Skip to contents

R6 class for CAMERA

Source: R/00CAMERA.R
CAMERA.Rd

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

Public fields

output

A list for the output

source

The source of the data.

exposure_ids

Exposures IDs obtained from IEU GWAS database (https://gwas.mrcieu.ac.uk) for each population

outcome_ids

Outcome IDs obtained from IEU GWAS database (https://gwas.mrcieu.ac.uk) for each population

exposure_metadata

Exposure metadata

outcome_metadata

Outcome metadata

radius

Genomic window size to extract SNPs

pops

Ancestry information for each population (i.e., AFR, AMR, EUR, EAS, SAS)

bfiles

Locations of LD reference files for each population (Download from: http://fileserve.mrcieu.ac.uk/ld/1kg.v3.tgz)

plink

Location of executable plink (version 1.90 is recommended)

clump_pop

Reference population for clumping

instrument_raw

Instruments for the exposures obtained from extract_instruments()

instrument_regions

Genomic regions around each instrument obtained from extract_instrument_regions()

ld_matrices

LD matrices for each instrument region obtained from regional_ld_matrices()

susie_results

Fine-mapping results from susie_finemap_regions()

paintor_results

Fine-mapping results from paintor_finemap_regions()

mscaviar_results

Fine-mapping results from MsCAVIAR_finemap_regions()

expected_replications

Expected replication of instruments across populations

instrument_region_zscores

Z scores for the SNPs in each instrument region obtained from scan_regional_instruments()

instrument_fema

Instruments selected by meta-analysis across populations obtained from fema_regional_instruments()

instrument_fema_regions

Meta-analysis results for each instrument region obtained from fema_regional_instruments()

instrument_maxz

Instruments selected by maximum Z score obtained from scan_regional_instruments()

instrument_susie

Instruments selected by susieR fine-mapping obtained from susie_finemap_regions()

instrument_paintor

Instruments selected by PAINTOR fine-mapping obtained from paintor_finemap_regions()

instrument_mscaviar

Instruments selected by MsCAVIAR fine-mapping obtained from MsCAVIAR_finemap_regions()

harmonised_data_check

Checks of the harmonised data

standardised_instrument_raw

Standardised instrument_raw obtained from standardise_data()

standardised_instrument_maxz

Standardised instrument_maxz obtained from standardise_data()

standardised_instrument_susie

Standardised instrument_susie obtained from standardise_data()

standardised_instrument_paintor

Standardised instrument_paintor obtained from standardise_data()

standardised_instrument_mscaviar

Standardised instrument_mscaviar obtained from standardise_data()

standardised_outcome

Standardised outcome data obtained from standardise_data()

instrument_specificity

Instrument specificity results obtained from estimate_instrument_specificity()

instrument_specificity_summary

Summary of the instrument specificity results obtained from estimate_instrument_specificity()

instrument_outcome

Outcome data for the instruments obtained from make_outcome_data()

instrument_outcome_regions

Outcome data for each instrument region imported with import_from_local()

harmonised_dat_sem

Harmonised data for the SEM analysis

harmonised_dat

Harmonised exposure and outcome data obtained from harmonise()

sem_result

SEM results obtained from perform_basic_sem()

pleiotropy_agreement

Agreement of pleiotropy outlier effects across populations obtained from pleiotropy()

pleiotropy_outliers

Pleiotropy outliers obtained from pleiotropy()

pleiotropy_Q_outliers

Heterogeneity for each pleiotropy outlier and population obtained from pleiotropy()

summary

Summary of the exposure, outcome and population metadata obtained from set_summary()

mrres

MR estimates obtained from cross_estimate()

instrument_heterogeneity_per_variant

Heterogeneity of the instrument-exposure associations for each variant obtained from estimate_instrument_heterogeneity_per_variant()

mrgxe_res

MR GxE results obtained from mrgxe()

Methods

Public methods

CAMERA$import()

Migrate the results from a previous CAMERA

Usage

CAMERA$import(x)

Arguments

x

A CAMERA object to import results from

CAMERA$assign()

Assign values to fields of the CAMERA object

Usage

CAMERA$assign(...)

Arguments

...

