Basic Concepts

We developed fcGENE using C++ object-oriented programming language. Therefore it allows us to handle high-dimensional data sets quickly. Our aim was to construct fcGENE as a complementary tool to PLINK by developing options for transforming SNP data into the formats required by different tools for GWA analysis. Therefore interface and commands of fcGENE are inspired by PLINK commands. For example just like in PLINK, PLINK-formatted files “example.ped” and “example.map” can be read in fcGENE with the Linux based command option: “./fcgene --ped example.ped --map example.map” or “./fcgene --file example”. The sequential order of the command options is unimportant throughout. Each option contains a command identifier. Commands are separated by “--”. fcGENE runs under Unix, Linux and Microsoft Windows operating systems; the latter by either implementing it in MINGW [17] or in the statistical software R [16]. For example, PLINK-formatted binary files can be read in R as follows:

system (“./fcgene --bim example.bim --fam example.fam --bed example.bed”).

Software validation

We have extensively checked and validated the correctness of the commands used in fcGENE. All functions related to format conversions were validated by performing multiple closed loops of format conversions, i.e. we converted genotype data from format “A” to format “B”, format “B” to format “C”, and finally, format “C” back to format “A”. After such a closed loop, we used Linux/Unix commands namely “diff” and “sdiff” to check if the first file containing original data in format “A” is identical to the result of the multiple conversions. Calculations of quality parameters (call rates, minor allele frequency (MAF), p-values of Hardy-Weinberg disequilibrium) were checked by comparing our results with those of PLINK. Many functions of fcGENE were also checked by repeating the same tasks via self-written Linux based shell-scripts or R scripts.

Limitations

Functionality of fcGENE is based on the file formats required by current versions of GWA analysis tools. If future versions of any of these GWA tools bring some changes in their file formats, an update of fcGENE may be necessary to handle the new data formats properly. Current version of fcGENE is not designed for purposes of statistical computations and data analyses except for a few summary statistics and issues of quality control necessary for data management.

Functional overview

fcGENE converts genotype data into different formats by loading inputs or outputs of a large variety of software packages such as PLINK, MaCH, IMPUTE, BEAGLE, BIMBAM, PHASE, fastPHASE and SNPTEST. It can also read and write compressed files (ending with “.gz”). If the original data is given in PLINK-format, we can use fcGENE to convert the data into the formats of either any imputation tool or that of other tools useful for genetic data analysis, such as SNPTEST, EIGENSOFT, HAPLOVIEW, GenABEL or VCF-tools. After completion of genotype imputation, fcGENE can be used to transform the imputation outputs back into the formats required for further analysis. A flowchart of possible data conversions is shown in Figure 1. In Figure 1, arrows pointing towards the fcGENE box imply that the data files of the programs from where the arrows start can be read and uploaded by fcGENE. Similarly, arrows pointing away from fcGENE address programs whose files can be generated. fcGENE can convert not only the inputs and outputs of imputation software, but also the imputation reference panels. This type of format conversion is required for example if we want to compare study genotypes and imputation reference panels.

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Figure 1. Flowchart of possible conversion steps for genotype data and the use of fcGENE during this process.

Arrows pointing towards the fcGENE box imply that the data files of the programmes from where the arrows start can be read and uploaded by fcGENE. Similarly, arrows pointing away from fcGENE address programmes whose files can be generated.

https://doi.org/10.1371/journal.pone.0097589.g001

Aside from the format converting tasks, fcGENE offers a number of additional options related to data management processes such as filtering SNPs and individuals according to pre-defined cut-offs of quality measures, generating templates of commands to run genotype imputation software, preparing phenotype files for SNPTEST software and GenABEL, and, creating scripts for EIGENSOFT and GenABEL.

Syntax and command options

fcGENE uses unique command identifiers to recognize command options and the names of files to be loaded. For example command identifiers “--ped” and “--dat” recognize genotype data given in MERLIN (MaCH) format. Commands of fcGENE are inspired by PLINK's command-line syntax. This makes fcGENE commands intuitive for PLINK users. The names of command options also hint to their functions. For example, if genotype SNP data is given in PLINK format, we can use the following commands to prepare inputs of EIGENSOFT and HAPLOVIEW respectively.

./fcgene --ped plink.ped --map plink.map --oformat eigensoft --out plink_eigensoft

./fcgene --ped plink.ped --map plink.map --oformat haploview --out plink_haploview

Here, the command “--oformat” performs translation of data into the specified formats, e.g. those of EIGENSOFT and HAPLOVIEW respectively. Details are explained in the next section. Names of output files generated by fcGENE can be specified by using option: “--out”. More examples of format conversions can be found in the supplementary document. Command options required to upload data in fcGENE are listed in Table S1.

