Introduction
A data frame of the instruments for an exposure is required. Each line has the information for one variant for one exposure. The minimum information required for MR analysis is the following:
-
SNP- rs ID -
beta- The effect size. If the trait is binary then log(OR) should be used -
se- The standard error of the effect size -
effect_allele- The allele of the SNP which has the effect marked inbeta
Other information that is useful for MR can also be provided:
-
other_allele- The non-effect allele -
eaf- The effect allele frequency -
Phenotype- The name of the phenotype for which the SNP has an effect
You can also provide the following extra information:
-
chr- Physical position of variant (chromosome) -
position- Physical position of variant (position) -
samplesize- Sample size for estimating the effect size -
ncase- Number of cases -
ncontrol- Number of controls -
pval- The P-value for the SNP’s association with the exposure -
units- The units in which the effects are presented -
gene- The gene or other annotation for the the SNP
Reading in from a file
The data can be read in from a text file using the
read_exposure_data function. The file must have a header
with column names corresponding to the columns described above.
Example 1: The default column names are used
An example of a text file with the default column names is provided as part of the package, the first few rows look like this:
Phenotype SNP beta se effect_allele other_allele eaf pval units gene samplesize
BMI rs10767664 0.19 0.0306122448979592 A T 0.78 5e-26 kg/m2 BDNF 225238
BMI rs13078807 0.1 0.0204081632653061 G A 0.2 4e-11 kg/m2 CADM2 221431
BMI rs1514175 0.07 0.0204081632653061 A G 0.43 8e-14 kg/m2 TNNI3K 207641
BMI rs1558902 0.39 0.0204081632653061 A T 0.42 5e-120 kg/m2 FTO 222476
BMI rs10968576 0.11 0.0204081632653061 G A 0.31 3e-13 kg/m2 LRRN6C 247166
BMI rs2241423 0.13 0.0204081632653061 G A 0.78 1e-18 kg/m2 LBXCOR1 227886The exact path to the file will be different on everyone’s computer, but it can be located like this:
bmi_file <- system.file("extdata", "bmi.txt", package = "TwoSampleMR")You can read the data in like this:
bmi_exp_dat <- read_exposure_data(bmi_file)
head(bmi_exp_dat)
#> SNP beta.exposure se.exposure effect_allele.exposure
#> 1 rs10767664 0.19 0.03061224 A
#> 2 rs13078807 0.10 0.02040816 G
#> 3 rs1514175 0.07 0.02040816 A
#> 4 rs1558902 0.39 0.02040816 A
#> 5 rs10968576 0.11 0.02040816 G
#> 6 rs2241423 0.13 0.02040816 G
#> other_allele.exposure eaf.exposure pval.exposure units.exposure gene.exposure
#> 1 T 0.78 5e-26 kg/m2 BDNF
#> 2 A 0.20 4e-11 kg/m2 CADM2
#> 3 G 0.43 8e-14 kg/m2 TNNI3K
#> 4 T 0.42 5e-120 kg/m2 FTO
#> 5 A 0.31 3e-13 kg/m2 LRRN6C
#> 6 A 0.78 1e-18 kg/m2 LBXCOR1
#> samplesize.exposure exposure mr_keep.exposure pval_origin.exposure
#> 1 225238 BMI TRUE reported
#> 2 221431 BMI TRUE reported
#> 3 207641 BMI TRUE reported
#> 4 222476 BMI TRUE reported
#> 5 247166 BMI TRUE reported
#> 6 227886 BMI TRUE reported
#> units.exposure_dat id.exposure data_source.exposure
#> 1 kg/m2 ImbABK textfile
#> 2 kg/m2 ImbABK textfile
#> 3 kg/m2 ImbABK textfile
#> 4 kg/m2 ImbABK textfile
#> 5 kg/m2 ImbABK textfile
#> 6 kg/m2 ImbABK textfileThe output from this function is a new data frame with standardised column names:
SNPexposurebeta.exposurese.exposureeffect_allele.exposureother_allele.exposureeaf.exposuremr_keep.exposurepval.exposurepval_origin.exposureid.exposuredata_source.exposureunits.exposuregene.exposuresamplesize.exposure
The function attempts to match the columns to the ones it expects. It also checks that the data type is as expected.
If the required data for MR to be performed is not present (SNP name,
effect size, standard error, effect allele) for a particular SNP, then
the column mr_keep.exposure will be FALSE.
