Package 'genepi.utils'

Title: GenEpi Utility Functions
Description: The genepi.utils package is a collection of utility functions for working with genetic epidemiology data.
Authors: Nicholas Sunderland [aut, cre]
Maintainer: Nicholas Sunderland <[email protected]>
License: MIT + file LICENSE
Version: 0.0.31
Built: 2024-09-17 16:20:27 UTC
Source: https://github.com/nicksunderland/genepi.utils

Help Index

as.data.table

Description

as.data.table

Usage

as.data.table(object, ...)

Arguments

object

GWAS object to covert to data.table
...

argument for data.table generic, ignored in this implementation

Chromosome & position data to variant RSID

Description

Chromosome & position data to variant RSID

Usage

chrpos_to_rsid(
  dt,
  chr_col,
  pos_col,
  ea_col = NULL,
  nea_col = NULL,
  flip = "allow",
  alt_rsids = FALSE,
  build = "b37_dbsnp156",
  dbsnp_dir = genepi.utils::which_dbsnp_directory(),
  parallel_cores = parallel::detectCores(),
  verbose = TRUE
)

Arguments

dt

a data.frame like object, or file path, with at least columns (chrom, pos, ea, nea)
chr_col

a string column name; chromosome position
pos_col

a string column name; base position
ea_col

a string column name; effect allele
nea_col

a string column name; non effect allele
flip

a string, options: "report", "allow", "no_flip"
alt_rsids

a logical, whether to return additional alternate RSIDs
build

a string, options: "b37_dbsnp156", "b38_dbsnp156" (corresponds to the appropriate data directory)
dbsnp_dir

a string file path to the dbSNP .fst file directory - see setup documentation
parallel_cores

an integer, the number of cores/workers to set up the future::multisession with
verbose

a logical, runtime reporting

Value

a data.table with an RSID column (or a list: 1-data.table; 2-list of alternate rsids IDs)

Clump a GWAS

Description

Clump variants in a GWAS using PLINK2 and an appropriate reference panel. For example, the 1000 genomes phase 3 data can be downloaded from the PLINK website (https://www.cog-genomics.org/plink/2.0/resources#phase3_1kg). To remove duplicates you can run:

plink2
–pfile all_phase3
–rm-dup force-first
–make-pgen
–out all_phase3_nodup

The path to the reference (without the plink extensions) should be passed as the plink_ref argument. The path to the plink2 executable should be passed as the plink2 argument.

Usage

clump(
  gwas,
  p1 = 1,
  p2 = 1,
  r2 = 0.1,
  kb = 250,
  plink2 = genepi.utils::which_plink2(),
  plink_ref = genepi.utils::which_1000G_reference(build = "GRCh37"),
  logging = TRUE,
  parallel_cores = parallel::detectCores()
)

Arguments

gwas

a data.frame like object with at least columns rsid, ea, oa, and p
p1

a numeric, the p-value threshold for inclusion as a clump
p2

a numeric, the p-value threshold for incorporation into a clump
r2

a numeric, the r2 value
kb

a integer, the window for clumping
plink2

a string, path to the plink executable
plink_ref

a string, path to the pfile genome reference
logging

a logical, whether to set the plink logging information as attributes (log, missing_id, missing_allele) on the returned data.table
parallel_cores

an integer, how many cores / threads to use

Value

a data.table with additional columns index (logical, whether the variant is an index SNP) and clump (integer, the clump the variant belongs to)

Clump MR object exposure

Description

Clump MR object exposure

Usage

clump_mr(
  x,
  p1 = 1,
  p2 = 1,
  r2 = 0.001,
  kb = 250,
  plink2 = genepi.utils::which_plink2(),
  plink_ref = genepi.utils::which_1000G_reference(build = "GRCh37"),
  parallel_cores = parallel::detectCores()
)

