Last updated: 2021-07-12

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This simulation uses UKB genotype data. We extract the genotype regions based on height GWAS result. There are 200 regions, each with 500 SNPs. We sample 50,000 individuals to simulate the data. We sample another 1000 samples to get reference LD matrix. We simulate data with 1,2,3 signals and PVE 0.005. We run susie_rss with L=10. We run FINEMAPv1.1 with oracle number of signals. We run FINEMAPv1.4 with oracle number of signals and max 4 signals. The reference panel has 500 samples.

PIP Calibration

SuSiE-RSS

SuSiE-RSS with refinement

CAVIAR

FINEMAP v1.1

FINEMAP v1.4

FINEMAP v1.4 L4

Power vs FDR

The left plot is SuSiE without refinement. The right plot is SuSiE with refinement.

Using in sample LD

Using ref LD

SuSiE-RSS with reference LD

FINEMAP v1.1 with reference LD

FINEMAP v1.4 with reference LD

FINEMAP v1.4 (L=4) with reference LD

CS

Overall

Without refinement

cs = readRDS('docs/assets/susierss_ukb_20210324_REF500_pve005/susierss_ukb_20210324_REF500_pve005_cs/susierss_ukb_cs_remdatallTRUE.rds')
rates = matrix(unlist(cs), length(cs), byrow = T)
rownames(rates) = names(cs)
colnames(rates) = c('discoveries', 'valid', 'size', 'purity', 'avgr2','expected', 'nonconverge',
                    'power', 'coverage', 'power_se', 'coverage_se')
rates = as.data.frame(rates)
rates$method = rownames(rates)
rates = rates[-c(2:8, 10:16, 19, 21, 23, 27, 29, 31, 33:36, 37, 39),]
rates = rates[-c(6,11),]
rates = rates[grep('refineFALSE', rates$method),]
methods = rates$method
rename_mets = gsub('_ldin', '', methods)
rename_mets = gsub('_ldrefout', '_ldref', rename_mets)
rename_mets = gsub('_ERNA', '', rename_mets)
rename_mets = gsub('_ERFALSE', '', rename_mets)
rename_mets = gsub('_AZFALSE', '', rename_mets)
rename_mets = gsub('_AZTRUE', '_AZ', rename_mets)
rename_mets = gsub('_pure', '', rename_mets)
rename_mets = gsub('finemapv4', 'FINEMAPv1.4', rename_mets)
rename = as.list(rename_mets)
names(rename) = methods
rates$method = sapply(rownames(rates), function(x) rename[[x]])
rates$method = gsub('_refineFALSE', '', rates$method)
rates$method = gsub('_lamb0$', '', rates$method)
library(kableExtra)
tb = rates[,c('method', 'nonconverge')]
rownames(tb) = NULL
t(tb) %>% kbl() %>% kable_styling()
method susie_suff susie_rss susie_rss_ldref susie_rss_ldref_lamb0.001 susie_rss_ldref_lambmlelikelihood susie_rss_ldref_AZ
nonconverge 0 0 1 0 0 9

With Refinement

cs = readRDS('docs/assets/susierss_ukb_20210324_REF500_pve005/susierss_ukb_20210324_REF500_pve005_cs/susierss_ukb_cs_remdatallTRUE.rds')
rates = matrix(unlist(cs), length(cs), byrow = T)
rownames(rates) = names(cs)
colnames(rates) = c('discoveries', 'valid', 'size', 'purity', 'avgr2','expected', 'nonconverge',
                    'power', 'coverage', 'power_se', 'coverage_se')
rates = as.data.frame(rates)
rates$method = rownames(rates)
rates = rates[-c(2:8, 10:16, 19, 21, 23, 27, 29, 31, 33:36, 37, 39),]
rates = rates[-c(6,11),]
rates = rates[grep('refineTRUE', rates$method),]
methods = rates$method
rename_mets = gsub('_ldin', '', methods)
rename_mets = gsub('_ldrefout', '_ldref', rename_mets)
rename_mets = gsub('_ERNA', '', rename_mets)
rename_mets = gsub('_ERFALSE', '', rename_mets)
rename_mets = gsub('_AZFALSE', '', rename_mets)
rename_mets = gsub('_AZTRUE', '_AZ', rename_mets)
rename_mets = gsub('_pure', '', rename_mets)
rename_mets = gsub('finemapv4', 'FINEMAPv1.4', rename_mets)
rename = as.list(rename_mets)
names(rename) = methods
rates$method = sapply(rownames(rates), function(x) rename[[x]])
rates$method = gsub('_refineTRUE', '', rates$method)
rates$method = gsub('_lamb0$', '', rates$method)
library(kableExtra)
tb = rates[,c('method', 'nonconverge')]
rownames(tb) = NULL
t(tb) %>% kbl() %>% kable_styling()
method susie_suff susie_rss susie_rss_ldref susie_rss_ldref_lamb0.001 susie_rss_ldref_lambmlelikelihood susie_rss_ldref_AZ
nonconverge 0 0 0 0 0 10

1 signal

Without refinement

With refinement

2 signals

Without refinement

With refinement

3 signals

Without refinement

With refinement


sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

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_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] kableExtra_1.3.4 workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6        highr_0.8         pillar_1.6.1      compiler_4.0.3   
 [5] later_1.1.0.1     git2r_0.28.0      tools_4.0.3       digest_0.6.27    
 [9] viridisLite_0.4.0 evaluate_0.14     lifecycle_1.0.0   tibble_3.1.2     
[13] pkgconfig_2.0.3   rlang_0.4.11      rstudioapi_0.13   yaml_2.2.1       
[17] xfun_0.22         stringr_1.4.0     httr_1.4.2        knitr_1.31       
[21] xml2_1.3.2        systemfonts_1.0.1 fs_1.5.0          vctrs_0.3.8      
[25] webshot_0.5.2     rprojroot_2.0.2   svglite_2.0.0     glue_1.4.2       
[29] R6_2.5.0          fansi_0.5.0       rmarkdown_2.7     magrittr_2.0.1   
[33] whisker_0.4       scales_1.1.1      promises_1.2.0.1  ellipsis_0.3.2   
[37] htmltools_0.5.1.1 rvest_1.0.0       colorspace_2.0-2  httpuv_1.5.5     
[41] utf8_1.2.1        stringi_1.5.3     munsell_0.5.0     crayon_1.4.1