Once instruments for the exposure trait have been specified, those variants need to be extracted from the outcome trait.
Available studies in IEU GWAS database
The IEU GWAS database (IGD) contains complete GWAS summary statistics from a large number of studies. You can browse them here:
To obtain details about the available GWASs programmatically do the following:
ao <- available_outcomes()
head(ao)
#> id trait ncase group_name year author consortium
#> 1 ieu-b-5103 Schizophrenia 1234 public 2022 Trubetskoy V PGC
#> 2 ieu-b-5102 Schizophrenia 52017 public 2022 Trubetskoy V PGC
#> 3 ieu-b-5101 Schizophrenia 12305 public 2022 Trubetskoy V PGC
#> 4 ieu-b-5100 Schizophrenia 64322 public 2022 Trubetskoy V PGC
#> 5 ieu-b-5099 Schizophrenia 76755 public 2022 Trubetskoy V PGC
#> 6 ieu-b-5098 Schizophrenia 5998 public 2022 Trubetskoy V PGC
#> sex pmid population unit
#> 1 Males and Females 35396580 Hispanic or Latin American logOR
#> 2 Males and Females 35396580 European logOR
#> 3 Males and Females 35396580 East Asian logOR
#> 4 Males and Females 35396580 Mixed logOR
#> 5 Males and Females 35396580 Mixed logOR
#> 6 Males and Females 35396580 African American or Afro-Caribbean logOR
#> sample_size build ncontrol category subcategory ontology
#> 1 4324 HG19/GRCh37 3090 Disease NA MONDO:0005090
#> 2 127906 HG19/GRCh37 75889 Disease NA MONDO:0005090
#> 3 27363 HG19/GRCh37 15058 Disease NA MONDO:0005090
#> 4 155269 HG19/GRCh37 90947 Disease NA MONDO:0005090
#> 5 320404 HG19/GRCh37 243649 Disease NA MONDO:0005090
#> 6 9824 HG19/GRCh37 3826 Disease NA MONDO:0005090
#> note mr
#> 1 <NA> NA
#> 2 <NA> NA
#> 3 <NA> NA
#> 4 Core - East Asian and European meta analysis NA
#> 5 Primary - meta analysis of Eur, East Asian, African American and Latino NA
#> 6 <NA> NA
#> nsnp doi coverage study_design priority sd
#> 1 NA <NA> <NA> <NA> NA NA
#> 2 NA <NA> <NA> <NA> NA NA
#> 3 NA <NA> <NA> <NA> NA NA
#> 4 NA <NA> <NA> <NA> NA NA
#> 5 NA <NA> <NA> <NA> NA NA
#> 6 NA <NA> <NA> <NA> NA NAFor 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.
Extracting particular SNPs from particular studies
If we want to perform MR of BMI against coronary heart disease, we need to identify the SNPs that influence the BMI, and then extract those SNPs from a GWAS on coronary heart disease.
Let’s get the Locke et al 2014 instruments for BMI as an example:
bmi_exp_dat <- extract_instruments(outcomes = 'ieu-a-2')
head(bmi_exp_dat)
#> pval.exposure samplesize.exposure chr.exposure se.exposure beta.exposure
#> 1 2.18198e-08 339152 1 0.0030 -0.0168
#> 2 4.56773e-11 339065 1 0.0031 0.0201
#> 3 5.05941e-14 313621 1 0.0087 0.0659
#> 4 5.45205e-10 338768 1 0.0029 0.0181
#> 5 1.88018e-28 338123 1 0.0030 0.0331
#> 6 2.28718e-40 339078 1 0.0037 0.0497
#> pos.exposure id.exposure SNP effect_allele.exposure
#> 1 47684677 ieu-a-2 rs977747 G
#> 2 78048331 ieu-a-2 rs17381664 C
#> 3 110082886 ieu-a-2 rs7550711 T
#> 4 201784287 ieu-a-2 rs2820292 C
#> 5 72837239 ieu-a-2 rs7531118 C
#> 6 177889480 ieu-a-2 rs543874 G
#> other_allele.exposure eaf.exposure exposure
#> 1 T 0.5333 Body mass index || id:ieu-a-2
#> 2 T 0.4250 Body mass index || id:ieu-a-2
#> 3 C 0.0339 Body mass index || id:ieu-a-2
