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BioMed Central
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BMC Bioinformatics
Open Access
Software
SimHap GUI: An intuitive graphical user interface for genetic
association analysis
Kim W Carter*1,4, Pamela A McCaskie2,3 and Lyle J Palmer3
Address: 1Western Australian Institute for Medical Research and UWA Centre for Medical Research, University of Western Australia, Perth,
Australia, 2School of Mathematics and Statistics, University of Western Australia, Perth, Australia, 3Centre for Genetic Epidemiology and
Biostatistics, University of Western Australia, Perth, Australia and 4Telethon Institute for Child Health Research, UWA Centre for Child Health
Research, University of Western Australia, 100 Roberts Rd, Subiaco, Western Australia 6008, Australia
Email: Kim W Carter* - kcarter@ichr.uwa.edu.au; Pamela A McCaskie - pmccask@cyllene.uwa.edu.au; Lyle J Palmer - lyle@cyllene.uwa.edu.au
* Corresponding author
Abstract
Background: 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.
Results: 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.
Conclusion: 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.
Background
While the growth in the volume of genetic data available
has led to many new discoveries, it is becoming increas-
ingly important to find ways in which to easily analyse
large of volumes of data. This is certainly the case with
genetic association studies, where high-throughput geno-
typing technologies have brought about the potential for
hundreds of thousands of data points per individual sub-
ject [1].
A graphical user interface (GUI) is still a rare feature
amongst currently available genetic analysis packages,
particularly those used to analyse single nucleotide poly-
morphisms (SNPs) or haplotypes. A well designed user
Published: 25 December 2008
BMC Bioinformatics 2008, 9:557 doi:10.1186/1471-2105-9-557
Received: 29 September 2008
Accepted: 25 December 2008
This article is available from: http://www.biomedcentral.com/1471-2105/9/557
© 2008 Carter et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
BMC Bioinformatics 2008, 9:557 http://www.biomedcentral.com/1471-2105/9/557
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interface would allow users without a comprehensive
knowledge of statistical modelling or command line oper-
ation to perform complex analyses.
Commercially available statistics software packages, such
as SPSS (SPSS Inc., 2008) and Stata (StataCorp. 2008),
may be useful, but are not specifically designed to analyse
genetic data, requiring sophisticated prior knowledge for
the end-user. Another major annoyance is the lack of inte-
gration between statistical and analytical packages [2],
often with one program required for epidemiological
analysis, a separate program for SNP analysis, and a third
used for haplotype analysis.
SimHap [3] is a statistical analysis package for genetic asso-
ciation testing, available in R [4], which amongst other
features, infers haplotypes for unrelated individuals with
unknown phase. Although various programs currently
exist for haplotype analysis, SimHap is unique in a
number of ways. It uses a multiple-imputation (MI) based
approach to test for association, which incorporates infor-
mation about uncertainty around inferred haplotypes.
This approach uses current expectation maximisation
(EM) methods for the estimation of haplotype frequen-
cies from unphased genotype data [5]. To utilize the pos-
terior distribution of diplotype (a haplotype pair)
probabilities, the MI approach of Rubin [6] was imple-
mented, where a series of "complete" data sets are gener-
ated containing all data from the original set as well as
additional dummy variables for each haplotype, the val-
ues of which indicate the number of copies of that haplo-
type observed in an individual's diplotype (0, 1 or 2). For
individuals with known phase (only one diplotype), the
values for these haplotype variables remain constant for
each of the generated data sets. For individuals with
ambiguous phase, their haplotype values will be sampled
from their predictive distribution, containing only those
diplotypes consistent with their genotypes. This is a novel
approach that provides an empirical distribution of the
haplotypic effects and their significance levels.
We have developed SimHap GUI as an intuitive graphical
tool for conducting genetic association analysis. At its
core, SimHap GUI utilises the SimHap R package and the
R statistical language. SimHap GUI is a novel custom-
designed integrated tool for conducting epidemiological,
single SNP and haplotype-based association analyses
within a single application, and provides a free alternative
to commercially available statistics packages.
Results and discussion
Implementation
SimHap GUI is written in Java (requires Java 1.5+) and
will operate on platforms where Java is available. This tool
has been successfully tested on Windows, Linux and
MacOS operating systems. SimHap GUI requires an
installation of the R statistics lanuguage (2.4.0+) and an
installation of the SimHap R package. This tool runs opti-
mally on a computer with a monitor resolution of 1024 ×
768, at least 128 Mb of RAM and a Pentium 4+ CPU. Sim-
Hap GUI has been successfully operated on datasets with
thousands of individuals, hundreds of phenotype varia-
bles, and thousands of SNPs. SimHap GUI is generally
only limited by the amount of system memory available
to Java.
