Last updated: 2022-07-18
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| File | Version | Author | Date | Message |
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| Rmd | 5651840 | Joelle Mbatchou | 2022-07-18 | session 3 practical |
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| Rmd | e1f9092 | Joelle Mbatchou | 2022-07-18 | session 3 practicals |
Before you begin:
require(data.table)
require(dplyr)
require(tidyr)
require(GWASTools)
require(ggplot2)We will be analyzing serum transferrin levels to identify whether there are associated variants in the genotype data. Serum transferrin is a biomarker which can give us information about iron levels in the body. Excessive iron can cause liver disease whereas iron deficiency can lead to anemia.
The file “Transferrin_pheno.txt”” contains the transferrin levels for a set of individuals and the file “Transferrin_height.bed” is a binary file in PLINK BED format with accompanying BIM and FAM files which contain the genotype data.
Here are some things to try:
Transferrin_pheno.txt and the
Transferrin_height.{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
plink2 --bfile Transferrin_height --pheno Transferrin_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>
)Compare your Manhattan plot to the one shown in Benjamin et al. (2009, AJHG).
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())
Now use REGENIE to perform a GWAS of the phenotype using a whole genome regression model.
Write the list of samples with non-missing phenotype to file (FID/IID columns).
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. Make sure to specify the ID of samples to analyze using
--keep. (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 LOCO. The basic command would look like
regenie --bed Transferrin_height --phenoFile Transferrin_pheno.txt --keep <nomiss.ids> --step 1 --bsize 1000 --qt --extract <plink_QC_pass_snplist> --out <output_prefix_step1>Skip this step and instead download the Step 1 output file transferrin_regenie_step1_1.loco
which contains the LOCO predictions for each chromosome.
Before running Step 2, generate the list file which has a single line
with phenotype name followed by path to *.loco file;
e.g.
Transferrin data/transferrin_regenie_step1_1.locoregenie --bed Transferrin_height --phenoFile Transferrin_pheno.txt --keep <nomiss.ids> --step 2 --bsize 400 --qt --pred <LOCO_list_file> --extract <plink_variant_list_file> --out <output_prefix_step2>
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7
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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[9] getPass_0.2-2 digest_0.6.25 jsonlite_1.7.2 evaluate_0.15
[13] tibble_2.1.3 lifecycle_1.0.1 pkgconfig_2.0.3 rlang_1.0.1
[17] cli_3.1.1 rstudioapi_0.13 yaml_2.2.1 xfun_0.31
[21] fastmap_1.1.0 httr_1.4.3 stringr_1.4.0 knitr_1.39
[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
[37] ps_1.7.0 promises_1.2.0.1 htmltools_0.5.2 ellipsis_0.3.2
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