Last updated: 2023-06-29

Checks: 7 0

Knit directory: SISG2023_Association_Mapping/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20230530)

The command set.seed(20230530) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: c3fd432

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version c3fd432. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    data/sim_rels_geno.bed
    Ignored:    exe/
    Ignored:    mk_website.R
    Ignored:    tmp/

Untracked files:
    Untracked:  .mk_website.R.swp
    Untracked:  analysis/SISGM15_prac4Solution.Rmd
    Untracked:  analysis/SISGM15_prac5Solution.Rmd
    Untracked:  analysis/SISGM15_prac6Solution.Rmd
    Untracked:  analysis/SISGM15_prac9Solution.Rmd
    Untracked:  analysis/Session01_practical_Key.Rmd
    Untracked:  analysis/Session02_practical_Key.Rmd
    Untracked:  analysis/Session03_practical_Key.Rmd
    Untracked:  analysis/Session07_practical_Key.Rmd
    Untracked:  analysis/Session08_practical_Key.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Session07_practical.Rmd) and HTML (docs/Session07_practical.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html c3fd432 Joelle Mbatchou 2023-06-29 Build site.
Rmd fd7086c Joelle Mbatchou 2023-06-29 cleanup edit
Rmd 5163a3d Joelle Mbatchou 2023-06-29 update year
Rmd 1f3a4dc Joelle Mbatchou 2023-06-26 add files for website

Before you begin:

  • Make sure that R is installed on your computer
  • For this lab, we will use the following R libraries:
library(data.table)
library(dplyr)
library(tidyr)
library(BEDMatrix)
library(SKAT)
library(ACAT)
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/.

Rare Variant Analysis

Introduction

We will look into a dataset collected on a quantitative phenotype which was first analyzed through GWAS and a signal was detected in chromosome 1. Let’s determine whether the signal is present when we focus on rare variation at the locus. In our analyses, we will define rare variants as those with \(MAF \leq 5\%\).

The file “rv_pheno.txt”” contains the phenotype measurements for a set of individuals and the file “rv_geno_chr1.bed” is a binary file in PLINK BED format with accompanying BIM and FAM files which contains the genotype data.

Exercises

Here are some things to try:

  1. Using PLINK, extract rare variants in a new PLINK BED file. (Hint: use options --max-maf to select rare variants and --maj-ref force so that the minor allele is the effect allele)
system("/data/SISG2023M15/exe/plink2 --bfile /data/SISG2023M15/data/rv_geno_chr1 --max-maf <..> --maj-ref force --make-bed --out <output_prefix>")
  1. Load the data in R:
  • Read in the SNPs using R function BEDMatrix() (hint: use option simple_names = TRUE to easily filter by sample IID later)
G <- BEDMatrix("<bed_file_prefix>", simple_names = TRUE)
  • Load the phenotype data from rv_pheno.txt
  • Keep only samples who are present both in the genotype as well as phenotype data and who don’t have missing values for the phenotype
  1. Examine the genotype data:
  • Compute the minor allele frequency (MAF) for each SNP and plot histogram. (hint: use na.rm=TRUE when calling mean())
  • Check for missing values (hint: use function is.na() which returns TRUE/FALSE value for missing status)
  1. Run the single variant association tests in PLINK (only for the extracted variants).
  • What would be your significance threshold after applying Bonferroni correction for the multiple tests (assume the significance level is 0.05)? Is anything significant after this correction?
  • Make a volcano plot (i.e. log10 p-values vs effect sizes). Which of the Burden/SKAT/ACAT tests do you expect will give us most power?

Reminder: The PLINK2 command would look like

system("/data/SISG2023M15/exe/plink2 --bfile <BED_file_with_extracted_SNPs> --pheno /data/SISG2023M15/data/rv_pheno.txt --pheno-name <pheno_name> --glm allow-no-covars --out <output_prefix>")
  1. We will first compare three collapsing/burden approaches:
  • CAST (Binary collapsing approach): for each individual, count where they have a rare allele at any of the sites
  • MZ Test/GRANVIL (Count based collapsing): for each individual, count the total number of sites where a rare allele is present
  • Weighted burden test: for each individual, take a weighted count of the rare alleles across sites (for the weights, use weights <- dbeta(MAF, 1, 25))

For each approach, first generate the burden scores vector then test it for association with the phenotype using lm() R function.

  1. Now use SKAT to test for an association. The basic command would look like
# fit null model (no covariates)
skat.null <- SKAT_Null_Model( <phenotype_vector> ~ 1 , out_type = "C")
# Run SKAT association test (returns a list - p-value is in `$p.value`)
SKAT( <genotype_matrix>, skat.null )
  1. Run the omnibus SKAT, but consider setting \(\rho\) (i.e.r.corr) to 0 and then 1.
  • Compare the results to using the CAST,MZ/GRANVIL and Weighted burden collapsing approaches in Question 5 as well as SKAT in Question 6. What tests do these \(\rho\) values correspond to? The basic command would look like
# Run SKATO association test specifying rho
SKAT( <genotype_matrix>, skat.null, r.corr = <rho_value>)
  1. Now the omnibus version of SKAT, but use the “optimal.adj” approach which searches across a range of rho values. The basic command would look like
# Run SKATO association test using grid of rho values
SKAT( <genotype_matrix>, skat.null, method="optimal.adj")
  1. Run ACATV on the single variant p-values. The basic command would look like
# `weights` vector is from Question 5
acat.weights <- weights * weights * MAF * (1 - MAF)
ACAT( <pvalues>, weights = acat.weights)
  1. Run ACATO combining the SKAT and BURDEN p-values (from Question 7) with the ACATV p-value (from Question 9). The basic command would look like
ACAT( c(<pvalue_SKAT>, <pvalue_Burden>, <pvalue_ACATV>))

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