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HyPrColoc

Source: R/hyprcoloc.R
hyprcoloc.Rd

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

Christopher Foley chris.neal.foley@gmail.com & James Staley jrstaley95@gmail.com

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)
#>                      T1           T2            T3           T4           T5
#> rs6694014    0.02791630 -0.030353270 -0.0006508550 -0.015820079  0.029344113
#> rs11206477  -0.01333565 -0.007925434 -0.0220791782 -0.024533654 -0.006170044
#> rs978479     0.02789307 -0.031569213  0.0013910408 -0.016449156  0.030562284
#> rs6684892    0.01109516 -0.035811867 -0.0041582437 -0.001093336  0.021029899
#> rs149881092 -0.02058398  0.033028644  0.0732322501 -0.062564186  0.031519527
#> rs2081705    0.02804342 -0.031658194 -0.0006283532 -0.017953736  0.029719314
#>                      T6          T7          T8            T9         T10
#> rs6694014   -0.03739309 -0.05015619 -0.03963766 -0.0497666615 -0.01565694
#> rs11206477   0.08915364  0.08788543  0.07824464 -0.0364999498 -0.07140001
#> rs978479    -0.03667044 -0.04993080 -0.03999140 -0.0485026083 -0.01742117
#> rs6684892   -0.04342024 -0.05462565 -0.02610322 -0.0516383902 -0.01373675
#> rs149881092  0.03339484  0.08794777  0.06747322  0.0002877561 -0.01701753
#> rs2081705   -0.03798465 -0.05052849 -0.04150411 -0.0498085699 -0.01767236
ses <- hyprcoloc::test.ses
head(ses)
#>                     T1         T2         T3         T4         T5         T6
#> rs6694014   0.01640805 0.01601499 0.01606328 0.01595009 0.01629387 0.01602093
#> rs11206477  0.01522992 0.01486595 0.01490668 0.01480196 0.01512465 0.01484631
#> rs978479    0.01642066 0.01602706 0.01607561 0.01596228 0.01630616 0.01603341
#> rs6684892   0.01738306 0.01696379 0.01701563 0.01689661 0.01726146 0.01696989
#> rs149881092 0.03917473 0.03823671 0.03833955 0.03807307 0.03890204 0.03825441
#> rs2081705   0.01643768 0.01604368 0.01609229 0.01597868 0.01632324 0.01604975
#>                     T7         T8         T9        T10
#> rs6694014   0.01618062 0.01625153 0.01639785 0.01626775
#> rs11206477  0.01499870 0.01506721 0.01522146 0.01508189
#> rs978479    0.01619313 0.01626394 0.01641083 0.01628007
#> rs6684892   0.01713951 0.01721824 0.01737041 0.01723253
#> rs149881092 0.03863522 0.03880163 0.03916327 0.03883610
#> rs2081705   0.01620976 0.01628044 0.01642748 0.01629694

# 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)