Activity

Investigating activity measures in relation to number of children

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

Jobs

Job involves heavy manual or physical work

d <- make_dat("ukb-b-2002", "ieu-b-4760")

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

## Extracting data for 25 SNP(s) from 1 GWAS(s)

## Harmonising Job involves heavy manual or physical work || id:ukb-b-2002 (ukb-b-2002) and Number of children || id:ieu-b-4760 (ieu-b-4760)

d_mr<-mr(d)

## Analysing 'ukb-b-2002' on 'ieu-b-4760'

d_mr

##   id.exposure id.outcome                             outcome
## 1  ukb-b-2002 ieu-b-4760 Number of children || id:ieu-b-4760
## 2  ukb-b-2002 ieu-b-4760 Number of children || id:ieu-b-4760
## 3  ukb-b-2002 ieu-b-4760 Number of children || id:ieu-b-4760
## 4  ukb-b-2002 ieu-b-4760 Number of children || id:ieu-b-4760
## 5  ukb-b-2002 ieu-b-4760 Number of children || id:ieu-b-4760
##                                                      exposure
## 1 Job involves heavy manual or physical work || id:ukb-b-2002
## 2 Job involves heavy manual or physical work || id:ukb-b-2002
## 3 Job involves heavy manual or physical work || id:ukb-b-2002
## 4 Job involves heavy manual or physical work || id:ukb-b-2002
## 5 Job involves heavy manual or physical work || id:ukb-b-2002
##                      method nsnp           b         se         pval
## 1                  MR Egger   25 -0.05946887 0.26368571 8.235594e-01
## 2           Weighted median   25  0.21706544 0.04510442 1.490468e-06
## 3 Inverse variance weighted   25  0.18730666 0.05513421 6.805795e-04
## 4               Simple mode   25  0.21688306 0.07369137 7.100743e-03
## 5             Weighted mode   25  0.23278079 0.05956726 6.648282e-04

mr_scatter_plot(d_mr,d)

## $`ukb-b-2002.ieu-b-4760`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-2002 ieu-b-4760

d <- make_dat("ieu-b-4760", "ukb-b-2002")

## Extracting data for 9 SNP(s) from 1 GWAS(s)

## Finding proxies for 1 SNPs in outcome ukb-b-2002

## Extracting data for 1 SNP(s) from 1 GWAS(s)

## Harmonising Number of children || id:ieu-b-4760 (ieu-b-4760) and Job involves heavy manual or physical work || id:ukb-b-2002 (ukb-b-2002)

d_mr<-mr(d)

## Analysing 'ieu-b-4760' on 'ukb-b-2002'

d_mr

##   id.exposure id.outcome
## 1  ieu-b-4760 ukb-b-2002
## 2  ieu-b-4760 ukb-b-2002
## 3  ieu-b-4760 ukb-b-2002
## 4  ieu-b-4760 ukb-b-2002
## 5  ieu-b-4760 ukb-b-2002
##                                                       outcome
## 1 Job involves heavy manual or physical work || id:ukb-b-2002
## 2 Job involves heavy manual or physical work || id:ukb-b-2002
## 3 Job involves heavy manual or physical work || id:ukb-b-2002
## 4 Job involves heavy manual or physical work || id:ukb-b-2002
## 5 Job involves heavy manual or physical work || id:ukb-b-2002
##                              exposure                    method nsnp          b
## 1 Number of children || id:ieu-b-4760                  MR Egger    8 -1.1429579
## 2 Number of children || id:ieu-b-4760           Weighted median    8  0.2037589
## 3 Number of children || id:ieu-b-4760 Inverse variance weighted    8  0.3171413
## 4 Number of children || id:ieu-b-4760               Simple mode    8  0.3887035
## 5 Number of children || id:ieu-b-4760             Weighted mode    8  0.2847020
##           se       pval
## 1 1.00872826 0.30042253
## 2 0.09886048 0.03929582
## 3 0.18893119 0.09322856
## 4 0.15225146 0.03793512
## 5 0.11049316 0.03665095

mr_scatter_plot(d_mr,d)

