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Today, I would like to share with you the relevant knowledge of how to use GWAS digital covariates. The content is detailed and the logic is clear. I believe most people still know too much about this knowledge, so share this article for your reference. I hope you can get something after reading this article. Let's take a look at it.

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.

These are all the contents of this article entitled "how to use GWAS Digital Covariates". Thank you for reading! I believe you will gain a lot after reading this article. The editor will update different knowledge for you every day. If you want to learn more knowledge, please pay attention to the industry information channel.

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