Package 'CheckSumStats'

Title: CheckSumStats
Description: CheckSumStats is an R package for checking the accuracy of meta- and summary-data from genome-wide association studies (GWAS) prior to their use in post-GWAS applications. For example, the package provides tools for checking that the reported effect allele and effect allele frequency columns are correct. It also checks for possible issues in the reported effect sizes that might introduce bias into downstream analyses.
Authors: Philip Haycock [aut, cre]
Maintainer: Philip Haycock <[email protected]>
License: MIT + file LICENSE
Version: 0.0.0.9000
Built: 2024-10-01 06:09:29 UTC
Source: https://github.com/MRCIEU/CheckSumStats

Help Index

A example dataset of genetic summary data for arachidonic acid

Description

The dataset contains summary association statistics for 436 SNPs, generated in linear regression models, from a genome-wide association study of arachidonic acid conducted by the CHARGE consortium. No post-GWAS filtering on allele frequency, imputation info score or number of studies has been performed. The selected SNPs correspond to three groups: 1) A MAF 1KG reference set, 2) GWAS catalog top hits for arachidonic acid and 3) GWAS top hits for arachidonic acid in the CHARGE study

Usage

ara_test_dat

Format

A data frame with 436 rows and 9 variables:

snp

SNP rsid

effect_allele

effect allele

other_allele

non-effect allele

effect_allele_freq

effect allele frequency

beta

change in arachidonic acid per copy of the effect allele

se

standard error for beta

p

p value statistic describing the association between the SNP and arachidonic acid

n

number of study participants

path_to_target_file

name of file used to generate example dataset

Source

http://www.chargeconsortium.com/main/results

GWAS top hits for arachidonic acid in the CHARGE consortium

Description

The dataset contains rsids for single nucleotide polymorphisms extracted from a genome-wide association study of arachidonic acid in the CHARGE consortium. The list was generated by 1) extracting all SNPs with P values <5e-8 (1063 SNPs in total); and then 2) performing LD clumping on the 1063 extracted SNPs (clump_r2 = 0.01, clump_kb=10000 ) using European participants from UK Biobank as a reference dataset. Clumping was performed using ieugwasr::ld_clump. No post-GWAS filtering on allele frequency, imputation info score or number of studies was performed on the GWAS summary statistics prior to the extraction of the SNPs.

Usage

charge_top_hits

Format

A charactor vector of length 210:

Source

http://www.chargeconsortium.com/main/results

GWAS top hits for arachidonic acid in the CHARGE consortium after post-GWAS cleaning

Description

The dataset contains rsids for single nucleotide polymorphisms extracted from a genome-wide association study of arachidonic acid in the CHARGE consortium. Prior to extraction of the rsids, SNPs were excluded if they had a minor allele frequency <=5%, an imputation r2 score <=0.5 or were present in only 1 study (out of a total of 5 studies in the meta-analysis). This filtering steps are based on the post-GWAS filtering steps described in Guan et al (PMID=24823311). The list of rsids was then generated by: 1) extracting all SNPs with P values <5e-8 (219 SNPs in total); and then 2) performing LD clumping on the 219 extracted SNPs (clump_r2 = 0.01, clump_kb=10000 ) using European participants from UK Biobank as a reference dataset (6 SNPs remained after LD clumping). Clumping was performed using ieugwasr::ld_clump.

Usage

charge_top_hits_cleaned

Format

A charactor vector of length 6:

Source

http://www.chargeconsortium.com/main/results

Make cow plot

Description

Combine all plots into a single plot using the cowplot package

Usage

combine_plots(
  Plot_list = NULL,
  out_file = NULL,
  return_plot = FALSE,
  width = 800,
  height = 1000,
  Title = "",
  Xlab = "",
  Ylab = "",
  Title_size = 0,
  Title_axis_size = 0,
  by2cols = TRUE,
  Ncol = 2,
  Tiff = FALSE
)

Arguments

Plot_list

plots to combine. Can either be vector of character strings giving the names of plot objects or a list of plot objects.
out_file

filepath to save the plot
return_plot

logical argument. If TRUE, plot is returned and is not save to out_file
width

width of plot
height

height of plot
Title

plot title
Xlab

label for X axis
Ylab

label for Y axis
Title_size

size of title
Title_axis_size

size of x axis title
by2cols

logical argument. If true, forces plot to have 2 columns
Ncol

number of columns
Tiff

save plot in tiff format. Default is set to FALSE. If set to FALSE, the plot is saved in png format. Not applicable if return_plot is set to TRUE.

