UPDG: U tilities package for data analysis of P ooled D NA G WAS
© Ho et al; licensee BioMed Central Ltd. 2012
Received: 2 August 2011
Accepted: 17 January 2012
Published: 17 January 2012
Despite being a well-established strategy for cost reduction in disease gene mapping, pooled DNA association study is much less popular than the individual DNA approach. This situation is especially true for pooled DNA genomewide association study (GWAS), for which very few computer resources have been developed for its data analysis. This motivates the development of UPDG (U tilities package for data analysis of P ooled D NA G WAS).
UPDG represents a generalized framework for data analysis of pooled DNA GWAS with the integration of Unix/Linux shell operations, Perl programs and R scripts. With the input of raw intensity data from GWAS, UPDG performs the following tasks in a stepwise manner: raw data manipulation, correction for allelic preferential amplification, normalization, nested analysis of variance for genetic association testing, and summarization of analysis results. Detailed instructions, procedures and commands are provided in the comprehensive user manual describing the whole process from preliminary preparation of software installation to final outcome acquisition. An example dataset (input files and sample output files) is also included in the package so that users can easily familiarize themselves with the data file formats, working procedures and expected output. Therefore, UPDG is especially useful for users with some computer knowledge, but without a sophisticated programming background.
UPDG provides a free, simple and platform-independent one-stop service to scientists working on pooled DNA GWAS data analysis, but with less advanced programming knowledge. It is our vision and mission to reduce the hindrance for performing data analysis of pooled DNA GWAS through our contribution of UPDG. More importantly, we hope to promote the popularity of pooled DNA GWAS, which is a very useful research strategy.
Over the years, many methods and algorithms have been developed for genetic association studies. With the availability of DNA microarrays and their common use in genomewide association study (GWAS), the dramatic increase in the number of markers to be handled poses a great challenge to the data analysis. Owing to the inability to analyze GWAS data manually, useful computer programs have been developed, but are mainly focused on the application for GWAS based on analysis of individual DNA samples (hereafter called individual DNA GWAS). Despite being a well-established strategy for cost reduction , association study based on pooled DNA is far less popular than the "individual DNA" approach. There are very few available resources for the analysis of pooled DNA GWAS data. This is especially true for pooled DNA GWAS that is conducted using the Illumina platform. The GenePool package  is supported for Linux environment only in its stable version although multiple environments (OSX, Windows and Unix-like) are supported in its new beta version. However, its documentation is rather brief. Another available software package is MPDA . Its graphical user interface is dependent on the proprietary MATLAB computing environment while its command line version relies on the MATLAB runtime environment. Another major constraint is that MPDA can only handle data from one or two data pools. In most applications, multiple DNA pools are constructed and each tested in multiple technical replicates - a study design that cannot be properly handled by MPDA. The SNPMaP package  was developed solely for the Affymetrix platform. Its functionality is limited to fundamental data manipulation and does not support subsequent association testing. Recently, GPFrontend and GPGraphics  were developed partly using modified source codes of gpextract and gpanalyze of GenePool. GPFrontend is essentially a wrapper tool for modified gpextract and gpanalyze of GenePool while GPGraphics incorporates graphical output functionality. Besides, this package is platform-independent and has been tested in Windows and Linux environments.
In order to facilitate the application of cost-saving pooled DNA GWAS, there is a need for more freely available platform-independent computer resources that are executable under different system environments for this purpose. With more software resources freely available, users are provided with more alternatives to suit their own specific needs. This motivates the development of UPDG - utilities package for data analysis of pooled DNA GWAS (Additional file 1). This utilities package consists of Unix/Linux shell operations and Perl programs for data manipulation, R scripts for data testing and a comprehensive user manual providing the instructions and procedures for pooled DNA GWAS data analysis. Users of UPDG are provided with a free, simple and platform-independent solution to pooled DNA GWAS from manipulation of raw data to summarization of analysis results.
UPDG manipulates raw GWAS data into the required data file formats. It implements pooled allele frequency estimation methods that incorporate adjustment for allelic preferential amplification/hybridization [6, 7] and normalization  along with the unadjusted pooled allele frequency estimation. Allelic preferential amplification/hybridization denotes the situation that equal dosage of two alleles in heterozygotes does not give equal fluorescence intensity signals in microarray-based genotyping experiments. UPDG also carries out nested analysis of variance (ANOVA) on replicates of DNA pools of subject groups [9, 10]. With unformatted results generated from R, it can summarize analysis results according to user-defined threshold. Pooled allele frequency estimates are also summarized for easy comparison.
Programs and interface
UPDG is a utilities package developed based mainly on Unix/Linux shell operations, Perl programming language and R programming language. Unix/Linux shell operations can be executed in Unix/Linux environment directly or in Windows environment upon the installation of Cygwin (http://www.cygwin.com/). All the Perl programs in the package can be executed in Unix/Linux environment directly or in Windows environment through the installation of ActivePerl (http://www.activestate.com/activeperl). Before executing the R scripts in UPDG, R should first be downloaded from the Comprehensive R Archive Network (CRAN) website (http://cran.r-project.org/) and installed. Alternatively, users can use the shell and batch scripts provided to execute a series of UPDG components sequentially and automatically. This can simplify the overall workflow. Through the use of UPDG, the following tasks can be made easy and automated: initial manipulation of raw intensity data, generation of pooled allele frequency data corrected for allelic preferential amplification [6, 7] and normalization , nested ANOVA analysis for genetic association testing [9, 10], and summarization of analysis results and pooled allele frequency estimates. A comprehensive user manual can be found within the UPDG package, describing the details of the procedures, data file formats and functionalities for various components of the package. A small example data set is also included in the package together with the expected output files.
