Clinically meaningful laboratory applications in the future will need to overcome significant barriers. Currently, there are not widely accepted methods and standards for performing genomic analysis using array platforms. There is also wide variation in the analytical and computational methods used in comparative genomic analysis. In addition, there is a paucity Inhibitors,research,lifescience,medical of standardized control biomaterials for use in analyses. Finally all of these quantitative

measures are highly sensitive to clinical specimen acquisition, preparation, and storage methods. Little comparative work on standards for controls and disease biospecimens has been done on establishing normal datasets for gene expression methods. Recently, a summary of these issues was addressed through a guidance document issued by the Centers for Disease Control and Prevention (CDC).17 The lack of highly annotated and fully characterized biospecimens with longitudinal phenotypic and demographic information remains a significant barrier for all of translational research Inhibitors,research,lifescience,medical in personalized medicine, but is most notable in large-scale genomic analyses.18 The application of the various genomic technology platforms has led to transformative Inhibitors,research,lifescience,medical research in population genetics.

Over the last several years, population-based research studies, such as the Framingham Heart Study, have enabled large-scale genomic analyses from clinical resources. Collectively, these genome-wide association http://www.selleckchem.com/products/H-89-dihydrochloride.html studies (GWAS), have enabled cross-study analyses from

Inhibitors,research,lifescience,medical publicly available databases known as dbGAP (database of genotype and phenotype).19 Over the past several years, hundreds of new GWAS results have yielded insights into multigene effects to a wide variety of human Inhibitors,research,lifescience,medical diseases and conditions. Many of these new mutations are identified in noncoding regions. Collectively, the discovery of these new associations is prompting more hypothesis generation about disease pathways than generating platforms for new diagnostics and therapeutics. These public resources are proving to be useful discovery resources for various disease areas, such as enough psychiatry, enabling consortia of investigators to use statistical analytic methods to map genetic architecture of common disorders.20 Information technologies in health care and impact on personalized medicine A key infrastructure needed to establish a medical practice environment for individualized decision making is a robust and facile information technology capability. The reasons for this are the dependency on key attributes about the patient’s health status, detailed data needs for phenotypic characteristics, and the complexity of the types of analytical data and decision algorithms that will be used to support more precise, preferred, and predictive health outcomes for the patient.