Value

plot

Compare the genetic effect sizes in the test dataset to the GWAS catalog

Description

Compare the direction of effects and effect allele frequency between the test dataset and the GWAS catalog, in order to identify effect allele meta data errors

Usage

compare_effect_to_gwascatalog(
  dat = NULL,
  efo = NULL,
  efo_id = NULL,
  trait = NULL,
  beta = NULL,
  se = NULL,
  gwas_catalog_ancestral_group = c("European", "East Asian"),
  exclude_palindromic_snps = TRUE,
  force_all_trait_study_hits = FALSE,
  distance_threshold = distance_threshold
)

Arguments

dat

the test dataset of interest
efo

trait of interest in the experimental factor ontology
efo_id

ID for trait of interest in the experimental factor ontology
trait

the trait of interest
beta

name of the column containing the SNP effect size
se

name of the column containing the standard error for the SNP effect size.
gwas_catalog_ancestral_group

restrict the comparison to these ancestral groups in the GWAS catalog. Default is set to (c("European","East Asian")
exclude_palindromic_snps

should the function exclude palindromic SNPs? default set to TRUE. If set to FALSE, then conflicts with the GWAS catalog could reflect comparison of different reference strands.
force_all_trait_study_hits

force the comparison to include GWAS hits from the test dataset if they are not in the GWAS catalog? This should be set to TRUE only if dat is restricted to GWAS hits for the trait of interest. This is useful for visualising whether the test trait study has an unusually larger number of GWAS hits, which could, in turn, indicate analytical issues with the summary statistics
distance_threshold

distance threshold for deciding if the GWAS hit in the test dataset is present in the GWAS catalog. For example, a distance_threshold of 25000 means that the GWAS hit in the test dataset must be within 25000 base pairs of a GWAS catalog association, otherwise it is reported as missing from the GWAS catalog.

Value

dataframe

Compare the genetic effect sizes in the test dataset to the GWAS catalog

Description

Compare the direction of effects and effect allele frequency between the test dataset and the GWAS catalog, in order to identify effect allele meta data errors

Usage

compare_effect_to_gwascatalog2(
  dat = NULL,
  efo = NULL,
  efo_id = NULL,
  trait = NULL,
  gwas_catalog_ancestral_group = c("European", "East Asian"),
  exclude_palindromic_snps = TRUE,
  map_association_to_study = FALSE,
  beta = "beta",
  se = "se",
  gwas_catalog = NULL,
  force_all_trait_study_hits = FALSE,
  distance_threshold = distance_threshold
)

Arguments

dat

the test dataset of interest
efo

trait of interest in the experimental factor ontology
efo_id

ID for trait of interest in the experimental factor ontology
trait

the trait of interest
gwas_catalog_ancestral_group

restrict the comparison to these ancestral groups in the GWAS catalog. Default is set to (c("European","East Asian")
exclude_palindromic_snps

should the function exclude palindromic SNPs? default set to TRUE. If set to FALSE, then conflicts with the GWAS catalog could reflect comparison of different reference strands.
map_association_to_study

map associations to study in GWAS catalog. This supports matching of results on PMID and study ancestry, which increases accuracy of comparisons, but is slow when there are large numbers of associations. Default = FALSE.
beta

name of the column containing the SNP effect size
se

name of the column containing the standard error for the SNP effect size.
gwas_catalog

user supplied data frame containing results from the GWAS catalog for the trait of interest. If set to NULL then the function will retrieve results from the GWAS catalog.
force_all_trait_study_hits

force the comparison to include GWAS hits from the test dataset if they are not in the GWAS catalog? This should be set to TRUE only if dat is restricted to GWAS hits for the trait of interest. This is useful for visualising whether the test trait study has an unusually larger number of GWAS hits, which could, in turn, indicate analytical issues with the summary statistics
distance_threshold

distance threshold for deciding if the GWAS hit in the test dataset is present in the GWAS catalog. For example, a distance_threshold of 25000 means that the GWAS hit in the test dataset must be within 25000 base pairs of a GWAS catalog association, otherwise it is reported as missing from the GWAS catalog.

