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

Abstract

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: (i) identify opportunities, challenges and resource needs for the development and application of genetic simulation models; (ii) improve the integration of tools for modeling and analysis of simulated data; and (iii) 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.

Genetic Data Simulators and their Applications: An Overview

“…This program certifies and promotes genetic simulators that meet criteria in areas such as accessibility, documentation, application, and support. It aims to establish and encourage the enforcement of standards for the development of genetic simulators and can hopefully help improve the quality of genetic simulators [Huann‐Sheng Chen et al., ].…”

Presidential address: Six open questions to genetic epidemiologists

“…On the cheat sheet (Table 1), it may be suggested to always provide free data, even if these are just artificial data sets, to allow others to use and check the code on. Producing meaningful artificial data that is similar enough to real-life scenarios is extremely difficult, and simulation of data should be recognized as a science in itself (Chen et al, 2015;König et al, 2016). If data and or code provided by others is used, acknowledging that adequately is important.…”

GESDB: a platform of simulation resources for genetic epidemiology studies

“…A group consisting of population geneticists, genetic epidemiologists and computational scientists addressed several current and emerging challenges and opportunities in genetic simulation studies in the ‘Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases’ workshop held at the National Institutes of Health in Bethesda, Maryland on 11–12 March 2014 ( 1 ). One of the challenges that was addressed is that researchers may have difficulties in choosing an appropriate simulation tool from a large number of existing tools.…”

“…This could lead to unfair comparisons of the method with other methods. One of the solutions is to create benchmark simulation datasets with detailed documentation for the simulation procedures so that the datasets can become standards for method comparisons ( 1 , 4 ). The opportunities discussed by the group included the creation of a server for sharing genetic simulation data, identification of common datasets for method comparisons, and encouragement of making simulated datasets publicly available.…”