TwoSampleMR
library(data.table)
library(TwoSampleMR)
stderr:
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
exp_raw <- fread("koges_bmi.txt.gz")
exp_raw <- subset(exp_raw,exp_raw$pval<5e-8)
exp_raw$phenotype <- "BMI"
exp_raw$n <- 72282
exp_dat <- format_data( exp_raw,
type = "exposure",
snp_col = "rsids",
beta_col = "beta",
se_col = "sebeta",
effect_allele_col = "alt",
other_allele_col = "ref",
eaf_col = "af",
pval_col = "pval",
phenotype_col = "phenotype",
samplesize_col= "n"
)
clumped_exp <- clump_data(exp_dat,clump_r2=0.01,pop="EAS")
stderr:
Warning message in .fun(piece, ...):
“Duplicated SNPs present in exposure data for phenotype 'BMI. Just keeping the first instance:
rs4665740
rs7201608
”
API: public: http://gwas-api.mrcieu.ac.uk/
Please look at vignettes for options on running this locally if you need to run many instances of this command.
Clumping rvi6Om, 2452 variants, using EAS population reference
Removing 2420 of 2452 variants due to LD with other variants or absence from LD reference panel
out_raw <- fread("hum0197.v3.BBJ.T2D.v1/GWASsummary_T2D_Japanese_SakaueKanai2020.auto.txt.gz",
select=c("SNPID","Allele1","Allele2","BETA","SE","p.value","N","AF_Allele2"))
out_raw$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"
)
stderr:
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>
1:4882232:t:<cn0>
1:5172414:g:<cn0>
1:5173809:t:<cn0>
1:5934301:g:<ins:me:alu>
1:6814818:a:<ins:me:alu>
1:7921468:c:<cn2>
1:8502010:t:<ins:me:alu>
1:8924066:c:<cn0>
1:9171841:c:<cn0>
1:9403667:a:<cn2>
1:9595360:a:<cn0>
1:9846036:c:<cn0>
1:10067190:g:<cn0>
1:10482499:g:<cn0>
1:11682873:t:<cn0>
1:11830220:t:<ins:me:sva>
1:11988599:c:<cn0>
1:12475666:t:<ins:me:sva>
1:12737575:a:<ins:me:alu>
1:12842004:a:<cn0>
1:14437074:t:<cn0>
1:14437868:a:<cn0>
1:14713511:t:<cn2>
1:14735732:g:<cn0>
1:15343948:g:<cn0>
1:16151682:c:<cn0>
1:16329336:t:<ins:me:sva>
1:16358741:g:<cn0>
1:17676165:a:<cn0>
1:19486410:c:<ins:me:alu>
1:19855608:a:<cn2>
1:20257109:t:<ins:me:alu>
1:20310746:g:<cn0>
1:20496899:c:<cn0>
1:20497183:c:<cn0>
1:20864015:t:<cn0>
1:20944751:c:<ins:me:alu>
1:21346279:a:<cn0>
1:21492591:c:<ins:me:alu>
1:21786418:t:<cn0>
1:22302473:t:<cn0>
1:22901908:t:<ins:me:alu>
1:23908383:g:<cn0>
1:24223580:g:<cn0>
1:24520350:g:<cn0>
1:24804603:c:<cn0>
1:25055152:g:<cn0>
1:26460095:a:<cn0>
1:26961278:g:<cn0>
1:29373390:t:<ins:me:alu>
1:31090520:t:<ins:me:alu>
1:31316259:t:<cn0>
1:31720009:a:<cn0>
1:32535965:g:<cn0>
1:32544371:a:<cn0>
1:33785116:c:<cn0>
1:35101427:c:<cn0>
1:35177287:g:<cn0>
1:35627104:t:<cn0>
1:36474694:t:<ins:me:alu>
1:36733282:t:<cn0>
1:37215810:a:<ins:me:alu>
1:37816478:a:<cn0>
1:38132306:t:<cn0>
1:39084231:a:<cn0>
1:39677675:t:<ins:me:alu>
1:40524704:t:<ins:me:alu>
1:40552356:a:<cn0>
1:40976681:g:<cn0>
1:41021684:a:<cn0>
1:41785500:a:<ins:me:line1>
1:42390318:c:<ins:me:alu>
1:43694061:t:<cn0>
1:44059290:a:<inv>
1:45021223:t:<cn0>
1:45708588:a:<cn0>
1:45822649:t:<cn0>
1:46333195:a:<ins:me:alu>
1:46794814:t:<ins:me:alu>
1:47267517:t:<cn0>
1:47346571:a:<cn0>
1:47623401:a:<cn0>
1:47913001:t:<cn0>
1:48820285:t:<ins:me:alu>
1:48972537:g:<ins:me:alu>
1:49357693:t:<ins:me:alu>
1:49428756:t:<ins:me:line1>
1:49861993:g:<ins:me:alu>
1:50912662:c:<ins:me:alu>
1:51102445:t:<cn0>
1:52146313:a:<cn0>
1:53594175:t:<cn0>
1:53595112:c:<cn0>
1:55092043:g:<cn0>
1:55341923:c:<cn0>
1:55342224:g:<cn0>
1:55927718:a:<cn0>
1:56268665:t:<ins:me:line1>
1:56405404:t:<ins:me:line1>
1:56879062:t:<ins:me:alu>
1:57100960:t:<ins:me:sva>
1:57208746:a:<cn0>
1:58722032:t:<cn2>
1:58743910:a:<cn0>
1:58795378:a:<cn0>
1:59205317:t:<ins:me:alu>
1:59591483:t:<ins:me:alu>
1:59871876:t:<ins:me:alu>
1:60046725:a:<cn0>
