Journal
GENETIC EPIDEMIOLOGY
Volume 36, Issue 1, Pages 22-35Publisher
WILEY
DOI: 10.1002/gepi.20652
Keywords
gene-gene interactions; gene-environment interactions; rare variants; next generation sequencing; complex phenotypes; simulations; computational resources
Funding
- NCI NIH HHS [P30 CA023108] Funding Source: Medline
- NHGRI NIH HHS [UM1 HG006493, U54 HG006493-01, U54 HG006493] Funding Source: Medline
- NHLBI NIH HHS [RC2 HL102926, RC2 HL102926-01] Funding Source: Medline
- NIAID NIH HHS [R01 AI059694] Funding Source: Medline
- NIA NIH HHS [U01 AG023746] Funding Source: Medline
- NIEHS NIH HHS [R01 ES019876] Funding Source: Medline
- NIGMS NIH HHS [R01 GM065450, P20 GM103534] Funding Source: Medline
- NLM NIH HHS [R01 LM009012, R01 LM010098] Funding Source: Medline
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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. Genet. Epidemiol. 36 : 22-35, 2012. (C) 2011 Wiley Periodicals, Inc.
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