TwoSampleMR Tutorial

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library(data.table)
library(TwoSampleMR)
library(data.table)
library(TwoSampleMR)

TwoSampleMR version 0.5.6 
[>] New: Option to use non-European LD reference panels for clumping etc
[>] Some studies temporarily quarantined to verify effect allele
[>] See news(package='TwoSampleMR') and https://gwas.mrcieu.ac.uk for further details

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exp_raw <- fread("koges_bmi.txt.gz")

exp_raw <- subset(exp_raw,exp_raw$pval<5e-8)

exp_raw$phenotype <- "BMI"

exp_raw$n <- 72282

exp_dat <- format_data( exp_raw,
    type = "exposure",
    snp_col = "rsids",
    beta_col = "beta",
    se_col = "sebeta",
    effect_allele_col = "alt",
    other_allele_col = "ref",
    eaf_col = "af",
    pval_col = "pval",
    phenotype_col = "phenotype",
    samplesize_col= "n"
)
clumped_exp <- clump_data(exp_dat,clump_r2=0.01,pop="EAS")
exp_raw <- fread("koges_bmi.txt.gz")

exp_raw <- subset(exp_raw,exp_raw$pval<5e-8)

exp_raw$phenotype <- "BMI"

exp_raw$n <- 72282

exp_dat <- format_data( exp_raw,
    type = "exposure",
    snp_col = "rsids",
    beta_col = "beta",
    se_col = "sebeta",
    effect_allele_col = "alt",
    other_allele_col = "ref",
    eaf_col = "af",
    pval_col = "pval",
    phenotype_col = "phenotype",
    samplesize_col= "n"
)
clumped_exp <- clump_data(exp_dat,clump_r2=0.01,pop="EAS") 

Warning message in .fun(piece, ...):
“Duplicated SNPs present in exposure data for phenotype 'BMI. Just keeping the first instance:
























rs4665740

















































































rs7201608









































”
API: public: http://gwas-api.mrcieu.ac.uk/

Please look at vignettes for options on running this locally if you need to run many instances of this command.

Clumping rvi6Om, 2452 variants, using EAS population reference

Removing 2420 of 2452 variants due to LD with other variants or absence from LD reference panel

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out_raw <- fread("hum0197.v3.BBJ.T2D.v1/GWASsummary_T2D_Japanese_SakaueKanai2020.auto.txt.gz",
                    select=c("SNPID","Allele1","Allele2","BETA","SE","p.value","N","AF_Allele2"))

out_raw$phenotype <- "T2D"

out_dat <- format_data( out_raw,
    type = "outcome",
    snp_col = "SNPID",
    beta_col = "BETA",
    se_col = "SE",
    effect_allele_col = "Allele2",
    other_allele_col = "Allele1",
    pval_col = "p.value",
    phenotype_col = "phenotype",
    samplesize_col= "n",
    eaf_col="AF_Allele2"
)
out_raw <- fread("hum0197.v3.BBJ.T2D.v1/GWASsummary_T2D_Japanese_SakaueKanai2020.auto.txt.gz",
                    select=c("SNPID","Allele1","Allele2","BETA","SE","p.value","N","AF_Allele2"))

out_raw$phenotype <- "T2D"

out_dat <- format_data( out_raw,
    type = "outcome",
    snp_col = "SNPID",
    beta_col = "BETA",
    se_col = "SE",
    effect_allele_col = "Allele2",
    other_allele_col = "Allele1",
    pval_col = "p.value",
    phenotype_col = "phenotype",
    samplesize_col= "n",
    eaf_col="AF_Allele2"
)

Warning message in format_data(out_raw, type = "outcome", snp_col = "SNPID", beta_col = "BETA", :
“effect_allele column has some values that are not A/C/T/G or an indel comprising only these characters or D/I. These SNPs will be excluded.”
Warning message in format_data(out_raw, type = "outcome", snp_col = "SNPID", beta_col = "BETA", :
“The following SNP(s) are missing required information for the MR tests and will be excluded
1:1142714:t:<cn0>
1:4288465:t:<ins:me:alu>
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harmonized_data <- harmonise_data(clumped_exp,out_dat,action=1)
harmonized_data <- harmonise_data(clumped_exp,out_dat,action=1)

Harmonising BMI (rvi6Om) and T2D (ETcv15)

In [18]:

