filter markers, subjects rgClean.py '$input_file.extra_files_path' '$input_file.metadata.base_name' '$title' '$mind' '$geno' '$hwe' '$maf' '$mef' '$mei' '$out_file1' '$out_file1.files_path' '$relfilter' '$afffilter' '$sexfilter' '$fixaff' No filter on relatedness Keep Founders only (pedigree m/f ID = "0") Keep Offspring only (one randomly chosen if >1 sibs in family) No filter on affection status Keep Controls only (affection='1') Keep Cases only (affection='2') No filter on gender status Keep Males only (pedigree gender='1') Keep Females only (pedigree gender='2')

.. class:: infomark **Syntax** - **Genotype data** is the input genotype file chosen from your current history - **Descriptive title** is the name to use for the filtered output file - **Missfrac threshold: subjects** is the threshold for missingness by subject. Subjects with more than this fraction missing will be excluded from the import - **Missfrac threshold: markers** is the threshold for missingness by marker. Markers with more than this fraction missing will be excluded from the import - **MaxMendel Individuals** Mendel error fraction above which to exclude subjects with more than the specified fraction of mendelian errors in transmission (for family data only) - **MaxMendel Families** Mendel error fraction above which to exclude families with more than the specified fraction of mendelian errors in transmission (for family data only) - **HWE** is the threshold for HWE test p values below which the marker will not be imported. Set this to -1 and all markers will be imported regardless of HWE p value - **MAF** is the threshold for minor allele frequency - SNPs with lower MAF will be excluded - **Filters** for founders/offspring or affected/unaffected or males/females are optionally available if needed - **Change Affection** is only needed if you want to change the affection status for creating new analysis datasets ----- **Attribution** This tool relies on the work of many people. It uses Plink http://pngu.mgh.harvard.edu/~purcell/plink/, and the R http://cran.r-project.org/ and Bioconductor http://www.bioconductor.org/ projects. respectively. In particular, http://pngu.mgh.harvard.edu/~purcell/plink/ has excellent documentation describing the parameters you can set here. This implementation is a Galaxy tool wrapper around these third party applications. It was originally designed and written for family based data from the CAMP Illumina run of 2007 by ross lazarus (ross.lazarus@gmail.com) and incorporated into the rgenetics toolkit. Rgenetics merely exposes them, wrapping Plink so you can use it in Galaxy. ----- **Summary** Reliable statistical inference depends on reliable data. Poor quality samples and markers may add more noise than signal, decreasing statistical power. Removing the worst of them can be done by setting thresholds for some of the commonly used technical quality measures for genotype data. Of course discordant replicate calls are also very informative but are not in scope here. Marker cleaning: Filters are available to remove markers below a specific minor allele frequency, beyond a Hardy Wienberg threshold, below a minor allele frequency threshold, or above a threshold for missingness. If family data are available, thresholds for Mendelian error can be set. Subject cleaning: Filters are available to remove subjects with many missing calls. Subjects and markers for family data can be filtered by proportions of Mendelian errors in observed transmission. Use the QC reporting tool to generate a comprehensive series of reports for quality control. Note that ancestry and cryptic relatedness should also be checked using the relevant tools. ----- .. class:: infomark **Tip** You can check that you got what you asked for by running the QC tool to ensure that the distributions are truncated the way you expect. Note that you do not expect that the thresholds will be exactly what you set - some bad assays and subjects are out in multiple QC measures, so you sometimes have more samples or markers than you exactly set for each threshold. Finally, the ordering of operations matters and Plink is somewhat restrictive about what it will do on each pass of the data. At least it's fixed. ----- This Galaxy tool was written by Ross Lazarus for the Rgenetics project It uses Plink for most calculations - for full Plink attribution, source code and documentation, please see http://pngu.mgh.harvard.edu/~purcell/plink/ plus some custom python code