The cerebral cortex plays an essential role in the integration of higher cognitive functions, while the striatum is intimately involved in the coordination of movement and executive function. A variety of developmental disorders have been associated with perturbations of these anatomic regions, including developmental dyslexia, autism, schizophrenia, Tourette's syndrome, and bipolar disorders to name but a few. This proposal seeks to map quantitative trait loci (QTL) that modulate absolute and relative magnitudes of glial and neuronal cell numbers and regional volume in both the neocortex and striatum of the mouse. Using a genetic reference population , classic QTL mapping techniques, bioinformatic haplotype mapping, and microarray analysis of gene expression profiles, a small but important set of genes that are responsible for some of the remarkable quantitative differences seen among inbred strains of mice will be mapped. The following questions will be addressed: (1) What is the phenotypic variation in the volume and number of neurons and glia in the neocortex and striatum? (2) Are these quantitative differences due to the segregation of QTLs that selectively modulate the genesis of neurons and glial cells? (3) Is variation in cell number and volume of different regions of the telencephalon controlled by separate QTLs-specifically are striatal volume and neuron number modulated by QTLs independent of those for cerebral cortex? To answer these questions, highly accurate, unbiased, and efficient estimates of cell number and volume will be assessed by stereology. These traits will be mapped using GeneNetwork, a public online resource for the investigation of systems genetics. The QTL intervals thus defined will be narrowed using haplotype maps derived from sequence databases of the two parent strains, and by the creation of a database of gene expression levels of the BXD recombinant inbred set at different stages of development. All data gathered from these experiments will be added to GeneNetwork and will be publicly accessible. The questions addressed in the proposal have important implications for a number of disease states, including a variety of developmental disorders. The extension of current transcriptome databases to encompass gene expression analysis at various stages of development will enable a systems genetic approach to understanding fundamental aspects of brain development.
