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SimHap GUI: An intuitive graphical user interface for genetic association analysis

Kim W Carter14*, Pamela A McCaskie23 and Lyle J Palmer3

Author Affiliations

1 Western Australian Institute for Medical Research and UWA Centre for Medical Research, University of Western Australia, Perth, Australia

2 School of Mathematics and Statistics, University of Western Australia, Perth, Australia

3 Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Perth, Australia

4 Telethon Institute for Child Health Research, UWA Centre for Child Health Research, University of Western Australia, 100 Roberts Rd, Subiaco, Western Australia 6008, Australia

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BMC Bioinformatics 2008, 9:557  doi:10.1186/1471-2105-9-557

Published: 25 December 2008



Researchers wishing to conduct genetic association analysis involving single nucleotide polymorphisms (SNPs) or haplotypes are often confronted with the lack of user-friendly graphical analysis tools, requiring sophisticated statistical and informatics expertise to perform relatively straightforward tasks. Tools, such as the SimHap package for the R statistics language, provide the necessary statistical operations to conduct sophisticated genetic analysis, but lacks a graphical user interface that allows anyone but a professional statistician to effectively utilise the tool.


We have developed SimHap GUI, a cross-platform integrated graphical analysis tool for conducting epidemiological, single SNP and haplotype-based association analysis. SimHap GUI features a novel workflow interface that guides the user through each logical step of the analysis process, making it accessible to both novice and advanced users. This tool provides a seamless interface to the SimHap R package, while providing enhanced functionality such as sophisticated data checking, automated data conversion, and real-time estimations of haplotype simulation progress.


SimHap GUI provides a novel, easy-to-use, cross-platform solution for conducting a range of genetic and non-genetic association analyses. This provides a free alternative to commercial statistics packages that is specifically designed for genetic association analysis.