#> 4 A 0.5083 Body mass index || id:ieu-a-2
#> 5 T 0.6083 Body mass index || id:ieu-a-2
#> 6 A 0.2667 Body mass index || id:ieu-a-2
#> mr_keep.exposure pval_origin.exposure data_source.exposure
#> 1 TRUE reported igd
#> 2 TRUE reported igd
#> 3 TRUE reported igd
#> 4 TRUE reported igd
#> 5 TRUE reported igd
#> 6 TRUE reported igdWe now need to find a suitable GWAS for coronary heart disease. We can search the available studies:
ao[grepl("heart disease", ao$trait), ]
#> id
#> 7958 finn-b-I9_CHD
#> 10829 ukb-b-3983
#> 12268 ukb-e-I25_AFR
#> 14221 ukb-b-2205
#> 14897 ieu-a-7
#> 15028 finn-b-I9_SECONDRIGHT_EXNONE
#> 16883 ukb-b-7436
#> 18046 ukb-a-534
#> 18048 ukb-e-I25_CSA
#> 20207 finn-b-I9_OTHHEART
#> 22249 finn-b-I9_VHD_EXNONE
#> 22725 finn-b-I9_PULMHEART
#> 23614 ieu-a-9
#> 24603 finn-b-FG_OTHHEART
#> 27414 ebi-a-GCST000998
#> 33264 finn-b-FG_PULMHEART
#> 35220 ukb-d-I9_CHD
#> 35231 finn-b-I9_OTHILLHEART
#> 36931 ukb-b-8184
#> 37302 finn-b-I9_OTHILLHEART_EXNONE
#> 37894 ukb-b-1668
#> 38319 finn-b-I9_IHD
#> 38448 finn-b-I9_RHEUFEV
#> 38602 ieu-a-8
#> 41994 finn-b-I9_ISCHHEART
#> 42095 ukb-d-I9_IHD
#> 42882 finn-b-I9_SECONDRIGHT
#> 43747 ukb-d-I9_CHD_NOREV
#> 45294 ieu-a-6
#> 46126 finn-b-I9_VHD
#> 46689 ukb-b-16606
#> trait
#> 7958 Major coronary heart disease event
#> 10829 Diagnoses - main ICD10: I25.9 Chronic ischaemic heart disease, unspecified
#> 12268 I25 Chronic ischaemic heart disease
#> 14221 Diagnoses - secondary ICD10: Z82.4 Family history of ischaemic heart disease and other diseases of the circulatory system
#> 14897 Coronary heart disease
#> 15028 Secondary right heart disease (no controls excluded)
#> 16883 Diagnoses - secondary ICD10: I25.1 Atherosclerotic heart disease
#> 18046 Diagnoses - main ICD10: I25 Chronic ischaemic heart disease
#> 18048 I25 Chronic ischaemic heart disease
#> 20207 Other heart diseases (I9_OTHHEART)
#> 22249 Valvular heart disease including rheumatic fever (no controls excluded)
#> 22725 Pulmonary heart disease, diseases of pulmonary circulation
#> 23614 Coronary heart disease
#> 24603 Other heart diseases (FG_OTHHEART)
#> 27414 Coronary heart disease
#> 33264 Pulmonary heart disease
#> 35220 Major coronary heart disease event
#> 35231 Other or ill-defined heart diseases
#> 36931 Diagnoses - secondary ICD10: I25.9 Chronic ischaemic heart disease, unspecified
#> 37302 Other or ill-defined heart diseases (no controls excluded)
#> 37894 Diagnoses - main ICD10: I25.1 Atherosclerotic heart disease
#> 38319 Ischaemic heart disease, wide definition
#> 38448 Rheumatic fever incl heart disease
#> 38602 Coronary heart disease
#> 41994 Ischemic heart diseases
#> 42095 Ischaemic heart disease, wide definition
#> 42882 Secondary right heart disease
#> 43747 Major coronary heart disease event excluding revascularizations
#> 45294 Coronary heart disease
#> 46126 Valvular heart disease including rheumatic fever
#> 46689 Diagnoses - secondary ICD10: I25.8 Other forms of chronic ischaemic heart disease
#> ncase group_name year author consortium sex
#> 7958 21012 public 2021 NA NA Males and Females
#> 10829 1195 public 2018 Ben Elsworth MRC-IEU Males and Females