Value

dataframe

Extract SNPs with P value below a specified threshold (e.g. significant SNPs)

Description

Exract the rows of the summary dataset of interest with P values below the specified threshold. This only works on linux/mac operating systems.

Usage

extract_sig_snps(
  path_to_target_file = NULL,
  p_val_col_number = NULL,
  p_threshold = 5e-08
)

Arguments

path_to_target_file

path to the target file. This contains the summary data for the trait of interest
p_val_col_number

the column number corresponding to the P values for the SNP-trait associations
p_threshold

Extract SNP-trait associtions with P values less than this value. Default set to 5e-8

Value

data frame

Extract SNPs

Description

Exract the summary data for the rsids of interest from a target study. This only works on linux/ mac operating systems. Will not work on Windows.

Usage

extract_snps(
  snplist = NULL,
  path_to_target_file = NULL,
  exact_match = TRUE,
  path_to_target_file_sep = "\t",
  Test.gz = FALSE,
  fill = FALSE,
  Comment = "#",
  Head = TRUE,
  get_sig_snps = FALSE,
  p_val_col_number = NULL,
  p_threshold = 5e-08
)

Arguments

snplist

a list of rsids of interest, either a character vector or path_to_target_file with the list of rsids
path_to_target_file

path to the target file This contains the summary data for the trait of #' interest
exact_match

search for exact matches. Default TRUE
path_to_target_file_sep

column/field separator. Default assumes that data is tab separated
Test.gz

is the target data a gz file? Default set to FALSE
fill

argument from read.table. logical. If ‘TRUE’ then in case the rows have unequal length, blank fields are implicitly added. Default is FALSE
Comment

comment to pass to comment.char in read.table. default = "#"
Head

Does the file have a header ? Default set to TRUE
get_sig_snps

also extract the top hits from the target file, not just the SNPs specified in snplist. logic TRUE or FALSE. Default set to FALSE
p_val_col_number

the column number corresponding to the P values for the SNP-trait associations
p_threshold

Extract SNP-trait associtions with P values less than this value. Default set to 5e-8

Value

data frame

Are hits in the GWAS catalog?

Description

Identify GWAS hits in the test dataset and see if they overlap with GWAS hits in the GWAS catalog.

Usage

find_hits_in_gwas_catalog(
  gwas_hits = NULL,
  trait = NULL,
  efo = NULL,
  efo_id = NULL,
  distance_threshold = 25000
)

Arguments

gwas_hits

the "GWAS hits" in the test dataset (e.g. SNP-trait associations with P<5e-8)
trait

the trait of interest
efo

trait of interest in the experimental factor ontology
efo_id

ID for trait of interest in the experimental factor ontology
distance_threshold

distance threshold for deciding if the GWAS hit in the test dataset is present in the GWAS catalog. For example, a distance_threshold of 25000 means that the GWAS hit in the test dataset must be within 25000 base pairs of a GWAS catalog association, otherwise it is reported as missing from the GWAS catalog.

Value

list

Flag allele frequency conflicts

Description

Flag allele frequency conflicts through comparison of reported allele frequency to minor allele frequency in the 1000 genomes super populations.

Usage

flag_af_conflicts(target_dat = NULL)

Arguments

target_dat

the dataset of interest. Data frame.

Value

list

Flag conflicts with the GWAS catalog

Description

Flag conflicts with the GWAS catalog through comparison of reported effect alleles and reported effect allele frequency.

Usage

flag_gc_conflicts(
  dat = NULL,
  beta = "lnor",
  se = "lnor_se",
  efo = NULL,
  trait = NULL,
  efo_id = NULL,
  gwas_catalog_ancestral_group = c("European", "East Asian"),
  exclude_palindromic_snps = TRUE
)

Arguments

dat

the test dataset of interest
beta

name of the column containing the SNP effect size
se

name of the column containing the standard error for the SNP effect size.
efo

trait of interest in the experimental factor ontology
trait

the trait of interest
efo_id

ID for trait of interest in the experimental factor ontology
gwas_catalog_ancestral_group

restrict the comparison to these ancestral groups in the GWAS catalog. Default is set to (c("European","East Asian")
exclude_palindromic_snps

should the function exclude palindromic SNPs? default set to TRUE. If set to FALSE, then conflicts with the GWAS catalog could reflect comparison of different reference strands.

