Introduction
The exposure data and outcome data are now obtained, e.g.:
bmi_exp_dat <- extract_instruments(outcomes = 'ieu-a-2')
chd_out_dat <- extract_outcome_data(snps = bmi_exp_dat$SNP, outcomes = 'ieu-a-7')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.
Dealing with strand issues
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:
Correct, unambiguous
exposure effect = 0.5
effect allele = A
other allele = G
outcome effect = 0.05
effect allele = A
other allele = GHere the effect allele on the exposure and the outcome is the same
Incorrect reference, unambiguous
exposure effect = 0.5
effect allele = A
other allele = G
outcome effect = -0.05
effect allele = C
other allele = THere 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.
Ambiguous
exposure effect = 0.5
effect allele = A
other allele = G
outcome effect = -0.05
effect allele = A
other allele = CHere the alleles do not correspond for the same SNP, so this SNP will be discarded from the analysis.
Palindromic SNP, inferrable
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 = 0.91Here 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.
Palindromic SNP, not inferrable
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 = 0.50This 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.
Options
There are three options to harmonising the data.
- Assume all alleles are presented on the forward strand
- Try to infer the forward strand alleles using allele frequency information
- Correct the strand for non-palindromic SNPs, but drop all palindromic SNPs
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).
Drop duplicate exposure-outcome summary sets
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 <- available_outcomes()
ao[ao$trait == "Body mass index", c("trait", "id", "pmid", "author", "sample_size", "nsnp")]
#> trait id pmid author
#> 3958 Body mass index ebi-a-GCST90103751 35051171 Wong HS
#> 4015 Body mass index ebi-a-GCST90095039 35399580 Fern<U+00E1>ndez-Rhodes L
#> 4020 Body mass index ebi-a-GCST90095034 35399580 Fern<U+00E1>ndez-Rhodes L
#> 6032 Body mass index ebi-a-GCST90029007 29892013 Loh PR
#> 6821 Body mass index ebi-a-GCST90025994 34226706 Barton AR
#> 7045 Body mass index ebi-a-GCST90018947 34594039 Sakaue S
#> 7259 Body mass index ebi-a-GCST90018727 34594039 Sakaue S
#> 10738 Body mass index ieu-a-94 23754948 Randall JC
#> 12717 Body mass index ieu-a-2 25673413 Locke AE
#> 14254 Body mass index ieu-a-95 23754948 Randall JC
#> 16676 Body mass index ieu-a-974 25673413 Locke AE
#> 19343 Body mass index bbj-a-3 28892062 Ishigaki K
#> 26475 Body mass index ebi-a-GCST006368 30108127 Hoffmann TJ
#> 28065 Body mass index ieu-b-4815 NA Howe LJ
#> 28419 Body mass index bbj-a-2 28892062 Ishigaki K
#> 32371 Body mass index ieu-b-4816 NA Howe LJ
#> 33869 Body mass index ieu-a-785 25673413 Locke AE
#> 39217 Body mass index ebi-a-GCST002783 25673413 Locke AE
#> 40065 Body mass index bbj-a-1 28892062 Ishigaki K
#> 43262 Body mass index ebi-a-GCST004904 28892062 Akiyama M
#> 43743 Body mass index ebi-a-GCST006802 26961502 Wood AR
#> 47894 Body mass index ieu-a-835 25673413 Locke AE
#> 48917 Body mass index ebi-a-GCST008025 31217584 Wojcik GL
#> 49208 Body mass index ieu-a-1089 26961502 Wood
#> sample_size nsnp
#> 3958 21930 6370138
#> 4015 330793 2401077
#> 4020 56161 8764141
#> 6032 532396 11973091
#> 6821 457756 4238669
#> 7045 359983 19066885
#> 7259 163835 12502877
#> 10738 60586 2736876
#> 12717 339224 2555511
#> 14254 73137 2736876
#> 16676 171977 2494613
#> 19343 72390 6108953
#> 26475 315347 27854527
#> 28065 51852 NA
#> 28419 85894 6108953
#> 32371 99998 7191606
#> 33869 152893 2477659
#> 39217 236781 2529499
#> 40065 158284 5961600
#> 43262 158284 5952516
#> 43743 119688 8580466
#> 47894 322154 2554668
#> 48917 21955 34343880
#> 49208 120286 8654252
ao[ao$trait == "Coronary heart disease", c("trait", "id", "pmid", "author", "ncase", "ncontrol", "nsnp")]
#> trait id pmid author ncase
#> 14897 Coronary heart disease ieu-a-7 26343387 Nikpay 60801
#> 23614 Coronary heart disease ieu-a-9 23202125 Deloukas 63746
#> 27414 Coronary heart disease ebi-a-GCST000998 21378990 Schunkert H 22233
#> 38602 Coronary heart disease ieu-a-8 21378990 Schunkert H 22233
#> 45294 Coronary heart disease ieu-a-6 21378988 Peden 15420
#> ncontrol nsnp
#> 14897 123504 9455779
#> 23614 130681 79129
#> 27414 64762 2415020
#> 38602 64762 2420361
#> 45294 15062 540233There 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 highest 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:
dat <- power_prune(dat, method = 1, dist.outcome = "binary")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:
dat <- power_prune(dat, method = 2, dist.outcome = "binary")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.