笔记 | GWAS 操作流程4-3：LM模型+因子协变量

邓飞

发布于 2020-05-29 10:59:21

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发布于 2020-05-29 10:59:21

文章被收录于专栏：育种数据分析之放飞自我育种数据分析之放飞自我

1. 协变量文件整理

第一列为FID 第二列为ID 第三列以后为协变量（注意，只能是数字，不能是字符！）

这里协变量文件为：

[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

这里第三列为性别，第四列为世代，这里，将世代作为因子，进行因子协变量的GWAS分析

2. 因子协变量

awk '{print $1,$2,$4}' cov.txt >cov1.txt

数据如下：

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. 使用plink的dummy coding转化为虚拟变量

plink --file b --covar cov1.txt --write-covar --dummy-coding

结果生成：

plink.cov

「注意：」这里的协变量，会减少一个水平，比如本来世代是由3,4，5三个世代，这里只有两个水平。plink文档是这样解释的：

That is, for a variable with K categories, K-1 new dummy variables are created. This new file can be used with --linear and --logistic, and a coefficient for each level would now be estimated for the first covariate (otherwise PLINK would have incorrectly treated the first covariate as an ordinal/ratio measure).

5 进行因子协变量GWAS分析LM模型

「代码：」

plink --file b --pheno phe.txt --allow-no-sex --linear --covar plink.cov --out re --hide-covar

「日志：」

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 plink.cov
  --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: 2 covariates 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.

「结果文件：」re.assoc.linear

「结果预览：」

4. 使用R语言进行结果比较lm+factor

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 + M7_1,data=dd)
summary(mod_M7)
mod_M9 = lm(phe.V3 ~ cov.V4 + M9_1,data=dd);summary(mod_M9)

「M7加上因子协变量结果：」