Value

list

Flag conflicts with the GWAS catalog

Description

Flag conflicts with the GWAS catalog through comparison of reported effect alleles and reported effect allele frequency.

Usage

flag_gc_conflicts2(gc_dat = NULL)

Arguments

gc_dat

dataset generated by compare_effect_to_gwascatalog2()

Value

list

format data

Description

Get the trait summary data ready for the QC checks.

Usage

format_data(
  dat = NULL,
  trait = NA,
  population = NA,
  ncase = NA,
  ncontrol = NA,
  rsid = NA,
  effect_allele = NA,
  other_allele = NA,
  beta = NA,
  se = NA,
  lnor = NA,
  lnor_se = NA,
  eaf = NA,
  p = NA,
  or = NA,
  or_lci = NA,
  or_uci = NA,
  chr = NA,
  pos = NA,
  z_score = NA,
  drop_duplicate_rsids = TRUE
)

Arguments

dat

the dataset to be formatted
trait

the name of the trait.
population

describe the population ancestry of the dataset
ncase

number of cases or name of the column specifying the number of cases
ncontrol

number of controls or name of the column specifying the number of controls. If your summary data was generated in a linear model of a continuous trait, use ncontrol to indicate the total sample size.
rsid

name of the column containing the rs number or identifiers for the genetic variants
effect_allele

name of the effect allele column
other_allele

name of the non-effect allele column
beta

name of the column containing the SNP effect sizes. Use this argument if your summary data was generated in a linear model of a continuous trait.
se

standard error for the beta. Use this argument if your summary data was generated in a linear model of a continuous trait.
lnor

name of the column containing the log odds ratio. If missing, tries to infer it from the odds ratio
lnor_se

name of the column containing the standard error for the log odds ratio. If missing, tries to infer it from 95% confidence intervals or pvalues
eaf

name of the effect allele frequency column
p

name of the pvalue columne
or

name of column containing the odds ratio
or_lci

name of column containing the lower 95% confidence interval for the odds ratio
or_uci

name of column containing the upper 95% confidence interval for the odds ratio
chr

name of the column containing the chromosome number for each genetic variant
pos

genomic position for the genetic variant in base pairs
z_score

effect size estimate divided by its standard error
drop_duplicate_rsids

drop duplicate rsids? logical. default TRUE. duplicate rsids may for example correspond to triallelic SNPs.

Value

data frame

get efo

Description

Retrieve the experimental factor ontology (EFO) for some trait of interest. EFOs are retrieved from ZOOMA https://www.ebi.ac.uk/spot/zooma/

Usage

get_efo(trait = NULL)

Arguments

trait

the trait of interest

Value

list

A example dataset of genetic summary data

Description

The dataset contains summary association statistics for 98 SNPs, generated in logistic regression models, from a genome-wide association study of glioma conducted by the GliomaScan consortium.

Usage

glioma_test_dat

Format

A data frame with 98 rows and 20 variables:

Locus

SNP rsid

Allele1

non-effect allele

Allele2

effect allele

MAF

SNP minor allele frequency in controls|cases

Geno_Counts

genotype counts in controls/cases

Subjects

Number of participants in study

p

p value statistic describing the association between the SNP and glioma

OR

odds ratio for glioma

OR_95._CI_l

lower 95% confidence interval

OR_95._CI_u

upper 95% confidence interval

CHROMOSOME

chromosome number

LOCATION

genomic coordinates in base pairs

controls

number of controls

cases

number of cases

eaf.controls

effect allele frequency in controls

Source

https://pubmed.ncbi.nlm.nih.gov/22886559/

GWAS top hits

Description

Extract results for top hits for the trait of interest from the NHGRI-EBI GWAS catalog

Usage

gwas_catalog_hits(
  trait = NULL,
  efo = NULL,
  efo_id = NULL,
  map_association_to_study = FALSE
)

Arguments

trait

the trait of interest as reported in the GWAS catalog
efo

trait of intersest in the experimental factor ontology
efo_id

ID for trait of interest in the experimental factor ontology
map_association_to_study

map associations to study in GWAS catalog. This supports matching of results on PMID and study ancestry, which increases accuracy of comparisons, but is slow when there are large numbers of associations. It is recommended that you run this function with map_association_to_study set to FALSE. Then, if large numbers of conflicting effect sizes are identified, re-run with this argument set to TRUE. Default = FALSE.