Arguments

x

an object of class MR description
p1

a numeric, the p-value threshold for inclusion as a clump
p2

a numeric, the p-value threshold for incorporation into a clump
r2

a numeric, the r2 value
kb

a integer, the window for clumping
plink2

a string, path to the plink executable
plink_ref

a string, path to the pfile genome reference
parallel_cores

an integer, how many cores / threads to use

Run collider bias assessment

Description

Run collider bias assessment

Usage

collider_bias(
  x,
  bias_method = "dudbridge",
  r2 = 0.001,
  p1 = 5e-08,
  kb = 250,
  plink2 = genepi.utils::which_plink2(),
  plink_ref = genepi.utils::which_1000G_reference(build = "GRCh37"),
  ip = 0.001,
  pi0 = 0.6,
  sxy1 = 1e-05,
  bootstraps = 100,
  weighted = TRUE,
  method = "Simex",
  B = 1000,
  seed = 2023
)

Arguments

x

an object of class MR
bias_method

a character or character vector, one or more of c("dudbridge", "slopehunter", "mr_ivw", "mr_egger", "mr_weighted_median", "mr_weighted_mode")
r2

a numeric 0-1, r2 used for clumping - set all clumping params to NA to turn off
p1

a numeric 0-1, p1 used for clumping - set all clumping params to NA to turn off
kb

an integer, kb used for clumping - set all clumping params to NA to turn off
plink2

a path, the plink2 binary
plink_ref

a path, the reference genome pfile
ip

a numeric 0-1, threshold for removing incidence variants; see xp_thresh SlopeHunter::hunt()
pi0

a numeric 0-1, proportion of SNPs in the incidence only cluster; see init_pi SlopeHunter::hunt()
sxy1

a numeric, the covariance between incidence and progression Gip SNPs; see init_sigmaIP SlopeHunter::hunt()
bootstraps

an integer, number of bootstraps to estimate SE; see M SlopeHunter::hunt()
weighted

see weighted indexevent::indexevent()
method

see method indexevent::indexevent()
B

see B indexevent::indexevent()
seed

seed, for reproducibility

Column object

Description

Column object

Usage

Column(name = class_missing, alias = class_missing, type = class_missing)

Arguments

name

the standard column name
alias

a character vector of aliases (other column names) for this column
type

a character, an atomic R type

Value

an S7 class genepi.utils::Column object

Slots

name

the standard column name

alias

a character vector of aliases (other column names) for this column

type

a character, an atomic R type

ColumnMap object

Description

A mapping to the standardised column names used in this package. Available names: 'rsid', 'chr', 'bp', 'ea', 'oa', 'eaf', 'p', 'beta', 'se', 'or', 'or_se', 'or_lb', 'or_ub', 'beta_lb', 'beta_ub', 'z', 'q_stat', 'i2', 'nstudies', 'n'

Usage

ColumnMap(x)

Arguments

x

either a list of Column class objects, a valid string for a pre-defined map: default, metal, ieu_ukb, ieugwasr, ns_map, gwama, giant, or a named character vector or list (standard name = old name)

Value

an S7 class genepi.utils::ColumnMap object

Slots

map

a list of Column class objects

Corrected Weighted Least Squares collider bias method

Description

Corrected Weighted Least Squares collider bias method

Usage

cwls(x, ...)

Arguments

x

an object of class MR
...

parameter sink, additional ignored parameters

Value

an object of class MRResult

Dudbridge collider bias method

Description

Dudbridge collider bias method

Usage

dudbridge(
  x,
  weighted = TRUE,
  prune = NULL,
  method = "Simex",
  B = 1000,
  lambda = seq(0.25, 5, 0.25),
  seed = 2018,
  ...
)

Arguments

x

an object of class MR
weighted

see indexevent::indexevent()
prune

see indexevent::indexevent()
method

see indexevent::indexevent()
B

see indexevent::indexevent()
lambda

see indexevent::indexevent()
seed

see indexevent::indexevent()
...

parameter sink, additional ignored parameters

Value

an object of class MRResult

Effect allele frequency plot

Description

Plotting reported effect allele frequencies (EAF) against a reference set to identify study variants which significantly deviate from the expected population frequencies.