## $`ieu-b-4760.ukb-b-2002`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ieu-b-4760 ukb-b-2002

Job involves mainly walking or standing

d <- make_dat("ukb-b-4461", "ieu-b-4760")

## Extracting data for 16 SNP(s) from 1 GWAS(s)

## Harmonising Job involves mainly walking or standing || id:ukb-b-4461 (ukb-b-4461) and Number of children || id:ieu-b-4760 (ieu-b-4760)

d_mr<-mr(d)

## Analysing 'ukb-b-4461' on 'ieu-b-4760'

d_mr

##   id.exposure id.outcome                             outcome
## 1  ukb-b-4461 ieu-b-4760 Number of children || id:ieu-b-4760
## 2  ukb-b-4461 ieu-b-4760 Number of children || id:ieu-b-4760
## 3  ukb-b-4461 ieu-b-4760 Number of children || id:ieu-b-4760
## 4  ukb-b-4461 ieu-b-4760 Number of children || id:ieu-b-4760
## 5  ukb-b-4461 ieu-b-4760 Number of children || id:ieu-b-4760
##                                                   exposure
## 1 Job involves mainly walking or standing || id:ukb-b-4461
## 2 Job involves mainly walking or standing || id:ukb-b-4461
## 3 Job involves mainly walking or standing || id:ukb-b-4461
## 4 Job involves mainly walking or standing || id:ukb-b-4461
## 5 Job involves mainly walking or standing || id:ukb-b-4461
##                      method nsnp         b         se         pval
## 1                  MR Egger   16 0.3780286 0.19163380 6.861678e-02
## 2           Weighted median   16 0.1981004 0.04009929 7.802372e-07
## 3 Inverse variance weighted   16 0.1291868 0.04533290 4.375563e-03
## 4               Simple mode   16 0.2251835 0.06003035 1.926369e-03
## 5             Weighted mode   16 0.2251835 0.05020273 4.357277e-04

mr_scatter_plot(d_mr,d)

## $`ukb-b-4461.ieu-b-4760`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-4461 ieu-b-4760

d <- make_dat("ieu-b-4760", "ukb-b-4461")

## Extracting data for 9 SNP(s) from 1 GWAS(s)

## Finding proxies for 1 SNPs in outcome ukb-b-4461

## Extracting data for 1 SNP(s) from 1 GWAS(s)

## Harmonising Number of children || id:ieu-b-4760 (ieu-b-4760) and Job involves mainly walking or standing || id:ukb-b-4461 (ukb-b-4461)

d_mr<-mr(d)

## Analysing 'ieu-b-4760' on 'ukb-b-4461'

d_mr

##   id.exposure id.outcome
## 1  ieu-b-4760 ukb-b-4461
## 2  ieu-b-4760 ukb-b-4461
## 3  ieu-b-4760 ukb-b-4461
## 4  ieu-b-4760 ukb-b-4461
## 5  ieu-b-4760 ukb-b-4461
##                                                    outcome
## 1 Job involves mainly walking or standing || id:ukb-b-4461
## 2 Job involves mainly walking or standing || id:ukb-b-4461
## 3 Job involves mainly walking or standing || id:ukb-b-4461
## 4 Job involves mainly walking or standing || id:ukb-b-4461
## 5 Job involves mainly walking or standing || id:ukb-b-4461
##                              exposure                    method nsnp          b
## 1 Number of children || id:ieu-b-4760                  MR Egger    8 -0.6739235
## 2 Number of children || id:ieu-b-4760           Weighted median    8  0.4020919
## 3 Number of children || id:ieu-b-4760 Inverse variance weighted    8  0.4502001
## 4 Number of children || id:ieu-b-4760               Simple mode    8  0.3790959
## 5 Number of children || id:ieu-b-4760             Weighted mode    8  0.4000037
##          se         pval
## 1 1.0554801 0.5467317524
## 2 0.1187779 0.0007111559
## 3 0.1852952 0.0151139230
## 4 0.1640121 0.0540759288
## 5 0.1329208 0.0196805444

mr_scatter_plot(d_mr,d)

## $`ieu-b-4760.ukb-b-4461`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ieu-b-4760 ukb-b-4461

These job types seem to have an effect on the number of children, but why?