Example 2: The text file has non-default column names
If the text file does not have default column names, this can still be read in as follows. Here are the first few rows of an example:
rsid,effect,SE,a1,a2,a1_freq,p-value,Units,Gene,n
rs10767664,0.19,0.030612245,A,T,0.78,5.00E-26,kg/m2,BDNF,225238
rs13078807,0.1,0.020408163,G,A,0.2,4.00E-11,kg/m2,CADM2,221431
rs1514175,0.07,0.020408163,A,G,0.43,8.00E-14,kg/m2,TNNI3K,207641
rs1558902,0.39,0.020408163,A,T,0.42,5.00E-120,kg/m2,FTO,222476Note that this is a CSV file, with commas separating fields. The file is located here:
bmi2_file <- system.file("extdata/bmi.csv", package = "TwoSampleMR")To read in this data:
bmi_exp_dat <- read_exposure_data(
filename = bmi2_file,
sep = ",",
snp_col = "rsid",
beta_col = "effect",
se_col = "SE",
effect_allele_col = "a1",
other_allele_col = "a2",
eaf_col = "a1_freq",
pval_col = "p-value",
units_col = "Units",
gene_col = "Gene",
samplesize_col = "n"
)
#> No phenotype name specified, defaulting to 'exposure'.
head(bmi_exp_dat)
#> SNP beta.exposure se.exposure effect_allele.exposure
#> 1 rs10767664 0.19 0.03061224 A
#> 2 rs13078807 0.10 0.02040816 G
#> 3 rs1514175 0.07 0.02040816 A
#> 4 rs1558902 0.39 0.02040816 A
#> 5 rs10968576 0.11 0.02040816 G
#> 6 rs2241423 0.13 0.02040816 G
#> other_allele.exposure eaf.exposure pval.exposure units.exposure gene.exposure
#> 1 T 0.78 5e-26 kg/m2 BDNF
#> 2 A 0.20 4e-11 kg/m2 CADM2
#> 3 G 0.43 8e-14 kg/m2 TNNI3K
#> 4 T 0.42 5e-120 kg/m2 FTO
#> 5 A 0.31 3e-13 kg/m2 LRRN6C
#> 6 A 0.78 1e-18 kg/m2 LBXCOR1
#> samplesize.exposure exposure mr_keep.exposure pval_origin.exposure
#> 1 225238 exposure TRUE reported
#> 2 221431 exposure TRUE reported
#> 3 207641 exposure TRUE reported
#> 4 222476 exposure TRUE reported
#> 5 247166 exposure TRUE reported
#> 6 227886 exposure TRUE reported
#> units.exposure_dat id.exposure data_source.exposure
#> 1 kg/m2 Ku3B84 textfile
#> 2 kg/m2 Ku3B84 textfile
#> 3 kg/m2 Ku3B84 textfile
#> 4 kg/m2 Ku3B84 textfile
#> 5 kg/m2 Ku3B84 textfile
#> 6 kg/m2 Ku3B84 textfileIf the Phenotype column is not provided (as is the case
in this example) then it will assume that the phenotype’s name is simply
“exposure”. This is entered in the exposure column. It can
be renamed manually:
bmi_exp_dat$exposure <- "BMI"Using an existing data frame
If the data already exists as a data frame in R then it can be
converted into the correct format using the format_data()
function. For example, here is some randomly created data:
random_df <- data.frame(
SNP = c("rs1", "rs2"),
beta = c(1, 2),
se = c(1, 2),
effect_allele = c("A", "T")
)
random_df
#> SNP beta se effect_allele
#> 1 rs1 1 1 A
#> 2 rs2 2 2 TThis can be formatted like so:
random_exp_dat <- format_data(random_df, type = "exposure")
#> No phenotype name specified, defaulting to 'exposure'.
#> Warning in format_data(random_df, type = "exposure"): The following columns are not present but are helpful for harmonisation
#> other_alleleeaf
#> Inferring p-values
random_exp_dat
#> SNP beta.exposure se.exposure effect_allele.exposure exposure
#> 1 rs1 1 1 A exposure
#> 2 rs2 2 2 T exposure
#> mr_keep.exposure pval.exposure pval_origin.exposure id.exposure
#> 1 TRUE 0.3173105 inferred 4nec1Z
#> 2 TRUE 0.3173105 inferred 4nec1Z
#> other_allele.exposure eaf.exposure
#> 1 NA NA
#> 2 NA NAObtaining instruments from existing catalogues
A number of sources of instruments have already been curated and are
available for use. They are provided as data objects in the
MRInstruments package. To install:
remotes::install_github("MRCIEU/MRInstruments")This package contains a number of data.frames, each of which is a repository of SNP-trait associations. How to access the data frames is detailed below:
GWAS catalog
The NHGRI-EBI GWAS catalog contains a catalog of significant associations obtained from GWASs. This version of the data is filtered and harmonised to contain associations that have the required data to perform MR, to ensure that the units used to report effect sizes from a particular study are all the same, and other data cleaning operations.