Usage

eaf_plot(
  gwas,
  eaf_col = "EAF",
  ref_eaf_col = "EUR_EAF",
  tolerance = 0.2,
  colours = list(missing = "#5B1A18", outlier = "#FD6467", within = "#7294D4"),
  title = NULL,
  facet_grid_row_col = NULL,
  facet_grid_col_col = NULL
)

Arguments

gwas

a data.table
eaf_col

a string, the column containing the study EAF data
ref_eaf_col

a string, the column containing the reference EAF data
tolerance

a numeric, frequency difference that determines outliers
colours

a 3 element list of colour codes, e.g. list(missing="#5B1A18", outlier="#FD6467", within="#7294D4")
title

a string, the plot title
facet_grid_row_col

(optional), a column by which to facet the plot by rows
facet_grid_col_col

(optional), a column by which to facet the plot by columns

Value

a ggplot

Generate random GWAS data

Description

Generates rows of synthetic GWAS summary stats data. Useful for developing plotting and other methods. No attempt is made to make this data at all realistic.

Usage

generate_random_gwas_data(n, seed = 2023)

Arguments

n

number of fake variants to generate
seed

seed, for reproducibility

Value

a data.table with columns SNP, CHR, BP, OA, EA, EAF, BETA, P, EUR_EAF

Extract variants from plink binary

Description

Extract variants from plink binary

Usage

get_pfile_variants(
  snp,
  win_kb,
  chr,
  from_bp,
  to_bp,
  plink2 = genepi.utils::which_plink2(),
  pfile = genepi.utils::which_1000G_reference(build = "GRCh37")
)

Arguments

snp

character, an rsid
win_kb

numeric, window size around snp in kb
chr

character, the chromosome (use instead of snp and win_kb, not in addition)
from_bp

numeric, the start base position (use instead of snp and win_kb, not in addition)
to_bp

numeric, the end base position (use instead of snp and win_kb, not in addition)
plink2

character / path, the plink2 executable
pfile

character / path, the plink pfile set

Value

a data.table

Get proxies for variants from plink binary

Description

Get proxies for variants from plink binary

Usage

get_proxies(
  x,
  stat = "r2-unphased",
  win_kb = 125,
  win_r2 = 0.8,
  win_ninter = Inf,
  proxy_eaf = NULL,
  plink2 = genepi.utils::which_plink2(),
  pfile = genepi.utils::which_1000G_reference(build = "GRCh37"),
  ...
)

Arguments

x

a character vector of rsids or a GWAS object
stat

character, the R stat to calculate, one of "r2-unphased", "r2-phased", "r-unphased", "r-phased"
win_kb

numeric, the window to look in around the variants
win_r2

numeric, the lower r2 limit to include in output, (for –r-phased and –r-unphased, this means |r|≥sqrt(0.2))
win_ninter

numeric, controls the maximum number of other variants allowed between variant-pairs in the report. Inf = off.
proxy_eaf

numeric, the minimal effect allele frequency for proxy variants. NULL = eaf filtering off.
plink2

character / path, the plink2 executable
pfile

character / path, the plink pfile set
...

other arguments (see below)
snps

a character vector (available if x is a GWAS object), a vector of rsids to ensure exist, or else try and find proxies for
then

a string (available if x is a GWAS object), either add (adds proxies to current GWAS) or subset (subsets GWAS to variants and potential proxies for variants in x)

Value

a data.table of variants and their proxies (if x is a character vector) or a GWAS object if x is a GWAS object.

GWAS object

Description

A GWAS object is a container for vectors of GWAS data, a correlation matrix, and meta-data regarding quality control procedures applied at the point of object creation / data import.

