Package 'CAMeRa'

Description

A simple wrapper function. Using a summary set, identify set of instruments for the traits, and peform SEM MR to test the association across the population.

Methods

Public methods

• CAMERA$import()

• CAMERA$assign()

• CAMERA$import_from_local()

• CAMERA$new()

• CAMERA$instrument_heterogeneity()

• CAMERA$estimate_instrument_specificity()

• CAMERA$replication_evaluation()

• CAMERA$check_phenotypes()

• CAMERA$cross_estimate()

• CAMERA$plot_cross_estimate()

• CAMERA$extract_instruments()

• CAMERA$extract_instrument_regions()

• CAMERA$scan_regional_instruments()

• CAMERA$plot_regional_instruments_maxz()

• CAMERA$regional_ld_matrices()

• CAMERA$susie_finemap_regions()

• CAMERA$paintor_finemap_regions()

• CAMERA$MsCAVIAR_finemap_regions()

• CAMERA$fema_regional_instruments()

• CAMERA$plot_regional_instruments()

• CAMERA$get_metadata()

• CAMERA$estimate_instrument_heterogeneity_per_variant()

• CAMERA$mrgxe()

• CAMERA$mrgxe_plot()

• CAMERA$mrgxe_plot_variant()

• CAMERA$make_outcome_data()

• CAMERA$make_outcome_local()

• CAMERA$harmonise()

• CAMERA$set_summary()

• CAMERA$pleiotropy()

• CAMERA$plot_pleiotropy()

• CAMERA$plot_pleiotropy_heterogeneity()

• CAMERA$perform_basic_sem()

• CAMERA$runsem()

• CAMERA$standardise_data()

• CAMERA$clone()

Method import()

Migrate the results from a previous CAMERA

Usage

CAMERA$import(x)

Arguments

Method assign()

Usage

CAMERA$assign(...)

Method import_from_local()

Usage

CAMERA$import_from_local(
  instrument_raw,
  instrument_outcome,
  instrument_regions,
  instrument_outcome_regions,
  exposure_ids,
  outcome_ids,
  pops,
  ...
)

Method new()

Create a new dataset and initialise an R interface

Usage

CAMERA$new(
  exposure_ids = NULL,
  outcome_ids = NULL,
  pops = NULL,
  bfiles = NULL,
  plink = NULL,
  radius = NULL,
  clump_pop = NULL,
  x = NULL
)

Arguments

Method instrument_heterogeneity()

Usage

CAMERA$instrument_heterogeneity(
  instrument = self$instrument_raw,
  alpha = "bonferroni",
  method = "ivw",
  outlier_removal = FALSE
)

Method estimate_instrument_specificity()

Usage

CAMERA$estimate_instrument_specificity(
  instrument,
  alpha = "bonferroni",
  winnerscurse = FALSE
)

Method replication_evaluation()

Usage

CAMERA$replication_evaluation(
  instrument = self$instrument_raw,
  ld = self$ld_matrices
)

Method check_phenotypes()

Usage

CAMERA$check_phenotypes(ids = self$exposure_ids)

Method cross_estimate()

Usage

CAMERA$cross_estimate(dat = self$harmonised_dat)

Method plot_cross_estimate()

Usage

CAMERA$plot_cross_estimate(est = self$mrres, qj_alpha = 0.05)

Method extract_instruments()

Usage

CAMERA$extract_instruments(exposure_ids = self$exposure_ids, ...)

Method extract_instrument_regions()

Usage

CAMERA$extract_instrument_regions(
  radius = self$radius,
  instrument_raw = self$instrument_raw,
  exposure_ids = self$exposure_ids
)

Method scan_regional_instruments()

Usage

CAMERA$scan_regional_instruments(
  instrument_raw = self$instrument_raw,
  instrument_regions = self$instrument_regions
)

Method plot_regional_instruments_maxz()

Usage

CAMERA$plot_regional_instruments_maxz(
  instrument_region_zscores = self$instrument_region_zscores,
  instruments = self$instrument_raw,
  region = 1:min(10, nrow(instruments)),
  comparison = FALSE
)