Value

data frame

Infer ancestry

Description

Infer possible ancestry through comparison of allele frequency amongst test dataset and 1000 genomes super populations. Returns list of Pearson correlation coefficients.

Usage

infer_ancestry(target_dat = NULL)

Arguments

target_dat

the dataset of interest. Data frame.

Value

list

Plot comparing the test study to the GWAS catalog

Description

Make a plot comparing signed Z scores, or effect allele frequency, between the test dataset and the GWAS catalog, in order to identify effect allele meta data errors

Usage

make_plot_gwas_catalog(
  dat = NULL,
  plot_type = "plot_zscores",
  efo_id = NULL,
  efo = NULL,
  trait = NULL,
  gwas_catalog_ancestral_group = c("European", "East Asian"),
  legend = TRUE,
  Title = "Comparison of Z scores between test dataset & GWAS catalog",
  Ylab = "Z score in test dataset",
  Xlab = "Z score in GWAS catalog",
  force_all_trait_study_hits = FALSE,
  exclude_palindromic_snps = TRUE,
  beta = "beta",
  se = "se",
  distance_threshold = 25000,
  return_dat = FALSE,
  map_association_to_study = FALSE,
  gwas_catalog = NULL,
  nocolour = FALSE,
  publication_quality = FALSE,
  gc_dat = NULL
)

Arguments

dat

the test dataset of interest
plot_type

compare Z scores or effect allele frequency? For comparison of Z scores set plot_type to "plot_zscores". For comparison of effect allele frequency set to "plot_eaf". Default is set to "plot_zscores"
efo_id

ID for trait of interest in the experimental factor ontology
efo

trait of interest in the experimental factor ontology
trait

the trait of interest
gwas_catalog_ancestral_group

restrict the comparison to these ancestral groups in the GWAS catalog. Default is set to (c("European","East Asian")
legend

include legend in plot. Default TRUE
Title

plot title
Ylab

label for Y axis
Xlab

label for X axis
force_all_trait_study_hits

force the plot to include GWAS hits from the outcome study if they are not in the GWAS catalog? This should be set to TRUE only if dat is restricted to GWAS hits for the trait of interest. This is useful for visualising whether the outcome/trait study has an unusually larger number of GWAS hits, which could, in turn, indicate that the summary statistics have not been adequately cleaned.
exclude_palindromic_snps

should the function exclude palindromic SNPs? default set to TRUE. If set to FALSE, then conflicts with the GWAS catalog could reflect comparison of different reference strands.
beta

name of the column containing the SNP effect size
se

name of the column containing the standard error for the SNP effect size.
distance_threshold

distance threshold for deciding if the GWAS hit in the test dataset is present in the GWAS catalog. For example, a distance_threshold of 25000 means that the GWAS hit in the test dataset must be within 25000 base pairs of a GWAS catalog association, otherwise it is reported as missing from the GWAS catalog.
return_dat

if TRUE, the dataset used to generate the plot is returned to the user and no plot is made.
map_association_to_study

map associations to study in GWAS catalog. This supports matching of results on PMID and study ancestry, which increases accuracy of comparisons, but is slow when there are large numbers of associations. Default = FALSE
gwas_catalog

user supplied data frame containing results from the GWAS catalog for the trait of interest. If set to NULL then the function will retrieve results from the GWAS catalog.
nocolour

if TRUE, effect size conflicts are illustrated using shapes rather than colours. Default FALSE
publication_quality

produce a high resolution image e.g. for publication purposes. Default FALSE
gc_dat

output of compare_effect_to_gwascatalog2. This will typically be ignored by most users. Default NULL

Value

plot

MAF plot

Description

Make a plot comparing minor allele frequency between test dataset and reference studies.