1:60048628:c:<cn0>
1:60470604:t:<ins:me:alu>
1:60487912:t:<cn0>
1:60715714:t:<ins:me:line1>
1:61144594:c:<ins:me:alu>
1:62082822:a:<cn0>
1:62113386:c:<cn0>
1:62479250:t:<cn0>
1:62622902:g:<cn0>
1:62654739:c:<cn0>
1:63841704:c:<ins:me:alu>
1:64720497:a:<cn0>
1:64850193:a:<ins:me:sva>
1:65346960:t:<ins:me:alu>
1:65412505:a:<cn0>
1:68375746:a:<cn0>
1:70061670:g:<ins:me:alu>
1:70091056:t:<ins:me:alu>
1:70093557:c:<ins:me:alu>
1:70412360:t:<ins:me:alu>
1:70424730:t:<cn2>
1:70820401:t:<cn0>
1:70912433:g:<ins:me:alu>
1:72449620:a:<cn0>
1:72755694:t:<cn0>
1:72766343:t:<cn0>
1:72778537:g:<cn0>
1:73092779:c:<cn2>
1:74312425:a:<cn0>
1:75148055:t:<ins:me:alu>
1:75192907:c:<ins:me:line1>
1:75301685:t:<ins:me:alu>
1:75557174:c:<ins:me:alu>
1:76392967:t:<ins:me:alu>
1:76416074:a:<ins:me:alu>
1:76900598:c:<cn0>
1:77577928:t:<ins:me:alu>
1:77634327:a:<ins:me:alu>
1:77764994:t:<ins:me:alu>
1:77830614:t:<cn0>
1:78446240:c:<ins:me:sva>
1:78607067:t:<ins:me:alu>
1:78649157:a:<cn0>
1:78800902:t:<ins:me:line1>
1:79108845:t:<ins:me:alu>
1:79331208:c:<ins:me:alu>
1:79582082:t:<ins:me:alu>
1:79855600:c:<cn0>
1:80221781:t:<cn0>
1:80299106:t:<ins:me:alu>
1:80504615:t:<cn0>
1:80554065:t:<cn0>
1:80955976:t:<ins:me:line1>
1:81422415:c:<cn0>
1:82312054:g:<ins:me:alu>
1:82850409:g:<ins:me:alu>
1:83041946:t:<cn0>
1:84056670:a:<cn0>
1:84388330:g:<cn0>
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1:84712009:g:<cn0>
1:84913274:c:<ins:me:alu>
1:85293152:g:<ins:me:alu>
1:85620127:t:<ins:me:alu>
1:85910957:g:<cn0>
1:86400829:t:<cn0>
1:86696940:a:<ins:me:alu>
1:87064962:c:<cn2>
1:87096974:c:<cn0>
1:87096990:t:<cn0>
1:88813625:t:<ins:me:alu>
1:89209563:t:<ins:me:alu>
1:89733616:t:<ins:me:line1>
1:89811425:g:<cn0>
1:90370569:t:<ins:me:alu>
1:90914512:g:<ins:me:line1>
1:91878937:g:<cn0>
1:92131841:g:<inv>
1:92232051:t:<cn0>
1:93291972:c:<cn0>
1:93498232:t:<ins:me:alu>
1:94288372:c:<cn0>
1:95192010:a:<ins:me:line1>
1:95342701:g:<ins:me:alu>
1:95522242:t:<cn0>
1:97458273:t:<inv>
1:98605297:t:<ins:me:alu>
1:99610528:a:<ins:me:alu>
1:99698454:g:<ins:me:alu>
1:100355940:a:<ins:me:alu>
1:100645536:g:<ins:me:alu>
1:100994221:g:<ins:me:alu>
1:101693230:t:<cn0>
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1:101978980:t:<ins:me:line1>
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1:104359763:g:<cn0>
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1:105832823:g:<cn0>
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1:107949843:t:<ins:me:alu>
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1:108369370:a:<cn0>
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1:109366972:g:<cn0>
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1:110225019:c:<cn0>
1:111013750:a:<cn0>
1:111472607:g:<cn0>
1:111802597:g:<ins:me:sva>
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1:111896187:c:<ins:me:sva>
1:112032284:t:<ins:me:alu>
1:112123691:t:<ins:me:alu>
1:112691740:a:<cn0>
1:112736007:a:<ins:me:alu>
1:112992009:t:<ins:me:alu>
1:113799625:g:<cn0>
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1:157173860:t:<cn0>
1:157363502:t:<ins:me:alu>
1:157540655:g:<cn0>
1:157887236:t:<inv>
1:158371473:a:<ins:me:alu>
1:158488410:a:<cn0>
1:158726918:a:<cn0>
1:160979498:c:<cn0>
1:162263027:t:<ins:me:alu>
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1:163314443:g:<ins:me:alu>
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1:165553149:t:<ins:me:line1>
1:165861400:t:<ins:me:sva>
1:166189445:t:<ins:me:alu>
1:167506110:g:<ins:me:alu>
1:167712862:g:<ins:me:alu>
1:168926083:a:<ins:me:sva>
1:169004356:c:<cn0>
1:169042039:c:<cn0>
1:169225213:t:<cn0>
1:169524859:t:<ins:me:line1>
1:170603451:a:<ins:me:alu>
