Fine-mapping
Siming Zhao
2026-04-27
Last updated: 2026-04-29
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Knit directory: QBS-statsgen/
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| Rmd | f627c66 | Siming | 2026-04-27 | Update exercise 2 content, add 2a-2c links to index |
| html | f627c66 | Siming | 2026-04-27 | Update exercise 2 content, add 2a-2c links to index |
| Rmd | a29f425 | simingz | 2024-01-23 | e2 |
| html | a29f425 | simingz | 2024-01-23 | e2 |
Before the class
Install R packages susieR and Rfast (the
latter speeds up credible set calculations):
install.packages("susieR")
install.packages("Rfast")Explore the data we will use the analysis
rm(list=ls())
library(susieR)Warning: package 'susieR' was built under R version 4.3.3data(N3finemapping)
attach(N3finemapping)names(N3finemapping)[1] "X" "chrom" "pos"
[4] "true_coef" "residual_variance" "Y"
[7] "allele_freq" "V" The genotype matrix is X, The phenotype matrix is
Y.
We focus on the first trait, let
y = Y[,1]
b = true_coef[,1]
which(b != 0)[1] 403 653 773Let’s perform a univariate analysis
sumstats <- univariate_regression(X, y)
z_scores <- sumstats$betahat / sumstats$sebetahat
log10p <- -log10(pchisq(z_scores^2,1,lower.tail=F))
susie_plot(z_scores,y="z",b=b)
fig.path you set was
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Fine mapping using individual level data
fitted <- susie(X, y, L = 10)HINT: For an X with many columns, please consider installingthe Rfast package for more efficient credible set (CS)calculations.By default, susie function computes 95% CS each containing one effect variable,
print(fitted$sets)$cs
$cs$L2
[1] 653
$cs$L1
[1] 773 777
$cs$L3
[1] 362 365 372 373 374 379 381 383 384 386 387 388 389 391 392 396 397 398 399
[20] 400 401 403 404 405 407 408 415
$purity
min.abs.corr mean.abs.corr median.abs.corr
L2 1.0000000 1.0000000 1.0000000
L1 0.9815726 0.9815726 0.9815726
L3 0.8686309 0.9640176 0.9720711
$cs_index
[1] 2 1 3
$coverage
[1] 0.9998236 0.9988858 0.9539811
$requested_coverage
[1] 0.95Plot Posterior Inclusion Probability
susie_plot(fitted, y="PIP", b=b, add_legend=T)
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| Version | Author | Date |
|---|---|---|
| a29f425 | simingz | 2024-01-23 |
Choice of prior effect size:
fitted2 = susie(X, y, L = 10, estimate_prior_variance = FALSE, scaled_prior_variance = 0.2)HINT: For an X with many columns, please consider installingthe Rfast package for more efficient credible set (CS)calculations.susie_plot(fitted2, y='PIP', b=b, add_legend=T)
fig.path you set was
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| Version | Author | Date |
|---|---|---|
| a29f425 | simingz | 2024-01-23 |
fitted2 = susie(X, y, L = 10, estimate_prior_variance = FALSE, scaled_prior_variance = 0.001)HINT: For an X with many columns, please consider installingthe Rfast package for more efficient credible set (CS)calculations.susie_plot(fitted2, y='PIP', b=b, add_legend=T)
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| Version | Author | Date |
|---|---|---|
| a29f425 | simingz | 2024-01-23 |
Fine-mapping with summary statistics via susie_rss
z-scores are provided and we can compute R from X.
R <- cor(X)
fitted_rss <- susie_rss(z_scores, R, L = 10, n = nrow(X))HINT: For large R or large XtX, consider installing the Rfast package for better performance.plot(fitted$pip, fitted_rss$pip, ylim=c(0,1))
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Credit
Credit to Gao Wang: https://statgenetics.github.io/statgen-courses/notebooks/finemapping.html#Fine-mapping-with-summary-statistics-via-susie_rss-6
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.6.1
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/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] susieR_0.14.2
loaded via a namespace (and not attached):
[1] Matrix_1.6-1.1 gtable_0.3.6 jsonlite_2.0.0 dplyr_1.2.0
[5] compiler_4.3.2 crayon_1.5.3 promises_1.5.0 tidyselect_1.2.1
[9] Rcpp_1.1.1 stringr_1.6.0 git2r_0.36.2 later_1.4.8
[13] jquerylib_0.1.4 scales_1.4.0 yaml_2.3.12 fastmap_1.2.0
[17] lattice_0.21-9 plyr_1.8.9 ggplot2_4.0.2 R6_2.6.1
[21] generics_0.1.4 mixsqp_0.3-54 workflowr_1.7.1 knitr_1.51
[25] tibble_3.3.1 rprojroot_2.0.4 RColorBrewer_1.1-3 bslib_0.10.0
[29] pillar_1.11.1 rlang_1.1.7 reshape_0.8.10 cachem_1.1.0
[33] stringi_1.8.7 httpuv_1.6.15 xfun_0.52 S7_0.2.1
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[41] magrittr_2.0.4 digest_0.6.39 grid_4.3.2 irlba_2.3.5.1
[45] lifecycle_1.0.5 vctrs_0.7.2 evaluate_1.0.5 glue_1.8.0
[49] farver_2.1.2 whisker_0.4.1 rmarkdown_2.30 matrixStats_1.5.0
[53] tools_4.3.2 pkgconfig_2.0.3 htmltools_0.5.9