Usage

make_plot_maf(
  ref_dat = NULL,
  ref_1000G = c("AFR", "AMR", "EAS", "EUR", "SAS", "ALL"),
  target_dat = NULL,
  eaf = "eaf",
  snp_target = "rsid",
  snp_reference = "SNP",
  ref_dat_maf = "MAF",
  target_dat_effect_allele = "effect_allele",
  target_dat_other_allele = "other_allele",
  ref_dat_minor_allele = "minor_allele",
  ref_dat_major_allele = "major_allele",
  trait = "trait",
  target_dat_population = "population",
  ref_dat_population = "population",
  target_study = "study",
  ref_study = "study",
  Title = "Comparison of allele frequency between test dataset & reference study",
  Ylab = "Allele frequency in test dataset",
  Xlab = "MAF in reference study",
  cowplot_title = "Allele frequency in test dataset vs 1000 genomes super populations",
  return_dat = FALSE,
  nocolour = FALSE,
  legend = TRUE,
  allele_frequency_conflict = 1,
  publication_quality = FALSE
)

Arguments

ref_dat

user supplied reference dataset. data frame. optional
ref_1000G

if ref_dat is NULL, the user should indicate the 1000 genomes reference study of interest. options are: AFR, AMR, EAS, EUR, SAS or ALL. Default is to make plots for all super populations
target_dat

the test dataset of interest. Data frame.
eaf

name of the effect allele frequency column in target_dat
snp_target

rsid column in target_dat
snp_reference

rsid column in ref_dat
ref_dat_maf

name of the minor allele frequency column in the reference dataset. Only necessary if ref_dat is specified
target_dat_effect_allele

name of the effect allele column in target_dat
target_dat_other_allele

name of the non-effect allele column in target_dat
ref_dat_minor_allele

name of the minor allele column in the reference dataset. Only necessary if ref_dat is specified
ref_dat_major_allele

name of the major allele column in the reference dataset. Only necessary if ref_dat is specified
trait

name of the trait corresponding to target_dat
target_dat_population

population ancestry of target_dat
ref_dat_population

name of column describing population ancestry of reference dataset. Only necessary if ref_dat is specified
target_study

column in target_dat indicating name of target study
ref_study

column in reference study indicating name of reference study. Only necessary if ref_dat is specified
Title

plot title
Ylab

Y label
Xlab

X label
cowplot_title

title of overall plot
return_dat

if TRUE, the dataset used to generate the plot is returned to the user and no plot is made.
nocolour

if TRUE, allele frequency conflicts are illustrated using shapes rather than colours.
legend

include legend in plot. Default TRUE
allele_frequency_conflict

how to define allele frequency conflicts. 1= flag SNPs in the test dataset whose reported minor allele has frequency >0.5. 2= additionally flag SNPs with allele frequency differening by more than 10 points from allele frequency in the reference dataset. Default = 1
publication_quality

produce a very high resolution image e.g. for publication purposes. Default FALSE

Value

plot

Predicted versus reported effect sizes

Description

Make a plot comparing the predicted effect sizes to the reported effect sizes.

Usage

make_plot_pred_effect(
  dat = NULL,
  Xlab = "Reported effect size",
  Ylab = "Expected effect size",
  subtitle = "",
  maf_filter = FALSE,
  bias = FALSE,
  Title = "Expected versus reported effect size",
  legend = TRUE,
  standard_errors = FALSE,
  pred_beta = "lnor_pred",
  pred_beta_se = "lnor_se_pred",
  beta = "lnor",
  se = "lnor_se",
  sd_est = "sd_est",
  exclude_1000G_MAF_refdat = TRUE,
  nocolour = FALSE,
  publication_quality = FALSE
)

Arguments

dat

the target dataset of interest
Xlab

label for X axis
Ylab

label for Y axis
subtitle

subtitle
maf_filter

minor allele frequency threshold. If not NULL, genetic variants with a minor allele frequency below this threshold are excluded
bias

logical argument. If TRUE, plots the % deviation of the expected from the reported effect size on the Y axis against the reported effect size on the X axis.
Title

plot title
legend

logical argument. If true, includes figure legend in plot
standard_errors

logical argument. If TRUE, plots the expected versus the reported standard errors for the effect sizes
pred_beta

name of column containing the predicted effect size
pred_beta_se

name of column containing the standard error for the predicted effect size
beta

name of column containing the reported effect size
se

name of column containing the standard error for the reported effect size
sd_est

the standard deviation of the phenotypic mean. Can either be a numeric vector of length 1 or name of the column in dat containing the standard deviation value (in which case should be constant across SNPs). Only applicable for continuous traits. If not supplied by the user, the standard deviation is approximated using sd_est, estimated by the predict_beta_sd() function. The sd_est is then used to standardise the reported effect size. If the reported effect size is already standardised (ie is in SD units) then sd_est should be set to NULL
exclude_1000G_MAF_refdat

exclude rsids from the 1000 genome MAF reference dataset.
nocolour

if TRUE, effect size conflicts are illustrated using shapes rather than colours. Default FALSE
publication_quality

produce a very high resolution image e.g. for publication purposes. Default FALSE

Value

plot

make a SNP list

Description

Create a list of rsids corresponding to "top hits" in the GWAS catalog, the 1000 genomes super popualtions and SNPs of specific interest to the user (e.g. genetic instruments/proxies for the exposure of interest).

