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SECA: SNP Effect Concordance Analysis

Nyholt DR (2014) SECA: SNP effect concordance analysis using genome-wide association summary results. Bioinformatics. 2014 Jul; 30(14):2086-8.

To run SECA:

SECA takes two input files containing GWAS summary results (described further below).
Note: to accelerate upload we recommend you compress your input files using zip (*.zip) or gzip (*.gz).

Upload GWAS results for dataset1:

Upload GWAS results for dataset2:

For LD estimation, use genotype data from 

For LD pruning, prioritise SNPs with smallest p-values (P1) in dataset1 

Email (results to be sent):

   

Approach:


Input format:


The GWAS summary results should be uploaded as a text file with five essential columns (tab or space-delimited).
Additional columns may be present, but they will not be used in the analyses.
Each dataset file must contain the following five columns of data with a header row listing (case insensitive) column names of 'SNP', 'EA', 'NEA', 'P', and 'BETA' or 'OR':

Column name Description of data in column
'SNP' SNP name (i.e., rsID). Column names of 'MARKER', 'SNPID' or 'rs_number' are also accepted.
'EA' Effect allele. Column names of 'A1', 'REF' or 'reference_allele' are also accepted.
'NEA' Non-effect allele. Column names of 'A2', 'ALT' or 'other_allele' are also accepted.
'P' P-value from the association test. Column names of 'PVALUE', 'PVAL' or 'p-value' are also accepted.
'BETA' or 'OR' Regression coefficient (BETA) [BETA=loge(OR)] or odds ratio (OR) [OR=exp(BETA)], for the effect allele (EA) relative to the non-effect allele (NEA). For example, a positive BETA (OR > 1) means EA increases risk relative to NEA. SECA will automatically convert OR values to BETA values. Column name of 'ZSCORE' is also accepted.

References: 

HapMap 3 (release 2) QC+ SNP genotype data formatted as PLINK files (hapmap3_r2_b36_fwd.qc.poly.tar.bz2) was downloaded from the Broad Institute's HapMap 3 webpage: http://www.broadinstitute.org/~debakker/hapmap3r2.html. HapMap 2 (release 23a) filtered (MAF > 0.01 and genotyping rate > 0.95) SNP genotype data (founders) formatted as PLINK files was downloaded from the PLINK resources webpage: http://pngu.mgh.harvard.edu/~purcell/plink/res.shtml. 1000 Genomes Project (1kGP) data (1000G PhaseI 2012 v3 Updated Integrated Phase 1 Release) was download from the MaCH download page and later converted to PLINK format. Note: only founder (unrelated) individuals are utilised in the LD estimation.

Nyholt DR (2014) SECA: SNP effect concordance analysis using genome-wide association summary results. Bioinformatics. 2014 Apr 1. [Epub ahead of print]. 

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am J Hum Genet. 2007 81(3):559-75. 

R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org

SECA's SNP effect alignment is based on METAL (Willer et al 2010) source code (generic-metal-2011-03-25.tar.gz).

Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010 26(17):2190-1.

Resources:

An R script ('SECA_Ranalysis_HeatmapPerm.R') allowing users to increase the number of replicates (default = 1000) for the permutation of heatmap p-values using the "independent_aligned_effects.txt" file generated by SECA can be downloaded here.


License agreement:

SECA is coded by Dale R. Nyholt.

Copyright (C) 2013, Dale R. Nyholt. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. The names of its contributors may not be used to endorse or promote products derived from this software without specific prior written permission.

4. We also request that use of this software be cited in publications as: Nyholt DR (2014) SECA: SNP effect concordance analysis using genome-wide association summary results. Bioinformatics. 2014 Apr 1. [Epub ahead of print].

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Page last updated April 16, 2014.
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