Method regional_ld_matrices()

Usage

CAMERA$regional_ld_matrices(
  instrument_regions = self$instrument_regions,
  bfiles = self$bfiles,
  pops = self$pops,
  plink = self$plink
)

Method susie_finemap_regions()

Usage

CAMERA$susie_finemap_regions(
  dat = self$instrument_regions,
  ld = self$ld_matrices
)

Method paintor_finemap_regions()

Usage

CAMERA$paintor_finemap_regions(
  region = self$instrument_regions,
  ld = self$ld_matrices,
  PAINTOR = "PAINTOR",
  workdir = tempdir()
)

Method MsCAVIAR_finemap_regions()

Usage

CAMERA$MsCAVIAR_finemap_regions(
  region = self$instrument_regions,
  ld = self$ld_matrices,
  MsCAVIAR = "MsCAVIAR",
  workdir = tempdir()
)

Method fema_regional_instruments()

Usage

CAMERA$fema_regional_instruments(
  method = "fema",
  instrument_regions = self$instrument_regions,
  instrument_raw = self$instrument_raw,
  n = self$exposure_metadata$sample_size
)

Method plot_regional_instruments()

Usage

CAMERA$plot_regional_instruments(
  region,
  instrument_regions = self$instrument_regions,
  meta_analysis_regions = self$instrument_fema_regions
)

Method get_metadata()

Usage

CAMERA$get_metadata(
  exposure_ids = self$exposure_ids,
  outcome_ids = self$outcome_ids
)

Method estimate_instrument_heterogeneity_per_variant()

Usage

CAMERA$estimate_instrument_heterogeneity_per_variant(dat = self$harmonised_dat)

Method mrgxe()

Usage

CAMERA$mrgxe(
  dat = self$harmonised_dat,
  variant_list = subset(self$instrument_heterogeneity_per_variant, Qfdr < 0.05)$SNP,
  nboot = 100
)

Method mrgxe_plot()

Usage

CAMERA$mrgxe_plot(mrgxe_res = self$mrgxe_res)

Method mrgxe_plot_variant()

Usage

CAMERA$mrgxe_plot_variant(
  variant = self$mrgxe_res %>% dplyr::filter(p.adjust(a_pval, "fdr") < 0.05) %>% {
  
      .$SNP
 },
  dat = self$harmonised_dat
)

Method make_outcome_data()

Usage

CAMERA$make_outcome_data(exp = self$instrument_raw, p_exp = 0.05/nrow(exp))

Method make_outcome_local()

Usage

CAMERA$make_outcome_local(
  exp = self$instrument_raw,
  out = self$instrument_outcome_regions,
  p_exp = 0.05/nreow(exp)
)

Method harmonise()

Usage

CAMERA$harmonise(exp = self$instrument_raw, out = self$instrument_outcome)

Method set_summary()

Usage

CAMERA$set_summary()

Method pleiotropy()

Usage

CAMERA$pleiotropy(harmonised_dat = self$harmonised_dat, mrres = self$mrres)

Method plot_pleiotropy()

Usage

CAMERA$plot_pleiotropy(dat = self$pleiotropy_outliers)

Method plot_pleiotropy_heterogeneity()

Usage

CAMERA$plot_pleiotropy_heterogeneity(
  dat = self$pleiotropy_Q_outliers,
  pthresh = 0.05
)

Method perform_basic_sem()

Usage

CAMERA$perform_basic_sem(harmonised_dat = self$harmonised_dat_sem)

Method runsem()

Usage

CAMERA$runsem(model, data, modname)

Method standardise_data()

Usage

CAMERA$standardise_data(
  dat = self$instrument_raw,
  standardise_unit = FALSE,
  standardise_scale = FALSE,
  scaling_method = "simple_mode"
)

Method clone()

The objects of this class are cloneable with this method.

Usage

CAMERA$clone(deep = FALSE)

Arguments

Description

A simple wrapper function for importing data from local files for use with the CAMERA class.