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harmonized_data
harmonized_data

A data.frame: 28 × 29
	SNP	effect_allele.exposure	other_allele.exposure	effect_allele.outcome	other_allele.outcome	beta.exposure	beta.outcome	eaf.exposure	eaf.outcome	remove	⋯	pval.exposure	se.exposure	samplesize.exposure	exposure	mr_keep.exposure	pval_origin.exposure	id.exposure	action	mr_keep	samplesize.outcome
	<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<lgl>	⋯	<dbl>	<dbl>	<dbl>	<chr>	<lgl>	<chr>	<chr>	<dbl>	<lgl>	<lgl>
1	rs10198356	G	A	G	A	0.044	0.027821816	0.450	0.46949841	FALSE	⋯	1.5e-17	0.0051	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
2	rs10209994	C	A	C	A	0.030	0.028433424	0.640	0.65770918	FALSE	⋯	2.0e-08	0.0054	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
3	rs10824329	A	G	A	G	0.029	0.018217119	0.510	0.56240335	FALSE	⋯	1.7e-08	0.0051	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
4	rs10938397	G	A	G	A	0.036	0.044554736	0.280	0.29915686	FALSE	⋯	1.0e-10	0.0056	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
5	rs11066132	T	C	T	C	-0.053	-0.031928806	0.160	0.24197159	FALSE	⋯	1.0e-13	0.0071	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
6	rs12522139	G	T	G	T	-0.037	-0.010749243	0.270	0.24543922	FALSE	⋯	1.8e-10	0.0057	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
7	rs12591730	A	G	A	G	0.037	0.033042812	0.220	0.25367536	FALSE	⋯	1.5e-08	0.0065	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
8	rs13013021	T	C	T	C	0.070	0.104075223	0.907	0.90195307	FALSE	⋯	1.9e-15	0.0088	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
9	rs1955337	T	G	T	G	0.036	0.019593503	0.300	0.24112816	FALSE	⋯	7.4e-11	0.0056	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
10	rs2076308	C	G	C	G	0.037	0.041352038	0.310	0.31562874	FALSE	⋯	3.4e-11	0.0055	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
11	rs2278557	G	C	G	C	0.034	0.021211196	0.320	0.29052039	FALSE	⋯	7.4e-10	0.0055	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
12	rs2304608	A	C	A	C	0.031	0.046669515	0.470	0.44287320	FALSE	⋯	1.1e-09	0.0051	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
13	rs2531995	T	C	T	C	0.031	0.043316015	0.370	0.33584772	FALSE	⋯	5.2e-09	0.0053	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
14	rs261967	C	A	C	A	0.032	0.048970828	0.440	0.39718313	FALSE	⋯	3.5e-10	0.0051	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
15	rs35332469	C	T	C	T	-0.035	0.008075598	0.220	0.17678428	FALSE	⋯	3.6e-08	0.0063	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
16	rs35560038	T	A	T	A	-0.047	0.073935089	0.590	0.61936434	FALSE	⋯	1.4e-19	0.0052	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
17	rs3755804	T	C	T	C	0.043	0.022854134	0.280	0.30750660	FALSE	⋯	1.5e-14	0.0056	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
18	rs4470425	A	C	A	C	-0.030	-0.020844137	0.450	0.44152032	FALSE	⋯	4.9e-09	0.0051	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
19	rs476828	C	T	C	T	0.067	0.078651859	0.270	0.25309742	FALSE	⋯	2.8e-31	0.0057	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
20	rs4883723	A	G	A	G	0.039	0.021370910	0.280	0.22189601	FALSE	⋯	8.3e-12	0.0057	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
21	rs509325	G	T	G	T	0.065	0.035691759	0.280	0.26816326	FALSE	⋯	7.8e-31	0.0057	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
22	rs55872725	T	C	T	C	0.090	0.121517023	0.120	0.20355108	FALSE	⋯	1.8e-31	0.0077	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
23	rs6089309	C	T	C	T	-0.033	-0.018669833	0.700	0.65803267	FALSE	⋯	3.5e-09	0.0056	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
24	rs6265	T	C	T	C	-0.049	-0.031642696	0.460	0.40541994	FALSE	⋯	6.1e-22	0.0051	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
25	rs6736712	G	C	G	C	-0.053	-0.029716899	0.917	0.93023505	FALSE	⋯	2.1e-08	0.0095	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
26	rs7560832	C	A	C	A	-0.150	-0.090481195	0.012	0.01129784	FALSE	⋯	2.0e-09	0.0250	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
27	rs825486	T	C	T	C	-0.031	0.019073554	0.690	0.75485104	FALSE	⋯	3.1e-08	0.0056	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA
28	rs9348441	A	T	A	T	-0.036	0.179230794	0.470	0.42502848	FALSE	⋯	1.3e-12	0.0051	72282	BMI	TRUE	reported	rvi6Om	1	TRUE	NA