To use the GWAS catalog:
library(MRInstruments)
data(gwas_catalog)
head(gwas_catalog)
#> Phenotype_simple
#> 1 Eosinophil percentage of white cells
#> 2 Eosinophil counts
#> 3 Medication use (agents acting on the renin-angiotensin system)
#> 4 Post bronchodilator FEV1
#> 5 DNA methylation variation (age effect)
#> 6 Ankylosing spondylitis
#> MAPPED_TRAIT_EFO
#> 1 eosinophil percentage of leukocytes
#> 2 eosinophil count
#> 3 Agents acting on the renin-angiotensin system use measurement
#> 4 forced expiratory volume, response to bronchodilator
#> 5 DNA methylation
#> 6 ankylosing spondylitis
#> MAPPED_TRAIT_EFO_URI
#> 1 http://www.ebi.ac.uk/efo/EFO_0007991
#> 2 http://www.ebi.ac.uk/efo/EFO_0004842
#> 3 http://www.ebi.ac.uk/efo/EFO_0009931
#> 4 http://www.ebi.ac.uk/efo/EFO_0004314, http://purl.obolibrary.org/obo/GO_0097366
#> 5 http://purl.obolibrary.org/obo/GO_0006306
#> 6 http://www.ebi.ac.uk/efo/EFO_0003898
#> Initial_sample_description
#> 1 172,378 European ancestry individuals
#> 2 172,275 European ancestry individuals
#> 3 62,752 European ancestry cases, 174,778 European ancestry controls
#> 4 10,094 European ancestry current and former smoker individuals, 3,260 African American current and former smoker individuals, 178 current and former smoker individuals
#> 5 Up to 954 individuals
#> 6 921 Turkish ancestry cases, 907 Turkish ancestry controls, 422 Iranian ancestry cases, 754 Iranian ancestry controls
#> Replication_sample_description STUDY.ACCESSION
#> 1 <NA> GCST004600
#> 2 <NA> GCST004606
#> 3 <NA> GCST007930
#> 4 <NA> GCST003262
#> 5 <NA> GCST006660
#> 6 <NA> GCST007844
#> Phenotype Phenotype_info
#> 1 Eosinophil percentage of white cells
#> 2 Eosinophil counts
#> 3 Medication use (agents acting on the renin-angiotensin system)
#> 4 Post bronchodilator FEV1
#> 5 DNA methylation variation (age effect)
#> 6 Ankylosing spondylitis
#> PubmedID Author Year SNP chr bp_ens_GRCh38 Region gene
#> 1 27863252 Astle WJ 2016 rs1000005 21 33060745 21q22.11 AP000282.2
#> 2 27863252 Astle WJ 2016 rs1000005 21 33060745 21q22.11 AP000282.2
#> 3 31015401 Wu Y 2019 rs1000010 3 11562645 3p25.3 VGLL4
#> 4 26634245 Lutz SM 2015 rs10000225 4 144312789 4q31.21 Intergenic
#> 5 30348214 Zhang Q 2018 rs10000513 4 160334994 4q32.1 NR
#> 6 30946743 Li Z 2019 rs10000518 4 11502867 4p15.33 HS3ST1
#> Gene_ens effect_allele other_allele beta se pval
#> 1 AP000282.2,LINC00945 C G -0.02652552 0.003826531 2e-13
#> 2 AP000282.2,LINC00945 C G -0.02481715 0.003571429 7e-12
#> 3 G A -0.03724189 0.006377551 6e-09
#> 4 Intergenic A T -0.04400000 0.009420188 3e-06
#> 5 NR <NA> <NA> NA NA 4e-08
#> 6 G A 0.73396926 NA 6e-06
#> units eaf date_added_to_MRBASE
#> 1 unit decrease 0.589400 2019-08-29
#> 2 unit decrease 0.589400 2019-08-29
#> 3 unit decrease 0.351806 2019-08-29
#> 4 NR unit decrease 0.350000 2019-08-29
#> 5 <NA> NA 2019-08-29
#> 6 <NA> NA 2019-08-29For example, to obtain instruments for body mass index using the Speliotes et al 2010 study:
bmi_gwas <-
subset(gwas_catalog,
grepl("Speliotes", Author) &
Phenotype == "Body mass index")
bmi_exp_dat <- format_data(bmi_gwas)Metabolites
Independent top hits from GWASs on 121 metabolites in whole blood are
stored in the metab_qtls data object. Use
?metab_qtls to get more information.