Named arguments, where each name is a field and each value is assigned to it

CAMERA$import_from_local()

Import instrument and outcome data from local summary statistics, e.g. as organised by CAMERA_local

Usage

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

Arguments

instrument_raw

Data frame of instruments for the exposures

instrument_outcome

Data frame of instrument associations with the outcomes

instrument_regions

List of genomic regions around each instrument for the exposures

instrument_outcome_regions

List of genomic regions around each instrument for the outcomes

exposure_ids

Exposures IDs obtained from IEU GWAS database (https://gwas.mrcieu.ac.uk) for each population

exposure_ids

ID for the exposure. Default is x$exposure_ids.

exposure_ids

IDs for the exposures. Default is x$exposure_ids.

outcome_ids

Outcome IDs obtained from IEU GWAS database (https://gwas.mrcieu.ac.uk) for each population

outcome_ids

IDs for the outcomes. Default is x$outcome_ids.

pops

Ancestry information for each population (i.e., AFR, AMR, EUR, EAS, SAS)

...

Further named arguments passed to assign()

CAMERA$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

exposure_ids

Exposures IDs obtained from IEU GWAS database (https://gwas.mrcieu.ac.uk) for each population

exposure_ids

ID for the exposure. Default is x$exposure_ids.

exposure_ids

IDs for the exposures. Default is x$exposure_ids.

outcome_ids

Outcome IDs obtained from IEU GWAS database (https://gwas.mrcieu.ac.uk) for each population

outcome_ids

IDs for the outcomes. Default is x$outcome_ids.

pops

Ancestry information for each population (i.e., AFR, AMR, EUR, EAS, SAS)

bfiles

Locations of LD reference files for each population (Download from: http://fileserve.mrcieu.ac.uk/ld/1kg.v3.tgz)

plink

Location of executable plink (version 1.90 is recommended)

radius

Genomic window size to extract SNPs

clump_pop

Reference population for clumping

x

Import data where available

CAMERA$instrument_heterogeneity()

The function evaluates heterogeneity in the association of selected instruments and the exposure/outcome between the populations. Heterogeneity (Q statistics) is calcuated based on an IVW or simple MODE MR estimator. The instruments can be identified using "Raw", "MaxZ", or fine-mapping (Susie, PAINTOR) methods.

Usage

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

Arguments

instrument

Intsruments for the exposure that are selected by using the provided methods in CAMERA (x$instrument_raw, x$instrument_maxz, x$instrument_susie, x$instrument_paintor). Default is x$instrument_raw.

alpha

Statistical threshold to determine significance. Default is "bonferroni", which is eqaul to 0.05/number of the instruments. For each pair of populations, the instruments that are significant (p < alpha) in the reference population are taken and their associations in the replication population are regressed on their associations in the reference population. If the instruments have the same effects in both populations the slope (agreement) is expected to be close to 1.

method

Method used to estimate the agreement slope, either "ivw" (inverse variance weighted, using TwoSampleMR::mr_ivw()) or "simple_mode" (using TwoSampleMR::mr_simple_mode()). Default is "ivw".

outlier_removal

Remove outliers identified by radial IVW MR before estimating heterogeneity. Only the first two exposures are used to identify outliers. Default is FALSE.

Returns

Data frame with one row for each ordered pair of populations, with columns:

  • Reference, Replication: IDs of the reference and replication datasets

  • nsnp: number of instruments significant in the reference dataset that are also present in the replication dataset

  • agreement: slope of the regression of the replication associations on the reference associations

  • se, pval: standard error of agreement and p-value for the test of the null hypothesis that the slope is 0 (i.e. no association between the instrument associations in the two populations)

  • Q, Q_pval ("ivw" only): Cochran's Q statistic and its p-value for the null hypothesis that all instruments share the same slope, i.e. no heterogeneity in the agreement between populations

  • I2 ("ivw" only): the proportion of the variation in the ratio of the associations that is due to heterogeneity rather than chance, calculated as (Q - nsnp) / Q

CAMERA$estimate_instrument_specificity()

The function estimates what fraction of the instuments is expected to be replicated across the populations under the hypothesis that the effect estimates are the same.

