Plotting the effect sizes after adjustment using mashr
Last updated: 2021-09-26
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Knit directory: fitnessGWAS/
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library(dplyr)
library(readr)
library(ggplot2)
library(gridExtra)
library(tidyr)
db <- DBI::dbConnect(RSQLite::SQLite(),
"data/derived/annotations.sqlite3")
# Results for all 1,613,615 SNPs, even those that are in 100% LD with others (these are grouped up by the SNP_clump column)
all_snps <- tbl(db, "univariate_lmm_results")
# All SNPs and SNP groups that are in <100% LD with one another (n = 1,207,357)
SNP_clumps <- all_snps %>% select(-SNP) %>% distinct() %>% collect(n=Inf)
# Subsetting variable to get the approx-LD subset of SNPs
LD_subset <- !is.na(SNP_clumps$LFSR_female_early_mashr_ED)Inspecting the effect of adaptive shrinkage on the SNP effect size estimates
The plots reveal that the R package mashr, which implements multivariate adaptive shrinkage, was effective at shrinking the effect size estimates towards zero. The amount of shrinkage applied was slightly stronger when applying mashr using data-driven covariance matrices, as opposed to ‘canonical’ covariance matrices that were selected a priori by us.
hex_plot <- function(x, y, xlab, ylab){
filter(SNP_clumps,LD_subset) %>%
ggplot(aes_string(x, y)) +
geom_abline(linetype = 2) +
geom_vline(xintercept = 0, linetype = 3) +
geom_hline(yintercept = 0, linetype = 3) +
stat_binhex(bins = 200, colour = "#FFFFFF00") +
scale_fill_distiller(palette = "Purples") +
coord_cartesian(xlim = c(-1,1), ylim = c(-0.55, 0.3)) +
theme_minimal() + xlab(xlab) + ylab(ylab) +
theme(legend.position = "none")
}
grid.arrange(
hex_plot("beta_female_early_raw",
"beta_female_early_mashr_canonical",
"Raw estimate of SNP\neffect size from LMM",
"Corrected estimate from\nmashr (canonical)"),
hex_plot("beta_female_early_raw",
"beta_female_early_mashr_ED",
"Raw estimate of SNP\neffect size from LMM",
"Corrected estimate from\nmashr (data-driven)"),
hex_plot("beta_female_early_mashr_canonical",
"beta_female_early_mashr_ED",
"Corrected estimate from\nmashr (canonical)",
"Corrected estimate from\nmashr (data-driven)"),
ncol = 3)
Figure SX: Plots comparing the raw effect sizes for each locus (calculated by linear mixed models implemented in GEMMA, LMM) with the effect sizes obtained using adaptive shrinkage implemented in mashr (either in ‘data-driven’ or ‘canonical’ methods). The dashed line shows \(y = x\), such that the first two plots illustrate that both forms of shrinkage moved both negative and positive effects towards zero. The third plot illustrates that very similar effect sizes were obtained whether we used the data-driven or canonical method.
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidyr_1.1.0 gridExtra_2.3 ggplot2_3.3.2 readr_2.0.0
[5] dplyr_1.0.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 highr_0.8 RColorBrewer_1.1-2 dbplyr_1.4.4
[5] compiler_4.0.3 pillar_1.4.4 later_1.0.0 git2r_0.27.1
[9] tools_4.0.3 bit_1.1-15.2 digest_0.6.25 lattice_0.20-41
[13] memoise_1.1.0 RSQLite_2.2.0 evaluate_0.14 lifecycle_0.2.0
[17] tibble_3.0.1 gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.6
[21] DBI_1.1.0 yaml_2.2.1 hexbin_1.28.1 xfun_0.22
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