data(metab_qtls)
head(metab_qtls)
#> phenotype chromosome position SNP effect_allele other_allele eaf
#> 1 AcAce 8 9181395 rs2169387 G A 0.870251
#> 2 AcAce 11 116648917 rs964184 C G 0.857715
#> 3 Ace 6 12042473 rs6933521 C T 0.120256
#> 4 Ala 2 27730940 rs1260326 C T 0.638817
#> 5 Ala 2 65220910 rs2160387 C T 0.403170
#> 6 Ala 12 47201814 rs4554975 G A 0.644059
#> beta se pval n_studies n
#> 1 0.085630 0.015451 3.61e-08 11 19257
#> 2 -0.096027 0.014624 6.71e-11 11 19261
#> 3 -0.091667 0.015885 8.10e-09 14 24742
#> 4 -0.104582 0.009940 7.40e-26 13 22569
#> 5 -0.071001 0.009603 1.49e-13 14 24793
#> 6 -0.069135 0.009598 6.12e-13 14 24792For example, to obtain instruments for Alanine:
ala_exp_dat <- format_metab_qtls(subset(metab_qtls, phenotype == "Ala"))Proteins
Independent top hits from GWASs on 47 protein levels in whole blood
are stored in the proteomic_qtls data object. Use
?proteomic_qtls to get more information.
data(proteomic_qtls)
head(proteomic_qtls)
#> analyte chr position SNP gene location annotation other_allele
#> 1 CFHR1 1 196698945 rs12144939 CFH cis missense T
#> 2 IL6r 1 154425456 rs12126142 IL6R cis missense A
#> 3 ApoA4 11 116677723 rs1263167 APOA4 cis intergenic G
#> 4 SELE 9 136149399 rs507666 ABO trans intronic A
#> 5 FetuinA 3 186335941 rs2070633 AHSG cis missense T
#> 6 ACE 17 61566031 rs4343 ACE cis synonymous A
#> effect_allele eaf maf pval beta se
#> 1 G 0.643 0.357 8.99e-143 -1.108 0.04355258
#> 2 G 0.608 0.392 1.81e-106 0.850 0.03878364
#> 3 A 0.803 0.197 2.64e-54 -0.919 0.05922332
#> 4 G 0.809 0.191 1.01e-52 -0.882 0.05771545
#> 5 C 0.676 0.324 2.88e-44 -0.629 0.04506925
#> 6 G 0.508 0.492 6.66e-44 0.493 0.03547679For example, to obtain instruments for the ApoH protein:
apoh_exp_dat <-
format_proteomic_qtls(subset(proteomic_qtls, analyte == "ApoH"))Gene expression levels
Independent top hits from GWASs on 32432 gene identifiers and in 44
tissues are available from the GTEX study in gtex_eqtl. Use
?gtex_eqtl to get more information.
data(gtex_eqtl)
head(gtex_eqtl)
#> tissue gene_name gene_start SNP snp_position
#> 1 Adipose Subcutaneous RP4-669L17.10 1:317720 rs2519065 1:787151
#> 2 Adipose Subcutaneous RP11-206L10.1 1:661611 rs11804171 1:723819
#> 3 Adipose Subcutaneous RP11-206L10.3 1:677193 rs149110718 1:759227
#> 4 Adipose Subcutaneous RP11-206L10.2 1:700306 rs148649543 1:752796
#> 5 Adipose Subcutaneous RP11-206L10.9 1:714150 rs12184279 1:717485
#> 6 Adipose Subcutaneous RP11-206L10.8 1:736259 rs10454454 1:754954
#> effect_allele other_allele beta se pval n
#> 1 A G 0.551788 0.0747180 2.14627e-12 298
#> 2 A T -0.917475 0.1150060 4.99967e-14 298
#> 3 T C 0.807571 0.1776530 8.44694e-06 298
#> 4 T C 0.745393 0.0958531 1.82660e-13 298
#> 5 A C 1.927250 0.2247390 9.55098e-16 298
#> 6 A G 1.000400 0.1787470 5.61079e-08 298For example, to obtain instruments for the IRAK1BP1 gene expression levels in subcutaneous adipose tissue:
irak1bp1_exp_dat <-
format_gtex_eqtl(subset(
gtex_eqtl,
gene_name == "IRAK1BP1" & tissue == "Adipose Subcutaneous"
))
#> Warning in format_data(gtex_eqtl_subset, type = type, phenotype_col = type, : The following columns are not present but are helpful for harmonisation
#> eaf
#> Inferring p-valuesDNA methylation levels
Independent top hits from GWASs on 0 DNA methylation levels in whole
blood across 5 time points are available from the ARIES study in
aries_mqtl. Use ?aries_mqtl to get more
information.
