significant single-SNP associations in case-control studies gpass.pl ${input1.extra_files_path}/${input1.metadata.base_name}.map ${input1.extra_files_path}/${input1.metadata.base_name}.ped $output $fdr gpass **Dataset formats** The input dataset must be in lped_ format, and the output is tabular_. (`Dataset missing?`_) .. _lped: ./static/formatHelp.html#lped .. _tabular: ./static/formatHelp.html#tab .. _Dataset missing?: ./static/formatHelp.html ----- **What it does** GPASS (Genome-wide Poisson Approximation for Statistical Significance) 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. The program has two main functionalities: 1. Detect significant single-SNP associations at a user-specified false discovery rate (FDR). *Note*: a "typical" definition of FDR could be FDR = E(# of false positive SNPs / # of significant SNPs) This definition however is very inappropriate for association mapping, since SNPs are highly correlated. Our FDR is defined differently to account for SNP correlations, and thus will obtain a proper FDR in terms of "proportion of false positive loci". 2. Approximate the significance of a list of candidate SNPs, adjusting for multiple comparisons. If you have isolated a few SNPs of interest and want to know their significance in a GWAS, you can supply the GWAS data and let the program specifically test those SNPs. *Also note*: the number of SNPs in a study cannot be both too small and at the same time too clustered in a local region. A few hundreds of SNPs, or tens of SNPs spread in different regions, will be fine. The sample size cannot be too small either; around 100 or more individuals (case + control combined) will be fine. Otherwise use permutation. ----- **Example** - input map file:: 1 rs0 0 738547 1 rs1 0 5597094 1 rs2 0 9424115 etc. - input ped file:: 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 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 etc. - output dataset, showing significant SNPs and their p-values and FDR:: #ID chr position Statistics adj-Pvalue FDR rs35 chr1 136606952 4.890849 0.991562 0.682138 rs36 chr1 137748344 4.931934 0.991562 0.795827 rs44 chr2 14423047 7.712832 0.665086 0.218776 etc. ----- **Reference** Zhang Y, Liu JS. (2010) Fast and accurate significance approximation for genome-wide association studies. Submitted.