Colocalization across SuSiE-suite fits
Gao Wang, Anjing Liu and William Denault
Source:vignettes/fsusie_colocalization.Rmd
fsusie_colocalization.RmdThis vignette runs a colocalization analysis on a pair of SuSiE-suite
fine-mapping fits with
susieR::susie_post_outcome_configuration(method = "coloc_pairwise"),
a port of the coloc::coloc.bf_bf pairwise-ABF recipe
maintained inside susieR. Calling the susieR port keeps the workflow
self-contained and adds no soft dependency on the coloc
package. The recipe applies to any pair of susie-class fits
(susieR::susie(), mfsusieR::fsusie(),
mfsusieR::mfsusie(), mvsusieR::mvsusie(),
susieR::susie_rss()) because each returns the
(class = "susie", $sets$cs_index, $lbf_variable) triple
that susie_post_outcome_configuration() reads.
susie_rss() covers the GWAS summary-statistics case.
We illustrate with fsusie() (single-outcome functional
fine-mapping); mfsusie() works the same way when both fits
cover multiple outcomes. For pre-fit covariate adjustment, see the covariates
vignette.
Data
data(N3finemapping)
X <- N3finemapping$X[, seq_len(150)]
n <- nrow(X); p <- ncol(X); T_m <- 64
colnames(X) <- paste0("SNP", seq_len(p))
positions <- seq_len(T_m)
shape <- exp(-((positions - T_m / 2)^2) / (2 * (T_m / 6)^2))
beta1 <- matrix(0, p, T_m); beta1[42, ] <- 1.2 * shape
beta2 <- matrix(0, p, T_m); beta2[42, ] <- 0.9 * shape # shared causal
beta3 <- matrix(0, p, T_m); beta3[88, ] <- 0.9 * shape # distinct
Y1 <- X %*% beta1 + matrix(rnorm(n * T_m, sd = 0.4), n)
Y2 <- X %*% beta2 + matrix(rnorm(n * T_m, sd = 0.4), n)
Y3 <- X %*% beta3 + matrix(rnorm(n * T_m, sd = 0.4), n)Colocalization
susie_post_outcome_configuration(list(fit1, fit2), method = "coloc_pairwise")
reads the credible-set indices from each fit’s $sets$cs and
the per-effect log-Bayes-factor matrix from $lbf_variable.
Both are populated by fsusie() / mfsusie() on
the standard fit path, so the call is a one-liner per pair of fits. The
returned $coloc_pairwise data.frame reports the posterior
probabilities of five hypotheses for each pair of credible sets:
| meaning | |
|---|---|
PP.H0 |
no causal in either trait |
PP.H1 |
causal in trait 1 only |
PP.H2 |
causal in trait 2 only |
PP.H3 |
causal in both, but at distinct variants |
PP.H4 |
shared causal variant |
hit1 and hit2 name the lead variant in each
fit; l1 and l2 are the credible-set indices
being compared; trait1 and trait2 index the
input fits.
Shared causal signals
fit1 and fit2 were simulated with the same
causal SNP at SNP42. The pairwise summary should put nearly
all posterior mass on PP.H4 (shared causal):
res_shared <- susieR::susie_post_outcome_configuration(
list(fit1, fit2), method = "coloc_pairwise")
res_shared$coloc_pairwise## trait1 trait2 l1 l2 hit1 hit2 PP.H0 PP.H1 PP.H2 PP.H3
## 1 trait_1 trait_2 1 1 SNP42 SNP42 0 6.789301e-151 7.082075e-208 0
## PP.H4
## 1 1PP.H4 is essentially 1 and
hit1 == hit2 == SNP42, matching the simulation.
Distinct causal signals
fit1 carries a signal at SNP42; fit3
carries one at SNP88. The pairwise summary should put posterior mass on
PP.H3 (both have a causal, but at different variants):
res_distinct <- susieR::susie_post_outcome_configuration(
list(fit1, fit3), method = "coloc_pairwise")
res_distinct$coloc_pairwise## trait1 trait2 l1 l2 hit1 hit2 PP.H0 PP.H1 PP.H2 PP.H3
## 1 trait_1 trait_2 1 1 SNP42 SNP88 0 0 3.541038e-205 1
## PP.H4
## 1 2.243415e-208PP.H3 is essentially 1, with hit1 = SNP42
and hit2 = SNP88.
Notes
The pairwise-ABF recipe assumes the user already has a pair of
fine-mapping fits, possibly from different SuSiE-suite packages. When
subject-level data is available for every outcome, the natural
alternative is mfsusie() jointly across them: the
multi-outcome fit shares signal across modalities and identifies
per-outcome and shared credible sets in one pass without a post-hoc
combination step. The pairwise recipe is essential when one or more
inputs are summary-statistics fits, e.g., a GWAS
susie_rss() fit colocalized against a molecular-QTL
fsusie() fit, where the underlying individual-level data is
not available. Joint mfsusie() fine-mapping that takes GWAS
summary statistics as one of its outcomes is future work.
For colocalization across more than two outcomes, see the multi-outcome intro; the joint fit avoids the post-hoc combine but rides on mfSuSiE’s modeling assumptions and is less modular than integrating separate SuSiE-suite fits.
Session info
This is the version of R and the packages that were used to generate these results.
## R version 4.4.3 (2025-02-28)
## Platform: x86_64-conda-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
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## attached base packages:
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## [1] susieR_0.16.1 mfsusieR_0.0.2
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