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1 <tool id="hgv_gpass" name="GPASS" version="1.0.0">
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2 <description>significant single-SNP associations in case-control studies</description>
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3
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4 <command interpreter="perl">
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5 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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6 </command>
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7
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8 <inputs>
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9 <param name="input1" type="data" format="lped" label="Dataset"/>
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10 <param name="fdr" type="float" value="0.05" label="FDR"/>
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11 </inputs>
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12
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13 <outputs>
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14 <data name="output" format="tabular" />
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15 </outputs>
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16
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17 <requirements>
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18 <requirement type="package">gpass</requirement>
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19 </requirements>
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20
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21 <!-- we need to be able to set the seed for the random number generator
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22 <tests>
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23 <test>
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24 <param name='input1' value='gpass_and_beam_input' ftype='lped' >
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25 <metadata name='base_name' value='gpass_and_beam_input' />
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26 <composite_data value='gpass_and_beam_input.ped' />
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27 <composite_data value='gpass_and_beam_input.map' />
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28 <edit_attributes type='name' value='gpass_and_beam_input' />
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29 </param>
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30 <param name="fdr" value="0.05" />
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31 <output name="output" file="gpass_output.txt" />
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32 </test>
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33 </tests>
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34 -->
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35
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36 <help>
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37 **Dataset formats**
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38
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39 The input dataset must be in lped_ format, and the output is tabular_.
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40 (`Dataset missing?`_)
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41
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42 .. _lped: ./static/formatHelp.html#lped
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43 .. _tabular: ./static/formatHelp.html#tab
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44 .. _Dataset missing?: ./static/formatHelp.html
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45
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46 -----
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47
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48 **What it does**
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49
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50 GPASS (Genome-wide Poisson Approximation for Statistical Significance)
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51 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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52
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53 The program has two main functionalities:
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54
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55 1. Detect significant single-SNP associations at a user-specified false
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56 discovery rate (FDR).
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57
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58 *Note*: a "typical" definition of FDR could be
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59 FDR = E(# of false positive SNPs / # of significant SNPs)
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60
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61 This definition however is very inappropriate for association mapping, since SNPs are
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62 highly correlated. Our FDR is
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63 defined differently to account for SNP correlations, and thus will obtain
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64 a proper FDR in terms of "proportion of false positive loci".
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65
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66 2. Approximate the significance of a list of candidate SNPs, adjusting for
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67 multiple comparisons. If you have isolated a few SNPs of interest and want
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68 to know their significance in a GWAS, you can supply the GWAS data and let
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69 the program specifically test those SNPs.
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70
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71
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72 *Also note*: the number of SNPs in a study cannot be both too small and at the same
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73 time too clustered in a local region. A few hundreds of SNPs, or tens of SNPs
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74 spread in different regions, will be fine. The sample size cannot be too small
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75 either; around 100 or more individuals (case + control combined) will be fine.
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76 Otherwise use permutation.
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77
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78 -----
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79
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80 **Example**
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81
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82 - input map file::
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83
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84 1 rs0 0 738547
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85 1 rs1 0 5597094
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86 1 rs2 0 9424115
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87 etc.
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88
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89 - input ped file::
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90
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91 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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92 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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93 etc.
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94
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95 - output dataset, showing significant SNPs and their p-values and FDR::
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96
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97 #ID chr position Statistics adj-Pvalue FDR
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98 rs35 chr1 136606952 4.890849 0.991562 0.682138
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99 rs36 chr1 137748344 4.931934 0.991562 0.795827
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100 rs44 chr2 14423047 7.712832 0.665086 0.218776
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101 etc.
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102
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103 -----
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104
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105 **Reference**
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106
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107 Zhang Y, Liu JS. (2010)
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108 Fast and accurate significance approximation for genome-wide association studies.
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109 Submitted.
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110
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111 </help>
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112 </tool>
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