Main functions

Two most important functions of fcGENE are format conversion of raw genotype data and transformation of imputed data into the formats required for different GWA tools. fcGENE can convert sets of genotype data of different formats into the formats of any of the specified imputation tools. Similarly, the outputs of different imputation software can be converted back either into PLINK format or into the formats of other software. Table 1 summarizes corresponding important command options implemented for fcGENE.

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Table 1. Major command options of fcGENE.

https://doi.org/10.1371/journal.pone.0097589.t001

Imputation outputs are either most likely genotypes of SNPs for each individual or the genotype probability distribution for each SNP and individual. In addition, the outputs are either provided in rows per individual (e.g. MaCH- and MINIMAC-outputs) or one, two or three columns per individual (e.g. IMPUTE- and BEAGLE-outputs). fcGENE can convert all kinds of imputation outputs. After loading an original or imputed dataset into fcGENE, it can be converted into the format of any of the specified programs using the “--oformat” command. Further details about the command “--oformat” can be found in Table S2. Table S3 describes commands which are optional to use. These optional commands are used to update phenotype and SNP information, to calculate quality measures of SNPs and individuals, to apply corresponding filters and to split or merge genotype data. For example, MaCH-outputs do not contain SNP annotations such as base pair position. However, HAPLOVIEW requires base pair position to identify the genetic distance between two SNPs which can be loaded to fcGENE using command “--snpinfo”. Similarly, IMPUTE-outputs do not contain any kind of pedigree information. Therefore it is necessary to update the original phenotype information with “--pedinfo” option, before the impute outputs are converted into other data formats such as those required for PLINK. Files required to update SNP and pedigree information are automatically generated by fcGENE when data transformation is initiated. Alternatively, one can use “--write-snpinfo” and “--write-pedinfo” commands to generate SNP and pedigree information respectively. Detailed information on different command options can be found in the supplementary file and fcGENE's documentation, which is distributed together with the source code through its open source home page [18].

To analyse genotype - phenotype associations using allele dosages, one can convert the imputation output for example into PLINK's dosage-file-format using command option “--oformat plink-dosage”. Moreover, imputed data can be converted into other tools like HAPLOVIEW, EIGENSOFT and SNPTEST as well. Another output option for imputed data is to create a file containing dosages of minor alleles calculated on the basis of genotype probability distributions. This is achieved by the command “--oformat recodeA-dose”. The most recent version of BEAGLE (BEAGLE4) requires VCF-format [19]. The current version of fcGENE (1.0.7) can export VCF-formatted files using the option “--oformat vcf” (see Table S2). A read-option for VCF-formatted data will be added in a future version of the software.

Auxiliary functions

For the convenient application of fcGENE, we implemented a number of additional options and features, namely options related to execution of multiple commands at a time, data management like merging, splitting, exclusion of SNPs and individuals, quality control of both raw and imputed genotypes, generation of templates for software commands and updating phenotype and SNP information. Some of these functions are described below.

Execution of multiple tasks

fcGENE can execute multiple tasks, i.e. fcGENE can process two or more tasks by one command. Each new task, except the first, starts with identifier “--new-start” and ends with “--new-end”. This command can be used for example to merge two or more sets of genotype data. The following example command reads two different PLINK-formatted files and convert the first one into MaCH and second one into IMPUTE format.

./fcgene --gens example1_impute.gens --oformat mach

--new-start --dosage example2.dose --fam example2.fam --map example2.map\

--oformat impute --out impute/example2 --new-end --out mach/example1

If option “--merge” is also used (see supplementary document) within “--new-start” and “--new-end” then the genotype data processed within these two identifiers, are merged with the genotype data loaded first (i.e. command given outside of the “--new-start” and “ --new-end” environment). Examples of merging data are given in the supplementary document.

Strand alignment

Strand alignment between genotype data set and reference data set is crucial for GWA analysis and imputation. Generally, reference panels such as HapMap are given as ‘+’ strand but data might be genotyped with respect to negative strand. If two samples at a SNP are genotyped at different strands, it can be easily recognized except for C/G or A/T SNPs. PLINK has the option to detect opposite strand alignments between cases and controls (“--flip-scan”). fcGENE supports the comparison of strand information between genotyped SNP data and reference panels using this PLINK's “--flip-scan” feature in the following way: (1) Use fcGENE to merge study genotypes and the corresponding reference panel, (2) use fcGENE to convert the merged data into PLINK format and assign a dummy case (genotyped data) and control (reference) status using option “--force”, (3) use PLINK to detect the strand mismatches of ambiguous markers applying command option “--flip-scan” or “--flip-scan-verbose” [1] on the merged data, (4) create a list of SNPs whose strand needs to be flipped, (5) use PLINK to flip the strand and (6) use fcGENE finally to convert the corrected genotypes into the format required by the desired imputation tool.