Methods

Public methods

• CAMERA_local$new()

• CAMERA_local$standardise()

• CAMERA_local$read_file()

• CAMERA_local$pool_tophits()

• CAMERA_local$organise_data()

• CAMERA_local$fixed_effects_meta_analysis_fast()

• CAMERA_local$organise()

• CAMERA_local$clone()

Method new()

Create a new dataset and initialise an R interface

Usage

CAMERA_local$new(
  metadata,
  ld_ref,
  plink_bin,
  mc.cores = 1,
  radius = 25000,
  pthresh = 5e-08,
  minmaf = 0.01
)

Arguments

Method standardise()

Usage

CAMERA_local$standardise(
  d,
  ea_col = "ea",
  oa_col = "oa",
  beta_col = "beta",
  eaf_col = "eaf",
  chr_col = "chr",
  pos_col = "pos",
  vid_col = "vid"
)

Method read_file()

Usage

CAMERA_local$read_file(m, minmaf = 0.01)

Method pool_tophits()

Usage

CAMERA_local$pool_tophits(
  rawdat,
  tophits,
  metadata,
  radius = 250000,
  pthresh = 5e-08,
  mc.cores = 10
)

Method organise_data()

Usage

CAMERA_local$organise_data(
  metadata = self$metadata,
  plink_bin = self$plink_bin,
  ld_ref = self$ld_ref,
  pthresh = self$pthresh,
  minmaf = self$minmaf,
  radius = self$radius,
  mc.cores = self$mc.cores
)

Method fixed_effects_meta_analysis_fast()

Usage

CAMERA_local$fixed_effects_meta_analysis_fast(beta_mat, se_mat)

Method organise()

Usage

CAMERA_local$organise()

Method clone()

The objects of this class are cloneable with this method.

Usage

CAMERA_local$clone(deep = FALSE)

Arguments

Description

For a single variant estiamted in different sub groups.

Usage

egger_bootstrap(b_gx, se_gx, b_gy, se_gy, nboot = 1000)

Arguments

Details

Estimate the degree of pleiotropy using MR GxE. This method uses a negative control type approach based on an assumption that the instrument-exposure association is uncorrelated with the pleiotropic effect. Therefore, as the instrument-exposure association reduces in magnitude, the effect on the outcome will reduce towards an intercept term which represents the pleiotropic effect.

Standard errors are obtained from parametric bootstrap

Value

List

• a = intercept estimate (pleiotropy)

• b = slope estimate (b_iv effect)

• a_se = standard error of intercept

• b_se = standard error of slope

• a_pval = p-value of intercept estimate

• b_pval = p-value of slope estimate

• a_mean = mean value of intercept from bootstraps

• b_mean = mean value of slope estimates from bootstraps

Description

Perform fixed effects meta analysis for one association

Usage

fixed_effects_meta_analysis(beta_vec, se_vec, infl = 10000)

Arguments

Value

list of results

Description

Assumes effects across studies are all on the same scale

Usage

fixed_effects_meta_analysis_fast(beta_mat, se_mat)

Arguments

Value

list of meta analysis betas and SEs

Description

Sometimes estimates appear unstable. They are likely unreliable and best to not use for heterogeneity analyses etc.

Usage

identify_blownup_estimates(b, se, infl)

Arguments

Value

index of betas to remove

Description

Taken from Okbay et al 2016. Under the assumption that all discovery effects are unbiased, what fraction of associations would replicate in the replication dataset, given the differential power of the discovery and replication datasets. Uses standard error of the replication dataset to account for differences in sample size and distribution of independent variable

Usage

prop_overlap(b_disc, b_rep, se_disc, se_rep, alpha)

Arguments

Value

List of results

• res: aggregate expected replication rate vs observed replication rate

• variants: per variant expected replication rates

Description

Uses weighted Z scores following advice from https://onlinelibrary.wiley.com/doi/full/10.1111/j.1420-9101.2005.00917.x Suggested weights are 1/se^2 However se is scale dependent and it would be ideal to avoid scale issues at this stage So using instead calculate expected se based on n and af. Assumes continuous traits in the way it uses n (i.e. not case control aware at the moment)

Usage

z_meta_analysis(beta_mat, se_mat, n, eaf_mat)

Arguments