1:170991168:c:<ins:me:alu>
1:171358314:t:<ins:me:alu>
1:172177959:g:<cn0>
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1:179607260:a:<cn0>
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1:181601286:t:<ins:me:alu>
1:181853551:g:<ins:me:alu>
1:182420857:t:<ins:me:alu>
1:183308627:a:<cn0>
1:185009806:t:<cn0>
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1:188174657:t:<ins:me:alu>
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1:188438213:t:<ins:me:alu>
1:188615934:g:<ins:me:alu>
1:189247039:a:<ins:me:alu>
1:190052658:t:<cn0>
1:190309695:t:<cn0>
1:190773296:t:<ins:me:alu>
1:190874469:t:<ins:me:alu>
1:191466954:t:<ins:me:line1>
1:191580781:a:<ins:me:alu>
1:191817437:c:<ins:me:alu>
1:191916438:t:<cn0>
1:192008678:t:<ins:me:line1>
1:192262268:a:<ins:me:line1>
1:193549655:c:<ins:me:line1>
1:193675125:t:<ins:me:alu>
1:193999047:t:<cn0>
1:194067859:t:<ins:me:alu>
1:194575585:t:<cn0>
1:194675140:c:<ins:me:alu>
1:195146820:c:<ins:me:alu>
1:195746415:a:<ins:me:line1>
1:195885406:g:<cn0>
1:195904499:g:<cn0>
1:196464453:a:<ins:me:line1>
1:196602664:a:<cn0>
1:196728877:g:<cn0>
1:196734744:a:<cn0>
1:196761370:t:<ins:me:alu>
1:197756784:c:<inv>
1:197894025:c:<cn0>
1:198093872:c:<ins:me:alu>
1:198243300:t:<ins:me:alu>
1:198529696:t:<ins:me:line1>
1:198757296:t:<cn0>
1:198773749:t:<cn0>
1:198815313:a:<ins:me:alu>
1:202961159:t:<ins:me:alu>
1:203684252:t:<cn0>
1:204238474:c:<ins:me:alu>
1:204345055:t:<ins:me:alu>
1:204381864:c:<cn0>
1:205178526:t:<inv>”
harmonized_data <- harmonise_data(clumped_exp,out_dat,action=1)
stderr:
Harmonising BMI (rvi6Om) and T2D (ETcv15)
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
res <- mr(harmonized_data)
stderr:
Analysing 'rvi6Om' on 'hff6sO'
| id.exposure |
id.outcome |
outcome |
exposure |
method |
nsnp |
b |
se |
pval |
| 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 |
mr_heterogeneity(harmonized_data)
| id.exposure |
id.outcome |
outcome |
exposure |
method |
Q |
Q_df |
Q_pval |
| 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 |
mr_pleiotropy_test(harmonized_data)
| id.exposure |
id.outcome |
outcome |
exposure |
egger_intercept |
se |
pval |
| rvi6Om |
hff6sO |
T2D |
BMI |
-0.03603697 |
0.0305241 |
0.2484472 |
res_single <- mr_singlesnp(harmonized_data)
| exposure |
outcome |
id.exposure |
id.outcome |
samplesize |
SNP |
b |
se |
p |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs10198356 |
0.6323140 |
0.2082837 |
2.398742e-03 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs10209994 |
0.9477808 |
0.3225814 |
3.302164e-03 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs10824329 |
0.6281765 |
0.3246214 |
5.297739e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs10938397 |
1.2376316 |
0.2775854 |
8.251150e-06 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs11066132 |
0.6024303 |
0.2232401 |
6.963693e-03 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs12522139 |
0.2905201 |
0.2890240 |
3.148119e-01 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs12591730 |
0.8930490 |
0.3076687 |
3.700413e-03 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs13013021 |
1.4867889 |
0.2207777 |
1.646925e-11 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs1955337 |
0.5442640 |
0.2994146 |
6.910079e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs2076308 |
1.1176226 |
0.2657969 |
2.613132e-05 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs509325 |
0.5491040 |
0.1598196 |
5.908641e-04 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs55872725 |
1.3501891 |
0.1259791 |
8.419325e-27 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs6089309 |
0.5657525 |