Pre-imputation quality control

It is common to implement a series of quality control (QC) steps at SNP-wise and sample-wise level before and after genotype imputation so that different confounding factors, which might affect imputation quality, can be ruled out. Typical SNP-wise QC measures comprise p-value of HWE-test, MAF and genotyping call rate [20]. Similarly, sample-wise genotyping call rate is also often applied as filter criterion. In the command line of fcGENE, one can prescribe the thresholds of these SNP-wise and sample-wise quality measures so that SNPs or individuals violating these criteria are automatically discarded. While filtering SNPs and samples, the default process of fcGENE calculates all quality measures without excluding any SNPs and samples of the uploaded data, and then filters SNPs and samples according to the specified thresholds. SNP-wise and individual-wise thresholds can be assigned by the command options “--filter-snp” and “--filter-indiv” respectively.

Generating templates of software commands

fcGENE can create not only files required by specified software, but it also generates scripts required to run the software. For example if we use IMPUTE, we have to provide lower and upper limits of the base pair positions to be imputed. Moreover, if we analyze a whole chromosome with IMPUTE, we may have to split it into smaller chunks to parallelize computations. Since it is cumbersome to specify the upper and lower limits of the chunks manually, fcGENE generates both, a list of upper and lower limits of base pair positions for each chunk, a Perl script that can execute all commands of the chunking process, and, a list of commands used to impute the chunks as well.

To start the imputation process, we may require a set of imputation reference panels. Imputation commands only work properly if one provides filenames of the reference panel with correct file identifiers. To support this process, fcGENE generates appropriate templates which can be edited with respect to filenames and folders of the reference panel.

While converting files into EIGENSOFT format, fcGENE also generates parameter files and command templates necessary for running SMARTPCA and SMARTEIGENSTRAT. More precisely the extra files generated by fcGENE are: a parameter file for SMARTPCA, a parameter file for SMARTEIGENSTRAT, an R-script for generating PCA-Plots and for modifying outputs of SMARTPCA to be compatible with the format of SMARTEIGENSTRAT, and finally, a Linux-script (alternative way of using R) to run SMARTPCA, SMARTEIGENSTRAT, EIGENPLOT and TWSTATS. More information on the parameter files and the different packages of EIGENSOFT can be found on its official website [21].

Post-imputation quality control

fcGENE can filter poorly imputed SNPs on the basis of their imputation quality and allele frequency. Imputation quality metrics differ between imputation tools. For example, MaCH uses quality parameter “Rsq” to assess imputation quality, while IMPUTE calculates so called “quality” and “info” scores (see corresponding publications for more information regarding definition of these QC parameters). The choice of corresponding filter options is rather intuitive: For example, filtering SNPs with values of MaCH-imputation quality score (i.e. Rsq score) lower than 0.3, is achieved by “--rsq 0.3”. Similarly, filtering SNPs with allele frequency <1% is achieved by the option “--maf-thresh 0.01”. An overview of possible options regarding post-imputation quality control can be found in Table S3. Detailed information on the filtering process are given in the manual of fcGENE [18].

Updates of SNP and sample information

To create a list of SNPs and individual ids from the uploaded genotype data, fcGENE provides command options “--write-snplist” and “--write-pedlist” respectively. Not all software need whole information related to SNPs and samples contained in genotype data. Therefore it may be necessary to drop or add SNP and sample information before one converts genotype data between formats. Moreover, it may be necessary to define coding and non-coding allele before reading and converting raw genotype data and probability distributions of imputed genotypes. fcGENE provides command options “--snpinfo” and “--pedinfo” respectively to add or update SNP information like rsid, allele information, base pair position etc, and pedigree information like pedigree ids, phenotype status and sex information.

Identifying individuals in pedigree data format requires family, parental and individual ids. However, some software like SHAPEIT or EIGENSOFT accept only one ID for each individual. In such a case, fcGENE can create hybrid ids by combining family-ids, individual-ids, paternal ids or maternal ids using the command option “--iid”. The process for creating such hybrid sample ids is explained in Table S4 and in fcGENE's documentation file in more detail.

While converting data for SNPTEST, we may require covariate information. Option “--covar” reads files given in plink-formatted covariate file and updates necessary information prior to data transformation.