data(aries_mqtl)
head(aries_mqtl)
#> SNP timepoint cpg beta pval se snp_chr
#> 1 esv2656832 1 cg21826606 0.3459 1.60408e-26 0.03265336 1
#> 2 esv2658098 1 cg22681495 -0.6263 1.55765e-66 0.03643240 15
#> 3 esv2660043 1 cg24276624 -0.5772 3.16370e-26 0.05481823 11
#> 4 esv2660043 1 cg11157765 -0.5423 1.33928e-22 0.05583777 11
#> 5 esv2660673 1 cg05832925 -0.5919 2.88011e-50 0.03982467 11
#> 6 esv2660769 1 cg05859533 -0.6224 1.49085e-58 0.03868158 16
#> snp_pos effect_allele other_allele eaf sex age units
#> 1 25591901 I R 0.3974 mixed Birth SD units
#> 2 86057007 D R 0.2076 mixed Birth SD units
#> 3 69982552 D R 0.1450 mixed Birth SD units
#> 4 69982552 D R 0.1450 mixed Birth SD units
#> 5 74024905 D R 0.1671 mixed Birth SD units
#> 6 57725395 D R 0.2136 mixed Birth SD units
#> island_location cpg_chr cpg_pos gene gene_location cis_trans
#> 1 N_Shore 1 25593055 cis
#> 2 15 86058755 AKAP13 Body cis
#> 3 11 69982941 ANO1 Body cis
#> 4 11 69982996 ANO1 Body cis
#> 5 S_Shelf 11 74026371 cis
#> 6 16 57727230 CCDC135 TSS1500 cisFor example, to obtain instruments for cg25212131 CpG DNA methylation levels in at birth:
cg25212131_exp_dat <-
format_aries_mqtl(subset(aries_mqtl, cpg == "cg25212131" &
age == "Birth"))IEU GWAS database
The IEU GWAS database contains the entire summary statistics for thousands of GWASs. You can browse them here: https://gwas.mrcieu.ac.uk/
You can use this database to define the instruments for a particular exposure. You can also use this database to obtain the effects for constructing polygenic risk scores using different p-value thresholds.
You can check the status of the API:
ieugwasr::api_status()To obtain a list and details about the available GWASs do the following:
ao <- available_outcomes()
head(ao)For information about authentication see https://mrcieu.github.io/ieugwasr/articles/guide.html#authentication.
The available_outcomes() function returns a table of all
the available studies in the database. Each study has a unique ID. e.g.,
You might obtain
head(subset(ao, select = c(trait, id)))
#> trait id
#> 1 Schizophrenia ieu-b-5103
#> 2 Schizophrenia ieu-b-5102
#> 3 Schizophrenia ieu-b-5101
#> 4 Schizophrenia ieu-b-5100
#> 5 Schizophrenia ieu-b-5099
#> 6 Schizophrenia ieu-b-5098To extract instruments for a particular trait using a particular study, for example to obtain SNPs for body mass index using the Locke et al. 2015 GIANT study, you specify the study ID as follows:
bmi2014_exp_dat <- extract_instruments(outcomes = 'ieu-a-2')
str(bmi2014_exp_dat)
#> 'data.frame': 79 obs. of 15 variables:
#> $ pval.exposure : num 2.18e-08 4.57e-11 5.06e-14 5.45e-10 1.88e-28 ...
#> $ samplesize.exposure : num 339152 339065 313621 338768 338123 ...
#> $ chr.exposure : chr "1" "1" "1" "1" ...
#> $ se.exposure : num 0.003 0.0031 0.0087 0.0029 0.003 0.0037 0.0031 0.003 0.0038 0.003 ...