In [6]:

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res <- mr(harmonized_data)
res <- mr(harmonized_data)

Analysing 'rvi6Om' on 'hff6sO'

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res
res

A data.frame: 5 × 9
id.exposure	id.outcome	outcome	exposure	method	nsnp	b	se	pval
<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<dbl>	<dbl>	<dbl>
rvi6Om	hff6sO	T2D	BMI	MR Egger	28	1.3337580	0.69485260	6.596064e-02
rvi6Om	hff6sO	T2D	BMI	Weighted median	28	0.6298980	0.08516315	1.399605e-13
rvi6Om	hff6sO	T2D	BMI	Inverse variance weighted	28	0.5598956	0.23225806	1.592361e-02
rvi6Om	hff6sO	T2D	BMI	Simple mode	28	0.6097842	0.13305429	9.340189e-05
rvi6Om	hff6sO	T2D	BMI	Weighted mode	28	0.5946778	0.12680355	7.011481e-05

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mr_heterogeneity(harmonized_data)
mr_heterogeneity(harmonized_data)

A data.frame: 2 × 8
id.exposure	id.outcome	outcome	exposure	method	Q	Q_df	Q_pval
<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>
rvi6Om	hff6sO	T2D	BMI	MR Egger	670.7022	26	1.000684e-124
rvi6Om	hff6sO	T2D	BMI	Inverse variance weighted	706.6579	27	1.534239e-131

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mr_pleiotropy_test(harmonized_data)
mr_pleiotropy_test(harmonized_data)

A data.frame: 1 × 7
id.exposure	id.outcome	outcome	exposure	egger_intercept	se	pval
<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>
rvi6Om	hff6sO	T2D	BMI	-0.03603697	0.0305241	0.2484472

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res_single <- mr_singlesnp(harmonized_data)
res_single <- mr_singlesnp(harmonized_data)

