Last updated: 2023-06-12

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Introduction

The goal of this analysis is to benchmark the newly developed mr.mash.rss (aka mr.mash with summary statistics) against already existing methods in the task of predicting phenotypes from genotypes. To do so, we used real genotypes from the array data of the UK Biobank. We randomly sampled 105,000 nominally unrelated (\(r_A\) < 0.025) individuals of European ancestry (i.e., Caucasian and white British fields). After retaining variants with minor allele frequency (MAF) > 0.01, minor allele count (MAC) > 5 genotype missing rate < 0.1 and Hardy-Weinberg Equilibrium (HWE) test p-value > \(1 *10^{-10}\), our data consisted of 595,071 genetic variants (i.e., our predictors).

Equal effects scenario

###Load libraries
library(ggplot2)
library(cowplot)

repz <- 1:20
prefix <- "output/prediction_accuracy/ukb_caucasian_white_british_unrel_100000"
scenarioz <- "equal_effects_indep_resid"
methodz <- c("mr_mash_rss", "ldpred2_auto", "ldpred2_auto_gwide")
metric <- "r2"
traitz <- 1:5

i <- 0

n_col <- 6
n_row <- length(repz) * length(scenarioz) * length(methodz) * length(traitz)
res <- as.data.frame(matrix(NA, ncol=n_col, nrow=n_row))
colnames(res) <- c("rep", "scenario", "method", "trait", "metric", "score")



for(sce in scenarioz){
  for(met in methodz){
    for(repp in repz){
      dat <- readRDS(paste0(prefix, "_", sce, "_", met, "_pred_acc_", repp, ".rds"))
      
      for(trait in traitz){
        i <- i + 1
        
        res[i, 1] <- repp
        res[i, 2] <- sce
        res[i, 3] <- met
        res[i, 4] <- trait
        res[i, 5] <- metric
        res[i, 6] <- dat$r2[trait]
      }
    }
  }
}

res <- transform(res, scenario=as.factor(scenario),
                      method=as.factor(method),
                      trait=as.factor(trait))

p_methods_shared <- ggplot(res, aes(x = trait, y = score, fill = method)) +
  geom_boxplot(color = "black", outlier.size = 1, width = 0.85) +
  stat_summary(fun=mean, geom="point", shape=23,
               position = position_dodge2(width = 0.87,   
                                          preserve = "single")) +
  ylim(0.3, 0.51) +
  scale_fill_manual(values = c("pink", "red", "green"))+ #, labels = c("g-lasso", "smt-lasso", "e-net")) +
  labs(x = "Trait", y = expression(italic(R)^2), title = "Equal effects", fill="Method") +
  geom_hline(yintercept=0.5, linetype="dotted", linewidth=1, color = "black") +
  theme_cowplot(font_size = 16) +
  theme(plot.title = element_text(hjust = 0.5, size=14))

print(p_methods_shared)


sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.5 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/libs/gnu9/openblas/0.3.7/lib/libopenblasp-r0.3.7.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] cowplot_1.1.1 ggplot2_3.4.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.10      highr_0.10       pillar_1.9.0     compiler_4.1.2  
 [5] bslib_0.4.2      later_1.3.0      jquerylib_0.1.4  git2r_0.31.0    
 [9] workflowr_1.7.0  tools_4.1.2      digest_0.6.31    gtable_0.3.3    
[13] jsonlite_1.8.4   evaluate_0.20    lifecycle_1.0.3  tibble_3.2.1    
[17] pkgconfig_2.0.3  rlang_1.1.0      cli_3.6.1        rstudioapi_0.14 
[21] yaml_2.3.7       xfun_0.37        fastmap_1.1.1    withr_2.5.0     
[25] dplyr_1.1.1      stringr_1.5.0    knitr_1.42       generics_0.1.3  
[29] fs_1.6.1         vctrs_0.6.1      sass_0.4.5       tidyselect_1.2.0
[33] rprojroot_2.0.3  grid_4.1.2       glue_1.6.2       R6_2.5.1        
[37] fansi_1.0.4      rmarkdown_2.20   farver_2.1.1     magrittr_2.0.3  
[41] whisker_0.4.1    scales_1.2.1     promises_1.2.0.1 htmltools_0.5.4 
[45] colorspace_2.1-0 httpuv_1.6.9     labeling_0.4.2   utf8_1.2.3      
[49] stringi_1.7.12   munsell_0.5.0    cachem_1.0.7