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113 lines
4.1 KiB
XML
113 lines
4.1 KiB
XML
<tool id="hgv_gpass" name="GPASS" version="1.0.0">
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<description>significant single-SNP associations in case-control studies</description>
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<command interpreter="perl">
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gpass.pl ${input1.extra_files_path}/${input1.metadata.base_name}.map ${input1.extra_files_path}/${input1.metadata.base_name}.ped $output $fdr
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</command>
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<inputs>
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<param name="input1" type="data" format="lped" label="Dataset"/>
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<param name="fdr" type="float" value="0.05" label="FDR"/>
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</inputs>
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<outputs>
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<data name="output" format="tabular" />
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</outputs>
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<requirements>
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<requirement type="package">gpass</requirement>
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</requirements>
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<!-- we need to be able to set the seed for the random number generator
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<tests>
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<test>
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<param name='input1' value='gpass_and_beam_input' ftype='lped' >
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<metadata name='base_name' value='gpass_and_beam_input' />
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<composite_data value='gpass_and_beam_input.ped' />
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<composite_data value='gpass_and_beam_input.map' />
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<edit_attributes type='name' value='gpass_and_beam_input' />
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</param>
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<param name="fdr" value="0.05" />
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<output name="output" file="gpass_output.txt" />
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</test>
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</tests>
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-->
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<help>
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**Dataset formats**
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The input dataset must be in lped_ format, and the output is tabular_.
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(`Dataset missing?`_)
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.. _lped: ./static/formatHelp.html#lped
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.. _tabular: ./static/formatHelp.html#tab
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.. _Dataset missing?: ./static/formatHelp.html
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-----
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**What it does**
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GPASS (Genome-wide Poisson Approximation for Statistical Significance)
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detects significant single-SNP associations in case-control studies at a user-specified FDR. Unlike previous methods, this tool can accurately approximate the genome-wide significance and FDR of SNP associations, while adjusting for millions of multiple comparisons, within seconds or minutes.
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The program has two main functionalities:
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1. Detect significant single-SNP associations at a user-specified false
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discovery rate (FDR).
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*Note*: a "typical" definition of FDR could be
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FDR = E(# of false positive SNPs / # of significant SNPs)
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This definition however is very inappropriate for association mapping, since SNPs are
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highly correlated. Our FDR is
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defined differently to account for SNP correlations, and thus will obtain
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a proper FDR in terms of "proportion of false positive loci".
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2. Approximate the significance of a list of candidate SNPs, adjusting for
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multiple comparisons. If you have isolated a few SNPs of interest and want
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to know their significance in a GWAS, you can supply the GWAS data and let
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the program specifically test those SNPs.
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*Also note*: the number of SNPs in a study cannot be both too small and at the same
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time too clustered in a local region. A few hundreds of SNPs, or tens of SNPs
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spread in different regions, will be fine. The sample size cannot be too small
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either; around 100 or more individuals (case + control combined) will be fine.
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Otherwise use permutation.
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-----
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**Example**
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- input map file::
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1 rs0 0 738547
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1 rs1 0 5597094
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1 rs2 0 9424115
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etc.
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- input ped file::
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1 1 0 0 1 1 G G A A A A A A A A A G A A G G G G A A G G G G G G A A A A A G A A G G A G A G A A G G A A G G A A G G A G A A G G A A G G A A A G A G G G A G G G G G A A A G A A G G G G G G G G A G A A A A A A A A
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1 1 0 0 1 1 G G A G G G A A A A A G A A G G G G G G A A G G A G A G G G G G A G G G A G A A G G A G G G A A G G G G A G A G G G A G A A A A G G G G A G A G G G A G A A A A A G G G A G G G A G G G G G A A G G A G
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etc.
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- output dataset, showing significant SNPs and their p-values and FDR::
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#ID chr position Statistics adj-Pvalue FDR
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rs35 chr1 136606952 4.890849 0.991562 0.682138
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rs36 chr1 137748344 4.931934 0.991562 0.795827
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rs44 chr2 14423047 7.712832 0.665086 0.218776
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etc.
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-----
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**Reference**
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Zhang Y, Liu JS. (2010)
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Fast and accurate significance approximation for genome-wide association studies.
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Submitted.
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</help>
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</tool>
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