#> $ beta.exposure : num -0.0168 0.0201 0.0659 0.0181 0.0331 0.0497 -0.0227 0.0221 0.0209 0.0175 ...
#> $ pos.exposure : int 47684677 78048331 110082886 201784287 72837239 177889480 49589847 96924097 164567689 181550962 ...
#> $ id.exposure : chr "ieu-a-2" "ieu-a-2" "ieu-a-2" "ieu-a-2" ...
#> $ SNP : chr "rs977747" "rs17381664" "rs7550711" "rs2820292" ...
#> $ effect_allele.exposure: chr "G" "C" "T" "C" ...
#> $ other_allele.exposure : chr "T" "T" "C" "A" ...
#> $ eaf.exposure : num 0.5333 0.425 0.0339 0.5083 0.6083 ...
#> $ exposure : chr "Body mass index || id:ieu-a-2" "Body mass index || id:ieu-a-2" "Body mass index || id:ieu-a-2" "Body mass index || id:ieu-a-2" ...
#> $ mr_keep.exposure : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
#> $ pval_origin.exposure : chr "reported" "reported" "reported" "reported" ...
#> $ data_source.exposure : chr "igd" "igd" "igd" "igd" ...This returns a set of LD clumped SNPs that are GWAS significant for BMI. You can specify various parameters for this function:
-
p1= P-value threshold for keeping a SNP -
clump= Whether or not to return independent SNPs only (default isTRUE) -
r2= The maximum LD R-square allowed between returned SNPs -
kb= The distance in which to search for LD R-square values
By changing changing the p1 parameter it is possible to
obtain SNP effects for constructing polygenic risk scores.
Clumping
For standard two sample MR it is important to ensure that the instruments for the exposure are independent. Once instruments have been identified for an exposure variable, the IEU GWAS database can be used to perform clumping.
You can provide a list of SNP IDs, the SNPs will be extracted from 1000 genomes data, LD calculated between them, and amongst those SNPs that have LD R-square above the specified threshold only the SNP with the lowest P-value will be retained. To do this, use the following command:
bmi_exp_dat <- clump_data(bmi2014_exp_dat)
str(bmi_exp_dat)
#> 'data.frame': 30 obs. of 16 variables:
#> $ SNP : chr "rs10767664" "rs13078807" "rs1514175" "rs1558902" ...
#> $ beta.exposure : num 0.19 0.1 0.07 0.39 0.11 0.13 0.06 0.09 0.13 0.06 ...
#> $ se.exposure : num 0.0306 0.0204 0.0204 0.0204 0.0204 ...
#> $ effect_allele.exposure: chr "A" "G" "A" "A" ...
#> $ other_allele.exposure : chr "T" "A" "G" "T" ...
#> $ eaf.exposure : num 0.78 0.2 0.43 0.42 0.31 0.78 0.41 0.24 0.21 0.21 ...
#> $ pval.exposure : num 5e-26 4e-11 8e-14 5e-120 3e-13 ...
#> $ units.exposure : chr "kg/m2" "kg/m2" "kg/m2" "kg/m2" ...
#> $ gene.exposure : chr "BDNF" "CADM2" "TNNI3K" "FTO" ...
#> $ samplesize.exposure : int 225238 221431 207641 222476 247166 227886 209051 218439 209849 220081 ...
#> $ exposure : chr "BMI" "BMI" "BMI" "BMI" ...
#> $ mr_keep.exposure : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
#> $ pval_origin.exposure : chr "reported" "reported" "reported" "reported" ...
#> $ units.exposure_dat : chr "kg/m2" "kg/m2" "kg/m2" "kg/m2" ...
#> $ id.exposure : chr "FXhiAH" "FXhiAH" "FXhiAH" "FXhiAH" ...
#> $ data_source.exposure : chr "textfile" "textfile" "textfile" "textfile" ...The clump_data() function takes any data frame that has
been formatted to be an exposure data type of data frame. Note that for
the instruments in the MRInstruments package the SNPs are already LD
clumped.
Note: The LD reference panel only includes SNPs (no INDELs). There are five super-populations from which LD can be calculated, by default European samples are used. Only SNPs with MAF > 0.01 within-population are available.
NOTE: If a variant is dropped from your unclumped data it could be because it is absent from the reference panel. For more flexibility, including using your own LD reference data, see here: https://mrcieu.github.io/ieugwasr/