Function reference • 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
Others included for completeness, but the main functions you need should be in the sections above
`Var.data()`	Var.data
`Var.data.cc()`	Var.data
`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
`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
`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
`plot_dataset()` `plot_dataset()`	plot a coloc dataset
`print(<coloc_abf>)`	print.coloc_abf
`process.dataset()`	process.dataset
`sdY.est()`	Estimate trait variance, internal function
`subset_dataset()`	subset_dataset

Reference

Coloc data structure

Bayesian enumeration of hypotheses

Sensitivity analysis

Relaxing the single variant assumption

Others