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Detect epistasis candidate SNP pair based on bayes factor
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README.md

RILStEp

RILStEp(Recombinant Inbred Lines Stepwise Epistasis analysis) package is the epistasis analysis tool. This package enables to detect the epistatic relationships between SNPs for RIL population by comparison of 2 models based on bayes factor.
publication: Not yet

Requirements

Other than CRAN package, GWASpoly package is required for RILStEp package.
please download zip files from here
and install install.packages("GWASpoly_download/GWASpoly_1.3.tar.gz", repos = NULL, type = "source")
or install.packages("GWASpoly_download/GWASpoly_1.3.zip", repos = NULL, type = "source")

Installation

You can install RILStEp from GitHub with:

# install.packages("devtools")
devtools::install_github("slt666666/RILStEp")

Example

This is a basic example script:

library(RILStEp)

### loading dataset
loaded_data <- load_data("phenotype.csv", "genotype.csv", "trait1")

### check all combinations of 2 SNPs
result1 <- rilstep(loaded_data, "result1", core_num = 8)

### using 1 SNP in each 500 SNPs
result2 <- rilstep(loaded_data, "result2", interval = 500)

### specify QTL-like SNPs by user.
result3 <- rilstep(loaded_data, "result3", qtls = c("chr08_19928351", "chr09_3909046"))

### using SNPs in specific regions
result4 <- rilstep(loaded_data, "result4", region = c("chr03_2132221:chr10_9330401", "chr03_2132221:chr10_9330401"))

### example
result5 <- rilstep(loaded_data, "result5", interval = 500, region = c("chr03_2132221:chr10_9330401", "chr03_2132221:chr10_9330401"), core_num = 8)

option region=c() can specify regions. rilstep check all combinations of SNPs in specified regions.

region = c("chr03_1234")                                    # chr03_1234 x all SNPs
region = c("chr03_1234:chr10_5678")                         # chr03_1234 ~ chr10_5678 x all SNPs
region = c("chr03_1234", "chr10_5678")                      # chr03_1234 x chr10_5678
region = c("chr03_1234:chr10_5678", "chr04_9012:chr7_3456") # chr03_1234~chr10_5678 x chr04_9012~chr7_3456

Input data format

input files should be csv format.

phenotype data

Name trait1
Sample1 12.3
Sample2 23.4
SampleXX 78.9

genotype data

Marker Chrom Position Sample1 Sample2 SampleXX
1 chr01 1234 2 2 2
2 chr01 3456 2 0 0
3 chr01 9999 0 0 1
XXX chr12 4869786 0 0 2

marker coded as {0,1,2} = {AA,AB,BB}
A, B: each parental genotype
!! Only bi-allelic markers are allowed !!

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