hyprcoloc: HyPrColoc

In jrs95/hyprcoloc: Hypothesis Prioritisation in multi-trait Colocalization (HyPrColoc)

HyPrColoc

Description

hyprcoloc is a function used to identify clusters of colocalized traits and candidate causal SNPs in genomic regions

Usage

hyprcoloc(
  effect.est,
  effect.se,
  binary.outcomes = rep(0, dim(effect.est)[2]),
  trait.subset = c(1:dim(effect.est)[2]),
  trait.names = c(1:dim(effect.est)[2]),
  snp.id = c(1:dim(effect.est)[1]),
  ld.matrix = diag(1, dim(effect.est)[1], dim(effect.est)[1]),
  trait.cor = diag(1, dim(effect.est)[2], dim(effect.est)[2]),
  sample.overlap = matrix(rep(1, dim(effect.est)[2]^2), nrow = dim(effect.est)[2]),
  bb.alg = TRUE,
  bb.selection = "regional",
  reg.steps = 1,
  reg.thresh = "default",
  align.thresh = "default",
  prior.1 = 1e-04,
  prior.c = 0.02,
  prior.12 = NULL,
  sensitivity = FALSE,
  sense.1 = 1,
  sense.2 = 2,
  uniform.priors = FALSE,
  ind.traits = FALSE,
  snpscores = FALSE
)

Arguments

effect.est	matrix of snp regression coefficients (i.e. regression beta values) in the genomic region
effect.se	matrix of standard errors associated with the beta values
binary.outcomes	a binary vector of dimension the number of traits: 1 represents a binary trait 0 otherwise
trait.subset	vector of trait names (or number) from the full trait list: used for trageted colocalization analysis in a region
trait.names	vector of trait names corresponding to the columns in the effect.est matrix
snp.id	vector of SNP IDs
ld.matrix	LD matrix
trait.cor	matrix of pairwise correlations between traits
sample.overlap	matrix of pairwise sample overlap between traits
bb.alg	branch and bound algorithm: TRUE, employ BB algorithm; FALSE, do not
bb.selection	branch and bound algorithm type, e.g. regional or alignment selection
reg.steps	regional step paramter
reg.thresh	threshold probability beyond which traits are believed to share a regional association signal
align.thresh	threshold probability beyond which traits are believed to align at a single causal variant
prior.1	prior probability of a SNP being associated with one trait
prior.c	conditional colocalization prior: probability of a SNP being associated with an additional trait given that the SNP is associated with at least 1 other trait
prior.12	COLOC prior p12: prior probability of a SNP being associated with any two traits
sensitivity	perform senstivity analysis
sense.1	first sensitivity analysis
sense.2	second sensitivity analysis
uniform.priors	uniform priors
ind.traits	are the traits independent or to be treated as independent
snpscores	output estimated posterior probability explained each SNP

Value

A data.frame of HyPrColoc results: each row is a cluster of colocalized traits or is coded NA (if no colocalization is identified)

If snpscores = TRUE: additionally returns a list of posterior probability explained by each SNPs and for each cluster of colocalized traits identified

Author(s)

Christopher Foley [e-mail] & James Staley [e-mail]

Examples

# Regression coefficients and standard errors from ten GWAS studies
# (Traits 1-5, 6-8 & 9-10 are the clusters of colocalized traits)
betas <- hyprcoloc::test.betas
head(betas)
ses <- hyprcoloc::test.ses
head(ses)

# Trait names and SNP IDs
traits <- paste0("T", 1:10)
rsid <- rownames(betas)

# Colocalisation analyses
results <- hyprcoloc(betas, ses, trait.names = traits, snp.id = rsid)

jrs95/hyprcoloc documentation built on April 10, 2024, 4:11 a.m.