Last updated: 2025-10-12

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Knit directory: Improved_LD_SuSiE/

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This is an illustration of using SuSiE-RSS to finemap a blood cell trait, reticulocyte percentage, using summary statistics from a PLINK association analysis of the UK Biobank data. Here we focus on a region on chromosome 1 near the gene PKLR which is known to contain a rare missense variant rs116100695 causing red cell pyruvate kinase deficiency (see Astle et al, Cell, 2016 and the OMIM entry).

To run this analysis, you will first need to download the association statistics from the UK Biobank data and the LD matrix. Download these two files from the Box folder and move them into the “data” directory of this repository:

bloodcells_chr1.153094636.157301801.z.rds
bloodcells_chr1.153094636.157301801.matrix.gz

Load the packages needed for this analysis:

library(data.table)
library(susieR)
library(ggplot2)
library(ggrepel)
library(cowplot)

Set the seed for reproducibility:

set.seed(1)

Get the z-scores:

dat <- readRDS("data/bloodcells_chr1.153094636.157301801.z.rds")
n <- dat$n
z <- dat$Z[,"Reticulocyte_perc"]
pos <- dat$pos$POS/1e6
ids <- dat$pos$ID
names(z) <- ids

Import the “in-sample” LD matrix:

R <- fread("data/bloodcells_chr1.153094636.157301801.matrix.gz",
           sep = " ",verbose = FALSE)
R <- as.matrix(R)
rownames(R) <- ids
colnames(R) <- ids

Run SuSiE-RSS with at most 5 “single effects” (i.e., causal SNPs):

fit <- susie_rss(z,R,n,L = 5,min_abs_corr = 0,coverage = 0.95,
                 max_iter = 100,verbose = TRUE)
fit$lbf
fit$V
# [1] "objective:-353173.931322825"
# [1] "objective:-353173.606429479"
# [1] "objective:-353173.597867136"
# [1] "objective:-353173.597254807"
# [1] 64.4319821 42.7729293  4.4479161  1.6243978  0.4958185
# [1] 6.160781e-04 4.377370e-04 1.180881e-04 6.350878e-05 2.699047e-05

This is my custom function to visualize the PIPs and the CSs:

susie_pip_plot <-
  function (fit,
            max_snps = 100,
            font_size = 10,
            cs_colors = c("#e41a1c","#4daf4a","#ff7f00","#ffff33",
                          "#a65628","#f781bf","#984ea3","#377eb8")) {
  cs  <- fit$sets$cs
  pip <- fit$pip
  m   <- length(cs)
  pdat_cs <- NULL
  for (i in 1:m) {
    j <- cs[[i]]
    if (length(j) < max_snps) {
      x <- data.frame(cs  = sprintf("CS%d (%d SNPs)",i,length(j)),
                      pos = pos[j],
                      id  = "",
                      pip = pip[j])
      k <- which.max(x$pip)
      x[k,"id"] <- ids[j[k]]
      pdat_cs <- rbind(pdat_cs,x)
    } 
  }
  pdat_cs <- transform(pdat_cs,cs = factor(cs))
  pdat_pip <- data.frame(pip = pip,pos = pos)
  return(ggplot() +
    geom_point(data = pdat_pip,
               mapping = aes(x = pos,y = pip),
               size = 1,color = "black") +
    geom_point(data = pdat_cs,
               mapping = aes(x = pos,y = pip,color = cs),
               size = 2) +
    geom_text_repel(data = pdat_cs,
                    mapping = aes(x = pos,y = pip,label = id),
                    size = 3,min.segment.length = 0,max.overlaps = Inf) +
    scale_x_continuous(breaks = seq(0,300,0.5)) +
    scale_color_manual(values = cs_colors) +
    labs(x = "base-pair position on chromosome 1 (Mb)",
         y = "PIP",
         color = "CS") +
    theme_cowplot(font_size = font_size) +
    theme(legend.position = "bottom",
          legend.direction = "vertical"))
}

This PIP plot summarizes the results of the fine-mapping analysis:

susie_pip_plot(fit)

Version Author Date
17d3b7f Peter Carbonetto 2025-10-12

SuSiE identified the missense variant but also identified 2 SNPs within 500 kb of PKLR with fairly high confidence.

Notes

The files bloodcells_chr1.153094636.157301801.z.rds and bloodcells_chr1.153094636.157301801.matrix.gz were originally downloaded from the subdirectories regions_zscores_maf001_info6 and regions_ld_maf001_info6 within directory /gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/BloodCells on randi.


sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS 15.6.1
# 
# 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: America/Chicago
# tzcode source: internal
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_1.1.3     ggrepel_0.9.6     ggplot2_3.5.2     susieR_0.14.22   
# [5] data.table_1.17.6 workflowr_1.7.1  
# 
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#  [7] evaluate_1.0.4      grid_4.3.3          RColorBrewer_1.1-3 
# [10] fastmap_1.2.0       R.oo_1.26.0         plyr_1.8.9         
# [13] rprojroot_2.0.4     jsonlite_2.0.0      Matrix_1.6-5       
# [16] R.utils_2.12.3      processx_3.8.3      whisker_0.4.1      
# [19] reshape_0.8.9       ps_1.7.6            mixsqp_0.3-54      
# [22] promises_1.3.3      httr_1.4.7          scales_1.4.0       
# [25] jquerylib_0.1.4     cli_3.6.5           rlang_1.1.6        
# [28] crayon_1.5.3        R.methodsS3_1.8.2   withr_3.0.2        
# [31] cachem_1.1.0        yaml_2.3.10         parallel_4.3.3     
# [34] tools_4.3.3         dplyr_1.1.4         httpuv_1.6.14      
# [37] RcppZiggurat_0.1.6  Rfast_2.1.0         vctrs_0.6.5        
# [40] R6_2.6.1            matrixStats_1.2.0   lifecycle_1.0.4    
# [43] git2r_0.33.0        stringr_1.5.1       fs_1.6.6           
# [46] irlba_2.3.5.1       pkgconfig_2.0.3     callr_3.7.5        
# [49] RcppParallel_5.1.10 pillar_1.11.0       bslib_0.9.0        
# [52] later_1.4.2         gtable_0.3.6        glue_1.8.0         
# [55] Rcpp_1.1.0          xfun_0.52           tibble_3.3.0       
# [58] tidyselect_1.2.1    rstudioapi_0.15.0   knitr_1.50         
# [61] dichromat_2.0-0.1   farver_2.1.2        htmltools_0.5.8.1  
# [64] labeling_0.4.3      rmarkdown_2.29      compiler_4.3.3     
# [67] getPass_0.2-4