Title | Next generation analytic tools for large scale genetic epidemiology studies of complex diseases. |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Mechanic, LE, Chen, H-S, Amos, CI, Chatterjee, N, Cox, NJ, Divi, RL, Fan, R, Harris, EL, Jacobs, K, Kraft, P, Leal, SM, McAllister, K, Moore, JH, Paltoo, DN, Province, MA, Ramos, EM, Ritchie, MD, Roeder, K, Schaid, DJ, Stephens, M, Thomas, DC, Weinberg, CR, Witte, JS, Zhang, S, Zöllner, S, Feuer, EJ, Gillanders, EM |
Journal | Genet Epidemiol |
Volume | 36 |
Issue | 1 |
Pagination | 22-35 |
Date Published | 2012 Jan |
ISSN | 1098-2272 |
Keywords | Data Mining, Gene-Environment Interaction, Genetic Variation, Genome-Wide Association Study, Humans, Molecular Epidemiology, National Institutes of Health (U.S.), Neoplasms, Phenotype, United States |
Abstract | Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized. |
DOI | 10.1002/gepi.20652 |
Alternate Journal | Genet. Epidemiol. |
PubMed ID | 22147673 |
PubMed Central ID | PMC3368075 |
Grant List | P20 GM103534 / GM / NIGMS NIH HHS / United States U54 HG006493 / HG / NHGRI NIH HHS / United States RC2 HL102926 / HL / NHLBI NIH HHS / United States P30 CA023108 / CA / NCI NIH HHS / United States R01 ES019876 / ES / NIEHS NIH HHS / United States U54 HG006493-01 / HG / NHGRI NIH HHS / United States R01 LM010098 / LM / NLM NIH HHS / United States R01 AI059694 / AI / NIAID NIH HHS / United States R01 LM009012 / LM / NLM NIH HHS / United States UM1 HG006493 / HG / NHGRI NIH HHS / United States U01 AG023746 / AG / NIA NIH HHS / United States RC2 HL102926-01 / HL / NHLBI NIH HHS / United States |