Time spent

Time spend outdoors in summer

d <- make_dat("ukb-b-969", "ieu-b-4760")

## Extracting data for 47 SNP(s) from 1 GWAS(s)

## Finding proxies for 6 SNPs in outcome ieu-b-4760

## Extracting data for 6 SNP(s) from 1 GWAS(s)

## Harmonising Time spend outdoors in summer || id:ukb-b-969 (ukb-b-969) and Number of children || id:ieu-b-4760 (ieu-b-4760)

d_mr<-mr(d)

## Analysing 'ukb-b-969' on 'ieu-b-4760'

d_mr

##   id.exposure id.outcome                             outcome
## 1   ukb-b-969 ieu-b-4760 Number of children || id:ieu-b-4760
## 2   ukb-b-969 ieu-b-4760 Number of children || id:ieu-b-4760
## 3   ukb-b-969 ieu-b-4760 Number of children || id:ieu-b-4760
## 4   ukb-b-969 ieu-b-4760 Number of children || id:ieu-b-4760
## 5   ukb-b-969 ieu-b-4760 Number of children || id:ieu-b-4760
##                                        exposure                    method nsnp
## 1 Time spend outdoors in summer || id:ukb-b-969                  MR Egger   45
## 2 Time spend outdoors in summer || id:ukb-b-969           Weighted median   45
## 3 Time spend outdoors in summer || id:ukb-b-969 Inverse variance weighted   45
## 4 Time spend outdoors in summer || id:ukb-b-969               Simple mode   45
## 5 Time spend outdoors in summer || id:ukb-b-969             Weighted mode   45
##             b         se         pval
## 1 -0.07509373 0.24816696 7.636586e-01
## 2  0.23007003 0.03996388 8.564764e-09
## 3  0.24620858 0.05108961 1.441700e-06
## 4  0.28194721 0.08667379 2.196909e-03
## 5  0.26258227 0.07351945 8.737846e-04

mr_scatter_plot(d_mr,d)

## $`ukb-b-969.ieu-b-4760`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1   ukb-b-969 ieu-b-4760

d <- make_dat("ieu-b-4760", "ukb-b-969")

## Extracting data for 9 SNP(s) from 1 GWAS(s)

## Finding proxies for 1 SNPs in outcome ukb-b-969

## Extracting data for 1 SNP(s) from 1 GWAS(s)

## Harmonising Number of children || id:ieu-b-4760 (ieu-b-4760) and Time spend outdoors in summer || id:ukb-b-969 (ukb-b-969)

d_mr<-mr(d)

## Analysing 'ieu-b-4760' on 'ukb-b-969'

d_mr

##   id.exposure id.outcome                                       outcome
## 1  ieu-b-4760  ukb-b-969 Time spend outdoors in summer || id:ukb-b-969
## 2  ieu-b-4760  ukb-b-969 Time spend outdoors in summer || id:ukb-b-969
## 3  ieu-b-4760  ukb-b-969 Time spend outdoors in summer || id:ukb-b-969
## 4  ieu-b-4760  ukb-b-969 Time spend outdoors in summer || id:ukb-b-969
## 5  ieu-b-4760  ukb-b-969 Time spend outdoors in summer || id:ukb-b-969
##                              exposure                    method nsnp         b
## 1 Number of children || id:ieu-b-4760                  MR Egger    8 0.1799593
## 2 Number of children || id:ieu-b-4760           Weighted median    8 0.3764852
## 3 Number of children || id:ieu-b-4760 Inverse variance weighted    8 0.3847310
## 4 Number of children || id:ieu-b-4760               Simple mode    8 0.6083308
## 5 Number of children || id:ieu-b-4760             Weighted mode    8 0.5758519
##           se         pval
## 1 0.84822246 0.8390055642
## 2 0.09709814 0.0001055925
## 3 0.13694088 0.0049623450
## 4 0.12517053 0.0018351208
## 5 0.13439203 0.0036335568

mr_scatter_plot(d_mr,d)