The SimHap GUI interface is written in Java Swing, and
uses the Synthetica look-and-feel suite [7] to enhance the
useability and functionality of the interface (compared
with standard Swing interfaces). We have also utilised the
Swing Worker [8] library, which provides a mechanism
for providing updates to the user interface while running
long analytical tasks, such as performing thousands of
haplotype simulations. Both Synthetica and Swing
Worker are provided with the SimHap GUI installation.
SimHap GUI is provided as a single cross-platform
installer, using the IzPack [9] packaging system, which
provides a simple standardised graphical installer tool
that both technical and non-technical users will be com-
fortable with.
Graphical User Interface (GUI)
SimHap GUI allows the user to conduct association anal-
ysis of binary, quantitative, longitudinal and survival
(right-censored) outcomes using phenotypic data, and
genetic SNP data and haplotype data, in unrelated indi-
viduals.
One key feature of SimHap GUI is the workflow interface,
which guides the user through each logical step of the ana-
lytical process. This workflow concept is central to provid-
ing an intuitive user interface accessible to both novice
and advanced users.
The user initially selects a standard comma separated
value (CSV) file containing phenotypic information for a
set of individuals (one row of data per person), as can be
obtained from most spreadsheet and statistics software.
The user also selects a CSV file containing genotypes for a
series of SNP markers for the same individuals (not
required for non-genetic modelling), and selects the char-
acter(s) signifying missing data in the input files. SimHap
GUI examines the input files to ensure correct formatting,
completeness, and the correct corresponding individual
identifier between phenotype and genotype files. Geno-
type files are examined to ensure biallelic SNPs are input,
where the user is given the option to remove multi-allelic
markers. Once data checking is complete, the user can
choose to perform epidemiological modelling (without
genetic markers), single SNP association analysis, or hap-
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lotype association analysis. Users are guided through each
of these analytical tasks in a straight-forward series of
steps, with a standardised model building screen central
to each of the analysis types.
Figure 1 is an example of the model building screen for a
single SNP analysis with a quantitative outcome using
SimHap GUI. At the top of the screen, hdl (cholesterol)
has been selected as the outcome of interest, with the out-
come normally distributed (Untransformed). Log base 10
and natural log of the outcome are available to transform
non-Normally distributed outcomes. In the MAIN
EFFECTS section are the available and selected covariates
for this model, namely sex, age, bmi and smoke. Covariates
can also be added as squared or cubic terms, logged (base
10 or natural log), and as factors (for categorical terms). In
the GENOTYPES section are the available and selected
SNPs to be analysed in the model. SNP covariates are
denoted with the S_ prefix, while the _add, _dom and _rec
terms refer to analysing the SNP under an additive, dom-
inant or recessive genetic model. SNPs can also be ana-
lysed under a codominant model by adding the SNP as a
factor. In the INTERACTIONS section are available and
selected covariate terms to be analysed for statistical inter-
actions; in this case, an interaction between sex and SNP_1
under a codominant model. Additional files 1, 2, 3, 4, 5,
6, 7, 8 provide a graphical representation of each of the
phases of analysis for an example single SNP analysis. The
SimHap GUI software manual also provides a detailed
description of the analysis process.
Case Studies
SimHap GUI, and its earlier Beta 1 and Beta 2.1 releases,
have been extensively utilised in a range of genetics
projects recently published.
In the area of cancer research, SimHap GUI has been used
in studies such as Sak et al [10], to examine the association
Example SimHap GUI model building interfaceFigure 1
Example SimHap GUI model building interface.
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between polymorphisms in the XPC gene and bladder
cancer susceptibility. Choudhury et al [11] also examined
haplotypes of DNA repair proteins to find genetic variants
that may modulate predisposition to bladder cancer.
SimHap GUI has been used extensively in the field of car-
diovascular disease genetics. Several studies has used this
tool to examine SNP and haplotype effects of genes
related to abdominal aortic aneurysm [12-14]. Studies by
both Horne et al [15] and McCaskie et al [16] have used
SimHap GUI to investigate the association between
genetic variation in the cholesteryl ester transfer protein
gene and cardiovascular disease. SimHap GUI has also
been used to investigate SNP and haplotype associations
with metabolic syndrome [17-20] and atherosclerosis
[21-24] related outcomes.
In the area of genetic epidemiology related to the Mende-
lian Randomization (MR) technique, a number of groups
have utilised SimHap GUI. Brunner and colleagues [25]
used SimHap GUI to generate haplotypes for three tagging
polymorphisms from the C-reactive protein (CRP) gene in
a study of 5,274 men and women. Studies by Lawlor et al
[26] and Kivimaki et al [27] similarly this software for
analysis of CRP mutations using MR.
Other diverse studies include the use of SimHap GUI to
investigate genetic influences of the melanocortin 1 recep-
tor with sensitivity to photochemotherapy [28], polymor-
phisms within the macrophage migration inhibitory
factor with relation to acute lung injury in patients with
sepsis [29], associations between cytokine polymor-
phisms and outcomes after renal transplantation [30],
and genetic predictors for the development of microalbu-
minuria in children with type 1 diabetes [31].
