The past few years have witnessed extraordinary advances in molecular genetic techniques and the accumulation of structural genomics information and resources in both human and model organisms. With the development of new technologies and the availability of resources like the sequence of eukaryotic genomes, problems of a previously unthinkable scope are now being routinely solved in neuroscience and many other areas of biomedical research. The results of these studies, in turn, are having, and will continue to have, profound impact on experimental approaches and designs for manipulating genes, the genome, and model organisms as a means of gaining insights into nervous system functioning and complex behavior. Methods in Genomic Neuroscience provides newcomers and experienced researchers with a reference guide for applying powerful, state-of-the-art molecular genetic techniques to the study of neural and behavioral systems. It thereby provides a foundation by which data on gene expression and function may be used to develop new therapeutic strategies for brain diseases. Authored by researchers in the forefront of genomic neuroscience, the book addresses state-of-the-art tools and technologies for global analysis of genes influencing the nervous system and its disorders. An emphasis is placed on massively parallel approaches for analyzing the avalanche of data that is being generated from the full genomic sequence of humans and model systems. Specific topics include human sequence variation, methods for discovering disease vulnerability genes, gene-environmental interaction, gene expression analysis using DNA microarrays, random mutagenesis, gene trap approaches for studying brain development, neural stem cells, gene targeting, and gene delivery.This may very well simplify things and may alleviate the need for further sequencing after linkage or association has been found. ... Technology to do so does not yet exist. ... Some forms of genetic interaction (principally when the mutants have equal and additive effects) are relatively easy to ... Once variation is detected, the problem will likely reduce to either scenario I or II, or some combination thereof.
|Title||:||Methods in Genomic Neuroscience|
|Author||:||Hemin R. Chin, Steven O. Moldin|
|Publisher||:||CRC Press - 2001-09-26|