Usage

GWAS(
  dat,
  map = "default",
  drop = FALSE,
  fill = FALSE,
  fill_rsid = FALSE,
  missing_rsid = "fill_CHR:BP",
  parallel_cores = parallel::detectCores(),
  dbsnp_dir = genepi.utils::which_dbsnp_directory(),
  filters = list(beta_invalid = "!is.infinite(beta) & abs(beta) < 20", eaf_invalid =
    "eaf > 0 & eaf < 1", p_invalid = "!is.infinite(p)", se_invalid = "!is.infinite(se)",
    alleles_invalid = "!is.na(ea) & !is.na(oa)", chr_missing = "!is.na(chr)", bp_missing
    = "!is.na(bp)", beta_missing = "!is.na(beta)", se_missing = "!is.na(se)", p_missing =
    "!is.na(p)", eaf_missing = "!is.na(eaf)"),
  reference = NULL,
  ref_map = NULL,
  verbose = TRUE,
  ...
)

Arguments

dat

a valid string file path to be read by data.table::fread or a data.table::data.table object; the GWAS data source
map

a valid input to the ColumnMap class constructor (a predefined map string id, a named list or character vector, or a ColumMap object)
drop

a logical, whether to drop data source columns not in the column map
fill

a logical, whether to add (NAs) missing columns present in the column map but not present in the data source
fill_rsid

either FALSE or a valid argument for the chrpos_to_rsid build argument, e.g. "b37_dbsnp156"
missing_rsid

a string, how to handle missing rsids: one of "fill_CHR:BP", "fill_CHR:BP_OA_EA", "overwrite_CHR:BP", "overwrite_CHR:BP:OA:EA", "none", or "leave"
parallel_cores

an integer, number of cores to used for RSID mapping, default is maximum machine cores
dbsnp_dir

path to the dbsnp directory of fst files see chrpos_to_rsid dbsnp_dir argument
filters

a list of named strings, each to be evaluated as an expression to filter the data during the quality control steps (above)
reference

a valid string file path to be read by data.table::fread or a data.table::data.table object; the reference data
ref_map

a valid input to the ColumnMap class constructor (a predefined map id (a string), a named list or character vector, or a ColumMap object) defining at least columns rsid (or chr, bp), ea, oa and eaf.
verbose

a logical, whether to print details
...

variable capture to be passed to the constructor, e.g. individual vectors for the slots, rather that dat

Value

an S7 class genepi.utils::GWAS object

Slots

rsid

character, variant ID - usually in rs12345 format, however this can be changed with the missing_rsid argument

chr

character, chromosome identifier

bp

integer, base position

ea

character, effect allele

oa

character, other allele

eaf

numeric, effect allele frequency

beta

numeric, effect size

se

numeric, effect size standard error

p

numeric, p-value

n

integer, total number of samples

ncase

integer, number of cases

strand

character, the strand + or -

imputed

logical, whether imputed

info

numeric, the info score

q

numeric, the Q statistic for meta analysis results

q_p

numeric, the Q statistic P-value

i2

numeric, the I2 statistic

proxy_rsid

character, proxy variant ID

proxy_chr

character, proxy chromosome identifier

proxy_bp

integer, proxy base position

proxy_ea

character, proxy effect allele

proxy_oa

character, proxy other allele

proxy_eaf

numeric, proxy effect allele frequency

proxy_r2

numeric, proxy r2 with rsid

trait

character, the GWAS trait

id

character, the GWAS identifier

source

character, data source; either the file path, or "data.table" if loaded directly

correlation

matrix, a correlation matrix of signed R values between variants

map

ColumnMap, a mapping of class ColumnMap

qc

list, a named list of filters; name is the filter expression and value is an integer vector of rows that fail the filter

Harmonise GWAS effects

Description

Harmonise effects across two GWAS datasets. The gwas objects are expected to be in standard format - see standardise_gwas(). Corrects strand for non-palindromic SNPs (flip BETAs). Drop palindromic variants. Drop non-matching variant alleles.
This function is based on the same function in the Slopehunter and TwoSampleMR packages. I have re-written it here to use the standardised column names and use pure data.table syntax for increased processing speed.

