Genetic simulation tools for post-genome wide association studies of complex diseases

Huann Sheng Chen, Carolyn M. Hutter, Leah E. Mechanic, Christopher I. Amos, Vineet Bafna, Elizabeth R. Hauser, Ryan D. Hernandez, Chun Li, David A. Liberles, Kimberly Mcallister, Jason H. Moore, Dina N. Paltoo, George J. Papanicolaou, Bo Peng, Marylyn D. Ritchie, Gabriel Rosenfeld, John S. Witte, Elizabeth M. Gillanders, Eric J. Feuer

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)11-19
Number of pages9
JournalGenetic Epidemiology
Issue number1
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Genetics(clinical)


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