Last updated: 2023-06-29
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SISG2023_Association_Mapping/
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Before you begin:
library(data.table)
library(dplyr)
library(tidyr)
library(GWASTools)
library(ggplot2)The R template to do the exercises is here.
Note: if on the online server, set your working directory to your home directory using in R
setwd("home/<username>/")The data files are in the folder
/data/SISG2023M15/data/.
We will be analyzing a simulated data set which contains sample structure to better understand the impact it can have in GWAS analyses if not accounted for. We will perform GWAS on a quantitative phenotype which was simulated to have high heritability and be highly polygenic.
The file “sim_rels_pheno.txt””
contains the phenotype measurements for a set of individuals and the
file “sim_rels_geno.bed” is a binary file in PLINK BED format with
accompanying BIM and FAM files which contains the genotype data
at null variants (i.e. simulated as not associated with
the phenotype).
How should we expect the QQ/Manhatthan plots to look like under this
scenario?
Here are some things to try:
sim_rels_pheno.txt and the
sim_rels_geno.{bed,bim,fam} genotype files. Only perform
association test on SNPs that pass the following quality control
threshold filters:The basic command would look like
system("/data/SISG2023M15/exe/plink2 --bfile /data/SISG2023M15/data/sim_rels_geno --pheno /data/SISG2023M15/data/sim_rels_pheno.txt --pheno-name <pheno_name> --maf <min_MAF> --geno <max_miss> --hwe <hwe_p_thresh> --glm allow-no-covars --out <output_prefix>")manhattanPlot() R function. The basic command would look
likemanhattanPlot(
p = <pvalues>,
chromosome = <chromosomes>,
thinThreshold = 1e-4,
main= <title>
)qqPlot() R function. The basic command would look likeqqPlot(
pval = <pvalues>,
thinThreshold = 1e-4,
main= <title>
)Compute the genomic control inflation factor \(\lambda_{GC}\) based on the p-values.
(Hint: convert p-values to \(\chi^2_1\)
test statistics using the R function qchisq()). Is there
evidence of possible inflation due to confounding?
Now use REGENIE to perform a GWAS of the phenotype using a whole genome regression model.
We want to use high quality variants in the Step 1 null model
fitting. Using PLINK, apply QC filters to remove variants with MAF below
5%, missingness above 1%, HWE p-value below 0.001, minor allele count
(MAC) below 20. (hint: use --write-snplist to store list of
variants passing QC without making a new BED file)
Run REGENIE Step 1 to fit the null model and obtain polygenic predictions using a leave-one-chromosome-out (LOCO) scheme. The basic command would look like
system("/data/SISG2023M15/exe/regenie --bed /data/SISG2023M15/data/sim_rels_geno --phenoFile /data/SISG2023M15/data/sim_rels_pheno.txt --step 1 --loocv --bsize 1000 --qt --extract <plink_QC_pass_snplist> --out <output_prefix_step1>")system("/data/SISG2023M15/exe/regenie --bed /data/SISG2023M15/data/sim_rels_geno --phenoFile /data/SISG2023M15/data/sim_rels_pheno.txt --step 2 --bsize 400 --qt --pred <output_prefix_step1>_pred.list --extract <plink_GWAS_snplist> --out <output_prefix_step2>")
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Australia/Brisbane
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] vctrs_0.6.2 cli_3.6.1 knitr_1.43 rlang_1.1.1
[5] xfun_0.39 stringi_1.7.12 promises_1.2.0.1 jsonlite_1.8.5
[9] workflowr_1.7.0 glue_1.6.2 rprojroot_2.0.3 git2r_0.32.0
[13] htmltools_0.5.5 httpuv_1.6.11 sass_0.4.6 fansi_1.0.4
[17] rmarkdown_2.22 evaluate_0.21 jquerylib_0.1.4 tibble_3.2.1
[21] fastmap_1.1.1 yaml_2.3.7 lifecycle_1.0.3 whisker_0.4.1
[25] stringr_1.5.0 compiler_4.3.0 fs_1.6.2 Rcpp_1.0.10
[29] pkgconfig_2.0.3 rstudioapi_0.14 later_1.3.1 digest_0.6.31
[33] R6_2.5.1 utf8_1.2.3 pillar_1.9.0 magrittr_2.0.3
[37] bslib_0.5.0 tools_4.3.0 cachem_1.0.8