4.3 Article Proceedings Paper

Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases

期刊

GENETIC EPIDEMIOLOGY
卷 39, 期 1, 页码 11-19

出版社

WILEY
DOI: 10.1002/gepi.21870

关键词

genetic simulation; rare variants; next-generation sequencing; complex phenotypes; computational resources

资金

  1. Surveillance Research Program (SRP) of the Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute (NCI)
  2. Direct For Computer & Info Scie & Enginr
  3. Div Of Information & Intelligent Systems [1318386] Funding Source: National Science Foundation

向作者/读者索取更多资源

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.

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