, 2013). Such technologies will make it possible to ascertain the specific segments of noncoding DNA that are utilized by each cell population and to connect specific disease-associated variants

to perturbations of specific types of neurons and glia. The recent success of genetic studies for highly polygenic brain disorders such as schizophrenia creates both a historical scientific opportunity and a formidable challenge for neurobiology. The opportunity inherent in having an initial molecular “parts list” for these disorders http://www.selleckchem.com/products/pci-32765.html is clear. However, the challenges are also substantial. Historically, neurobiologists have investigated gene function by making highly penetrant mutations in individual genes, studying their effects on isogenic backgrounds, often inbred laboratory mouse strains, and focusing on phenotypes that are outside the range of natural phenotypic variation. In this way, a great deal has been learned about some SB431542 order aspects of rare and often severe monogenic diseases, whether of the nervous system or of other organ systems (Shahbazian et al., 2002 and Peça et al., 2011). However, as described above, the genetic architecture of common polygenic diseases is quite different from either

the severe mutations of rare monogenic disorders or artificial mutations (such as knockouts) made in laboratory mice. The genetic architecture of common polygenic diseases involves natural polymorphisms, including regulatory variants, whose ultimate contribution to phenotype is just one piece of a larger puzzle; such variants segregate on genetic backgrounds that contain many other risk and protective factors. The resulting challenges have led some to suggest that biology should focus on the component of genetic architecture that derives from rare, protein-altering mutations that are assumed to have large effects (McClellan and King, 2010). We think that to do

so would miss the far larger scientific opportunity emerging Thiamine-diphosphate kinase from studies of polygenic disorders. Indeed to do so might miss the most important opportunities to address common serious diseases. We recognize, however, that successful neurobiological analysis of polygenic disorders will require relatively new technologies and experimental approaches at scales that have not been typical for neuroscience. For example, the interrogation of large numbers of disease-associated genes and an even larger number of allelic variants within them, both individually and likely in combination, will require new approaches to living model systems. It would neither be practical nor likely given the modest penetrance of relevant alleles to make thousands of transgenic mice.