Next generation analytic tools for large scale genetic epidemiology studies of complex diseases
✍ Scribed by Leah E. Mechanic; Huann-Sheng Chen; Christopher I. Amos; Nilanjan Chatterjee; Nancy J. Cox; Rao L. Divi; Ruzong Fan; Emily L. Harris; Kevin Jacobs; Peter Kraft; Suzanne M. Leal; Kimberly McAllister; Jason H. Moore; Dina N. Paltoo; Michael A. Province; Erin M. Ramos; Marylyn D. Ritchie; Kathryn Roeder; Daniel J. Schaid; Matthew Stephens; Duncan C. Thomas; Clarice R. Weinberg; John S. Witte; Shunpu Zhang; Sebastian Zöllner; Eric J. Feuer; Elizabeth M. Gillanders
- Publisher
- John Wiley and Sons
- Year
- 2011
- Tongue
- English
- Weight
- 161 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0741-0395
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✦ Synopsis
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. Genet. Epidemiol. 36 : 22–35, 2012. © 2011 Wiley Periodicals, Inc.