Usage

harmonise(
  gwas1,
  gwas2,
  gwas1_trait = "incidence",
  gwas2_trait = "progression",
  merge = NULL
)

Arguments

gwas1

a data.frame like object or file path, GWAS 1
gwas2

a data.frame like object or file path, GWAS 2
gwas1_trait

a string, suffix to add to gwas1 column names e.g. progression –> columns chr_progression, bp_progression, ..., etc.
gwas2_trait

a string, suffix to add to gwas2 column names e.g. progression –> columns chr_progression, bp_progression, ..., etc.
merge

a named character vector c(gwas1_col=gwas2_col) of the columns to join on; e.g. c("CHR1"="CHR2","BP1"="BP2"); can be NULL, in which case the gwases must be data.tables and already have keys set

Value

a data.table, the harmonised dataset

References

Slopehunter https://github.com/Osmahmoud/SlopeHunter

TwoSampleMR https://github.com/MRCIEU/TwoSampleMR

Calculate LD matrix

Description

Based on the ieugwasr function (see reference)

Usage

ld_matrix(
  dat,
  colmap = NULL,
  method = "r",
  plink2 = genepi.utils::which_plink2(),
  plink_ref = genepi.utils::which_1000G_reference(build = "GRCh37"),
  ukbb_ref = NULL
)

Arguments

dat

data.frame like object, or file path, with at least column rsid; if columns ea,oa,beta,eaf are provided then the variants will be return harmonised to the reference panel (effect allele, data = major allele, reference)
colmap

a list, mapping to columns list(rsid=?,ea=?,oa=?,beta=?,eaf=?) where ? can be a character vector in the case of harmonised datasets. Warning - it is assumed that harmonised datasets are indeed harmonised, if not, any unharmonised variants will be inappropriately removed.
method

a string, either r or r2
plink2

a string, path to the plink executable
plink_ref

a string, path to the pfile genome reference
ukbb_ref

path to a UKBB reference file

Value

an LD matrix if only variants provided, else if alleles provided a list(dat=harmonised data, ld_mat=ld_matrix)

References

ieugwasr::ld_matrix_local()

Liftover GWAS positions

Description

Determine GWAS build and liftover to required build. This is the same function from the GwasDataImport package, the only difference being that you can specify the build rather than it trying to guess the build (which fails if you are trying to liftover small segments of the genome).

Usage

lift(
  gwas,
  from = "Hg19",
  to = "Hg38",
  snp_col = "snp",
  chr_col = "chr",
  pos_col = "pos",
  ea_col = "ea",
  oa_col = "oa",
  remove_duplicates = TRUE
)

Arguments

gwas

a data.table, or file path, chr, pos, snp name, effect allele, non-effect allele columns
from

which build to lift from, one of c("Hg18", "Hg19", "Hg38")
to

which build to lift over to, one of c("Hg18", "Hg19", "Hg38")
snp_col

Name of SNP column name. Optional. Uses less certain method of matching if not available
chr_col

Name of chromosome column name. Required
pos_col

Name of position column name. Required
ea_col

Name of effect allele column name. Optional. Might lead to duplicated rows if not presented
oa_col

Name of other allele column name. Optional. Might lead to duplicated rows if not presented
remove_duplicates

a logical, whether to remove duplicate IDs

Value

data.table with updated position columns

References

https://github.com/MRCIEU/GwasDataImport

Manhattan plot

Description

Create a Manhattan plot with ggplot2 geom_point.

Usage

manhattan(
  gwas,
  highlight_snps = NULL,
  highlight_win = 100,
  annotate_snps = NULL,
  colours = c("#d9d9d9", "#bfbfbf"),
  highlight_colour = "#e15758",
  highlight_shape = 16,
  highlight_alpha = 1,
  sig_line_1 = 5e-08,
  sig_line_2 = NULL,
  y_limits = c(NULL, NULL),
  title = NULL,
  subtitle = NULL,
  base_text_size = 14,
  hit_table = FALSE,
  max_table_hits = 10,
  downsample = 0.9,
  downsample_pval = 0.7
)

Arguments

gwas

a data.table with a minimum of columns SNP, CHR, BP, and P
highlight_snps

(optional) a character vector of SNPs to highlight
highlight_win

(optional) a numeric, the number of kb either side of the highlight_snps to also highlight (i.e create peaks)
annotate_snps

(optional) a character vector of SNPs to annotate
colours

(optional) a character vector colour codes to be replicated along the chromosomes
highlight_colour

(optional) a character colour code; the colour to highlight points in
highlight_shape

