Skip to content

Latest commit

 

History

History
89 lines (53 loc) · 4.31 KB

README.md

File metadata and controls

89 lines (53 loc) · 4.31 KB
Raw

UA Blackberry GWAS Project

Step 1. Filtering original VCF data in readVCF_ARonly.R

Description: Because not all samples in the ARK_133901 GWAS panel are tetraploid, it is necessary to remove the higher ploidy Oregon materials and then remove entries that no longer contain variants. This R script hard codes the location of a local text file containing the sample IDs of samples that we wish to include. All others are removed. This script processed a 125,000 SNP x 495 sample vcf in about 15 minutes on a normal laptop.

Installing R package dependencies:

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("VariantAnnotation")

inputs:

  • The original, compressed and tabix indexed vcf file containing all samples
  • Text file containing subset sample IDs

output:

  • An uncompressed, filtered vcf file containing only the desired samples with non-variants removed

Step 2. Tetraploid SNP calls and GWASpoly formatting in Dogify.R

Description: This script makes tetraploid SNP calls using the multidog() function in the updog package, which supports parallel computing. This script runs multidog incrementally over a user-assigned number of SNPS to avoid memory exhaustion. Then, the estimated SNP allele dosage calls in the 'inddf' data frame are reformatted for GWASpoly. This script will take a while and should be run on a high performance computing system if available.

Installing R package dependencies:

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("VariantAnnotation")
install.packages("tidyverse")
install.packages("updog")

Inputs:

  • Uncompressed VCF file
  • SLURM job file (Optional. For running on AHPCC pinnacle)

Output:

  • CSV file containing tetraploid SNP calls formatted for GWASpoly

Step 3. Using GWASpoly to perform association analysis

Description: GWASpoly reads in formatted genotypic and phenotypic data with read.GWASpoly() to generate a covariance matrix with set.K() (accounting for population structure). Finally, GWASpoly() does the heavy-lifting by performing the association analysis on the dataset returned by set.K(). A detailed vignette and manual are available on the github page (see link above). Below is a sample script with placeholders for user-specific inputs.

Installing R package dependencies:

library(devtools)
install_github("jendelman/GWASpoly")
install.packages("ggplot2")
install.packages("rrBLUP")

Sample Script for UA blackberry GWAS:

library(GWASpoly)
library(ggplot2)
library("rrBLUP")

# reading in genotypic and phenotypic datasets
data1 <- read.GWASpoly(ploidy = 4, pheno.file= "YourPathToPheno.csv", geno.file= "YourPathToGeno.csv", format= "numeric", , n.traits = YourNumber, delim = ",")

# set the covariance matrix for population structure
data2 <- set.K(data1)

# run the association analysis
data3 <- GWASpoly(mydata2, models = c("additive", "1-dom"), traits = c("trait1", "trait2", "etc..."))

# set the significance threshold
data4 <- set.threshold(MarkerSig, method="Bonferroni", level=0.05)

# extract the significant QTLs into a dataframe
myQTLs <- get.QTLs(data4)

# inspect qqplots
qqplot(data4)

# inspect Manhattan plots
manhattan.plot(plot4)

References

Gerard, D., Ferrao, L. F. V., Garcia, A. A. F., & Stephens, M. (2018). Genotyping Polyploids from Messy Sequencing Data. >Genetics, 210(3), 789-807. doi: 10.1534/genetics.118.301468.

Rosyara, U. R., De Jong, W. S., Douches, D. S., & Endelman, J. B. (2016). Software for genome-wide association studies in >autopolyploids and its application to potato. The plant genome, 9(2), plantgenome2015-08.