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Rmd c1092d9 Joelle Mbatchou 2022-07-18 session 7 exercises

Before you begin:

  • Make sure that R is installed on your computer
  • For this lab, we will use the following R libraries:
require(data.table)
require(dplyr)
require(tidyr)
require(BEDMatrix)
require(SKAT)
require(ACAT)
require(ggplot2)

The R template to do the exercises is here.

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)

  2. 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)
  • 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
  1. Run the single variant association tests in PLINK (only for the extracted variants).
  • Compute the minimum p-value across SNPs and apply Bonferroni correction for the multiple testing (hint: take \(\min(1, p\cdot K)\), where \(p\) is the minimum p-value and \(K\) is the number of tests). Is anything significant after adjusting for multiple testing?
  • 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

plink2 --bfile <BED_file_with_extracted_SNPs> --pheno 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 Qesution 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 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.0

loaded via a namespace (and not attached):
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[13] tibble_2.1.3     lifecycle_1.0.1  pkgconfig_2.0.3  rlang_1.0.4     
[17] cli_3.1.1        rstudioapi_0.13  yaml_2.2.1       xfun_0.31       
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[25] sass_0.4.0       fs_1.5.2         vctrs_0.3.8      rprojroot_2.0.3 
[29] glue_1.6.1       R6_2.4.1         processx_3.5.3   fansi_0.4.1     
[33] rmarkdown_2.14   callr_3.7.0      magrittr_1.5     whisker_0.4     
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