## $`ieu-b-4760.ukb-b-969`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ieu-b-4760  ukb-b-969

Time spent watching television (TV)

d <- make_dat("ukb-b-5192", "ieu-b-4760")

## Extracting data for 113 SNP(s) from 1 GWAS(s)

## Finding proxies for 9 SNPs in outcome ieu-b-4760

## Extracting data for 9 SNP(s) from 1 GWAS(s)

## Harmonising Time spent watching television (TV) || id:ukb-b-5192 (ukb-b-5192) and Number of children || id:ieu-b-4760 (ieu-b-4760)

## Removing the following SNPs for incompatible alleles:
## rs1889996

d_mr<-mr(d)

## Analysing 'ukb-b-5192' on 'ieu-b-4760'

d_mr

##   id.exposure id.outcome                             outcome
## 1  ukb-b-5192 ieu-b-4760 Number of children || id:ieu-b-4760
## 2  ukb-b-5192 ieu-b-4760 Number of children || id:ieu-b-4760
## 3  ukb-b-5192 ieu-b-4760 Number of children || id:ieu-b-4760
## 4  ukb-b-5192 ieu-b-4760 Number of children || id:ieu-b-4760
## 5  ukb-b-5192 ieu-b-4760 Number of children || id:ieu-b-4760
##                                               exposure
## 1 Time spent watching television (TV) || id:ukb-b-5192
## 2 Time spent watching television (TV) || id:ukb-b-5192
## 3 Time spent watching television (TV) || id:ukb-b-5192
## 4 Time spent watching television (TV) || id:ukb-b-5192
## 5 Time spent watching television (TV) || id:ukb-b-5192
##                      method nsnp           b         se         pval
## 1                  MR Egger  111  0.07006501 0.12818181 5.857663e-01
## 2           Weighted median  111  0.08129001 0.03035367 7.404226e-03
## 3 Inverse variance weighted  111  0.12445604 0.02872653 1.474645e-05
## 4               Simple mode  111 -0.06716342 0.09846625 4.966121e-01
## 5             Weighted mode  111 -0.05919764 0.09630088 5.400122e-01

mr_scatter_plot(d_mr,d)

## $`ukb-b-5192.ieu-b-4760`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-5192 ieu-b-4760

d <- make_dat("ieu-b-4760", "ukb-b-5192")

## Extracting data for 9 SNP(s) from 1 GWAS(s)

## Finding proxies for 1 SNPs in outcome ukb-b-5192

## Extracting data for 1 SNP(s) from 1 GWAS(s)

## Harmonising Number of children || id:ieu-b-4760 (ieu-b-4760) and Time spent watching television (TV) || id:ukb-b-5192 (ukb-b-5192)

d_mr<-mr(d)

## Analysing 'ieu-b-4760' on 'ukb-b-5192'

d_mr

##   id.exposure id.outcome                                              outcome
## 1  ieu-b-4760 ukb-b-5192 Time spent watching television (TV) || id:ukb-b-5192
## 2  ieu-b-4760 ukb-b-5192 Time spent watching television (TV) || id:ukb-b-5192
## 3  ieu-b-4760 ukb-b-5192 Time spent watching television (TV) || id:ukb-b-5192
## 4  ieu-b-4760 ukb-b-5192 Time spent watching television (TV) || id:ukb-b-5192
## 5  ieu-b-4760 ukb-b-5192 Time spent watching television (TV) || id:ukb-b-5192
##                              exposure                    method nsnp
## 1 Number of children || id:ieu-b-4760                  MR Egger    8
## 2 Number of children || id:ieu-b-4760           Weighted median    8
## 3 Number of children || id:ieu-b-4760 Inverse variance weighted    8
## 4 Number of children || id:ieu-b-4760               Simple mode    8
## 5 Number of children || id:ieu-b-4760             Weighted mode    8
##             b         se      pval
## 1 -0.85963048 0.60641903 0.2060995
## 2  0.08544530 0.06102656 0.1614735
## 3  0.20503795 0.12048519 0.0887987
## 4  0.13985524 0.08122506 0.1287745
## 5  0.07767199 0.06858995 0.2947559

mr_scatter_plot(d_mr,d)

## $`ieu-b-4760.ukb-b-5192`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ieu-b-4760 ukb-b-5192

Time spent outside in summer is related but seems to also be reverse causal. Time spent watching tv is very different depending on the MR method used.