Usage

make_snplist(
  trait = NULL,
  efo_id = NULL,
  efo = NULL,
  ref1000G_superpops = TRUE,
  snplist_user = NULL
)

Arguments

trait

the name of the trait in the NHGRI-EBI GWAS catalog
efo_id

experimental factor ontology ID for trait of interest
efo

experimental factor ontology for the trait of interest
ref1000G_superpops

include reference SNPs from 1000 genomes super populations. Default=TRUE
snplist_user

character vector of user specified rsids.

Value

character vector

Examples

snplist<-make_snplist(efo_id="EFO_0006859",ref1000G_superpops=FALSE)

Predicted standardised beta

Description

Predict the standardised beta using sample sise, Z score and minor allele frequency. Returns the predicted standardised beta, proportion of phenotypic variance explained by the SNP (r2) and F statistic for each SNP

Usage

predict_beta_sd(
  dat = NULL,
  beta = "beta",
  se = "se",
  eaf = "eaf",
  sample_size = "ncontrol",
  pval = "p"
)

Arguments

dat

the outcome dataset of interest
beta

the effect size column
se

the standard error column
eaf

the effect allele frequency column
sample_size

the sample size column
pval

name of the p value column

Value

data frame with predicted standardised beta, r2 and F stat statistics and estimated standard deviation

Predicted log odds ratio

Description

Predict the log odds ratio, using the Harrison approach. https://seanharrisonblog.com/2020/. The log odds ratio is inferred from the reported number of cases and controls, Z scores and minor allele frequency

Usage

predict_lnor_sh(dat = NULL)

Arguments

dat

the outcome dataset of interest

Value

data frame

A dataset of reference allele frequencies from 1000 genomes superpopulations

Description

The dataset contains minor allele frequency for 2297 SNPs that have minor allele frequency 0.1-0.3 across each superpopulation in the 1000 genomes project.

Usage

refdat_1000G_superpops

Format

A data frame with 13782 rows and 8 variables:

CHR

chromosome number

SNP

SNP rsid

minor_allele

SNP minor allele

major_allele

SNP major allele

MAF

SNP minor allele frequency

NCHROBS

number of observed chromosomes

population

1000 genomes superpopulation: AFR=African; ALL=all individuals; AMR = Ad Mixed American; EAS=East Asian; EUR=European; SAS=South Asian

Source

https://www.internationalgenome.org/home

Transform betas

Description

Transform betas from a linear model to a log odds ratio scale. Assumes betas have been derived from a linear model of case-control status regressed on SNP genotype (additively coded).

Usage

transform_betas(dat = NULL, effect = "lnor", effect.se = "se")

Arguments

dat

the target dataset rsids
effect

the column containing the beta. We wish to transform this to a log odds ratio scale
effect.se

standard error for the beta

Value

data frame

ZZ plot

Description

Calculate Z scores from the reported P values (Zp) and the reported log odds ratios (Zlnor). Construct a scatter plot of Zp and Zlnor

Usage

zz_plot(
  dat = NULL,
  Title = "ZZ plot",
  Ylab = "Z score inferred from p value",
  Xlab = "Z score inferred from effect size and standard error",
  beta = "lnor",
  se = "lnor_se",
  exclude_1000G_MAF_refdat = TRUE,
  publication_quality = FALSE
)

Arguments

dat

the target dataset of interest
Title

plot title
Ylab

label for Y axis
Xlab

label for X axis
beta

the name of the column containing the SNP effect size
se

the name of the column containing the standard error for the SNP effect size
exclude_1000G_MAF_refdat

exclude rsids from the 1000 genome MAF reference dataset.
publication_quality

produce a very high resolution image e.g. for publication purposes. Default FALSE

Value

plot