Package index • coloc

Coloc data structure

Description of coloc data structure

check_dataset() check.dataset(): check_dataset

Bayesian enumeration of hypotheses

Two trait colocalisation and single trait fine mapping under single causal variant assumptions

coloc.abf(): Fully Bayesian colocalisation analysis using Bayes Factors

finemap.abf(): Bayesian finemapping analysis

Sensitivity analysis

Check how robust inference is to changing prior parameter values

sensitivity(): Prior sensitivity for coloc

Relaxing the single variant assumption

use conditioning or masking to account for multiple independent causal variants per trait

coloc.susie(): run coloc using susie to detect separate signals

runsusie(): Run susie on a single coloc-structured dataset

Visualisation

see stuff

plot_dataset(): plot a coloc dataset

plot_datasets(): plot a pair of coloc datasets, highlighting the snps that are common between them

plot_extended_datasets(): Draw extended plot of summary statistics for two coloc datasets

Others

included for completeness, but the main functions you need should be in the sections above

annotate_susie(): annotate susie_rss output for use with coloc_susie

approx.bf.estimates(): Internal function, approx.bf.estimates

approx.bf.p(): Internal function, approx.bf.p

bin2lin(): binomial to linear regression conversion

check_alignment() check.alignment(): check alignment

-package coloc-package: Colocalisation tests of two genetic traits

coloc.bf_bf(): Coloc data through Bayes factors

coloc.detail(): Bayesian colocalisation analysis with detailed output

coloc.process(): Post process a coloc.details result using masking

coloc.signals(): Coloc with multiple signals per trait

coloc.susie_bf(): run coloc using susie to detect separate signals

coloc_test_data: Simulated data to use in testing and vignettes in the coloc package

combine.abf(): combine.abf

credible.sets(): credible.sets

est_cond(): generate conditional summary stats

estgeno.1.ctl() estgeno.1.cse(): estgeno1

find.best.signal(): Pick out snp with most extreme Z score

findends(): trim a dataset to central peak(s)

findpeaks(): trim a dataset to only peak(s)

finemap.bf(): Finemap data through Bayes factors

finemap.signals(): Finemap multiple signals in a single dataset

logbf_to_pp(): logbf 2 pp

logdiff(): logdiff

logsum(): logsum

map_cond(): find the next most significant SNP, conditioning on a list of sigsnps

map_mask(): find the next most significant SNP, masking a list of sigsnps

plot(<coloc_abf>): plot a coloc_abf object

print(<coloc_abf>): print.coloc_abf

process.dataset(): process.dataset

sdY.est(): Estimate trait variance, internal function

subset_dataset(): subset_dataset

Var.data.cc(): Var.data

Var.data(): Var.data

eqtlgen_density_data: eQTLGen estimated distance density