#> 12268 302 public 2020 Pan-UKB team NA Males and Females
#> 14221 9330 public 2018 Ben Elsworth MRC-IEU Males and Females
#> 14897 60801 public 2015 Nikpay CARDIoGRAMplusC4D Males and Females
#> 15028 428 public 2021 NA NA Males and Females
#> 16883 5771 public 2018 Ben Elsworth MRC-IEU Males and Females
#> 18046 8755 public 2017 Neale Neale Lab Males and Females
#> 18048 1205 public 2020 Pan-UKB team NA Males and Females
#> 20207 62081 public 2021 NA NA Males and Females
#> 22249 38209 public 2021 NA NA Males and Females
#> 22725 4564 public 2021 NA NA Males and Females
#> 23614 63746 public 2013 Deloukas CARDIoGRAMplusC4D Males and Females
#> 24603 58173 public 2021 NA NA Males and Females
#> 27414 22233 public 2011 Schunkert H NA NA
#> 33264 4185 public 2021 NA NA Males and Females
#> 35220 10157 public 2018 Neale lab NA Males and Females
#> 35231 713 public 2021 NA NA Males and Females
#> 36931 5861 public 2018 Ben Elsworth MRC-IEU Males and Females
#> 37302 713 public 2021 NA NA Males and Females
#> 37894 12171 public 2018 Ben Elsworth MRC-IEU Males and Females
#> 38319 31640 public 2021 NA NA Males and Females
#> 38448 573 public 2021 NA NA Males and Females
#> 38602 22233 public 2011 Schunkert H CARDIoGRAM Males and Females
#> 41994 30952 public 2021 NA NA Males and Females
#> 42095 20857 public 2018 Neale lab NA Males and Females
#> 42882 428 public 2021 NA NA Males and Females
#> 43747 10157 public 2018 Neale lab NA Males and Females
#> 45294 15420 public 2011 Peden C4D Males and Females
#> 46126 38209 public 2021 NA NA Males and Females
#> 46689 5738 public 2018 Ben Elsworth MRC-IEU Males and Females
#> pmid population unit sample_size
#> 7958 NA European NA NA
#> 10829 NA European SD 463010
#> 12268 NA African American or Afro-Caribbean NA 6636
#> 14221 NA European SD 463010
#> 14897 26343387 Mixed log odds 184305
#> 15028 NA European NA NA
#> 16883 NA European SD 463010
#> 18046 NA European SD 337199
#> 18048 NA South Asian NA 8876
#> 20207 NA European NA NA
#> 22249 NA European NA NA
#> 22725 NA European NA NA
#> 23614 23202125 Mixed log odds 194427
#> 24603 NA European NA NA
#> 27414 21378990 European logOR 86995
#> 33264 NA European NA NA
#> 35220 NA European NA 361194
#> 35231 NA European NA NA
#> 36931 NA European SD 463010
#> 37302 NA European NA NA
#> 37894 NA European SD 463010
#> 38319 NA European NA NA
#> 38448 NA European NA NA
#> 38602 21378990 European log odds 86995
#> 41994 NA European NA NA
#> 42095 NA European NA 361194
#> 42882 NA European NA NA
#> 43747 NA European NA 361194
#> 45294 21378988 Mixed log odds 30482
#> 46126 NA European NA NA
#> 46689 NA European SD 463010
#> build ncontrol category subcategory ontology
#> 7958 HG19/GRCh37 197780 Binary NA NA
#> 10829 HG19/GRCh37 461815 Binary NA NA
#> 12268 HG19/GRCh37 6334 Binary NA NA
#> 14221 HG19/GRCh37 453680 Binary NA NA
#> 14897 HG19/GRCh37 123504 Disease Cardiovascular NA
#> 15028 HG19/GRCh37 218364 Binary NA NA
#> 16883 HG19/GRCh37 457239 Binary NA NA
#> 18046 HG19/GRCh37 328444 NA NA NA
#> 18048 HG19/GRCh37 7671 Binary NA NA
#> 20207 HG19/GRCh37 156711 Binary NA NA
#> 22249 HG19/GRCh37 180583 Binary NA NA