0.3347009 |
9.096620e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs6265 |
0.6457693 |
0.1901871 |
6.851804e-04 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs6736712 |
0.5606962 |
0.3448784 |
1.039966e-01 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs7560832 |
0.6032080 |
0.2904972 |
3.785077e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs825486 |
-0.6152759 |
0.3500334 |
7.878772e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs9348441 |
-4.9786332 |
0.2572782 |
1.992909e-83 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
All - Inverse variance weighted |
0.5598956 |
0.2322581 |
1.592361e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
All - MR Egger |
1.3337580 |
0.6948526 |
6.596064e-02 |
res_loo <- mr_leaveoneout(harmonized_data)
res_loo
| exposure |
outcome |
id.exposure |
id.outcome |
samplesize |
SNP |
b |
se |
p |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs10198356 |
0.5562834 |
0.2424917 |
2.178871e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs10209994 |
0.5520576 |
0.2388122 |
2.079526e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs10824329 |
0.5585335 |
0.2390239 |
1.945341e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs10938397 |
0.5412688 |
0.2388709 |
2.345460e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs11066132 |
0.5580606 |
0.2417275 |
2.096381e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs12522139 |
0.5667102 |
0.2395064 |
1.797373e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs12591730 |
0.5524802 |
0.2390990 |
2.085075e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs13013021 |
0.5189715 |
0.2386808 |
2.968017e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs1955337 |
0.5602635 |
0.2394505 |
1.929468e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs2076308 |
0.5431355 |
0.2394403 |
2.330758e-02 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs4883723 |
0.5602050 |
0.2397325 |
1.945000e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs509325 |
0.5608429 |
0.2468506 |
2.308693e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs55872725 |
0.4419446 |
0.2454771 |
7.180543e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs6089309 |
0.5597859 |
0.2388902 |
1.911519e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs6265 |
0.5547068 |
0.2436910 |
2.282978e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs6736712 |
0.5598815 |
0.2387602 |
1.902944e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs7560832 |
0.5588113 |
0.2396229 |
1.969836e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs825486 |
0.5800026 |
0.2367545 |
1.429330e-02 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
rs9348441 |
0.7378967 |
0.1366838 |
6.717515e-08 |
| BMI |
T2D |
rvi6Om |
hff6sO |
NA |
All |
0.5598956 |
0.2322581 |
1.592361e-02 |
harmonized_data$"r.outcome" <- get_r_from_lor(
harmonized_data$"beta.outcome",
harmonized_data$"eaf.outcome",
45383,
132032,
0.26,
model = "logit",
correction = FALSE
)
out <- directionality_test(harmonized_data)
out
stderr:
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
| id.exposure |
id.outcome |
exposure |
outcome |
snp_r2.exposure |
snp_r2.outcome |
correct_causal_direction |
steiger_pval |
| rvi6Om |
ETcv15 |
BMI |
T2D |
0.02125453 |
0.005496427 |
TRUE |
NA |
res <- mr(harmonized_data)
p1 <- mr_scatter_plot(res, harmonized_data)
p1[[1]]
res_single <- mr_singlesnp(harmonized_data)
p2 <- mr_forest_plot(res_single)
p2[[1]]
res_loo <- mr_leaveoneout(harmonized_data)
p3 <- mr_leaveoneout_plot(res_loo)
p3[[1]]
res_single <- mr_singlesnp(harmonized_data)
p4 <- mr_funnel_plot(res_single)
p4[[1]]