With recent explosion in the diversity and volume of genetic data generated, an increasing number of genetic simulation programs have been developed to aid the development of statistical methods for the analysis of such data in all genetic-related disciplines (Escalona et al., 2016; Hoban et al., 2012; Peng et al., 2015; Ritchie and Bush, 2010). Despite the recognized importance of genetic simulation tools, it is often difficult to discover and select the right simulation tool for a particular study due to differences in the type of genetic data of interest, simulation methods, features, terminologies and assumptions (Mechanic et al., 2012), and lack of external evaluations of the usability and maintenance status of published simulators. To address these issues, we want to encourage use of the Genetic Simulation Resources (GSR) online catalog and search tool (https://popmodels.cancercontrol.cancer.gov/gsr/) and participation in the GSR Certification Program. GSR was created in 2013 to help researchers sort through and compare a large number of genetic simulation programs to identify the best tool for their research topic (Peng et al., 2013). The GSR catalog currently features more than 100 simulators that use various simulation techniques (coalescent and forward-time simulations are the most common methods), and with application areas such as linkage analysis, genome wide association and sequencing analyses. GSR records basic information, features of the simulation programs and lists typical applications of the software. After reviewing the software listed in GSR, we realized that the quality and usefulness of published simulation tools varied greatly due to inaccessible source code, lack of or incomplete documentation, difficulties in installation and execution, lack of support from authors and lack of programs maintenance (Peng et al., 2015). An approach to address questions about the quality and utility of tools was suggested in a workshop in 2013 (Chen et al., 2015), to evaluate genetic simulation tools based on a defined checklist of features that may benefit end users. Therefore, the GSR Certification Program was developed and implemented to appraise tools based on criteria in the categories of accessibility, documentation, application and support (https://popmodels.cancercontrol.cancer.gov/gsr/certification/). It is hoped that evaluating software based on these criteria will encourage improvements in reporting and documentation of simulation software tools. Moreover, the GSR certification criteria ensure that these programs are more readily findable and usable by the research community and consistent with the FAIR (Findability, Accessibility, Interoperability and Reusability) principles for scientific data management and stewardship (Wilkinson et al., 2016). The GSR Certification Program is overseen by the GSR Certification Committee, which currently consists of NIH staff and extramural researchers. The manuscript authors invite volunteers who wish to participate in the evaluation process to contact the corresponding author of this letter. The GSR Certification Committee is in process of reviewing all packages cataloged in GSR, beginning with newer software packages and packages that are considered actively maintained and broadly used in the scientific community. The review process consists of two stages; an initial review where a GSR moderator completes a questionnaire based on publicly available information (e.g. publication, web site, documentation, Pubmed citation, issue tracker), and a panel review during which three members of the GSR Certification Committee decide about award of any certificates (Accessibility, Documentation, Application and Support). Simulators with one or more GSR certificates are prominently listed on the GSR web site. Simulators which obtain all 4 GSR certificates are invited to place a GSR Certified tile on the homepages for their software. The GSR Certification Program provides a service to the community by evaluating genetic simulation software, with the ultimate goal of promoting the development and application of genetic simulation software. With Bioinformatics being the target journal for many simulation programs, it would be beneficial to authors, the journal and the scientific community if package authors are encouraged to register their simulation programs at the GSR where they are evaluated to conform to basic guidelines for accessibility, documentation, application and support for GSR certifications. We believe this policy could facilitate dissemination of software and ensure the quality and usability of genetic simulation software. The development and maintenance of GSR has been supported by contracts from the National Cancer Institute, National Institutes of Health [HHSN261201100558P] and [HHSN261201300002I]. Conflict of Interest: none declared.

SUMMARY: Many simulation methods and programs have been developed to simulate genetic data of the human genome. These data have been widely used, for example, to predict properties of populations retrospectively or prospectively according to mathematically intractable genetic models, and to assist the validation, statistical inference and power analysis of a variety of statistical models. However, owing to the differences in type of genetic data of interest, simulation methods, evolutionary features, input and output formats, terminologies and assumptions for different applications, choosing the right tool for a particular study can be a resource-intensive process that usually involves searching, downloading and testing many different simulation programs. Genetic Simulation Resources (GSR) is a website provided by the National Cancer Institute (NCI) that aims to help researchers compare and choose the appropriate simulation tools for their studies. This website allows authors of simulation software to register their applications and describe them with well-defined attributes, thus allowing site users to search and compare simulators according to specified features. AVAILABILITY: http://popmodels.cancercontrol.cancer.gov/gsr.

BACKGROUND: The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives. RESULTS: We developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600) generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306-315.]. Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data (case:control 1:2 and 1:4, 10 SNPs) in the context of non-interacting SNPs. CONCLUSION: PIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations.