Title: Adaptation of the Coloc Method for PheWAS
Version: 1.4.1
Maintainer: Ichcha Manipur <im504@cam.ac.uk>
Description: A Bayesian method for Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and traits, while simultaneously addressing confounding due to linkage disequilibrium. For details see Manipur et al (2023) <doi:10.1101/2023.06.29.546856>.
License: GPL-3
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 7.2.3
Depends: R (≥ 3.5.0)
URL: https://github.com/ichcha-m/cophescan, https://ichcha-m.github.io/cophescan/
BugReports: https://github.com/ichcha-m/cophescan/issues
Imports: Rcpp (≥ 1.0.7), coloc, data.table, ggplot2, ggrepel, pheatmap, methods, viridis, stats, grDevices, magrittr, utils, matrixStats, dplyr
Suggests: knitr, testthat (≥ 3.0.0), rmarkdown, RColorBrewer, ggpubr
Collate: 'cophescan-package.R' 'singlevar.R' 'multivarsusie.R' 'multitrait.R' 'cophe_hyp_predict.R' 'copheplots.R' 'testdata.R' 'RcppExports.R' 'metrop_hier_priors.R' 'zzz.R'
LinkingTo: Rcpp, RcppArmadillo
Config/testthat/edition: 3
ByteCompile: true
NeedsCompilation: yes
Packaged: 2024-06-11 14:52:08 UTC; ichcha
Author: Ichcha Manipur [aut, cre], Chris Wallace [aut]
Repository: CRAN
Date/Publication: 2024-06-11 15:20:20 UTC

The 'cophescan' package.

Description

Coloc adapted Phenome-wide Scans

Estimate the Hc.cutoff for the required FDR

Description

Estimate the Hc.cutoff for the required FDR

Usage

Hc.cutoff.fdr(ppHc, fdr = 0.05, return_plot = TRUE)

Arguments

ppHc

a vector containing the PP.Hc (the posterior probability of causal association) of all tests

fdr

FDR default: 0.05

return_plot

default: TRUE, plot the fdr estimated at the different Hc.cutoff

Value

the Hc.cutoff value for the specified FDR, if return_plot is True returns a plot showing the FDR calculated at different Hc thresholds

adjust_priors

Description

adjust fixed priors when nsnps in region is high

Usage

adjust_priors(
  nsnps,
  pa = 3.82e-05,
  pc = 0.00182,
  p1 = NULL,
  p2 = NULL,
  p12 = NULL
)

Arguments

nsnps

number of SNPs

pa

prior probability that a non-query variant is causally associated with the query trait (cophescan prior), default 3.82e-5

pc

prior probability that the query variant is causally associated with the query trait (cophescan prior), default 1.82e-3 (cophescan prior)

p1

prior probability a SNP is associated with trait 1, (coloc prior), pc derived by using pc = p12/p1+p12; use p1, p2, p12 only when pa and pc are unavailable (See vignettes)

p2

prior probability a SNP is associated with trait 2, (coloc prior), pa derived by using pa = p2

p12

prior probability a SNP is associated with both traits, (coloc prior), pc derived by using pc = p12/p1+p12

Value

vector of pn, pa and pc adjusted prior probabilities

Average of priors: pnk, pak and pck

Description

Average of priors: pnk, pak and pck

Usage

average_piks(params, nsnps, covar_vec, nits, thin, covar = FALSE)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

nsnps

number of snps

covar_vec

Vector of the covariate

nits

Number of iterations run in mcmc

thin

thinning

covar

logical: was the covariate inflormation used? default: False

Value

average pik matrix of priors: pnk, pak and pck

Average of priors: pnk, pak and pck from list (memory intensive)

Description

Average of priors: pnk, pak and pck from list (memory intensive)

Usage

average_piks_list(params, nsnps, covar_vec, nits, thin, covar = FALSE)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

nsnps

number of snps

covar_vec

Vector of the covariate

nits

Number of iterations run in mcmc

thin

thinning

covar

logical: was the covariate inflormation used? default: False

Value

average pik matrix of priors: pnk, pak and pck

Average of posterior probabilities: Hn, Ha and Hc

Description

Average of posterior probabilities: Hn, Ha and Hc

Usage

average_posterior_prob(
  params,
  lbf_mat,
  nsnps,
  covar_vec,
  nits,
  thin,
  covar = FALSE
)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

lbf_mat

matrix of log bayes factors: lBF.Ha and lBF.Hc

nsnps

number of snps

covar_vec

Vector of the covariate

nits

Number of iterations run in mcmc

thin

thinning

covar

logical: was the covariate inflormation used? default: False

Value

matrix with average of all the posterior probabilities: Hn, Ha and Hc

Average of posterior probabilities: Hn, Ha and Hc from list (memory intensive)

Description

Average of posterior probabilities: Hn, Ha and Hc from list (memory intensive)

Usage

average_posterior_prob_list(
  params,
  lbf_mat,
  nsnps,
  covar_vec,
  nits,
  thin,
  covar = FALSE
)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

lbf_mat

matrix of log bayes factors: lBF.Ha and lBF.Hc

nsnps

number of snps

covar_vec

Vector of the covariate

nits

Number of iterations run in mcmc

thin

thinning

covar

logical: was the covariate inflormation used? default: False

Value

matrix with average of all the posterior probabilities: Hn, Ha and Hc

combine.bf

Description

Calculate posterior probabilities for all the configurations

Usage

combine.bf(lBF_df, pn, pa, pc)

Arguments

lBF_df

dataframe with log bayes factors of hypothesis Ha and Hn: column names should be lBF.Ha and lBF.Hc

pn

prior probability that none of the SNPs/variants in the region are associated with the query trait

pa

prior probability that a non-query variant is causally associated with the query trait

pc

prior probability that the query variant is causally associated with the query trait

Value

named numeric vector of posterior probabilities and bayes factors

Author(s)

Ichcha Manipur

extract data through Bayes factors

Description

a dataset represented by Bayes factors from SuSIE

Usage

cophe.bf_bf(
  sus_dat,
  cred_set,
  querysnpid,
  querytrait,
  pn = NULL,
  pa = NULL,
  pc = NULL,
  ret_pp = TRUE
)

Arguments

sus_dat

a list with the output of running susie

cred_set

credible set extracted from susie

querysnpid

Id of the query variant

querytrait

Query trait name

pn

prior probability that none of the SNPs/variants in the region are associated with the query trait

pa

prior probability that a non-query variant is causally associated with the query trait

pc

prior probability that the query variant is causally associated with the query trait

Value

bayes factors of signals

See Also

cophe.susie

Predict cophescan hypothesis for tested associations

Description

Predict cophescan hypothesis for tested associations

Usage

cophe.hyp.predict(
  cophe.res,
  grouping.vars = c("querysnp", "querytrait"),
  Hc.cutoff = 0.6,
  Hn.cutoff = 0.2
)

Arguments

cophe.res

results obtained from cophe.single, cophe.susie or cophe.multitrait or data.frame with the following columns: PP.Hn, PP.Hc, PP.Ha, querysnp, querytrait

grouping.vars

This is important for results from cophe.susie where there are multiple signals. These will be collapsed into one call. If you want to return all signals set this to a single variable eg: grouping.vars = c('querysnp')

Hc.cutoff

threshold for PP.Hc above which the associations are called Hc

Hn.cutoff

threshold for PP.Hn above which the associations are called Hn

Value

returns dataframe with posterior probabilties of Hn, Hc and Ha with the predicted hypothesis based on the provided cut.offs.

See Also

cophe.single, cophe.susie, cophe.multitrait, , multitrait.simplify

Run cophescan on multiple traits at once

Description

Run cophescan on multiple traits at once

Usage

cophe.multitrait(
  trait.dat,
  querysnpid,
  querytrait.names,
  LDmat = NULL,
  method = "single",
  simplify = FALSE,
  predict.hyp = TRUE,
  Hn.cutoff = 0.2,
  Hc.cutoff = 0.6,
  est.fdr.based.cutoff = FALSE,
  fdr = 0.05,
  ...
)

Arguments

trait.dat

Named(traits) list of coloc structured data for k traits (Total number of traits)

querysnpid

vector of query variant ids = length(trait.dat), if the same variant

querytrait.names

vector of names for the query traits, if the names of the multi.dat list contain the trait names please pass querytrait.names=names(multi.dat)

LDmat

LD matrix

method

either 'single' for cophe.single or 'susie' for cophe.susie

simplify

if TRUE removes intermediate results from output using 'multitrait.simplify'

predict.hyp

if TRUE predicts the hypothesis based on the provided thresholds for pp.Hc and pp.Hn (overrides simplify) using cophe.hyp.predict

Hn.cutoff

threshold for PP.Hc above which the associations are called Hc

Hc.cutoff

threshold for PP.Hc above which the associations are called Hn

est.fdr.based.cutoff

if True calculates the Hc.cutoff using 1-mean(PP.Hc)|PP.Hc > cutoff

fdr

fdr threshold to estimate Hc.cutoff

...

additional arguments of priors for cophe.susie or cophe.single

Value

if simplify is False returns multi-trait list of lists, each with:

if simplify is TRUE only returns dataframe with posterior probabilties of Hn, Hc and Ha with no intermediate results if predict.hyp is TRUE returns a dataframe with output of simplify and the predicted hypotheses for all associations

Author(s)

Ichcha Manipur

Prepare data for cophe.single

Description

Prepare data for cophe.single

Usage

cophe.prepare.dat.single(dataset, querysnpid, MAF = NULL)

Arguments

dataset

a list with specifically named elements defining the query trait dataset to be analysed.

querysnpid

Id of the query variant, (id in dataset$snp)

Value

named list containing the per snp BFs (df) and position of the query variant (querypos)

Author(s)

Ichcha Manipur

Prepare data for cophe.susie

Description

Prepare data for cophe.susie

Usage

cophe.prepare.dat.susie(dataset, querysnpid, susie.args)

Arguments

dataset

a list with specifically named elements defining the query trait dataset to be analysed.

querysnpid

Id of the query variant, (id in dataset$snp)

susie.args

a named list of additional arguments to be passed to runsusie

Value

a list with the output of running susie

See Also

cophe.susie

Bayesian cophescan analysis using Approximate Bayes Factors

Description

Bayesian cophescan analysis under single causal variant assumption

Usage

cophe.single(
  dataset,
  querysnpid,
  querytrait,
  MAF = NULL,
  pa = 3.82e-05,
  pc = 0.00182,
  p1 = NULL,
  p2 = NULL,
  p12 = NULL
)

Arguments

dataset

a list with specifically named elements defining the query trait dataset to be analysed.

querysnpid

Id of the query variant, (id in dataset$snp)

querytrait

Query trait name

MAF

Minor allele frequency vector

pa

prior probability that a non-query variant is causally associated with the query trait (cophescan prior), default 3.82e-5

pc

prior probability that the query variant is causally associated with the query trait (cophescan prior), default 1.82e-3 (cophescan prior)

p1

prior probability a SNP is associated with trait 1, (coloc prior), pc derived by using pc = p12/p1+p12; use p1, p2, p12 only when pa and pc are unavailable (See vignettes)

p2

prior probability a SNP is associated with trait 2, (coloc prior), pa derived by using pa = p2

p12

prior probability a SNP is associated with both traits, (coloc prior), pc derived by using pc = p12/p1+p12

Details

This function calculates posterior probabilities of different causal variant configurations under the assumption of a single causal variant for each trait.

If regression coefficients and variances are available, it calculates Bayes factors for association at each SNP. If only p values are available, it uses an approximation that depends on the SNP's MAF and ignores any uncertainty in imputation. Regression coefficients should be used if available. Find more input data structure details in the coloc package

Value

a list of two data.frames:

Author(s)

Ichcha Manipur

Examples

library(cophescan)
data(cophe_multi_trait_data)
query_trait_1 <- cophe_multi_trait_data$summ_stat[['Trait_1']]
querysnpid <- cophe_multi_trait_data$querysnpid
res.single <- cophe.single(query_trait_1, querysnpid = querysnpid, querytrait='Trait_1')
summary(res.single)

cophe.single.lbf

Description

Calculate log bayes factors for each hypothesis (Single causal variant assumption)

Usage

cophe.single.lbf(dataset, querysnpid, querytrait, MAF = NULL)

Arguments

dataset

a list with specifically named elements defining the query trait dataset to be analysed.

querysnpid

Id of the query variant, (id in dataset$snp)

querytrait

Query trait name

MAF

Minor allele frequency vector

Value

data frame with log bayes factors for Hn and Ha hypotheses

Author(s)

Ichcha Manipur

See Also

cophe.single

Examples

library(cophescan)
data(cophe_multi_trait_data)
query_trait_1 <- cophe_multi_trait_data$summ_stat[['Trait_1']]
querysnpid <- cophe_multi_trait_data$querysnpid
res.single.lbf <- cophe.single.lbf(query_trait_1, querysnpid = querysnpid, querytrait='Trait_1')
res.single.lbf

run cophe.susie using susie to detect separate signals

Description

Check if a variant causally associated in one trait might be causal in another trait

Usage

cophe.susie(
  dataset,
  querysnpid,
  querytrait,
  pa = 3.82e-05,
  pc = 0.00182,
  p1 = NULL,
  p2 = NULL,
  p12 = NULL,
  susie.args = list()
)

Arguments

dataset

either a list with specifically named elements defining the dataset to be analysed. (see check_dataset)

querysnpid

Id of the query variant

querytrait

Query trait name

pa

prior probability that a non-query variant is causally associated with the query trait (cophescan prior), default 3.82e-5

pc

prior probability that the query variant is causally associated with the query trait (cophescan prior), default 1.82e-3

p1

prior probability a SNP is associated with trait 1, (coloc prior), pc derived by using pc = p12/p1+p12; use p1, p2, p12 only when pa and pc are unavailable (See vignettes)

p2

prior probability a SNP is associated with trait 2, (coloc prior), pa derived by using pa = p2

p12

prior probability a SNP is associated with both traits, (coloc prior), pc derived by using pc = p12/p1+p12

susie.args

a named list of additional arguments to be passed to runsusie

Value

a list, containing elements

Author(s)

Ichcha Manipur

Examples

library(cophescan)
data(cophe_multi_trait_data)
query_trait_1 <- cophe_multi_trait_data$summ_stat[['Trait_1']]
querysnpid <- cophe_multi_trait_data$querysnpid
query_trait_1$LD <- cophe_multi_trait_data$LD
res.susie <- cophe.susie(query_trait_1, querysnpid = querysnpid, querytrait='Trait_1')
summary(res.susie)

cophe.susie.lbf

Description

Calculate log bayes factors for each hypothesis (SuSIE - multiple causal variant assumption)

Usage

cophe.susie.lbf(
  dataset,
  querysnpid,
  querytrait,
  switch = TRUE,
  susie.args = list(),
  MAF = NULL
)

Arguments

dataset

a list with specifically named elements defining the query trait dataset to be analysed.

querysnpid

Id of the query variant, (id in dataset$snp)

querytrait

Query trait name

switch

Set switch=TRUE to obtain single BF when credible sets not found with SuSIE

susie.args

a named list of additional arguments to be passed to runsusie

MAF

Minor allele frequency vector

Value

data frame with log bayes factors for Hn and Ha hypotheses

Author(s)

Ichcha Manipur

See Also

cophe.susie

Examples

library(cophescan)
data(cophe_multi_trait_data)
query_trait_1 <- cophe_multi_trait_data$summ_stat[['Trait_1']]
query_trait_1$LD <- cophe_multi_trait_data$LD
querysnpid <- cophe_multi_trait_data$querysnpid
res.susie.lbf <- cophe.susie.lbf(query_trait_1, querysnpid = querysnpid,
                                  querytrait='Trait_1', switch=T)
res.susie.lbf

Heatmap of multi-trait cophescan results

Description

Heatmap of multi-trait cophescan results

Usage

cophe_heatmap(
  multi.dat,
  querysnpid,
  query_trait_names,
  thresh_Hc = 0.5,
  thresh_Ha = 0.5,
  ...
)

Arguments

multi.dat

multi trait cophescan results returned from cophe.multitrait or formatted in the same way with multitrait.simplify

querysnpid

query variant

query_trait_names

names of phenotypes corresponding to the multi.dat results

thresh_Hc

Hc threshold to be displayed

thresh_Ha

Ha threshold to be displayed

...

additional arguments to be passed to pheatmap

Value

heatmap of posterior probabilities of the phentypes above the set threshold

Simulated multi-trait data

Description

Simulated multi-trait data

Usage

data(cophe_multi_trait_data)

Format

list of coloc structred datasets for 24 traits (cophe_multi_trait_data$summ_stat), LD matrix (cophe_multi_trait_data$LD) and the id of the query snp (cophe_multi_trait_data$querysnpid). #' The trait dataset are simulated summary statistics (1000 SNPs) for 10 Hn, 10 Ha and 10 Hc.

cophe_plots showing the Ha and Hc of all traits and labelled above the specified threshold

Description

cophe_plots showing the Ha and Hc of all traits and labelled above the specified threshold

Usage

cophe_plot(
  multi.dat,
  querysnpid,
  query_trait_names,
  thresh_Hc = 0.5,
  thresh_Ha = 0.5,
  beta_p = NULL,
  traits.dat = NULL,
  group_pheno = NULL
)

Arguments

multi.dat

multi trait cophescan results returned from cophe.multitrait or multitrait.simplify

querysnpid

query variant (only a single variant for PheWAS plots)

query_trait_names

list of phenotype names

thresh_Hc

Hc threshold to be displayed

thresh_Ha

Ha threshold to be displayed

beta_p

data.frame (from the get.beta function) with four columns : 1. "beta_plot": indicating beta direction (p or n) 2. "beta_plot": -log10(pval) of the queried variant 3. "querysnp" 4. "querytrait".

traits.dat

list of multi-trait coloc structured datasets

group_pheno

Vector with additional grouping of phenotypes

Value

cophescan plots of Ha and Hc

See Also

cophe.single, cophe.susie, cophe.multitrait, , multitrait.simplify

Extract beta and p-values of queried variant

Description

Extract beta and p-values of queried variant

Usage

get_beta(traits.dat, querysnpid, querytrait)

Arguments

traits.dat

list of coloc structured dataset

querysnpid

vector of querysnpid

querytrait

vector of querytrait names

Value

data.frame with one column named beta_plot: indicating beta direction (n/p) and another column named pval_plot with -log10(pval) of the queried variant

Calculation of the posterior prob of Hn, Ha and Hc

Description

Calculation of the posterior prob of Hn, Ha and Hc

Usage

get_posterior_prob(params, lbf_mat, nsnps, covar_vec, covar = FALSE)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

lbf_mat

matrix of log bayes factors: lBF.Ha and lBF.Hc

nsnps

number of snps

covar_vec

Vector of the covariate

covar

logical: should the covariate inflormation be used? default: False

Value

posterior prob of Hn, Ha and Hc

hypothesis.priors

Description

Estimate priors for each hypothesis

Usage

hypothesis.priors(nsnps, pn, pa, pc)

Arguments

nsnps

number of SNPs

pn

prior probability that none of the SNPs/variants in the region are associated with the query trait

pa

prior probability that a non-query variant is causally associated with the query trait

pc

prior probability that the query variant is causally associated with the query trait

Value

hypotheses priors

Author(s)

Ichcha Manipur

dnorm for alpha

Description

dnorm for alpha

Usage

logd_alpha(a, alpha_mean = -10, alpha_sd = 0.5)

Arguments

a

current alpha

alpha_mean

prior for the mean of alpha

alpha_sd

prior for the standard deviation of alpha

Value

log dnorm

dgamma for beta

Description

dgamma for beta

Usage

logd_beta(b, beta_shape = 2, beta_scale = 2)

Arguments

b

current beta

beta_shape

prior for the shape (gamma distibution) of beta

beta_scale

prior for the scale of beta

Value

log dgamma

dgamma for gamma

Description

dgamma for gamma

Usage

logd_gamma(g, gamma_shape = 2, gamma_scale = 2)

Arguments

g

current gamma

gamma_shape

prior for the shape (gamma distibution) of gamma

gamma_scale

prior for the scale of gamma

Value

log dgamma

Log likelihood calculation

Description

Log likelihood calculation

Usage

loglik(params, lbf_mat, nsnps, covar_vec, covar = FALSE)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

lbf_mat

matrix of log bayes factors: lBF.Ha and lBF.Hc

nsnps

number of snps

covar_vec

Vector of the covariate

covar

logical: should the covariate inflormation be used? default: False

Value

logpost flog of the posteriors

Log posterior calculation

Description

Log posterior calculation

Usage

logpost(params, lbf_mat, nsnps, covar_vec, covar = FALSE)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

lbf_mat

matrix of log bayes factors: lBF.Ha and lBF.Hc

nsnps

number of snps

covar_vec

Vector of the covariate

covar

logical: should the covariate inflormation be used? default: False

Value

logpost flog of the posteriors

Calculate log priors

Description

Calculate log priors

Usage

logpriors(
  params,
  covar = FALSE,
  alpha_mean = -10,
  alpha_sd = 0.5,
  beta_shape = 2,
  beta_scale = 2,
  gamma_shape = 2,
  gamma_scale = 2
)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

covar

logical: Should the covariate inflormation be used? default: False

alpha_mean

prior for the mean of alpha

alpha_sd

prior for the standard deviation of alpha

beta_shape

prior for the shape (gamma distibution) of beta

beta_scale

prior for the scale of beta

gamma_shape

prior for the shape (gamma distibution) of gamma

gamma_scale

prior for the scale of gamma

Value

log priors

logsum

Description

Internal function, logsum Function directly taken from coloc This function calculates the log of the sum of the exponentiated logs taking out the max, i.e. insuring that the sum is not Inf

Usage

logsum(x)

Arguments

x

numeric vector

Value

max(x) + log(sum(exp(x - max(x))))

Log sum

Description

Log sum

Usage

logsumexp(x)

Arguments

x

vector of log scale values to be added

Value

log sum of input

Run the hierarchical mcmc model to infer priors

Description

Run the hierarchical mcmc model to infer priors

Usage

metrop_run(
  lbf_mat,
  nsnps,
  covar_vec,
  covar = FALSE,
  nits = 10000L,
  thin = 1L,
  alpha_mean = -10,
  alpha_sd = 0.5,
  beta_shape = 2,
  beta_scale = 2,
  gamma_shape = 2,
  gamma_scale = 2
)

Arguments

lbf_mat

matrix of log bayes factors: lBF.Ha and lBF.Hc

nsnps

number of snps

covar_vec

Vector of the covariate

covar

logical: Should the covariate inflormation be used? default: False

nits

Number of iterations run in mcmc

thin

thinning

alpha_mean

prior for the mean of alpha

alpha_sd

prior for the standard deviation of alpha

beta_shape

prior for the shape (gamma distibution) of beta

beta_scale

prior for the scale of beta

gamma_shape

prior for the shape (gamma distibution) of gamma

gamma_scale

prior for the scale of gamma

Value

named list of log likelihood (ll) and parameters: alpha, beta and gamma

Simplifying the output obtained from cophe.multitrait, cophe.single or cophe.susie

Description

Simplifying the output obtained from cophe.multitrait, cophe.single or cophe.susie

Usage

multitrait.simplify(multi.dat, only_BF = FALSE)

Arguments

multi.dat

output obtained from cophe.multitrait, cophe.single or cophe.susie

only_BF

return only bayes factors and not posterior probabilities (default=FALSE)

Value

dataframe with posterior probabilties of Hn, Hc and Ha

Conversion of parameters alpha, beta and gamma to pnk, pak and pck

Description

Conversion of parameters alpha, beta and gamma to pnk, pak and pck

Usage

pars2pik(params, nsnps, covar_vec, covar = FALSE)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

nsnps

number of snps

covar_vec

Vector of the covariate

covar

logical: should the covariate information be used? default: False

Value

pik matrix of priors: pnk, pak and pck

Initiate parameters alpha, beta and gamma

Description

Initiate parameters alpha, beta and gamma

Usage

pars_init(
  covar = FALSE,
  alpha_mean = -10,
  alpha_sd = 0.5,
  beta_shape = 2,
  beta_scale = 2,
  gamma_shape = 2,
  gamma_scale = 2
)

Arguments

covar

logical: Should the covariate inflormation be used? default: False

alpha_mean

prior for the mean of alpha

alpha_sd

prior for the standard deviation of alpha

beta_shape

prior for the shape (gamma distibution) of beta

beta_scale

prior for the scale of beta

gamma_shape

prior for the shape (gamma distibution) of gamma

gamma_scale

prior for the scale of gamma

Value

params \alpha, \beta and \gamma

per.snp.priors

Description

Estimate per snp priors

Usage

per.snp.priors(
  nsnps,
  pa = 3.82e-05,
  pc = 0.00182,
  p1 = NULL,
  p2 = NULL,
  p12 = NULL
)

Arguments

nsnps

number of SNPs

pa

prior probability that a non-query variant is causally associated with the query trait (cophescan prior), default 3.82e-5

pc

prior probability that the query variant is causally associated with the query trait (cophescan prior), default 1.82e-3 (cophescan prior)

p1

prior probability a SNP is associated with trait 1, (coloc prior), pc derived by using pc = p12/p1+p12; use p1, p2, p12 only when pa and pc are unavailable (See vignettes)

p2

prior probability a SNP is associated with trait 2, (coloc prior), pa derived by using pa = p2

p12

prior probability a SNP is associated with both traits, (coloc prior), pc derived by using pc = p12/p1+p12

Value

priors at the query variant

Author(s)

Ichcha Manipur

List of priors: pn, pa and pc over all iterations

Description

List of priors: pn, pa and pc over all iterations

Usage

piks(params, nsnps, covar_vec, covar = FALSE)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

nsnps

number of snps

covar_vec

Vector of the covariate

covar

logical: was the covariate inflormation used? default: False

Value

List of priors (len: iterations): pnk, pak and pck

Plot region Manhattan for a trait highlighting the queried variant

Description

Plot region Manhattan for a trait highlighting the queried variant

Usage

plot_trait_manhat(trait.dat, querysnpid, alt.snpid = NULL)

Arguments

trait.dat

dataset used as input for running cophescan

querysnpid

the id of the causal variant as present in trait.dat$snp, plotted in red

alt.snpid

the id of the other variants as a vector to be plotted, plotted in blue

Value

regional manhattan plot

List of posterior probabilities: Hn, Ha and Hc over all iterations

Description

List of posterior probabilities: Hn, Ha and Hc over all iterations

Usage

posterior_prob(params, lbf_mat, nsnps, covar_vec, covar = FALSE)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

lbf_mat

matrix of log bayes factors: lBF.Ha and lBF.Hc

nsnps

number of snps

covar_vec

Vector of the covariate

covar

logical: was the covariate inflormation used? default: False

Value

List of posterior probabilties (len: iterations): Hn, Ha and Hc

Prepare data for plotting

Description

Prepare data for plotting

Usage

prepare_plot_data(
  multi.dat,
  querysnpid,
  query_trait_names,
  thresh_Ha = 0.5,
  thresh_Hc = 0.5,
  hmp = FALSE,
  cophe.plot = TRUE
)