Genotyping platform and data file format
All the genotyping experiments (pooled samples and individual samples) were performed with genomic DNA using the Illumina Human610-Quad BeadChips with 620901 markers, median inter-marker spacing of 2.7kb and 100% median genomic coverage in Asians (Illumina). Despite that UPDG was tested using data generated from the Illumina platform, data from other genotyping platforms can also be handled by UPDG provided that the data are first transformed to the required data formats for the corresponding components of UPDG. Detailed data file formats can be found in the user manual of UPDG. In particular, intensity data from Affymetrix CEL files can first be extracted using such free packages as SNPMaP  (an R package that can process CEL files to generate raw intensity data) or using the Affymetrix power tools (APT) provided by Affymetrix. Once the required data files are prepared, data analysis can then be handled by UPDG.
Results and Discussion
Summary of various components of UPDG.
Combines genotype and intensity data of individual DNA GWAS from separate files
Estimates allele frequencies of markers from DNA pools and performs adjustment correcting for allelic preferential amplification with the methods based on Hoogendoorn et al. (2000), Meaburn et al. (2006) and Craig et al. (2005), and generates input data files for the subsequent step of nested ANOVA
Removes SNPs with minor allele frequencies and call rates below a user-specified threshold and generates filtered input data file for nested ANOVA
Carries out nested ANOVA in R environment
Organizes results from nested ANOVA and generates a summary of markers with p values below a user-specified threshold
Generates summary information on estimated allele frequencies for markers
An example of Illumina Human610-Quad BeadChip data
UPDG was used to process and analyze real data obtained using the Illumina Human610-Quad BeadChips. Pooled DNA dataset consisted of 6 case pools and 6 control pools. Each pool was constructed by mixing equal amounts of 50 individual DNA samples and hence 6 case pools were created from 300 case samples and 6 control pools from 300 control samples. Each pool was tested in 3 technical replicates. Individual DNA dataset was obtained from a group of 100 cases and 100 controls. The Illumina Human610-Quad BeadChip contains 620901 markers. For pooled DNA dataset, 598821 SNPs remained for further analysis after filtering for SNPs with NCBI reference SNP (rs) numbers. Subsequent extraction of autosomal markers reduced the number of SNPs down to 582539. After further quality checking for minor allele frequency (0.01) and genotype call rate for markers (80%), there remained 581714 SNPs (adjustment based on Hoogendoorn et al. ), 581714 SNPs (adjustment based on Meaburn et al. ), 522692 SNPs (normalization based on Craig et al.  and adjustment based on Meaburn et al. ) and 581724 SNPs (unadjusted estimation). If a relatively lenient p value threshold (0.0001) was adopted for nested ANOVA analysis in at least one of the four datasets, 15 SNPs were suggestive and would be further confirmed by genotyping individual DNA samples.
Accuracy of allele frequency estimation for DNA pools.
Allele frequency difference
(Pools - Individual samples)
% of SNPs with over-estimated allele frequencyc
Case - Control
Case - Control
Case - Control
Case - Control
Accuracy of the correction factors for adjusting allelic preferential amplification depends on the number of heterozygotes individually tested by the same platform. The more heterozygous samples are tested individually, the more accurate the correction factors are, but the less cost-effective the DNA pooling approach becomes. As the main targets of GWAS are common variants (usually at least a minor allele frequency of 5%), we expect to find on average 2-3 heterozygous subjects upon testing 20-30 individuals. To strike a balance between these two extremes, we therefore recommend genotyping at least 20-30 individual samples together with pooled DNA samples with the same whole-genome genotyping platform.
UPDG integrates the functionalities of various programming environments (Unix/Linux shell, Perl and R) and provides the users with a one-stop service for pooled DNA GWAS analysis. Up to now, there are very few resources available for pooled DNA GWAS analysis, especially for Illumina platform. Existing resources require intense prior knowledge on programming and statistics. It is impossible to do pooled DNA GWAS analysis manually and hence use of computer programs is definitely required to achieve this. With limited resources available, scientists will be hindered and become reluctant to carry out pooled DNA GWAS despite being a well-established strategy for cost reduction. Different programming environments have their own edges and drawbacks. It is usually difficult to stick with one single programming language in complicated tasks like pooled GWAS analysis, but learning many programming languages at the same time is not feasible for most users. It is our intention to contribute our UPDG utilities package to the field of pooled DNA GWAS so that this useful research strategy will no longer be intractable to scientists with less advanced programming knowledge. UPDG provides users with an alternative choice of utilities packages for manipulating and analyzing their pooled DNA GWAS data. More importantly, it is our vision that the useful strategy of pooled DNA GWAS can gain in popularity by reducing the hindrance to data manipulation and analysis.
Availability and Requirements
UPDG package for data analysis of pooled DNA GWAS (package components including Perl programs and R scripts, an example dataset and a user manual) is freely available (Additional file 1).
Project name: UPDG - Utilities package for data analysis of pooled DNA GWAS
Operating system: Platform independent
Programming language: Unix/Linux shell, Perl and R
Other requirements: ActivePerl, and Cygwin under Windows environment
License: GNU GPL v3
Any restriction to use by non-academics: On request and citation
List of Abbreviations
Analysis of variance
Genomewide association study.
This work is a contribution of the Centre for Myopia Research, The Hong Kong Polytechnic University. This work was supported by grants from The Hong Kong Polytechnic University (G-YX2V, J-BB7P and 87TP).
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