Usual walking pace

d <- make_dat("ukb-b-4711", "ieu-b-4760")

## Extracting data for 57 SNP(s) from 1 GWAS(s)

## Finding proxies for 7 SNPs in outcome ieu-b-4760

## Extracting data for 7 SNP(s) from 1 GWAS(s)

## Harmonising Usual walking pace || id:ukb-b-4711 (ukb-b-4711) and Number of children || id:ieu-b-4760 (ieu-b-4760)

d_mr<-mr(d)

## Analysing 'ukb-b-4711' on 'ieu-b-4760'

d_mr

##   id.exposure id.outcome                             outcome
## 1  ukb-b-4711 ieu-b-4760 Number of children || id:ieu-b-4760
## 2  ukb-b-4711 ieu-b-4760 Number of children || id:ieu-b-4760
## 3  ukb-b-4711 ieu-b-4760 Number of children || id:ieu-b-4760
## 4  ukb-b-4711 ieu-b-4760 Number of children || id:ieu-b-4760
## 5  ukb-b-4711 ieu-b-4760 Number of children || id:ieu-b-4760
##                              exposure                    method nsnp
## 1 Usual walking pace || id:ukb-b-4711                  MR Egger   57
## 2 Usual walking pace || id:ukb-b-4711           Weighted median   57
## 3 Usual walking pace || id:ukb-b-4711 Inverse variance weighted   57
## 4 Usual walking pace || id:ukb-b-4711               Simple mode   57
## 5 Usual walking pace || id:ukb-b-4711             Weighted mode   57
##               b         se         pval
## 1 -0.0887748470 0.19300214 6.473523e-01
## 2 -0.1024025097 0.05208555 4.929348e-02
## 3 -0.1954489295 0.04670936 2.859667e-05
## 4 -0.0003859447 0.13061219 9.976528e-01
## 5 -0.0098774764 0.11701479 9.330298e-01

mr_scatter_plot(d_mr,d)

## $`ukb-b-4711.ieu-b-4760`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-4711 ieu-b-4760

d <- make_dat("ieu-b-4760", "ukb-b-4711")

## Extracting data for 9 SNP(s) from 1 GWAS(s)

## Finding proxies for 1 SNPs in outcome ukb-b-4711

## Extracting data for 1 SNP(s) from 1 GWAS(s)

## Harmonising Number of children || id:ieu-b-4760 (ieu-b-4760) and Usual walking pace || id:ukb-b-4711 (ukb-b-4711)

d_mr<-mr(d)

## Analysing 'ieu-b-4760' on 'ukb-b-4711'

d_mr

##   id.exposure id.outcome                             outcome
## 1  ieu-b-4760 ukb-b-4711 Usual walking pace || id:ukb-b-4711
## 2  ieu-b-4760 ukb-b-4711 Usual walking pace || id:ukb-b-4711
## 3  ieu-b-4760 ukb-b-4711 Usual walking pace || id:ukb-b-4711
## 4  ieu-b-4760 ukb-b-4711 Usual walking pace || id:ukb-b-4711
## 5  ieu-b-4760 ukb-b-4711 Usual walking pace || id:ukb-b-4711
##                              exposure                    method nsnp
## 1 Number of children || id:ieu-b-4760                  MR Egger    8
## 2 Number of children || id:ieu-b-4760           Weighted median    8
## 3 Number of children || id:ieu-b-4760 Inverse variance weighted    8
## 4 Number of children || id:ieu-b-4760               Simple mode    8
## 5 Number of children || id:ieu-b-4760             Weighted mode    8
##             b         se      pval
## 1  0.45035718 0.63477970 0.5046557
## 2  0.02034838 0.05240105 0.6977792
## 3 -0.02407511 0.10674817 0.8215656
## 4  0.16980080 0.07125891 0.0486762
## 5  0.06734991 0.05324604 0.2463942

mr_scatter_plot(d_mr,d)

## $`ieu-b-4760.ukb-b-4711`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ieu-b-4760 ukb-b-4711

Very large differences between the MR methods, perhaps not as important as IVW method would imply.