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res_single
res_single

A data.frame: 30 × 9
	exposure	outcome	id.exposure	id.outcome	samplesize	SNP	b	se	p
	<chr>	<chr>	<chr>	<chr>	<lgl>	<chr>	<dbl>	<dbl>	<dbl>
1	BMI	T2D	rvi6Om	hff6sO	NA	rs10198356	0.6323140	0.2082837	2.398742e-03
2	BMI	T2D	rvi6Om	hff6sO	NA	rs10209994	0.9477808	0.3225814	3.302164e-03
3	BMI	T2D	rvi6Om	hff6sO	NA	rs10824329	0.6281765	0.3246214	5.297739e-02
4	BMI	T2D	rvi6Om	hff6sO	NA	rs10938397	1.2376316	0.2775854	8.251150e-06
5	BMI	T2D	rvi6Om	hff6sO	NA	rs11066132	0.6024303	0.2232401	6.963693e-03
6	BMI	T2D	rvi6Om	hff6sO	NA	rs12522139	0.2905201	0.2890240	3.148119e-01
7	BMI	T2D	rvi6Om	hff6sO	NA	rs12591730	0.8930490	0.3076687	3.700413e-03
8	BMI	T2D	rvi6Om	hff6sO	NA	rs13013021	1.4867889	0.2207777	1.646925e-11
9	BMI	T2D	rvi6Om	hff6sO	NA	rs1955337	0.5442640	0.2994146	6.910079e-02
10	BMI	T2D	rvi6Om	hff6sO	NA	rs2076308	1.1176226	0.2657969	2.613132e-05
11	BMI	T2D	rvi6Om	hff6sO	NA	rs2278557	0.6238587	0.2968184	3.556906e-02
12	BMI	T2D	rvi6Om	hff6sO	NA	rs2304608	1.5054682	0.2968905	3.961740e-07
13	BMI	T2D	rvi6Om	hff6sO	NA	rs2531995	1.3972908	0.3130157	8.045689e-06
14	BMI	T2D	rvi6Om	hff6sO	NA	rs261967	1.5303384	0.2921192	1.616714e-07
15	BMI	T2D	rvi6Om	hff6sO	NA	rs35332469	-0.2307314	0.3479219	5.072217e-01
16	BMI	T2D	rvi6Om	hff6sO	NA	rs35560038	-1.5730870	0.2018968	6.619637e-15
17	BMI	T2D	rvi6Om	hff6sO	NA	rs3755804	0.5314915	0.2325073	2.225933e-02
18	BMI	T2D	rvi6Om	hff6sO	NA	rs4470425	0.6948046	0.3079944	2.407689e-02
19	BMI	T2D	rvi6Om	hff6sO	NA	rs476828	1.1739083	0.1568550	7.207355e-14
20	BMI	T2D	rvi6Om	hff6sO	NA	rs4883723	0.5479721	0.2855004	5.494141e-02
21	BMI	T2D	rvi6Om	hff6sO	NA	rs509325	0.5491040	0.1598196	5.908641e-04
22	BMI	T2D	rvi6Om	hff6sO	NA	rs55872725	1.3501891	0.1259791	8.419325e-27
23	BMI	T2D	rvi6Om	hff6sO	NA	rs6089309	0.5657525	0.3347009	9.096620e-02
24	BMI	T2D	rvi6Om	hff6sO	NA	rs6265	0.6457693	0.1901871	6.851804e-04
25	BMI	T2D	rvi6Om	hff6sO	NA	rs6736712	0.5606962	0.3448784	1.039966e-01
26	BMI	T2D	rvi6Om	hff6sO	NA	rs7560832	0.6032080	0.2904972	3.785077e-02
27	BMI	T2D	rvi6Om	hff6sO	NA	rs825486	-0.6152759	0.3500334	7.878772e-02
28	BMI	T2D	rvi6Om	hff6sO	NA	rs9348441	-4.9786332	0.2572782	1.992909e-83
29	BMI	T2D	rvi6Om	hff6sO	NA	All - Inverse variance weighted	0.5598956	0.2322581	1.592361e-02
30	BMI	T2D	rvi6Om	hff6sO	NA	All - MR Egger	1.3337580	0.6948526	6.596064e-02

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res_loo <- mr_leaveoneout(harmonized_data)
res_loo
res_loo <- mr_leaveoneout(harmonized_data)
res_loo

