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CAMERA_local class

Source: R/organise_local_data.R
CAMERA_local.Rd

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

Create the object with CAMERA_local$new() and then call its organise() method. This reads the summary statistics files listed in metadata, finds and clumps the top hits for each dataset, pools them across populations, and extracts the region around each instrument from every dataset. The results are stored in the instrument_raw, instrument_outcome, instrument_regions and instrument_outcome_regions fields, which can be passed to the import_from_local() method of a CAMERA object.

See vignette("import-local", package = "CAMeRa") for a worked example.

Details

metadata must be a data frame with one row per summary statistics file (i.e. per trait and population) and the following columns:

  • what: either "exposure" or "outcome"

  • trait: name of the trait, the same for all populations of a trait

  • pop: population, e.g. "EUR"; must match the pop column of ld_ref

  • id: a unique identifier for the dataset

  • fn: path to the summary statistics file, which is read with data.table::fread()

  • chr_col, pos_col, ea_col, oa_col, eaf_col, beta_col, se_col, pval_col: the name or number of the column in the file containing the chromosome, base pair position, effect allele, other allele, effect allele frequency, effect estimate, standard error, and p-value respectively

Other columns are ignored. Variants are identified by chromosome, position and alleles (as chr:pos_a1_a2, with the alleles in alphabetical order), so all files must use the same genome build.

ld_ref must be a data frame with columns pop and bfile, giving for each population the path to a PLINK binary fileset (without the .bed/.bim/.fam extension) used as the LD reference for clumping.

Top hits are only taken from datasets with at least two variants with p-value below pthresh. Currently organise() uses the first exposure trait and the first outcome trait in metadata.

See also

CAMERA, vignette("import-local", package = "CAMeRa")

Public fields

metadata

Data frame with information about the data. One row per dataset. See Details for the required columns

ld_ref

Data frame with two columns - pop = population (referencing the pop values in metadata), bfile = path to plink file for that reference

mc.cores

The number of processor cores to use

plink_bin

Location of executable plink (version 1.90 is recommended)

radius

Genomic window size to extract SNPs

minmaf

Minimum allele frequency per dataset

pthresh

P-value threshold for instrument inclusion

instrument_raw

A data frame of pooled instruments across all ancestries, that has been extracted from each ancestry for the exposure traits

instrument_outcome

Instruments in instrument_raw extracted from the outcome datasets

instrument_regions

Named list of data frames of length number of unique instruments in instrument_raw. Names of each item are the instruments. Each item is a list of regional extracts around the instrument from each population exposure study.

instrument_outcome_regions

As per instrument_regions but for the outcome datasets.

Methods

Public methods

CAMERA_local$new()

Create a new CAMERA_local object

Usage

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

Arguments

metadata

Data frame with information about the data. One row per dataset. See Details for the required columns

ld_ref

Data frame with two columns - pop = population (referencing the pop values in metadata), bfile = path to plink file for that reference. See Details

plink_bin

Location of executable plink (version 1.90 is recommended)

mc.cores

Number of cores to use

radius

Genomic window size to extract SNPs

radius

Default 250000

pthresh

P-value threshold for instrument inclusion

minmaf

Minimum allele frequency per dataset

CAMERA_local$standardise()

Standardise the allele coding so that the effect allele is the alphabetically first allele, flipping the effect estimate and allele frequency where needed, and create a variant ID of the form chr:pos_ea_oa

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"
)

Arguments

d

data.frame

ea_col

Column name for effect allele

oa_col

Column name for other allele

beta_col

Column name containing beta coefficients

eaf_col

Column name containing allele frequency for effect allele

chr_col

Column name containing chromosome

pos_col

Column name containing position

vid_col

Column name containing variant ID

Returns

d with standardised alleles and a variant ID column

CAMERA_local$read_file()

Read in the summary statistics file for one dataset, drop variants with minor allele frequency below minmaf, and standardise the alleles

Usage

CAMERA_local$read_file(m, minmaf = self$minmaf)

Arguments

m

File object

minmaf

Minimum allele frequency per dataset

Returns

A tibble with columns chr, pos, eaf, beta, se, pval, ea, oa and vid

CAMERA_local$pool_tophits()

Pool the top hits across datasets. For each trait, the regions within radius of its top hits are merged, and the variants in each region are extracted from every dataset. Within each region the variant with the largest absolute z-score for the trait is selected, considering only variants present in a number of datasets for which at least one such variant has p-value below pthresh

Usage

CAMERA_local$pool_tophits(
  rawdat,
  tophits,
  metadata,
  radius = 250000,
  pthresh = 5e-08,
  mc.cores = 1
)

Arguments

rawdat

The raw data

tophits

The top hits

metadata

Data frame with information about the data. One row per dataset. See Details for the required columns

radius

Genomic window size to extract SNPs

radius

Default 250000

pthresh

P-value threshold for instrument inclusion

mc.cores

Number of cores to use

Returns

A list with elements region_list (the merged regions for each trait), region_extract (the extracted variants in each region) and tophit_pool (the selected variant from each region in every dataset)

CAMERA_local$organise_data()

Read in the data (unless rawdat is supplied), find the top hits in each dataset by clumping the variants with p-value below pthresh using ieugwasr::ld_clump(), and pool them with pool_tophits()

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,
  rawdat = NULL
)

Arguments

metadata

Data frame with information about the data. One row per dataset. See Details for the required columns

plink_bin

Location of executable plink (version 1.90 is recommended)

ld_ref

Data frame with two columns - pop = population (referencing the pop values in metadata), bfile = path to plink file for that reference. See Details

pthresh

P-value threshold for instrument inclusion

minmaf

Minimum allele frequency per dataset

radius

Genomic window size to extract SNPs

radius

Default 250000

mc.cores

Number of cores to use

rawdat

The raw data

Returns

As for pool_tophits()

CAMERA_local$fixed_effects_meta_analysis_fast()

Fixed effect inverse variance weighted meta-analysis of each row of beta_mat and se_mat

Usage

CAMERA_local$fixed_effects_meta_analysis_fast(beta_mat, se_mat)

Arguments

beta_mat

Matrix of beta coefficients

se_mat

Matrix of SEs

Returns

A vector of p-values, one per row

CAMERA_local$organise()

Organise the data for the exposure and outcome, populating the instrument_raw, instrument_outcome, instrument_regions and instrument_outcome_regions fields

Usage

CAMERA_local$organise()

CAMERA_local$clone()

The objects of this class are cloneable with this method.

Usage

CAMERA_local$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.