Last updated: 2025-10-12
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Knit directory: Improved_LD_SuSiE/
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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.gzLoad 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) <- idsImport the “in-sample” LD matrix:
R <- fread("data/bloodcells_chr1.153094636.157301801.matrix.gz",
sep = " ",verbose = TRUE)
R <- as.matrix(R)
rownames(R) <- ids
colnames(R) <- ids
# OpenMP version (_OPENMP) 201811
# omp_get_num_procs() 8
# R_DATATABLE_NUM_PROCS_PERCENT unset (default 50)
# R_DATATABLE_NUM_THREADS unset
# R_DATATABLE_THROTTLE unset (default 1024)
# omp_get_thread_limit() 2147483647
# omp_get_max_threads() 8
# OMP_THREAD_LIMIT unset
# OMP_NUM_THREADS unset
# RestoreAfterFork true
# data.table is using 4 threads with throttle==1024. See ?setDTthreads.
# freadR.c has been passed a filename: /var/folders/9b/ck4lp8s140lcksryyh4dppdr0000gp/T//RtmpbkVxkq/filefb341eebd464
# [01] Check arguments
# Using 4 threads (omp_get_max_threads()=8, nth=4)
# NAstrings = [<<NA>>]
# None of the NAstrings look like numbers.
# show progress = 0
# 0/1 column will be read as integer
# Y/N column will be read as character
# [02] Opening the file
# Opening file /var/folders/9b/ck4lp8s140lcksryyh4dppdr0000gp/T//RtmpbkVxkq/filefb341eebd464
# File opened, size = 3.061GB (3286687694 bytes).
# Memory mapped ok
# [03] Detect and skip BOM
# [04] Arrange mmap to be \0 terminated
# \n has been found in the input and different lines can end with different line endings (e.g. mixed \n and \r\n in one file). This is common and ideal.
# [05] Skipping initial rows if needed
# Positioned on line 1 starting: <<1 -0.016736 -0.00106111 -0.001>>
# [06] Detect separator, quoting rule, and ncolumns
# Using supplied sep ' '
# sep=' ' with 100 lines of 17048 fields using quote rule 0
# Detected 17048 columns on line 1. This line is either column names or first data row. Line starts as: <<1 -0.016736 -0.00106111 -0.001>>
# Quote rule picked = 0
# fill=false and the most number of columns found is 17048
# [07] Detect column types, dec, good nrow estimate and whether first row is column names
# Number of sampling jump points = 10 because (3286687693 bytes from row 1 to eof) / (2 * 19613164 jump0size) == 83
# dec='.' detected based on a balance of 1704799 parsed fields
# Type codes (jump 000) : 99999999999999999999999999999999999999999999999999999999999999999999999999999999...9999999999 Quote rule 0
# Type codes (jump 010) : 99999999999999999999999999999999999999999999999999999999999999999999999999999999...9999999999 Quote rule 0
# 'header' determined to be false because there are some number columns and those columns do not have a string field at the top of them
# =====
# Sampled 1050 rows (handled \n inside quoted fields) at 11 jump points
# Bytes from first data row on line 1 to the end of last row: 3286490092
# Line length: mean=192865.54 sd=4221.24 min=184037 max=199377
# Estimated number of rows: 3286490092 / 192865.54 = 17041
# Initial alloc = 18745 rows (17041 + 9%) using bytes/max(mean-2*sd,min) clamped between [1.1*estn, 2.0*estn]
# =====
# [08] Assign column names
# [09] Apply user overrides on column types
# After 0 type and 0 drop user overrides : 99999999999999999999999999999999999999999999999999999999999999999999999999999999...9999999999
# [10] Allocate memory for the datatable
# Allocating 17048 column slots (17048 - 0 dropped) with 18745 rows
# [11] Read the data
# jumps=[0..20), chunk_size=164324504, total_size=3286687693
# Read 17048 rows x 17048 columns from 3.061GB (3286687694 bytes) file in 00:10.230 wall clock time
# [12] Finalizing the datatable
# Type counts:
# 17048 : float64 '9'
# =============================
# 0.009s ( 0%) Memory map 3.061GB file
# 0.943s ( 9%) sep=' ' ncol=17048 and header detection
# 0.005s ( 0%) Column type detection using 1050 sample rows
# 1.547s ( 15%) Allocation of 18745 rows x 17048 cols (2.381GB) of which 17048 ( 91%) rows used
# 7.727s ( 76%) Reading 20 chunks (0 swept) of 156.712MB (each chunk 852 rows) using 4 threads
# + 7.209s ( 70%) Parse to row-major thread buffers (grown 0 times)
# + 0.392s ( 4%) Transpose
# + 0.126s ( 1%) Waiting
# 0.000s ( 0%) Rereading 0 columns due to out-of-sample type exceptions
# 10.230s TotalThe file bloodcells_chr1.153094636.157301801.z.rds was
originally downloaded from the directory
/gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/BloodCells/regions_zscores_maf001_info6
on randi.
bloodcells_chr1.153094636.157301801.matrix.gz was downloaded # from /gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/BloodCells/ # regions_ld_maf001_info6 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
#
# loaded via a namespace (and not attached):
# [1] sass_0.4.10 generics_0.1.4 stringi_1.8.7 lattice_0.22-5
# [5] digest_0.6.37 magrittr_2.0.3 evaluate_1.0.4 grid_4.3.3
# [9] RColorBrewer_1.1-3 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 R.utils_2.12.3
# [17] processx_3.8.3 whisker_0.4.1 reshape_0.8.9 ps_1.7.6
# [21] mixsqp_0.3-54 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 crayon_1.5.3
# [29] R.methodsS3_1.8.2 withr_3.0.2 cachem_1.1.0 yaml_2.3.10
# [33] tools_4.3.3 dplyr_1.1.4 httpuv_1.6.14 vctrs_0.6.5
# [37] R6_2.6.1 matrixStats_1.2.0 lifecycle_1.0.4 git2r_0.33.0
# [41] stringr_1.5.1 fs_1.6.6 irlba_2.3.5.1 pkgconfig_2.0.3
# [45] callr_3.7.5 pillar_1.11.0 bslib_0.9.0 later_1.4.2
# [49] gtable_0.3.6 glue_1.8.0 Rcpp_1.1.0 xfun_0.52
# [53] tibble_3.3.0 tidyselect_1.2.1 rstudioapi_0.15.0 knitr_1.50
# [57] dichromat_2.0-0.1 farver_2.1.2 htmltools_0.5.8.1 rmarkdown_2.29
# [61] compiler_4.3.3 getPass_0.2-4