[show abstract][hide abstract] ABSTRACT: Deep exome resequencing is a powerful approach for delineating patterns of protein-coding variation among genes, pathways, individuals and populations. We analyzed exome data from 2,440 individuals of European and African ancestry as part of the National Heart, Lung, and Blood Institute's Exome Project, the aim of which is to discover novel genes and mechanisms that contribute to heart, lung and blood disorders. Each exome was sequenced to a mean coverage of 116×, allowing detailed inferences about the population genomic patterns of both common variation and rare coding variation. We identifi ed more than 500,000 single nucleotide variations, the majority of which were novel and rare (76% of variants had a minor allele frequency of less than 0.1%), refl ecting the recent dramatic increase in the size of the human population. The unprecedented magnitude of this dataset allowed us to rigorously characterize the large variation in nucleotide diversity among genes (ranging from 0 to 1.32%), as well as the role of positive and purifying selection in shaping patterns of protein-coding variation and the diff erential signatures of population structure from rare and common variation. This dataset provides a framework for personal genomics and is an important resource that will allow inferences of broad importance to human evolution and health. Hardly a month goes by without a new published report of a patient's genome being used diagnostically for clinical management in a diverse spectrum of disease areas, including gastroenterology, nephrology, neurology and oncology. The impression is that clinical genomics is already becoming semi-routine. However, a large and complex set of non-technical barriers needs to be overcome before genomics can truly be integrated into the practice of medicine and made widely available for patient care. Through the use of case studies, my presentation will elucidate issues relating to the needs and requirements of the workforce, the legal and regulatory aspects of 'laboratory-developed tests' and insurance reimbursement for 'multi-analyte diagnostics' . The roles of the Food and Drug Administration, the Centers for Medicare & Medicaid Services and the College of American Pathologists will be highlighted.
beyond the genome 2011; 09/2011 · 10.30 Impact Factor
[show abstract][hide abstract] ABSTRACT: Currently, cancer therapy remains limited by a "one-size-fits-all" approach, whereby treatment decisions are based mainly on the clinical stage of disease, yet fail to reference the individual's underlying biology and its role driving malignancy. Identifying better personalized therapies for cancer treatment is hindered by the lack of high-quality "omics" data of sufficient size to produce meaningful results and the ability to integrate biomedical data from disparate technologies. Resolving these issues will help translation of therapies from research to clinic by helping clinicians develop patient-specific treatments based on the unique signatures of patient's tumor. Here we describe the Georgetown Database of Cancer (G-DOC), a Web platform that enables basic and clinical research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment. While several rich data repositories for high-dimensional research data exist in the public domain, most focus on a single-data type and do not support integration across multiple technologies. Currently, G-DOC contains data from more than 2500 breast cancer patients and 800 gastrointestinal cancer patients, G-DOC includes a broad collection of bioinformatics and systems biology tools for analysis and visualization of four major "omics" types: DNA, mRNA, microRNA, and metabolites. We believe that G-DOC will help facilitate systems medicine by providing identification of trends and patterns in integrated data sets and hence facilitate the use of better targeted therapies for cancer. A set of representative usage scenarios is provided to highlight the technical capabilities of this resource.
Neoplasia (New York, N.Y.) 09/2011; 13(9):771-83. · 5.48 Impact Factor