(optional) a numeric shape code; the shape of the highlight points (see ggplot2 shape codes)
highlight_alpha

(optional) a numeric value between 0 and 1; the alpha of the highlighted points colour
sig_line_1

(optional) a numeric value (-log10(P)) for where to draw a horizontal line
sig_line_2

(optional) a numeric value (-log10(P)) for where to draw a second horizontal line
y_limits

(optional) a numeric length 2 vector c(min-Y, max-Y)
title

(optional) a string title
subtitle

(optional) a string subtitle
base_text_size

an integer, base_size for the ggplot2 theme
hit_table

(optional) a logical, whether to display a table of top hits (lowest P values)
max_table_hits

(optional) an integer, how many top hits to show in the table
downsample

(optional) a numeric between 0 and 1, the proportion by which to downsample by, e.g. 0.6 will remove 60% of points above the downsample_pval threshold (can help increase plotting speed with minimal impact on plot appearance)
downsample_pval

(optional) a numeric between 0 and 1, the p-values affected by downsampling, default >0.1

Value

a ggplot

Miami plot

Description

Create a Miami plot. Please look carefully at the parameters as these largely map to the manhattan() parameters, the main difference being that you need to supply a 2 element list of the parameter, one for the upper and one for the lower plot aspect of the Miami plot. Some parameters are not duplicated however - see the example defaults below.

Usage

miami(
  gwases,
  highlight_snps = list(top = NULL, bottom = NULL),
  highlight_win = list(top = 100, bottom = 100),
  annotate_snps = list(top = NULL, bottom = NULL),
  colours = list(top = c("#d9d9d9", "#bfbfbf"), bottom = c("#bfbfbf", "#d9d9d9")),
  highlight_colour = list(top = "#e15758", bottom = "#4f79a7"),
  highlight_shape = list(top = 16, bottom = 16),
  sig_line_1 = list(top = 5e-08, bottom = 5e-08),
  sig_line_2 = list(top = NULL, bottom = NULL),
  y_limits = list(top = c(NULL, NULL), bottom = c(NULL, NULL)),
  title = NULL,
  subtitle = list(top = NULL, bottom = NULL),
  base_text_size = 14,
  hit_table = FALSE,
  max_table_hits = 10,
  downsample = 0.1,
  downsample_pval = 0.1
)

Arguments

gwases

a list of 2 data.tables
highlight_snps

(optional) a character vector of SNPs to highlight
highlight_win

(optional) a numeric, the number of kb either side of the highlight_snps to also highlight (i.e create peaks)
annotate_snps

(optional) a character vector of SNPs to annotate
colours

(optional) a character vector colour codes to be replicated along the chromosomes
highlight_colour

(optional) a character colour code; the colour to highlight points in
highlight_shape

(optional) a numeric shape code; the shape of the highlight points (see ggplot2 shape codes)
sig_line_1

(optional) a numeric value (-log10(P)) for where to draw a horizontal line
sig_line_2

(optional) a numeric value (-log10(P)) for where to draw a second horizontal line
y_limits

(optional) a numeric length 2 vector c(min-Y, max-Y)
title

(optional) a string title
subtitle

(optional) a string subtitle
base_text_size

an integer, base_size for the ggplot2 theme
hit_table

(optional) a logical, whether to display a table of top hits (lowest P values)
max_table_hits

(optional) an integer, how many top hits to show in the table
downsample

(optional) a numeric between 0 and 1, the proportion by which to downsample by, e.g. 0.6 will remove 60% of points above the downsample_pval threshold (can help increase plotting speed with minimal impact on plot appearance)
downsample_pval

(optional) a numeric between 0 and 1, the p-values affected by downsampling, default >0.1

Value

a ggplot

MR object

Description

An MR object is a container for vectors and matrices of 2 or more GWAS data.

