iloci software is used to calculate the gene-gene interactions from GWAS data. This software was implemented by the OpenCL framework.

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1 iloci software iloci software is used to calculate the gene-gene interactions from GWAS data. This software was implemented by the OpenCL framework. Software requirements : 1. Linux or Mac operating system 2. Python 2.4 or later version 3. Java runtime version 1.6 or later 4. OpenCL driver and libraries 4.1 Linux x86/64 ( and- sdks/heterogeneous- computing/amd- accelerated- parallel- processing- app- sdk/) 4.2 MacOSX 10.7 Lion with build-in OpenCL framework 5. Optional (Queue management) for cluster computing environment, e.g. SGE (Sun Grid Engine) for parallel calculation. iloci software comprises three parts : 1. Pre-processing step Python script ( MergeInput.py ) prepares iloci input files. This input file is composition of three files : control and case genotype files and SNP annotation file. The input genotype of both cases and controls cannot contain any missing data. User must screen out or imputing the missing data. An example of genotyping data is shown below This sample has 20 individuals (columns) and 6 SNPs (rows). Genotypes are encoded with "0" : homozygous wide type, "1" : heterozygous and "2" : homozygous variant type.

2 The SNPs annotation file is tab-delimited format. The example is shown below. rs Chr1: FLJ22639 geneid:79854 near-gene-3_10k rs Chr1: LOC geneid: near-gene-3_10k rs Chr1: MIRN200B geneid: near-gene-5_10k rs Chr1: TNFRSF18 geneid:8784 near-gene-3_10k rs Chr1: SDF4 geneid:51150 intron rs Chr1: SDF4 geneid:51150 coding-synon The example data of 1963 cases and 2938 controls in 8000 SNPs are stored in files Gty_Cases_8000snps.txt, Gty_Ctrls_8000snps.txt and SNPs_8000.txt. script is used to combine the cases and controls genotyping data and the SNP identification from SNPs annotation file to the iloci input file. The example file is shown below. MergeInput.py The first line contains the number of individuals from case and control groups separated by tab respectively. The next lines contain the combination of genotyping data of cases follow by controls. The genotyping data starts exactly at the 21st characters of each line. User can prepare the iloci input file with following sample command. >./MergeInput.py Gty_Cases_8000snps.txt Gty_Ctrls_8000snps.txt SNPs_8000.txt Combined_Gty.txt - Gty_Cases_8000snps.txt is the genotype data of cases. - Gty_Ctrls_8000snps.txt is the genotype data of controls. - SNPs_8000.txt is the SNPs annotation file.

3 - Combined_Gty.txt is the file name to save the output. 2. Processing step requires the same script with different options to calculate ρ diff values of SNP pairs. The file iloci-main.jar was implemented using OpenCL framework and jocl (Java OpenCL wrapper). Users can provide parameters for iloci-main.jar program as shown below. > java Xmx2000m jar iloci-main.jar i Combined_TestData_0 j Combined_TestData_1 x 0 y 1 r 1000 f 2000 o Toprank_0_1 h Histogram_0_1 p 0 -Xmx2000m this option is used to reserve 2000 MB of memory for JVM. -i Combined_TestData_0 the first input file. -j Combined_TestData_1 the second input file. -x 0 the block position of input file 1. -y 1 the block position of the input file 2. The default value is 0. -r 1000 the number of range to store distribution (histogram) of ρ diff values (2.0/1000). The default value is f 2000 the number of top rank score of ρ diff values to store in the output file. The default value is o Toprank_0_1 the name of the output file to store the SNP pairs and ρ diff values. -h Histogram_0_1 the name of the output file to store in the histogram of the ρ diff values. -p 0 Specify the device used to perform iloci-main.jar program. This option is used when running on the machine that have heterogenous environment (multiple CPUs and GPUs). The default value is 0 (The first device) depending on the machine configuration.

4 The example above demonstrates when divide the full data set into small 4 blocks (0, 1, 2 and 3). The combination of the operations are 00, 01, 02, 03, 11, 12, 13, 22, 23 and 33. We provide the example script to perform the large data set in the file Job_submit.py. User can modify the script for running on your system. 3. Post-processing step Python script Combined_Toprank.py use to collect the whole results and select the top rank pairs. The other script Combined_Histogram.py is used to collect the ρ diff values and the frequency to plot the histogram. Create the list file that contains all of result files by using the simple UNIX command. If the toprank output files from processing step contain 3 files : Toprank_0_1, Toprank_0_2 and Toprank_1_2. User and use the ls command to create the list file as shown below. > ls Toprank_* > Toprank_TestData_list.txt Use Combined_Toprank.py to collect the required result. >./Combined_Toprank.py SNPs_8000.txt Toprank_TestData_list.txt 1000 Top_1000.txt The explanation of parameters are shown below. -SNPs_8000.txt -Toprank_TestData_list.txt SNPs annotation file. File contains the list of top rank output files The number of top rank pairs. -Top_1000.txt The final top rank output file. Create the list file of histogram results with ls command as same as Toprank files. >./Combined_Histogram.py Histogram_list.txt Histogram_500.txt The explanation of parameters are shown below. -Histogram_list.txt File contains the list of histogram output files.

5 -500 The range of the ρ diff values, this generates the bin values as 2.0/ The top ρ diff values to collect the frequency. -Histogram_500.txt The output file of ρ diff histogram. File Run_test.py is the python script for running the example files. This script do the complete processes include pre-processing, processing (called Job_submit.py) and post-processing (Combined_Toprank.py and Combined_Histogram.py). >./Run_test.py