The exposure data and outcome data are now obtained, e.g.:
ao <- available_outcomes() #> API: public: http://gwas-api.mrcieu.ac.uk/ bmi_exp_dat <- extract_instruments(outcomes='ieu-a-2') chd_out_dat <- extract_outcome_data(snps = bmi_exp_dat$SNP, outcomes = 'ieu-a-7') #> Extracting data for 79 SNP(s) from 1 GWAS(s)
but it is important to harmonise the effects. This means that the effect of a SNP on the exposure and the effect of that SNP on the outcome must each correspond to the same allele.
Note: The IEU GWAS database contains data that is already harmonised, meaning that the non-effect allele is aligned to the human genome reference sequence (build 37). It’s still recommended to harmonise, but in principle everything should be on the forward strand and effect alleles always relating to the same allele. Some discrepancies could arise if there are multi-allelic variants that are represented as different bi-allelic variants in different studies.
To harmonise the exposure and outcome data, do the following:
dat <- harmonise_data( exposure_dat = bmi_exp_dat, outcome_dat = chd_out_dat ) #> Harmonising Body mass index || id:ieu-a-2 (ieu-a-2) and Coronary heart disease || id:ieu-a-7 (ieu-a-7)
This creates a new data frame that has the exposure data and outcome data combined.
If there were 3 exposure traits and 3 outcome traits then there will be 9 sets of harmonisations being performed - harmonising the SNP effects of exposure trait 1 against outcome trait 1; exposure trait 1 against outcome trait 2; and so on.
Recent GWASs typically present the effects of a SNP in reference to the allele on the forward strand. But as reference panels are updated the forward strand sometimes changes, and GWASs from a few years ago aren’t guaranteed to be using forward strand conventions.
Some examples are shown below:
exposure effect = 0.5 effect allele = A other allele = G outcome effect = 0.05 effect allele = A other allele = G
Here the effect allele on the exposure and the outcome is the same
exposure effect = 0.5 effect allele = A other allele = G outcome effect = -0.05 effect allele = C other allele = T
Here the outcome GWAS is presenting the effect for the alternate allele on the reverse strand. We need to flip the outcome effect to 0.05 to correspond to the same allele as the exposure GWAS on the forward strand.
exposure effect = 0.5 effect allele = A other allele = G outcome effect = -0.05 effect allele = A other allele = C
Here the alleles do not correspond for the same SNP, so this SNP will be discarded from the analysis.
exposure effect = 0.5 effect allele = A other allele = T effect allele frequency = 0.11 outcome effect = -0.05 effect allele = A other allele = T effect allele frequency = 0.91
Here the alleles correspond, but it is a palindromic SNP, such that the alleles on the forward strand are the same as on the reverse strand (A/T on forward is T/A on the reverse). However, the allele frequency of the effect allele gives us information - if the outcome effect allele (A) were on the forward strand we would expect it to have a low allele frequency, but given it has a high frequency (0.91) we infer that the outcome GWAS is presenting the effect on the reverse strand for the alternative allele. We would flip the effect to 0.05 for the outcome GWAS.
exposure effect = 0.5 effect allele = A other allele = T effect allele frequency = 0.50 outcome effect = -0.05 effect allele = A other allele = T effect allele frequency = 0.50
This is similar to the above, except the allele frequency no longer gives us information about the strand. We would discard this SNP. This is done for any palindromic SNPs that have minor allele frequency above 0.42.
There are three options to harmonising the data.
By default, the
harmonise_data function uses option 2, but this can be modified using the
action argument, e.g.
harmonise_data(exposure_dat, outcome_dat, action=3).
After data harmonisation, users may find that their dataset contains duplicate exposure-outcome summary sets. This can arise, for example, when a GWAS consortium has released multiple results from separate GWAS analyses for the same trait. For example, there are multiple GWAS summary datasets for body mass index and coronary heart disease:
ao[ao$trait=="Body mass index",c("trait","id","pmid","author","sample_size","nsnp")] #> # A tibble: 15 x 6 #> trait id pmid author sample_size nsnp #> <chr> <chr> <int> <chr> <int> <int> #> 1 Body mass index ieu-a-974 25673413 Locke AE 171977 2494613 #> 2 Body mass index ieu-a-835 25673413 Locke AE 322154 2554668 #> 3 Body mass index ieu-a-94 23754948 Randall JC 60586 2736876 #> 4 Body mass index ebi-a-GCST006802 26961502 Wood AR 119688 8580466 #> 5 Body mass index bbj-a-3 28892062 Ishigaki K 72390 6108953 #> 6 Body mass index ieu-a-785 25673413 Locke AE 152893 2477659 #> 7 Body mass index ieu-a-2 25673413 Locke AE 339224 2555511 #> 8 Body mass index ieu-a-1089 26961502 Wood 120286 8654252 #> 9 Body mass index ebi-a-GCST008025 31217584 Wojcik GL 21955 34343880 #> 10 Body mass index ebi-a-GCST004904 28892062 Akiyama M 158284 5952516 #> 11 Body mass index ebi-a-GCST006368 30108127 Hoffmann TJ 315347 27854527 #> 12 Body mass index bbj-a-1 28892062 Ishigaki K 158284 5961600 #> 13 Body mass index bbj-a-2 28892062 Ishigaki K 85894 6108953 #> 14 Body mass index ieu-a-95 23754948 Randall JC 73137 2736876 #> 15 Body mass index ebi-a-GCST002783 25673413 Locke AE 236781 2529499 ao[ao$trait=="Coronary heart disease",c("trait","id","pmid","author","ncase","ncontrol","nsnp")] #> # A tibble: 5 x 7 #> trait id pmid author ncase ncontrol nsnp #> <chr> <chr> <int> <chr> <int> <int> <int> #> 1 Coronary heart dise~ ieu-a-8 21378990 Schunkert~ 22233 64762 2.42e6 #> 2 Coronary heart dise~ ebi-a-GCST0009~ 21378990 Schunkert~ 22233 64762 2.42e6 #> 3 Coronary heart dise~ ieu-a-6 21378988 Peden 15420 15062 5.40e5 #> 4 Coronary heart dise~ ieu-a-7 26343387 Nikpay 60801 123504 9.46e6 #> 5 Coronary heart dise~ ieu-a-9 23202125 Deloukas 63746 130681 7.91e4
There are therefore multiple potential combinations of body mass index and coronary heart disease, which would likely lead to duplicate MR analyses. We recommend that users prune their datasets so that only the exposure-outcome combination with the highested expected power is retained. This can be done by selecting the exposure-outcome summary set with the largest sample size for the outcome, using the power_prune function:
This drops the duplicate exposure-outcome sets with the smaller outcome sample size (number of cases for binary outcomes). Remaining duplicates are then dropped on the basis of the exposure sample size. However, if there are a large number of SNPs available to instrument an exposure, the outcome GWAS with the better SNP coverage may provide better power than the outcome GWAS with the larger sample size. This can occur, for example, if the larger outcome GWAS has used a targeted genotyping array. In such instances, it may be better to prune studies on the basis of instrument strength (i.e. variation in exposure explained by the instrumental SNPs) as well as sample size. This can be done by setting the method argument to 2:
This procedure drops duplicate exposure-outcome sets on the basis of instrument strength and sample size, and assumes that the SNP-exposure effects correspond to a continuous trait with a normal distribution (i.e. exposure should not be binary). The SNP-outcome effects can correspond to either a binary or continuous trait (default behaviour is to assume a binary distribution). If the exposure is binary then method 1 should be used.