Arguments

multi.dat

multi trait cophescan results returned from cophe.multitrait or multitrait.simplify

querysnpid

query variant

query_trait_names

vector of names of the query traits

thresh_Ha

Ha threshold to be displayed

thresh_Hc

Hc threshold to be displayed

hmp

return for heatmap

cophe.plot

default: TRUE, return for cophe_plot

Value

plot list

See Also

cophe_plot, cophe.susie, cophe.multitrait, multitrait.simplify default NULL

Proposal distribution

Description

Proposal distribution

Usage

propose(params, propsd = 0.5)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

propsd

Standard deviation for the proposal

Value

vector : proposal

Run the hierarchical Metropolis Hastings model to infer priors

Description

Run the hierarchical Metropolis Hastings model to infer priors

Usage

run_metrop_priors(
  multi.dat,
  covar = FALSE,
  covar_vec = NULL,
  is_covar_categorical = FALSE,
  nits = 10000,
  thin = 1,
  posterior = FALSE,
  avg_pik = TRUE,
  avg_posterior = TRUE,
  pik = FALSE,
  alpha_mean = -10,
  alpha_sd = 0.5,
  beta_shape = 2,
  beta_scale = 2,
  gamma_shape = 2,
  gamma_scale = 2
)

Arguments

multi.dat

matrix of bf values, rows=traits, named columns=("lBF.Ha","lBF.Hc","nsnps")

covar

whether to include covariates

covar_vec

vector of covariates

is_covar_categorical

only two categories supported (default=FALSE) - Experimental

nits

number of iterations

thin

burnin

posterior

default: FALSE, estimate posterior probabilities of the hypotheses

avg_pik

default: FALSE, estimate the average of the pik

avg_posterior

default: FALSE, estimate the average of the posterior probabilities of the hypotheses

pik

default: FALSE, inferred prior probabilities

alpha_mean

prior for the mean of alpha

alpha_sd

prior for the standard deviation of alpha

beta_shape

prior for the shape (gamma distibution) of beta

beta_scale

prior for the scale of beta

gamma_shape

prior for the shape (gamma distibution) of gamma

gamma_scale

prior for the scale of gamma

Value

List containing the posterior distribution of the parameters alpha, beta, gamma (if covariate included) and the loglikelihood

if avg_posterior=TRUE matrix with average of all the posterior probabilities of Hn, Ha and Hc

if avg_pik=TRUE matrix with average of all the priors: pn, pa and pc

data, nits and thin contain the input data, number of iterations and burnin respectively specified for the hierarchical model

sample alpha

Description

sample alpha

Usage

sample_alpha(alpha_mean = -10, alpha_sd = 0.5)

Arguments

alpha_mean

prior for the mean of alpha

alpha_sd

prior for the standard deviation of alpha

Value

sample from rnorm for \alpha

sample beta

Description

sample beta

Usage

sample_beta(beta_shape = 2, beta_scale = 2)

Arguments

beta_shape

prior for the shape (gamma distibution) of beta

beta_scale

prior for the scale of beta

Value

sample from rgamma for \beta

sample gamma

Description

sample gamma

Usage

sample_gamma(gamma_shape = 2, gamma_scale = 2)

Arguments

gamma_shape

prior for the shape (gamma distibution) of gamma

gamma_scale

prior for the scale of gamma

Value

sample from rgamma for \gamma

print the summary of results from cophescan single or susie

Description

print the summary of results from cophescan single or susie

Usage

## S3 method for class 'cophe'
summary(object, ...)

Arguments

object

Result from either cophe.susie or cophe.single

...

additional arguments affecting the summary produced.

Value

log bayes and posterior probabilities

See Also

cophe.single, cophe.susie

Target distribution

Description

Target distribution

Usage

target(params, lbf_mat, nsnps, covar_vec, covar = FALSE)

Arguments

params

Vector of parameters: \alpha, \beta and \gamma

lbf_mat

matrix of log bayes factors: lBF.Ha and lBF.Hc

nsnps

number of snps

covar_vec

Vector of the covariate

covar

logical: Should the covariate inflormation be used? default: False

Value

target