How to analyze interSNP results of DB_TEST_Singlemarker.txt
The first goal is to be able of run a clump over the obtained results. Our first approach will be take a Singlemarker result from interSNP, parse it and write the data in plink results format.
- First, plink data must contain:
CHR Chromosome SNP SNP ID BP Physical position (base-pair) A1 Minor allele name (based on whole sample) F_A Frequency of this allele in cases F_U Frequency of this allele in controls A2 Major allele name CHISQ Basic allelic test chi-square (1df) P Asymptotic p-value for this test OR Estimated odds ratio (for A1, i.e. A2 is reference)
- Second, interSNP file contains:
1 - No 2 - Chr 3 - rs_No 4 - Position 5 - Gene 6 - minor 7 - major 8 - MAF(All/Co/Ca) 9 - SNP_MR 10 - HWE_Ca 11 - HWE_Co 12 - P_Single-marker 13 - P_Corr 14 - A_Ca_N 15 - B_Ca_N 16 - A_Co_NB_Co_N 17 - A_Ca 18 - B_Ca 19 - A_Co 20 - B_Co 21 - OR_A 22 - LCL_A 23 - RCL_A 24 - OR_B 25 - LCL_B 26 - RCL_B
- So, the relation must be:
CHR --> Chr SNP --> rs_No BP --> Position A1 --> minor F_A --> MAF(All/Co/Ca) --> Ca F_U --> MAF(All/Co/Ca) --> Co A2 --> major CHISQ --> Not available, lets try skipping it P --> P_Single-marker OR --> OR_A
Instead try writting a perl pattern to catch the desired variables I'm going to use sed & awk first to write a csv like temporary file
echo "CHR SNP BP A1 F_A F_U A2 P OR" > tmpfile; awk -F'\t' '{print $2,$3,$4,$6,$8,$7,$12,$21}' myresults.file | sed 's/\(.*\) .*\/\(.*\)\/\([^ ]*\) \(.*\)/\1 \3 \2 \4/' | tail -n +2 >> tmpfile
Yep, I know is awful but it save me from writing a nearly to infinity matching regexp in perl.
- Now, this file is ready to feed plink! Something like this,
plink --bfile ~/data/Variomics/ADMURimpQC2 --clump test_clean.txt --clump-p1 0.01
produces this output:
@----------------------------------------------------------@ | PLINK! | v1.07 | 10/Aug/2009 | |----------------------------------------------------------| | (C) 2009 Shaun Purcell, GNU General Public License, v2 | |----------------------------------------------------------| | For documentation, citation & bug-report instructions: | | http://pngu.mgh.harvard.edu/purcell/plink/ | @----------------------------------------------------------@ Web-based version check ( --noweb to skip ) Recent cached web-check found... OK, v1.07 is current Writing this text to log file [ plink.log ] Analysis started: Wed Mar 6 14:06:03 2013 Options in effect: --bfile /home/osotolongo/data/Variomics/ADMURimpQC2 --clump test_clean.txt --clump-p1 0.01 Reading map (extended format) from [ /home/osotolongo/data/Variomics/ADMURimpQC2.bim ] 1034238 markers to be included from [ /home/osotolongo/data/Variomics/ADMURimpQC2.bim ] Reading pedigree information from [ /home/osotolongo/data/Variomics/ADMURimpQC2.fam ] 1088 individuals read from [ /home/osotolongo/data/Variomics/ADMURimpQC2.fam ] 1088 individuals with nonmissing phenotypes Assuming a disease phenotype (1=unaff, 2=aff, 0=miss) Missing phenotype value is also -9 319 cases, 769 controls and 0 missing 511 males, 577 females, and 0 of unspecified sex Reading genotype bitfile from [ /home/osotolongo/data/Variomics/ADMURimpQC2.bed ] Detected that binary PED file is v1.00 SNP-major mode Before frequency and genotyping pruning, there are 1034238 SNPs 1088 founders and 0 non-founders found 1130 heterozygous haploid genotypes; set to missing Writing list of heterozygous haploid genotypes to [ plink.hh ] Total genotyping rate in remaining individuals is 0.986847 0 SNPs failed missingness test ( GENO > 1 ) 0 SNPs failed frequency test ( MAF < 0 ) After frequency and genotyping pruning, there are 1034238 SNPs After filtering, 319 cases, 769 controls and 0 missing After filtering, 511 males, 577 females, and 0 of unspecified sex Parameters for --clump: p-value threshold for index SNPs = 0.01 Physical (kb) threshold for clumping = 250 LD (r-squared) threshold for clumping = 0.5 p-value threshold for clumped SNPs = 0.01 Reading results for clumping from [ test_clean.txt ] Extracting fields SNP and P Indexing on all files Writing clumped results file to [ plink.clumped ] Analysis finished: Wed Mar 6 14:17:58 2013
This look fine 
What happens then with the obtained results?
Well, we can make a bootstrap of the better results (e.g. 1k) and compare this with the frequency of markers by chromosome.
This first one is a self test. I mean, a bunch of random 1k SNPs of the DB against the SNPs DB frequency.
As expected, unless the RNG was rubbish, the linear regression is very good between both data. However if our results are half good the Pearson coefficient must be far from 0.9.
Look at this picture,
This was the above explained test over single marker results of interSNP test 5 against one of our DBs. The goodness of regression is self explanatory, results don't worth a dime.
Want to see what happens next? go to the DB_TEST_BestMarkerCombi2.txt example