Risk

Driving faster than motorway speed limit

d <- make_dat("ukb-b-4549", "ieu-b-4760")

## Extracting data for 25 SNP(s) from 1 GWAS(s)

## Harmonising Drive faster than motorway speed limit || id:ukb-b-4549 (ukb-b-4549) and Number of children || id:ieu-b-4760 (ieu-b-4760)

d_mr<-mr(d)

## Analysing 'ukb-b-4549' on 'ieu-b-4760'

d_mr

##   id.exposure id.outcome                             outcome
## 1  ukb-b-4549 ieu-b-4760 Number of children || id:ieu-b-4760
## 2  ukb-b-4549 ieu-b-4760 Number of children || id:ieu-b-4760
## 3  ukb-b-4549 ieu-b-4760 Number of children || id:ieu-b-4760
## 4  ukb-b-4549 ieu-b-4760 Number of children || id:ieu-b-4760
## 5  ukb-b-4549 ieu-b-4760 Number of children || id:ieu-b-4760
##                                                  exposure
## 1 Drive faster than motorway speed limit || id:ukb-b-4549
## 2 Drive faster than motorway speed limit || id:ukb-b-4549
## 3 Drive faster than motorway speed limit || id:ukb-b-4549
## 4 Drive faster than motorway speed limit || id:ukb-b-4549
## 5 Drive faster than motorway speed limit || id:ukb-b-4549
##                      method nsnp         b         se       pval
## 1                  MR Egger   25 0.7984066 0.42700353 0.07429638
## 2           Weighted median   25 0.1341501 0.05943337 0.02399858
## 3 Inverse variance weighted   25 0.2108394 0.09237041 0.02245730
## 4               Simple mode   25 0.1959355 0.10739567 0.08056272
## 5             Weighted mode   25 0.2121821 0.10159442 0.04753364

mr_scatter_plot(d_mr,d)

## $`ukb-b-4549.ieu-b-4760`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-4549 ieu-b-4760

d <- make_dat("ieu-b-4760", "ukb-b-4549")

## Extracting data for 9 SNP(s) from 1 GWAS(s)

## Finding proxies for 1 SNPs in outcome ukb-b-4549

## Extracting data for 1 SNP(s) from 1 GWAS(s)

## Harmonising Number of children || id:ieu-b-4760 (ieu-b-4760) and Drive faster than motorway speed limit || id:ukb-b-4549 (ukb-b-4549)

d_mr<-mr(d)

## Analysing 'ieu-b-4760' on 'ukb-b-4549'

d_mr

##   id.exposure id.outcome
## 1  ieu-b-4760 ukb-b-4549
## 2  ieu-b-4760 ukb-b-4549
## 3  ieu-b-4760 ukb-b-4549
## 4  ieu-b-4760 ukb-b-4549
## 5  ieu-b-4760 ukb-b-4549
##                                                   outcome
## 1 Drive faster than motorway speed limit || id:ukb-b-4549
## 2 Drive faster than motorway speed limit || id:ukb-b-4549
## 3 Drive faster than motorway speed limit || id:ukb-b-4549
## 4 Drive faster than motorway speed limit || id:ukb-b-4549
## 5 Drive faster than motorway speed limit || id:ukb-b-4549
##                              exposure                    method nsnp
## 1 Number of children || id:ieu-b-4760                  MR Egger    8
## 2 Number of children || id:ieu-b-4760           Weighted median    8
## 3 Number of children || id:ieu-b-4760 Inverse variance weighted    8
## 4 Number of children || id:ieu-b-4760               Simple mode    8
## 5 Number of children || id:ieu-b-4760             Weighted mode    8
##              b         se       pval
## 1  2.442338280 0.71991437 0.01462976
## 2 -0.187650858 0.08801489 0.03300394
## 3 -0.009187427 0.20008194 0.96337535
## 4 -0.220981011 0.14039904 0.15949894
## 5 -0.173685688 0.09983800 0.12546826

mr_scatter_plot(d_mr,d)