Usage

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

Arguments

instrument

Intsruments for the exposure that are selected by using the provided methods in CAMERA (x$instrument_raw, x$instrument_maxz, x$instrument_susie, x$instrument_paintor). Default is x$instrument_raw.

alpha

Statistical threshold to determine significance. Default is "bonferroni", which is eqaul to 0.05/number of the instruments.

winnerscurse

Use this option to correct winners' curse bias.

Details

For each pair of populations, the instruments that are significant (p < alpha) in the discovery population are compared with their associations in the replication population. Following Okbay et al. (2016), under the hypothesis that the instruments have the same effects in both populations, the expected number of instruments with the same sign, and the expected number with p < alpha, in the replication population are calculated taking account of the precision of the replication associations. These are compared with the observed numbers.

Returns

Data frame, also stored in x$instrument_specificity_summary, with columns:

  • discovery, replication: IDs of the discovery and replication datasets

  • metric: "Sign" (the instrument has the same sign in both populations) or "P-value" (the instrument has p < alpha in the replication population)

  • nsnp: number of instruments compared

  • datum, value: the "Expected" and "Observed" number of instruments meeting metric

  • pdiff: p-value from a binomial test of the null hypothesis that the observed number equals the expected number. A small p-value suggests the instruments replicate less (or more) than expected if their effects were the same in both populations.

Per variant results are stored in x$instrument_specificity, including the expected probabilities of sign agreement (sign) and replication (sig), and distinct, which flags instruments that were expected to replicate but did not (sig > 0.8 and replication p > 0.1), or were expected to have the same sign but did not (sign > 0.8).

CAMERA$replication_evaluation()

The function explains what contributes to the replication of gene-trait association between the populations, considering LD structure.

Usage

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

Arguments

instrument

Intsruments for the exposure that are selected by using the provided methods in CAMERA (x$instrument_raw, x$instrument_maxz, x$instrument_susie, x$instrument_paintor). Default is x$instrument_raw.

ld

LD matrix obtained by using x$regional_ld_matrices()

Returns

Table of the regression result

CAMERA$check_phenotypes()

This function evaluates how competitable genetic associations in population 1 are with those in population 2. The function checks whether 1) the chosen IDs for the exposure or outcome can be used for the further steps and 2) the units for the genetic associations are comparable between populations.

Usage

CAMERA$check_phenotypes(ids = self$exposure_ids)

Arguments

ids

ID for the exposure or the outcome. Default is x$exposure_ids.

Returns

Table of the result.

CAMERA$cross_estimate()

This method performs a IVW analysis on the harmonised data. It fits two linear models, one considering all populations and another considering each population separately. It then performs a fixed effects meta-analysis to estimate the heterogeneity across different populations. The results are stored in the mrres attribute of the CAMERA object.

Usage

CAMERA$cross_estimate(dat = self$harmonised_dat)

Arguments

dat

A data frame containing the harmonised data. It should have the columns beta.y, beta.x, se.y, and pops. If not provided, the method uses the harmonised_dat attribute of the CAMERA object.

Returns

Data frame, also stored in x$mrres, with one row for the estimate across all populations (pops == "All") and one row for each population, with columns:

  • pops: population

  • Estimate, Std. Error, t value, Pr(>|t|): the IVW MR estimate, i.e. the weighted regression of the SNP-outcome associations on the SNP-exposure associations through the origin with weights 1 / se.y^2, its standard error, t statistic and p-value

  • Qj, Qjpval, Qdf: for each population, the contribution of its estimate to the heterogeneity between populations and its p-value (1 degree of freedom), from a fixed effects meta analysis of the population estimates; a small p-value suggests the estimate in that population differs from the others. For the "All" row these are Cochran's Q statistic across all populations, its p-value (null hypothesis: the same causal effect in all populations) and its degrees of freedom.

CAMERA$plot_cross_estimate()

Plot the results from cross_estimate

Usage

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

Arguments

est

Results from cross_estimate(). Default is x$mrres.

qj_alpha

Significance threshold for highlighting heterogeneous estimates. Default is 0.05.

Returns

Plot

CAMERA$extract_instruments()

This function searches for GWAS significant SNPs (P < 5E-8) for a specified set of the exposures. This method is equivalant to the instrumnet extraction method for Multivariable MR. Reference here: https://mrcieu.github.io/TwoSampleMR/reference/mv_extract_exposures.html.

