Here we report a method for conducting GWAS that does not require the genotyping of large numbers of individuals. Instead, XP-GWAS (extreme phenotype GWAS) relies on genotyping pools of individuals from a diversity panel having extreme phenotypes. This analysis measures allele frequencies in the extreme pools, enabling the discovery of associations between genetic variants and traits of interest.
git clone [email protected]:yangjl/XP-GWAS.git
Input data should be a tab separated file with the column names matching the below example.
- Columns 1-3 should be "snpid" (SNP ID), "chr" (Chromosome name) and "pos" (Physical Position).
- Columns 4 and onwards, with column names like high_ref and high_alt, indicate the reference and alternative allele counts at each polymorphic site.
snpid chr pos high_ref high_alt low_ref low_alt random_ref random_alt
1 10_3005 10 3005 0 1 1 7 0 0
2 10_3219 10 3219 5 5 9 5 6 4
3 10_3452 10 3452 26 28 32 92 32 52
4 10_3523 10 3523 4 8 8 27 9 11
5 10_3658 10 3658 9 25 10 18 6 17
6 10_4099 10 4099 12 5 14 3 13 11
The following scripts should be ran in R console. An add-on package GenABEL should be installed first.
install.packages("GenABEL")
source("lib/xpgwas.R")
library("GenABEL")
### read the input data
input <- read.table("data/input_sample.txt", header=TRUE)
### get FDR corrected p-values for sites passed your filtering criterion (DEFAULT filter = 50)
### plotlambda: indicate whether to plot the genomic control results, default = TRUE.
qval <- xpgwas(input, filter=200, plotlambda=TRUE)
### save results
save(file="cache/qval.RData", list=c("input", "qval"))
source("lib/xpplot.R")
xpplot(qval)
For more information, see our tutorial.