## $`ieu-b-4760.ukb-b-4549`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ieu-b-4760 ukb-b-4549

Risk taking

d <- make_dat("ukb-b-14147", "ieu-b-4760")

## Extracting data for 29 SNP(s) from 1 GWAS(s)

## Finding proxies for 1 SNPs in outcome ieu-b-4760

## Extracting data for 1 SNP(s) from 1 GWAS(s)

## Harmonising Risk taking || id:ukb-b-14147 (ukb-b-14147) and Number of children || id:ieu-b-4760 (ieu-b-4760)

d_mr<-mr(d)

## Analysing 'ukb-b-14147' on 'ieu-b-4760'

d_mr

##   id.exposure id.outcome                             outcome
## 1 ukb-b-14147 ieu-b-4760 Number of children || id:ieu-b-4760
## 2 ukb-b-14147 ieu-b-4760 Number of children || id:ieu-b-4760
## 3 ukb-b-14147 ieu-b-4760 Number of children || id:ieu-b-4760
## 4 ukb-b-14147 ieu-b-4760 Number of children || id:ieu-b-4760
## 5 ukb-b-14147 ieu-b-4760 Number of children || id:ieu-b-4760
##                        exposure                    method nsnp         b
## 1 Risk taking || id:ukb-b-14147                  MR Egger   29 1.7581159
## 2 Risk taking || id:ukb-b-14147           Weighted median   29 0.3322063
## 3 Risk taking || id:ukb-b-14147 Inverse variance weighted   29 0.4415104
## 4 Risk taking || id:ukb-b-14147               Simple mode   29 0.3638135
## 5 Risk taking || id:ukb-b-14147             Weighted mode   29 0.2979975
##          se        pval
## 1 0.6419878 0.010792681
## 2 0.1122094 0.003070476
## 3 0.1441284 0.002189008
## 4 0.2494762 0.155881948
## 5 0.2308815 0.207365885

mr_scatter_plot(d_mr,d)

## $`ukb-b-14147.ieu-b-4760`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1 ukb-b-14147 ieu-b-4760

d <- make_dat("ieu-b-4760", "ukb-b-14147")

## Extracting data for 9 SNP(s) from 1 GWAS(s)

## Finding proxies for 1 SNPs in outcome ukb-b-14147

## Extracting data for 1 SNP(s) from 1 GWAS(s)

## Harmonising Number of children || id:ieu-b-4760 (ieu-b-4760) and Risk taking || id:ukb-b-14147 (ukb-b-14147)

d_mr<-mr(d)

## Analysing 'ieu-b-4760' on 'ukb-b-14147'

d_mr

##   id.exposure  id.outcome                       outcome
## 1  ieu-b-4760 ukb-b-14147 Risk taking || id:ukb-b-14147
## 2  ieu-b-4760 ukb-b-14147 Risk taking || id:ukb-b-14147
## 3  ieu-b-4760 ukb-b-14147 Risk taking || id:ukb-b-14147
## 4  ieu-b-4760 ukb-b-14147 Risk taking || id:ukb-b-14147
## 5  ieu-b-4760 ukb-b-14147 Risk taking || id:ukb-b-14147
##                              exposure                    method nsnp         b
## 1 Number of children || id:ieu-b-4760                  MR Egger    8 1.0905046
## 2 Number of children || id:ieu-b-4760           Weighted median    8 0.1131527
## 3 Number of children || id:ieu-b-4760 Inverse variance weighted    8 0.1704986
## 4 Number of children || id:ieu-b-4760               Simple mode    8 0.1890127
## 5 Number of children || id:ieu-b-4760             Weighted mode    8 0.1633722
##           se       pval
## 1 0.46790483 0.05859164
## 2 0.04788899 0.01813686
## 3 0.09696137 0.07867651
## 4 0.07592077 0.04162061
## 5 0.09171135 0.11805754

mr_scatter_plot(d_mr,d)

## $`ieu-b-4760.ukb-b-14147`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure  id.outcome
## 1  ieu-b-4760 ukb-b-14147

Seems to be a large effect of risk taking on number of children.