool Box


















Principal Investigator/Program Director Williams, Robert W.


Problems and issues addressed

Many genetic traits of medical and economic importance are complex quantitative traits. Such traits are usually affected by numerous genetic loci and also have significant environmental or nongenetic components. A complex trait is typically measured as a numerical value rather than a categorical phenotype. In recent years, with the development of numerous easily typed genetic markers based on DNA sequence polymorphisms, it has finally become practical to identify many of the individual loci that contribute to quantitative traits in mammalian populations (Lander and Botstein 1989) . In the introduction to this application, we reviewed the impact that complex trait analysis (also known as QTL analysis) is now having on structural, functional, and behavioral analysis of the CNS. In fact, in mice, a strong case can now be made that complex trait analysis is proving to be as important in the rapid advances in neurogenetics as is the analysis of transgenic, knockout, and mutant lines. In this section of the application, we provide a much more comprehensive bioinformatic and technical review of QTL mapping and the programs and utilities now used in this rapidly expanding field. We demonstrate that the work we have carried out up to the present meets and exceeds the standards of a Phase I Neuroinformatics Project. We explain how, with a relatively modest investment of funds, this project will significantly enhance the analysis of complex traits of the mouse CNS. The PPG should put QTL analysis into a new bioinformatic sphere in which collaboration becomes the new norm.

QTL analysis is a highly specialized area of research that may be unfamiliar even to readers with a strong genetics background. Recent papers and a tutorial by the PI (KM) and the coPI (RW) are included in the appendix and are also available online at <> and <>.

The Human Genome Project has provided extensive DNA sequence information for genomes of humans and model organisms. In the near future, this information is expected to be far more comprehensive and nearly complete. However, connecting this sequence information with traits of medical and economic importance will remain a challenge for many years. In many cases, the first step for connecting traits with sequence information will be QTL mapping, both in humans and in mice. QTL mapping can enumerate genes contributing to a trait, indicate an approximate chromosomal location for each gene, and estimate their relative importance. In this sense, QTL mapping is a classic forward genetic approach that starts with phenotypes and particular biological problems and then searches for genes that contribute to or modulate variance in those phenotypes. This approach is a compelling advantage for neuroscientists, who almost invariably become interested in the genetics of a specific phenomenon or mechanism because they have spent years studying itthe migration of neurons, modulation of cell proliferation and cell death, the control of receptor density, or even the source of emotionality. Traits like these can be systematically explored or genetically dissected using QTL methods (Williams 1998, 2000).

The feasibility of QTL mapping and its potential importance have stimulated theoretical work on mapping methods and software development to implement those methods. This work has included methods designed for human populations, where pedigree methods are necessary, and those designed for experimental organisms, where inbred lines are available. Although QTL mapping is now feasible, it remains beyond the reach of many neuroscientists because it requires specialized statistical methods and data sets consisting of tens or hundreds of thousands of genotypes derived from large crosses. Current mapping software makes many statistical methods available, but often in forms difficult for biologists to use. The required genotyping, which is expensive and time-consuming, is an even more serious deterrent. For example, as part of the PPG, our Genotyping and Mouse Colony Core will generate nearly 400,000 genotypes, a number far beyond the reach of most R01-supported laboratories. The Neurogenetics Tool Box, in combination with mapping resources and the huge collection of images and phenotypes generated in the other three projects, will substantially reduce these barriers and allow neuroscientists to exploit the power of QTL detection and mapping. (See the Introduction for specific examples.)