Usage

MR(
  exposure,
  outcome,
  harmonise_strictness = 2,
  correlation = NULL,
  verbose = TRUE
)

Arguments

exposure

a GWAS object or list of GWAS objects
outcome

a GWAS object
harmonise_strictness

an integer (1,2,3) corresponding to the TwoSampleMR harmonisation options of the same name.
correlation

a matrix, correlation matrix of signed R values between variants
verbose

a logical, print more information

Value

an S7 class genepi.utils::MR object

Slots

snps

character, variant ID

chr

character, chromosome identifier

bp

integer, base position

ea

character, effect allele

oa

character, other allele

eafx

numeric, exposure effect allele frequency

nx

integer, exposure total number of samples

ncasex

integer, exposure number of cases

bx

numeric, exposure effect size

bxse

numeric, exposure effect size standard error

px

numeric, exposure p-value

eafy

numeric, exposure effect allele frequency

ny

integer, exposure total number of samples

ncasey

integer, exposure number of cases

by

numeric, exposure effect size

byse

numeric, exposure effect size standard error

py

numeric, exposure p-value

exposure_id

character, the GWAS identifier

exposure

character, the GWAS exposure

outcome_id

character, the GWAS identifier

outcome

character, the GWAS outcome

group

integer, grouping variable used for plotting

index_snp

logical, whether the variant is an index variant (via clumping)

proxy_snp

character, the id of the proxy snp

ld_info

logical, whether there is LD information

info

data.frame, information about the loaded GWAS objects

correlation

matrix, a correlation matrix of signed R values between variants

Run Egger MR

Description

Run Egger MR

Usage

mr_egger(x, corr = FALSE, ...)

Arguments

x

an object of class MR
corr

a logical, whether to use the correlation matrix when running MR
...

parameter sink, not used

Run IVW MR

Description

Run IVW MR

Usage

mr_ivw(x, corr = FALSE, ...)

Arguments

x

an object of class MR
corr

a logical, whether to use the correlation matrix when running MR
...

parameter sink, not used

Run PC-GMM MR

Description

Run PC-GMM MR

Usage

mr_pcgmm(x, corr = TRUE, ...)

Arguments

x

an object of class MR
corr

a logical, whether to use the correlation matrix when running MR
...

parameter sink, not used

MR results to data.table

Description

MR results to data.table

Usage

mr_results_to_data_table(x)

Arguments

x

MRResult object to covert to data.table

Run weighted median MR

Description

Run weighted median MR

Usage

mr_weighted_median(x, corr = FALSE, ...)

Arguments

x

an object of class MR
corr

a logical, whether to use the correlation matrix when running MR
...

parameter sink, not used

Run weighted mode MR

Description

Run weighted mode MR

Usage

mr_weighted_mode(x, corr = FALSE, ...)

Arguments

x

an object of class MR
corr

a logical, whether to use the correlation matrix when running MR
...

parameter sink, not used

Coloc probability plot

Description

A plotting wrapper for the coloc package. Produces a ggplot for either the prior or posterior probability sensitivity analyses. See the coloc package vignettes for details.

Usage

plot_coloc_probabilities(coloc, rule = "H4 > 0.5", type = "prior", row = 1)

Arguments

coloc

coloc object, output from coloc::coloc.abf()
rule

a string, a valid rule indicating success e.g. "H4 > 0.5"
type

a string, either prior or posterior
row

an integer, row in a coloc.susie or coloc.signals object

Value

a ggplot

References

coloc

Plot MR results

Description

Plot MR results

Usage

plot_mr(mr, res)

Arguments

mr

an object of class MR
res

a data.table output from run_mr or other MR methods

QQ plot

Description

QQ plot

Usage

qq_plot(
  gwas,
  pval_col = "p",
  colours = list(raw = "#2166AC"),
  title = NULL,
  subtitle = NULL,
  plot_corrected = FALSE,
  facet_grid_row_col = NULL,
  facet_grid_col_col = NULL,
  facet_nrow = NULL,
  facet_ncol = NULL
)