Usage

CAMERA$extract_instruments(
  exposure_ids = self$exposure_ids,
  harmonise_strictness = 1,
  ...
)

Arguments

exposure_ids

ID for the exposure. Default is x$exposure_ids.

harmonise_strictness

Passed to TwoSampleMR::mv_extract_exposures(). Default is 1, which assumes all alleles are on the forward strand, as they are in OpenGWAS. The TwoSampleMR default of 2 infers the strand of palindromic SNPs from their allele frequencies, which wrongly flips palindromic SNPs whose allele frequencies are on different sides of 0.5 in different populations, and drops palindromic SNPs with intermediate allele frequencies.

...

Further arguments passed to TwoSampleMR::mv_extract_exposures()

Returns

Data frame in x$instrument_raw

CAMERA$extract_instrument_regions()

This function extract genomic regions around each instrument (e.g. 50kb) obtained from x$extract_instruments().

Usage

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

Arguments

radius

Set a range of the region to search

instrument_raw

A set of instruments obtained from x$extract_instruments()

exposure_ids

ID for the exposure. Default is x$exposure_ids.

Returns

Data frame in x$instrument_regions

CAMERA$scan_regional_instruments()

The function searches for a SNP that is best associated across the poupulations within the identified regions by using x$extract_instrument_regions().

Usage

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

Arguments

instrument_raw

Instruments for the exposures by using x$extract_instrument()

instrument_regions

Genomic regions identified by using x$extract_instrument_regions()

Returns

List of z scores for the chosen SNPs in x$instrument_region_zscores. Data frame in x$instrument_maxz

CAMERA$plot_regional_instruments_maxz()

The function draws a plot shows how CAMERA selected the instruments for the exposure from the selected genomic regions.

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
)

Arguments

instrument_region_zscores

Y axis. Z scores based on the SNP-exposure associations.

instruments

Use this option to draw a separate plot for the selcted instruments.

region

X axis. Number of genomic regions to be shown in the plot. Default is 10.

comparison

Use this option to compare the selected instruments by different instrument selection methods in one plot.

Returns

Plot

CAMERA$regional_ld_matrices()

Generate LD matrices for instrument regions. If we want to do fine mapping we need to get an LD matrix for the whole region (for each population) We then need to harmonise the LD matrix to the summary data, and the summary datasets to each other The function obtains an LD matrix for the selected genomic regions.

Usage

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

Arguments

instrument_regions

Genomic regions identified by using x$extract_instrument_regions()

bfiles

Location of LD reference files for each population (Download from: http://fileserve.mrcieu.ac.uk/ld/1kg.v3.tgz)

pops

Ancestry information for each population (i.e. AFR, AMR, EUR, EAS, SAS)

plink

Location of executable plink (version 1.90 is recommended)

Returns

Data frame of LD matrix (x$ld_matrices)

CAMERA$susie_finemap_regions()

Fine-mapping using susieR (https://github.com/stephenslab/susieR) to extract instruments for the exposure for multiple populations. The function identifies credible sets that overlap between the populations and determine the best SNP in a genomic region to be used as instrument.

Usage

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

Arguments

dat

Genomic regions identified by using x$extract_instrument_regions()

ld

LD matrix obtained by using x$regional_ld_matrices()

Returns

Result from susieR in x$susie_results. Data frame in x$instrument_susie

CAMERA$paintor_finemap_regions()

Fine-mapping using PAINTOR (https://github.com/gkichaev/PAINTOR_V3.0) to extract instruments for the exposure for multiple populations. The function chooses the SNP with highest posterior probability and associations in each exposure.

Usage

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

Arguments

region

Genomic regions identified by using x$extract_instrument_regions()

ld

LD matrix obtained by using x$regional_ld_matrices()

PAINTOR

Path to executable PAINTOR. Default="PAINTOR"

workdir

Working directory to save the output. Default=tempdir()

Returns

Result from PAINTOR in x$paintor_results. Data frame in x$instrument_paintor

CAMERA$MsCAVIAR_finemap_regions()

Fine-mapping using MsCAVIAR (https://github.com/nlapier2/MsCAVIAR) to extract instruments for the exposure for multiple populations.

