How to use GWAS digital covariates
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1. Covariant file arrangement
The first column is FID, the second column is ID, and the third column is a covariant (note that it can only be numbers, not characters! )
Here the covariable file is:
[dengfei@ny 03_linear_cov] $head cov.txt
1061 1061 F 3
1062 1062 M 3
1063 1063 F 3
1064 1064 F 3
1065 1065 F 3
1066 1066 F 3
1067 1067 F 3
1068 1068 M 3
1069 1069 M 3
1070 1070 M 3
Here, the third is gender and the fourth is generation. In order to facilitate operation, we take generation as numerical value and analyze covariates directly.
two。 Digital covariable awk'{print $1 cov.txt > cov1.txt
The data are as follows:
1061 1061 3
1062 1062 3
1063 1063 3
1064 1064 3
1065 1065 3
1066 1066 3
1067 1067 3
1068 1068 3
1069 1069 3
1070 1070 3
3. GWAS analysis of LM model with numerical covariates
"Code:"
Plink-file b-pheno phe.txt-allow-no-sex-linear-covar cov1.txt-out re
"Log:"
PLINK v1.90b5.3 64-bit (21 Feb 2018) www.cog-genomics.org/plink/1.9/
(C) 2005-2018 Shaun Purcell, Christopher Chang GNU General Public License v3
Logging to re.log.
Options in effect:
-- allow-no-sex
-- covar cov1.txt
-- file b
-- linear
-- out re
-- pheno phe.txt
515199 MB RAM detected; reserving 257599 MB for main workspace.
.ped scan complete (for binary autoconversion).
Performing single-pass. Bed write (10000 variants, 1500 people).
File: re-temporary.bed + re-temporary.bim + re-temporary.fam written.
10000 variants loaded from .bim file.
1500 people (0 males, 0 females, 1500 ambiguous) loaded from .fam.
Ambiguous sex IDs written to re.nosex.
1500 phenotype values present after-pheno.
Using 1 thread (no multithreaded calculations invoked).
-covar: 1 covariate loaded.
Before main variant filters, 1500 founders and 0 nonfounders present.
Calculating allele frequencies... Done.
10000 variants and 1500 people pass filters and QC.
Phenotype data is quantitative.
Writing linear model association results to re.assoc.linear... Done.
"result file:" re.assoc.linear
"result Preview:"
Insert a picture description here
The result here includes the test of the covariate. we do not need to output the covariable result, we can add the parameter:-- hide-covar
4. Use R language to compare results with library (data.table)
Geno = fread ("c.raw")
Geno [1:10,1:10]
Phe = fread ("phe.txt")
Cov = fread ("cov.txt")
Dd = data.frame (phe$V3,cov$V4,geno [, 7:20])
Head (dd)
Str (dd)
Mod_M7 = lm (phe.V3 ~ cov.V4 + M7room1Magna dataSecretdd)
Summary (mod_M7)
Mod_M9 = lm (phe.V3 ~ cov.V4 + M9001); summary (mod_M9)
M7 plus numerical covariates:
M9 plus numerical covariates:
The results are exactly the same.
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