Adding group labels in preparation for PCA analysis

To plot the result of PCA, software SMARTPCA expects an assignment of each sample to its population/ethnic group. To add this group labelling, fcGENE accepts an extra file (containing sample ids in the first column and group/population-labelling of the samples in the second column) using command option “--group-label”.

Coding genotypes as count of a given allele

fcGENE can code genotypes as counts of a given allelic reference. This type of coding is convenient for a variety of different statistical analyses such as regression models [14]. PLINK supports generation of files containing counts of minor alleles. However it only supports data transformation from the raw calls of genotype data resulting in numbers 0, 1 and 2 of the minor allele dose. Complementary to this option, fcGENE facilitates transformation of genotype probability distributions into PLINK's recodeA-formatted files but using expected allele doses of a given reference allele. More precisely, the raw files transformed by fcGENE can contain not only 0, 1 and 2 as reference allele counts but also expected allele doses of the reference allele, i.e. numbers between 0 and 2. The default reference allele is the minor allele. However, users can force fcGENE to take either the first, or the second or the major allele as reference. GWA analysis with this type of coding is useful especially if the uncertainty of imputation results is high [14].

To facilitate analyses of imputed genotype data with the statistical package R, fcGENE can convert sets of genotype data from different formats into standard text files with genotype codes either as counts of a reference allele (0,1 or 2), or as the expected dose of the reference allele. Text files contain rows for samples and columns for SNPs with simple headers for SNP identifier (e.g. rs-IDs) and a first column to identify subjects (i.e. sample ids). In order to write SNPs as rows and individuals as columns, an additional command option “--transpose” can be used. Command options “--oformat r” and “--oformat r-dose” are used to write the allele counts and expected doses of the reference alleles. Again, the default reference allele is the minor allele. However one can alter the reference allele using commands like “--force ref-allele = major”. Re-import of these types of allele counting data is also possible (command option “--rgeno”). More information on these formats can be found in the supplementary file.

An example workflow

The following example workflow demonstrates how fcGENE can be applied in different stages of GWA analysis, and how it interacts with PLINK. We assume that original genotype data are in PLINK format and saved as “example.ped” and “example.map” files. If we plan to create a plot for PCA analysis after quality control, one can use the following command to convert the data into EIGENSOFT format.

./fcgene --ped example.ped --filter-snp hwe = 1e-6,crate = 0.95,maf = 0.10\

--map example.map --oformat eigensoft --out example_eigensoft

This command filters SNPs with low quality and low minor allele frequencies, saves files and writes scripts necessary to run software EIGENSOFT at different stages. In addition, the command also creates R-scripts to plot PCA results on the basis of outputs of SMARTPCA.

In the next step, we aim to impute the PLINK-formatted data with IMPUTE using HapMap reference panel and to analyse the imputation output with SNPTEST after performing post-imputation quality control. We start this work by comparing the strand similarities between study genotypes and IMPUTE-formatted reference panels as explained in section “strand alignment”. A typical command line for this process is given below.

../fcgene --ped example.ped --example.map --force pheno = aff\

--new-start --impute-hap impute_ref.hap --impute-legend impute_ref.legend\

--merge --force pheno = unaff --new-end --oformat plink --out merged_data

After correcting the mismatched strand with PLINK, we can convert the study genotypes into IMPUTE format applying pre-imputation quality control as follows.

./fcgene --ped example.ped --map example.map\

--filter-snp hwe = 1e-6,crate = 0.95,maf = 0.01 --filter-indiv crate = 0.95\

--oformat impute --out example_impute

This command also generates the files necessary to run IMPUTE. After finishing genotype imputation, we can convert the results into SNPTEST format applying post-imputation quality filtering.

./fcgene --gens example_impute2 --thresh 0.9\

--info example _info --info-thresh 0.3 --filter-snp --maf-thresh 0.1\

--pedinfo example/impute.pedinfo --out example_snptest

Access statistics

fcGENE is widely used by several research institutions. We received a number of positive feedbacks of active users. Since the first release of fcGENE on 2012-09-26, the software has been downloaded more than 2000 times from more than 60 countries. The highest number of downloaders are from USA (N = 599), followed by UK (N = 214), Germany (N = 138) China (N = 131), and Spain (N = 70). These statistics were taken from fcGENE's official sourceforge website: http://sourceforge.net/projects/fcgene/ on 2014/03/12.

Future plans for software extensions

The recent version of fcGENE allows export of genotype data into the variant call format (VCF). In the next step, an option to import this data format will be added. We plan to develop fcGENE as format converter of family data and to add a graphical user interface (GUI) for windows users. We are also committed to update fcGENE if necessary, especially in case of changes of input and output formats of the addressed software packages.