Usage

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

Arguments

region

Genomic regions identified by using x$extract_instrument_regions()

ld

LD matrix obtained by using x$regional_ld_matrices()

MsCAVIAR

Path to executable MsCAVIAR. Default="MsCAVIAR"

workdir

Working directory to save the output. Default=tempdir()

Returns

Result from MsCAVIAR in x$mscaviar_results. Data frame in x$instrument_mscaviar

CAMERA$fema_regional_instruments()

Identify the best variant for each region by meta-analysing the associations across the populations

Usage

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

Arguments

method

Meta-analysis method, either "fema" (fixed effects meta-analysis) or "zma" (Z score meta-analysis). Default is "fema".

instrument_regions

Genomic regions identified by using x$extract_instrument_regions()

instrument_raw

Instruments for the exposures obtained from x$extract_instruments()

n

Sample sizes for each exposure, required for method = "zma". Default is the sample sizes from x$exposure_metadata.

Returns

Data frame of the selected instruments, also stored in x$instrument_fema

CAMERA$plot_regional_instruments()

Plot the associations in a region for each population and for the meta-analysis

Usage

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

Arguments

region

Name of the region to plot, i.e. one of names(x$instrument_regions)

instrument_regions

Genomic regions identified by using x$extract_instrument_regions()

meta_analysis_regions

Meta-analysis results for each region obtained from x$fema_regional_instruments()

Returns

Plot

CAMERA$get_metadata()

Get metadata for the exposures and outcomes from the OpenGWAS API

Usage

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

Arguments

exposure_ids

IDs for the exposures. Default is x$exposure_ids.

outcome_ids

IDs for the outcomes. Default is x$outcome_ids.

Returns

List of the exposure and outcome metadata, also stored in x$exposure_metadata and x$outcome_metadata

CAMERA$estimate_instrument_heterogeneity_per_variant()

Test for heterogeneity of effect estimates between populations. For each SNP this function will provide a Cochran's Q test statistic - a measure of heterogeneity of effect sizes between populations. A low p-value means high heterogeneity. In addition, for every SNP it gives a per population p-value - this can be interpreted as asking for each SNP is a particular giving an outlier estimate.

Usage

CAMERA$estimate_instrument_heterogeneity_per_variant(dat = self$harmonised_dat)

Arguments

dat

Harmonised data. Default is x$harmonised_dat.

Returns

Data frame of the Q statistic, its p-value and FDR adjusted p-value for each SNP, also stored in x$instrument_heterogeneity_per_variant

CAMERA$mrgxe()

Estimate the pleiotropic effect of each variant using MR GxE across populations, see egger_bootstrap()

Usage

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

Arguments

dat

Harmonised data. Default is x$harmonised_dat.

variant_list

SNPs to analyse. Default is the SNPs with FDR < 0.05 in x$instrument_heterogeneity_per_variant.

nboot

Number of bootstraps. Default is 100.

Returns

Data frame of the MR GxE results, also stored in x$mrgxe_res

CAMERA$mrgxe_plot()

Plot the MR GxE pleiotropy estimates for each variant

Usage

CAMERA$mrgxe_plot(mrgxe_res = self$mrgxe_res)

Arguments

mrgxe_res

Results from mrgxe(). Default is x$mrgxe_res.

Returns

Plot

CAMERA$mrgxe_plot_variant()

Plot the instrument-exposure against instrument-outcome associations across populations for selected variants

Usage

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

Arguments

variant

SNPs to plot. Default is the SNPs with FDR < 0.05 for the pleiotropy estimate in x$mrgxe_res.

dat

Harmonised data. Default is x$harmonised_dat.

Returns

Plot

CAMERA$make_outcome_data()

The function extracts summary statistics of given a list of instruments and the outcomes

Usage

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

Arguments

exp

Intsruments for the exposure that are selected by using the provided methods in CAMERA (x$instrument_raw, x$instrument_maxz, x$instrument_susie, x$instrument_paintor). Default is x$instrument_raw.

p_exp

Statistical threshold to determine significance. Default is "bonferroni", which is eqaul to 0.05/number of the instruments.

