Pairwise Allele Sharing rgGRR.py $i.extra_files_path/$i.metadata.base_name "$i.metadata.base_name" '$out_file1' '$out_file1.files_path' "$title1" '$n' '$Z'

.. class:: infomark **Explanation** This tool will calculate allele sharing among all subjects, one pair at a time. It outputs measures of average alleles shared and measures of variability for each pair of subjects and creates an interactive image where each pair is plotted in this mean/variance space. It is based on the GRR windows application available at http://www.sph.umich.edu/csg/abecasis/GRR/ The plot is interactive - you can unselect one of the relationships in the legend to remove all those points from the plot for example. Details of outlier pairs will pop up when the pointer is over them. e found by moving your pointer over them. This relies on a working browser SVG plugin - try getting one installed for your browser if the interactivity is broken. ----- **Syntax** - **Genotype file** is the input pedigree data chosen from available library Plink binary files - **Title** will be used to name the outputs so make it mnemonic and useful - **N** is left 0 to use all snps - otherwise you get a random sample - much quicker with little loss of precision > 5000 SNPS **Summary** Warning - this tool works pairwise so slows down exponentially with sample size. An LD-reduced dataset is strongly recommended as it will give good resolution with relatively few SNPs. Do not use all million snps from a whole genome chip - it's overkill - 5k is good, 10k is almost indistinguishable from 100k. SNP are sampled randomly from the autosomes - otherwise parent/child pairs will be separated by gender. This tool will estimate mean pairwise allele shareing among all subjects. Based on the work of Abecasis, it has been rewritten so it can run with much larger data sets, produces cross platform svg and runs on a Galaxy server, instead of being MS windows only. Written in is Python, it uses numpy, and the innermost loop is inline C so it can calculate about 50M SNPpairs/sec on a typical opteron server. Setting N to some (fraction) of available markers will speed up calculation - the difference is most painful for large subject N. The real cost is that every subject must be compared to every other one over all genotypes - this is an exponential problem on subjects. If you don't see the genotype data set you want here, it can be imported using one of the methods available from the Rgenetics Get Data tool. ----- **Attribution** Based on an idea from G. Abecasis implemented as GRR (windows only) at http://www.sph.umich.edu/csg/abecasis/GRR/ Ross Lazarus wrote the original pdf writer Galaxy tool version. John Ziniti added the C and created the slick svg representation. Copyright Ross Lazarus 2007 Licensed under the terms of the LGPL as documented http://www.gnu.org/licenses/lgpl.html