Coloc: using variant-specific priros in coloc

Jeffrey Pullin

2025-03-27

Source: vignettes/a07_variant-specific-priors.Rmd

Variant-specific priors in coloc 

## This is coloc version 6.0.0
data(coloc_test_data)
data(eqtlgen_density_data)
attach(coloc_test_data)
attach(eqtlgen_density_data)

By default coloc assumes that all variants in a region are equally likely to be causal for the traits of interest. However, there is substantial evidence that functional annotations and other genetic information can provide evidence about whether variants are causal.

Using general variant-specific information

To provide variant-specific information to coloc use the prior_weights1 or prior_weights2 arguments for coloc.abf(), coloc.susie() or coloc.bf(). The arguments specify non-negative weights that encode the ‘weight’ (proportional to the probability of a variant being causal). Specifying weights for either or both of the traits will automatically change the prior probability of a variant being causal for both traits.

For example, if we had prior information that the first 100 variants were twice as likely to be causal for trait 1 we could encode this in the following prior weights:

weights <- c(rep(2, 100), rep(1, 400))
coloc.abf(D1, D2, prior_weights1 = weights)
## PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf 
##  8.26e-19  2.91e-10  5.16e-12  8.18e-04  9.99e-01 
## [1] "PP abf for shared variant: 99.9%"
## Coloc analysis of trait 1, trait 2
## 
## SNP Priors
##    p1    p2   p12 
## 1e-04 1e-04 1e-05
## 
## Hypothesis Priors
##        H0   H1   H2       H3    H4
##  0.892505 0.05 0.05 0.002495 0.005
## 
## Posterior
##        nsnps           H0           H1           H2           H3           H4 
## 5.000000e+02 8.262373e-19 2.911768e-10 5.156170e-12 8.179198e-04 9.991821e-01

An example of variant-specific information

Pullin and Wallace (2024) describe the implementation of variant-specific priors in coloc and compare various sources of prior information. The best performing source of prior information was the density of the distance between an expression quantative trait loci (eQTL) and the gene’s transcription start site (TSS). These estimated densities show that eQTLs often lie close to the TSS, suggesting that we should expect variants that lie closer to the TSS to be more likely to be causal. The density estimated from the eQTLGen dataset, see Pullin and Wallace (2024) for details, is stored in the coloc dataset eqtlgen_density_data and we can visualise the density, where the middle of the interval is the TSS.

plot(
  eqtlgen_density_data$rel_dist,
  eqtlgen_density_data$dens_value
)

To illustrate the use of the TSS we augment coloc’s simulated data with simulated genomic positions in a 1Mb window around the TSS of PTPN22. Then using a simple function we can compute prior weights for these positions relative to different specified TSSs.

pos <- sample(113371759:114371759, 500, replace = FALSE)
D1$position <- pos
D2$position <- pos

compute_eqtl_tss_dist_weights <- function(pos, tss, density_data) {
  rel <- pos - tss
  closest <- numeric(length(pos))
  for (i in seq_along(pos)) {
    closest[[i]] <- which.min(abs(density_data$rel_dist - rel[[i]]))
  }
  out <- density_data$dens_value[closest]
  out <- out / sum(out)
  out
}

Computing the weights and applying them shows little impact on the probability of colocalisation because this simulated example has very strong evidence for colocalisation. In real data, the impact will likely be greater.

# Compute the weights relative to the PTPN22 TSS.
w1 <- compute_eqtl_tss_dist_weights(D1$position, 113871759, eqtlgen_density_data) 

coloc.abf(D1, D2)
## PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf 
##  1.38e-18  2.94e-10  8.59e-12  8.34e-04  9.99e-01 
## [1] "PP abf for shared variant: 99.9%"
## Coloc analysis of trait 1, trait 2
## 
## SNP Priors
##    p1    p2   p12 
## 1e-04 1e-04 1e-05
## 
## Hypothesis Priors
##        H0   H1   H2       H3    H4
##  0.892505 0.05 0.05 0.002495 0.005
## 
## Posterior
##        nsnps           H0           H1           H2           H3           H4 
## 5.000000e+02 1.377000e-18 2.937336e-10 8.593226e-12 8.338917e-04 9.991661e-01
coloc.abf(D1, D2, prior_weights1 = w1)
## PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf 
##  4.61e-17  1.19e-09  2.88e-10  6.44e-03  9.94e-01 
## [1] "PP abf for shared variant: 99.4%"
## Coloc analysis of trait 1, trait 2
## 
## SNP Priors
##    p1    p2   p12 
## 1e-04 1e-04 1e-05
## 
## Hypothesis Priors
##        H0   H1   H2       H3    H4
##  0.892505 0.05 0.05 0.002495 0.005
## 
## Posterior
##        nsnps           H0           H1           H2           H3           H4 
## 5.000000e+02 4.607613e-17 1.191318e-09 2.875401e-10 6.440915e-03 9.935591e-01