The wide range of example publications described here
highlights the significance of the SimHap GUI software
providing an easy-to-use powerful interface for both nov-
ice and advanced genetic association analyses.
GUI versus R package
One of the critical distinctions to make with the SimHap
GUI software is the difference between the SimHap R pack-
age, and the Java based interface described in this manu-
script. The backend SimHap R package simply provides the
statistical operations to conduct particular analytical
tasks, with the onus on the user to have technical knowl-
edge of the statistical methods being employed and exper-
tise with the command line interface of the R language.
Users who are not professional statisticians may be dis-
couraged by the difficulty of operating under a command-
line interface.
The SimHap GUI interface provides the functionality,
accessibility and the guided analytical approach that can-
not be found in the command line package. The user
interface is designed around the premise of a workflow
analysis model, which mimics the logical analytical proc-
esses required to conduct a particular statistical test. This
user-friendly, intuitive interface has been designed to sat-
isfy the needs of both the technical and non-technical sta-
tistical user, and does not require sophisticated
informatics knowledge to operate. Using the novel model
building interface, users can perform tasks ranging from
simple univariate linear modelling, through to more
sophisticated tasks such as multivariate modelling of lon-
gitudinal outcomes with gene:gene and gene:environ-
ment interactions. A standardised interface is provide for
users to conduct epidemiological (no genetics factors),
single SNP and haplotype association analyses.
Features of SimHap GUI that are not provided in the Sim-
Hap R package include: an intuitive GUI for model build-
ing and guiding the overall analysis process; sophisticated
data checking of phenotype and genotype data; automatic
conversion of data for single SNP and haplotype associa-
tion analysis; automatic calculation of allele frequencies
and genotype distribution; quantile-quantile plotting for
Normality of quantitative traits; and real-time estimation
of the haplotype imputation simulation progress. Sim-
Hap GUI implements all of the functions from the Sim-
Hap R package.
Conclusion
In summary, SimHap GUI provides a cross-platform, intu-
itive and integrated interface for conducting a range of
genetic and non-genetic association analyses.
Availability and requirements
- Project name: SimHap GUI
- Project home page: http://www.genepi.org.au/simhap
- Operating system(s): Platform independent (tested on
Windows, Linux and MacOS)
- Programming language: Java
- Other requirements: Java 1.5+; R 2.4.0+ (available from
http://www.r-project.org/); SimHap R package from
CRAN (available from http://cran.r-project.org/web/pack
ages/SimHap/index.html)
- Licence: Free for non-commercial use
Authors' contributions
KWC designed and developed the Java GUI interface.
PAM assisted with integration of statistical methods and
aided with design of the GUI. LJP supervised the design
and coordinated the development of the software.
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Additional material
Acknowledgements
KWC was supported by the Australian Research Council Discovery
Project DP0663247. This work was supported by the National Health and
Medical Research Council of Australia Project Grant 404009.
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Additional file 1
SimHap GUI file selection screen. This screenshot shows the selection of
phenotype and genotype CSV files for analysis in SimHap GUI.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-557-S1.png]
Additional file 2
SimHap GUI input parameter selection screen. Following selection of
input files, this screenshot shows the user specifying input parameters, and
a summary of the input data file characteristics.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-557-S2.png]
Additional file 3
SimHap GUI major allele selection screen. After the user has selected to
perform a 'single SNP' analysis, the user can specify the major allele for
polymorphism in the input genotype file (as illustrated in this screenshot).
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-557-S3.png]
Additional file 4
SimHap GUI normality plots. This screenshot shows the user checking
whether quantitative variables to be analysed are normally distributed.
This screen option is available when the user is ready to select a particular
type of outcome (binary, quantitative, longitudinal and right-censored)
for analysis.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-557-S4.png]
Additional file 5
SimHap GUI model building screen for single SNP analysis. This
screenshot shows the model building screen in SimHap GUI, where the
user has selected to analyse a quantitative outcome (HDL), and has
selected various covariates (SEX, AGE, BMI, SMOKE) and a polymor-
phism of interest (SNP1).
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-557-S5.png]
Additional file 6
SimHap GUI model parameters. This screenshot shows the display pre-
sented after the model building screen, where the user can specify addi-
tional subset parameters, and other statistical parameters.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-557-S6.png]
Additional file 7
SimHap GUI results summary. After the user has built their desired sta-
tistical model, SimHap GUI runs the analysis, and the summary results
are presented as illustrated in this screenshot. Statistically significant
results are highlighted in red for easy identification.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-557-S7.png]
Additional file 8
SimHap GUI detailed results summary. The screenshot shows the
detailed statistical information provided, in addition to the summary sta-
tistics described in the previous figure. For example, marginal means by
genotype group are provided in this detailed summary.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2105-9-557-S8.png]
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