Arguments

gwas

a data.frame like object or valid file path
pval_col

the P value column
colours

a 2 element list of colour codes (1-the uncorrected points, 2-the GC corrected points)
title

a string, the title for the plot
subtitle

a string, the subtitle for the plot
plot_corrected

a logical, whether to apply and plot the lambda correction
facet_grid_row_col

a string, the column name in gwas by which to facet the plot (rows)
facet_grid_col_col

a string, the column name in gwas by which to facet the plot (cols)
facet_nrow

an integer, passed to facet_wrap, the number of rows to facet by (if only facet_grid_row_col is provided)
facet_ncol

an integer, passed to facet_wrap, the number of cols to facet by (if only facet_grid_col_col is provided)

Value

a ggplot

Reset index SNP

Description

Reset index SNP

Usage

reset_index_snp(x)

Arguments

x

an object of class MR

Run MR

Description

Run MR

Usage

run_mr(
  x,
  corr = FALSE,
  methods = c("mr_ivw", "mr_egger", "mr_weighted_median", "mr_weighted_mode"),
  ...
)

Arguments

x

an object of class MR
corr

a logical, whether to use the correlation matrix when running MR
methods

a string, one of c('mr_ivw','mr_egger','mr_weighted_median','mr_weighted_mode', 'mr_pcgmm')
...

parameter sink, not used

Set the 1000G reference path

Description

Set the 1000G reference path

Usage

set_1000G_reference(path, build = "GRCh37")

Arguments

path

path to the 1000G reference pfile
build

one of c("GRCh37", "GRCh38")

Value

NULL, updated config file

Set dbSNP directory

Description

Set dbSNP directory

Usage

set_dbsnp_directory(path)

Arguments

path

path to the dbsnp directory

Value

NULL, updated config file

Set the LD matrix

Description

Set the LD matrix

Usage

set_ld_mat(x, correlation)

Arguments

x

an object of class MR
correlation

a matrix, the correlation ('r') matrix

Set the PLINK2 path

Description

Set the PLINK2 path

Usage

set_plink2(path)

Arguments

path

path to the PLINK2 executable

Value

NULL, updated config file

Slope-Hunter collider bias method

Description

Slope-Hunter collider bias method

Usage

slopehunter(
  x,
  ip = 0.001,
  pi0 = 0.6,
  sxy1 = 1e-05,
  bootstraps = 100,
  seed = 777,
  ...
)

Arguments

x

an object of class MR
ip

see xp_thresh SlopeHunter::hunt()
pi0

see init_pi SlopeHunter::hunt()
sxy1

see init_sigmaIP SlopeHunter::hunt()
bootstraps

see M SlopeHunter::hunt()
seed

see seed SlopeHunter::hunt()
...

parameter sink, additional ignored parameters

Value

an object of class MRResult

subset_gwas

Description

subset_gwas

Usage

subset_gwas(x, snps)

Arguments

x

GWAS object
snps

a vector, either row indicies (integers) into the GWAS object (e.g. obtained with filters such as which(GWAS'at'p < 5e-8), or rsids (characters) to be found in the GWAS rsid slot.

Value

GWAS object subsetted by snps

Convert to MendelianRandomization::MRInput object

Description

Convert to MendelianRandomization::MRInput object

Usage

to_MRInput(x, corr = FALSE)

Arguments

x

an object of class MR
corr

a logical, whether to use the correlation matrix when running MR

Convert to MendelianRandomization::MRMVInput object

Description

Convert to MendelianRandomization::MRMVInput object

Usage

to_MRMVInput(x, corr = FALSE)

Arguments

x

an object of class MR
corr

a logical, whether to use the correlation matrix when running MR

Get 1000G reference path(s)

Description

Get 1000G reference path(s)

Usage

which_1000G_reference(build = NULL)

Arguments

build

one of "GRCh37" or "GRCh38", or null to return both

Value

a string file path, the currently set 1000G reference path

Get available dbSNP builds

Description

Get available dbSNP builds

Usage

which_dbsnp_builds(build = NULL)

Arguments

build

a dbSNP build

Value

a list of available dbSNP builds - name(dbSNP build): value(directory_path)

Get dbSNP directory

Description

Get dbSNP directory

Usage

which_dbsnp_directory()

Value

a string file path, the currently set dbSNP directory path

Get plink2 path

Description

Get plink2 path

Usage

which_plink2()

Value

a string file path, the currently set plink2 path