#> 22725 HG19/GRCh37 214228 Binary NA NA
#> 23614 HG19/GRCh37 130681 Disease Cardiovascular NA
#> 24603 HG19/GRCh37 160619 Binary NA NA
#> 27414 HG19/GRCh37 64762 NA NA NA
#> 33264 HG19/GRCh37 214607 Binary NA NA
#> 35220 HG19/GRCh37 351037 Binary NA NA
#> 35231 HG19/GRCh37 156711 Binary NA NA
#> 36931 HG19/GRCh37 457149 Binary NA NA
#> 37302 HG19/GRCh37 218079 Binary NA NA
#> 37894 HG19/GRCh37 450839 Binary NA NA
#> 38319 HG19/GRCh37 187152 Binary NA NA
#> 38448 HG19/GRCh37 218219 Binary NA NA
#> 38602 HG19/GRCh37 64762 Disease Cardiovascular NA
#> 41994 HG19/GRCh37 187840 Binary NA NA
#> 42095 HG19/GRCh37 340337 Binary NA NA
#> 42882 HG19/GRCh37 214228 Binary NA NA
#> 43747 HG19/GRCh37 351037 Binary NA NA
#> 45294 HG19/GRCh37 15062 Disease Cardiovascular NA
#> 46126 HG19/GRCh37 156711 Binary NA NA
#> 46689 HG19/GRCh37 457272 Binary NA NA
#> note
#> 7958 I9_CHD
#> 10829 41202#I259: Output from GWAS pipeline using Phesant derived variables from UKBiobank
#> 12268 NA
#> 14221 41204#Z824: Output from GWAS pipeline using Phesant derived variables from UKBiobank
#> 14897 <NA>
#> 15028 I9_SECONDRIGHT_EXNONE
#> 16883 41204#I251: Output from GWAS pipeline using Phesant derived variables from UKBiobank
#> 18046 NA
#> 18048 NA
#> 20207 I9_OTHHEART
#> 22249 I9_VHD_EXNONE
#> 22725 I9_PULMHEART
#> 23614 <NA>
#> 24603 FG_OTHHEART
#> 27414 NA
#> 33264 FG_PULMHEART
#> 35220 NA
#> 35231 I9_OTHILLHEART
#> 36931 41204#I259: Output from GWAS pipeline using Phesant derived variables from UKBiobank
#> 37302 I9_OTHILLHEART_EXNONE
#> 37894 41202#I251: Output from GWAS pipeline using Phesant derived variables from UKBiobank
#> 38319 I9_IHD
#> 38448 I9_RHEUFEV
#> 38602 <NA>
#> 41994 I9_ISCHHEART
#> 42095 NA
#> 42882 I9_SECONDRIGHT
#> 43747 NA
#> 45294 <NA>
#> 46126 I9_VHD
#> 46689 41204#I258: Output from GWAS pipeline using Phesant derived variables from UKBiobank
#> mr nsnp doi coverage study_design priority sd
#> 7958 1 16380466 <NA> <NA> <NA> 0 NA
#> 10829 1 9851867 <NA> <NA> <NA> 1 NA
#> 12268 1 15478580 <NA> <NA> <NA> 0 NA
#> 14221 1 9851867 <NA> <NA> <NA> 1 NA
#> 14897 1 9455779 <NA> <NA> <NA> 1 NA
#> 15028 1 16380466 <NA> <NA> <NA> 0 NA
#> 16883 1 9851867 <NA> <NA> <NA> 1 NA
#> 18046 1 10894596 <NA> <NA> <NA> 1 NA
#> 18048 1 9811287 <NA> <NA> <NA> 0 NA
#> 20207 1 16380466 <NA> <NA> <NA> 0 NA
#> 22249 1 16380466 <NA> <NA> <NA> 0 NA
#> 22725 1 16380466 <NA> <NA> <NA> 0 NA
#> 23614 1 79129 <NA> <NA> <NA> 1 NA
#> 24603 1 16380466 <NA> <NA> <NA> 0 NA
#> 27414 1 2415020 <NA> <NA> <NA> 0 NA
#> 33264 1 16380466 <NA> <NA> <NA> 0 NA
#> 35220 1 13295130 <NA> <NA> <NA> 0 NA
#> 35231 1 16380177 <NA> <NA> <NA> 0 NA
#> 36931 1 9851867 <NA> <NA> <NA> 1 NA
#> 37302 1 16380466 <NA> <NA> <NA> 0 NA
#> 37894 1 9851867 <NA> <NA> <NA> 1 NA
#> 38319 1 16380466 <NA> <NA> <NA> 0 NA
#> 38448 1 16380466 <NA> <NA> <NA> 0 NA
#> 38602 1 2420361 <NA> <NA> <NA> 2 NA
#> 41994 1 16380466 <NA> <NA> <NA> 0 NA
#> 42095 1 13586589 <NA> <NA> <NA> 0 NA
#> 42882 1 16380459 <NA> <NA> <NA> 0 NA
#> 43747 1 13295130 <NA> <NA> <NA> 0 NA
#> 45294 1 540233 <NA> <NA> <NA> 3 NA
#> 46126 1 16380358 <NA> <NA> <NA> 0 NA
#> 46689 1 9851867 <NA> <NA> <NA> 1 NAThe most recent CARDIOGRAM GWAS is ID number ieu-a-7. We