A data.frame: 29 × 9
	exposure	outcome	id.exposure	id.outcome	samplesize	SNP	b	se	p
	<chr>	<chr>	<chr>	<chr>	<lgl>	<chr>	<dbl>	<dbl>	<dbl>
1	BMI	T2D	rvi6Om	hff6sO	NA	rs10198356	0.5562834	0.2424917	2.178871e-02
2	BMI	T2D	rvi6Om	hff6sO	NA	rs10209994	0.5520576	0.2388122	2.079526e-02
3	BMI	T2D	rvi6Om	hff6sO	NA	rs10824329	0.5585335	0.2390239	1.945341e-02
4	BMI	T2D	rvi6Om	hff6sO	NA	rs10938397	0.5412688	0.2388709	2.345460e-02
5	BMI	T2D	rvi6Om	hff6sO	NA	rs11066132	0.5580606	0.2417275	2.096381e-02
6	BMI	T2D	rvi6Om	hff6sO	NA	rs12522139	0.5667102	0.2395064	1.797373e-02
7	BMI	T2D	rvi6Om	hff6sO	NA	rs12591730	0.5524802	0.2390990	2.085075e-02
8	BMI	T2D	rvi6Om	hff6sO	NA	rs13013021	0.5189715	0.2386808	2.968017e-02
9	BMI	T2D	rvi6Om	hff6sO	NA	rs1955337	0.5602635	0.2394505	1.929468e-02
10	BMI	T2D	rvi6Om	hff6sO	NA	rs2076308	0.5431355	0.2394403	2.330758e-02
11	BMI	T2D	rvi6Om	hff6sO	NA	rs2278557	0.5583634	0.2394924	1.972992e-02
12	BMI	T2D	rvi6Om	hff6sO	NA	rs2304608	0.5372557	0.2377325	2.382639e-02
13	BMI	T2D	rvi6Om	hff6sO	NA	rs2531995	0.5419016	0.2379712	2.277590e-02
14	BMI	T2D	rvi6Om	hff6sO	NA	rs261967	0.5358761	0.2376686	2.415093e-02
15	BMI	T2D	rvi6Om	hff6sO	NA	rs35332469	0.5735907	0.2378345	1.587739e-02
16	BMI	T2D	rvi6Om	hff6sO	NA	rs35560038	0.6734906	0.2217804	2.391474e-03
17	BMI	T2D	rvi6Om	hff6sO	NA	rs3755804	0.5610215	0.2413249	2.008503e-02
18	BMI	T2D	rvi6Om	hff6sO	NA	rs4470425	0.5568993	0.2392632	1.993549e-02
19	BMI	T2D	rvi6Om	hff6sO	NA	rs476828	0.5037555	0.2443224	3.922224e-02
20	BMI	T2D	rvi6Om	hff6sO	NA	rs4883723	0.5602050	0.2397325	1.945000e-02
21	BMI	T2D	rvi6Om	hff6sO	NA	rs509325	0.5608429	0.2468506	2.308693e-02
22	BMI	T2D	rvi6Om	hff6sO	NA	rs55872725	0.4419446	0.2454771	7.180543e-02
23	BMI	T2D	rvi6Om	hff6sO	NA	rs6089309	0.5597859	0.2388902	1.911519e-02
24	BMI	T2D	rvi6Om	hff6sO	NA	rs6265	0.5547068	0.2436910	2.282978e-02
25	BMI	T2D	rvi6Om	hff6sO	NA	rs6736712	0.5598815	0.2387602	1.902944e-02
26	BMI	T2D	rvi6Om	hff6sO	NA	rs7560832	0.5588113	0.2396229	1.969836e-02
27	BMI	T2D	rvi6Om	hff6sO	NA	rs825486	0.5800026	0.2367545	1.429330e-02
28	BMI	T2D	rvi6Om	hff6sO	NA	rs9348441	0.7378967	0.1366838	6.717515e-08
29	BMI	T2D	rvi6Om	hff6sO	NA	All	0.5598956	0.2322581	1.592361e-02

In [29]:

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harmonized_data$"r.outcome" <- get_r_from_lor(
  harmonized_data$"beta.outcome",
  harmonized_data$"eaf.outcome",
  45383,
  132032,
  0.26,
  model = "logit",
  correction = FALSE
)
harmonized_data$"r.outcome" <- get_r_from_lor(
  harmonized_data$"beta.outcome",
  harmonized_data$"eaf.outcome",
  45383,
  132032,
  0.26,
  model = "logit",
  correction = FALSE
)

In [34]:

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out <- directionality_test(harmonized_data)
out
out <- directionality_test(harmonized_data)
out

r.exposure and/or r.outcome not present.

Calculating approximate SNP-exposure and/or SNP-outcome correlations, assuming all are quantitative traits. Please pre-calculate r.exposure and/or r.outcome using get_r_from_lor() for any binary traits

A data.frame: 1 × 8
id.exposure	id.outcome	exposure	outcome	snp_r2.exposure	snp_r2.outcome	correct_causal_direction	steiger_pval
<chr>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<lgl>	<dbl>
rvi6Om	ETcv15	BMI	T2D	0.02125453	0.005496427	TRUE	NA

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res <- mr(harmonized_data)
p1 <- mr_scatter_plot(res, harmonized_data)
p1[[1]]
res <- mr(harmonized_data)
p1 <- mr_scatter_plot(res, harmonized_data)
p1[[1]]

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res_single <- mr_singlesnp(harmonized_data)
p2 <- mr_forest_plot(res_single)
p2[[1]]
res_single <- mr_singlesnp(harmonized_data)
p2 <- mr_forest_plot(res_single)
p2[[1]]

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res_loo <- mr_leaveoneout(harmonized_data)
p3 <- mr_leaveoneout_plot(res_loo)
p3[[1]]
res_loo <- mr_leaveoneout(harmonized_data)
p3 <- mr_leaveoneout_plot(res_loo)
p3[[1]]

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res_single <- mr_singlesnp(harmonized_data)
p4 <- mr_funnel_plot(res_single)
p4[[1]]
res_single <- mr_singlesnp(harmonized_data)
p4 <- mr_funnel_plot(res_single)
p4[[1]]

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