Returns

Data frame in x$instrument_outcome

CAMERA$make_outcome_local()

The function extracts summary statistics of given a list of instruments and the outcomes from local data

Usage

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

Arguments

exp

Intsruments for the exposure that are selected by using the provided methods in CAMERA (x$instrument_raw, x$instrument_fema, x$instrument_susie, x$instrument_paintor). Default is x$instrument_raw.

out

Outcome data for each instrument region. Default is x$instrument_outcome_regions.

p_exp

Statistical threshold to determine significance. Default is "bonferroni", which is eqaul to 0.05/number of the instruments.

Returns

Data frame in x$instrument_outcome

CAMERA$harmonise()

This function harmonises the alleles and effects between the exposure and outcome.

Usage

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

Arguments

exp

Intsruments for the exposure that are selected by using the provided methods in CAMERA (x$instrument_raw, x$instrument_fema, x$instrument_susie, x$instrument_paintor). Default is x$instrument_raw.

out

Intsruments for the outcome by using make_outcome_data(). Default is x$instrument_outcome.

Returns

Data frame in x$harmonised_dat_sem

CAMERA$set_summary()

Generate summary of exposure, outcome and population metadata

Usage

CAMERA$set_summary()

Returns

data frame

CAMERA$pleiotropy()

Estimate similarity of horizontal pleiotropy across ancestries. For each ancestry, identify outliers in the MR analysis based on per-variatn Q statistics. Then estimate the deviation from the main estimates for all outliers across all ancestries. Finally, determine if the pleiotropy deviation is consistent across all ancestries

Usage

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

Arguments

harmonised_dat

Outcome from harmonise function

mrres

Outcome from cross_estimate

Returns

  • data frame of outliers pleiotropy_outliers

  • data frame of heterogeneity for each outlier / ancestry combination pleiotropy_Q_outliers

  • data frame of agreement of outlier effects pleiotropy_agreement

CAMERA$plot_pleiotropy()

Plot pleiotropy results

Usage

CAMERA$plot_pleiotropy(dat = self$pleiotropy_outliers)

Arguments

dat

Output from pleiotropy - pleiotropy_outliers

Returns

plot

CAMERA$plot_pleiotropy_heterogeneity()

Plot pleiotropy results per variant

Usage

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

Arguments

dat

Output from pleiotropy - pleiotropy_Q_outliers

pthresh

p-value from Q statistic for inclusion in plots

Returns

plot

CAMERA$perform_basic_sem()

Perform basic SEM analysis of the joint estimates in multiple ancestries.

Usage

CAMERA$perform_basic_sem(harmonised_dat = self$harmonised_dat_sem)

Arguments

harmonised_dat

Harmonised dataset obtained by using harmonised_dat()

Returns

Table of the SEM results. Summary of the results available in x$sem_result.

CAMERA$runsem()

Fit an SEM model using lavaan

Usage

CAMERA$runsem(model, data, modname)

Arguments

model

lavaan model syntax

data

Data frame of the data for the model

modname

Name of the model used to label the results

Returns

Data frame of the SEM estimates for each population

CAMERA$standardise_data()

This function standardises the betas and SEs for the instruments-exposure/outcome associations when unit information is not matched across the populations. In case of large differences in genetic effects observed between the populations (e.g. due to sample size difference), the function scales the betas and SEs.

Usage

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

Arguments

dat

Instruments for the exposure that are selected by using the provided methods in CAMERA (x$instrument_raw, x$instrument_maxz, x$instrument_susie, x$instrument_paintor). Default is x$instrument_raw.

dat

A data frame containing the harmonised data. It should have the columns beta.y, beta.x, se.y, and pops. If not provided, the method uses the harmonised_dat attribute of the CAMERA object.

dat

Genomic regions identified by using x$extract_instrument_regions()

dat

Harmonised data. Default is x$harmonised_dat.

standardise_unit

Use this option if unit information is not matched.

standardise_scale

Use this option if genetic effects are substantially different due to study power.

scaling_method

Choose the methods to obtain scaling units (MR estimates of exposure 1 and exposure 2 or outcome 1 and outcome 2). Default is "simple_mode".

CAMERA$clone()

The objects of this class are cloneable with this method.

Usage

CAMERA$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.