can extract the BMI SNPs from this GWAS as follows:
chd_out_dat1 <- extract_outcome_data(
snps = bmi_exp_dat$SNP,
outcomes = 'ieu-a-7'
)The extract_outcome_data() function is flexible. The
snps argument only requires an array of rsIDs, and the
outcomes argument can be a vector of outcomes, e.g.
chd_out_dat2 <- extract_outcome_data(
snps = c("rs234", "rs17097147"),
outcomes = c('ieu-a-2', 'ieu-a-7')
)will extract the two SNPs from each of the outcomes
ieu-a-2 and ieu-a-7.
LD proxies
By default if a particular requested SNP is not present in the outcome GWAS then a SNP (proxy) that is in LD with the requested SNP (target) will be searched for instead. LD proxies are defined using 1000 genomes European sample data. The effect of the proxy SNP on the outcome is returned, along with the proxy SNP, the effect allele of the proxy SNP, and the corresponding allele (in phase) for the target SNP.
The parameters for handling LD proxies are as follows:
-
proxies= TRUE or FALSE (TRUE by default) -
rsq= numeric value of minimum rsq to find a proxy. Default is 0.8, minimum is 0.6 -
palindromes= Allow palindromic SNPs? Default is 1 (yes) -
maf_threshold= If palindromes allowed then what is the maximum minor allele frequency of palindromes allowed? Default is 0.3.
Using local GWAS summary data
If you have GWAS summary data that is not present in IEU GWAS database, this can still be used to perform analysis.
Supposing there is a GWAS summary file called “gwas_summary.csv” with e.g. 2 million rows and it looks like this:
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,222476
...
...To extract the exposure SNPs from this data, we would use the following command:
outcome_dat <- read_outcome_data(
snps = bmi_exp_dat$SNP,
filename = "gwas_summary.csv",
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"
)This returns an outcome data frame with only the SNPs that were requested (if those SNPs were present in the “gwas_summary.csv” file).
Outcome data format
The extract_outcome_data function returns a table of SNP
effects for the requested SNPs on the requested outcomes. The format of
the data is similar to the exposure data format, except the main columns
are as follows:
SNPbeta.outcomese.outcomesamplesize.outcomencase.outcomencontrol.outcomepval.outcomeeaf.outcomeeffect_allele.outcomother_allele.outcomeunits.outcomeoutcomeconsortium.outcomeyear.outcomepmid.outcomeid.outcomeoriginalname.outcomeproxy.outcometarget_snp.outcomeproxy_snp.outcometarget_a1.outcometarget_a2.outcomeproxy_a1.outcomeproxy_a2.outcomemr_keep.outcomedata_source.outcome
More advanced use of local data
We have developed a summary data format called “GWAS VCF”, which is designed to store GWAS results in a strict and performant way. It is possible to use this format with the TwoSampleMR package. Going down this avenue also allows you to use LD proxy functionality using your own LD reference files (or ones that we provide). For more details, see this package that explains the format and how to query it in R:
https://github.com/mrcieu/gwasvcf
and this package for how to connect the data to other packages including TwoSampleMR