Health

Investigating health measures in relation to number of live births

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

Overall

Overall health rating

d <- make_dat("ukb-b-6306", "ukb-b-1209")

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

## Extracting data for 109 SNP(s) from 1 GWAS(s)

## Harmonising Overall health rating || id:ukb-b-6306 (ukb-b-6306) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

d_mr<-mr(d)

## Analysing 'ukb-b-6306' on 'ukb-b-1209'

d_mr

##   id.exposure id.outcome                                outcome
## 1  ukb-b-6306 ukb-b-1209 Number of live births || id:ukb-b-1209
## 2  ukb-b-6306 ukb-b-1209 Number of live births || id:ukb-b-1209
## 3  ukb-b-6306 ukb-b-1209 Number of live births || id:ukb-b-1209
## 4  ukb-b-6306 ukb-b-1209 Number of live births || id:ukb-b-1209
## 5  ukb-b-6306 ukb-b-1209 Number of live births || id:ukb-b-1209
##                                 exposure                    method nsnp
## 1 Overall health rating || id:ukb-b-6306                  MR Egger  109
## 2 Overall health rating || id:ukb-b-6306           Weighted median  109
## 3 Overall health rating || id:ukb-b-6306 Inverse variance weighted  109
## 4 Overall health rating || id:ukb-b-6306               Simple mode  109
## 5 Overall health rating || id:ukb-b-6306             Weighted mode  109
##           b         se         pval
## 1 0.5872102 0.26346803 2.791971e-02
## 2 0.1749643 0.05506427 1.485735e-03
## 3 0.2265477 0.05267119 1.698991e-05
## 4 0.1246454 0.14591620 3.948707e-01
## 5 0.1331159 0.14584132 3.634099e-01

mr_scatter_plot(d_mr,d)

## $`ukb-b-6306.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-6306 ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-b-6306")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and Overall health rating || id:ukb-b-6306 (ukb-b-6306)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-b-6306'

d_mr

##   id.exposure id.outcome                                outcome
## 1  ukb-b-1209 ukb-b-6306 Overall health rating || id:ukb-b-6306
## 2  ukb-b-1209 ukb-b-6306 Overall health rating || id:ukb-b-6306
## 3  ukb-b-1209 ukb-b-6306 Overall health rating || id:ukb-b-6306
## 4  ukb-b-1209 ukb-b-6306 Overall health rating || id:ukb-b-6306
## 5  ukb-b-1209 ukb-b-6306 Overall health rating || id:ukb-b-6306
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##            b         se         pval
## 1 -0.3144043 0.47779140 5.269860e-01
## 2  0.1273513 0.03250944 8.952254e-05
## 3  0.1470591 0.05739943 1.040623e-02
## 4  0.1529336 0.04350272 5.580233e-03
## 5  0.1461899 0.04472225 8.446857e-03

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-b-6306`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-1209 ukb-b-6306

Eyes

Age started wearing glasses or contact lenses

d <- make_dat("ukb-b-5801", "ukb-b-1209")

## Extracting data for 84 SNP(s) from 1 GWAS(s)

## Harmonising Age started wearing glasses or contact lenses || id:ukb-b-5801 (ukb-b-5801) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

d_mr<-mr(d)

## Analysing 'ukb-b-5801' on 'ukb-b-1209'

d_mr

##   id.exposure id.outcome                                outcome
## 1  ukb-b-5801 ukb-b-1209 Number of live births || id:ukb-b-1209
## 2  ukb-b-5801 ukb-b-1209 Number of live births || id:ukb-b-1209
## 3  ukb-b-5801 ukb-b-1209 Number of live births || id:ukb-b-1209
## 4  ukb-b-5801 ukb-b-1209 Number of live births || id:ukb-b-1209
## 5  ukb-b-5801 ukb-b-1209 Number of live births || id:ukb-b-1209
##                                                         exposure
## 1 Age started wearing glasses or contact lenses || id:ukb-b-5801
## 2 Age started wearing glasses or contact lenses || id:ukb-b-5801
## 3 Age started wearing glasses or contact lenses || id:ukb-b-5801
## 4 Age started wearing glasses or contact lenses || id:ukb-b-5801
## 5 Age started wearing glasses or contact lenses || id:ukb-b-5801
##                      method nsnp          b         se         pval
## 1                  MR Egger   84 0.07000614 0.08287710 0.4007371456
## 2           Weighted median   84 0.06150364 0.03983979 0.1226430448
## 3 Inverse variance weighted   84 0.10911289 0.03050572 0.0003478249
## 4               Simple mode   84 0.03390377 0.08477117 0.6902247443
## 5             Weighted mode   84 0.04673003 0.05599215 0.4063489239

mr_scatter_plot(d_mr,d)

## $`ukb-b-5801.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-5801 ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-b-5801")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and Age started wearing glasses or contact lenses || id:ukb-b-5801 (ukb-b-5801)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-b-5801'

d_mr

##   id.exposure id.outcome
## 1  ukb-b-1209 ukb-b-5801
## 2  ukb-b-1209 ukb-b-5801
## 3  ukb-b-1209 ukb-b-5801
## 4  ukb-b-1209 ukb-b-5801
## 5  ukb-b-1209 ukb-b-5801
##                                                          outcome
## 1 Age started wearing glasses or contact lenses || id:ukb-b-5801
## 2 Age started wearing glasses or contact lenses || id:ukb-b-5801
## 3 Age started wearing glasses or contact lenses || id:ukb-b-5801
## 4 Age started wearing glasses or contact lenses || id:ukb-b-5801
## 5 Age started wearing glasses or contact lenses || id:ukb-b-5801
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##             b         se       pval
## 1 0.006128052 0.56531682 0.99158756
## 2 0.070968839 0.04775014 0.13721197
## 3 0.165835508 0.06489580 0.01060621
## 4 0.062444868 0.07211916 0.40686242
## 5 0.049881605 0.06424703 0.45548240

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-b-5801`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-1209 ukb-b-5801

Blood

Haematocrit percentage

d <- make_dat("ukb-d-30030_irnt", "ukb-b-1209")

## Extracting data for 274 SNP(s) from 1 GWAS(s)

## Finding proxies for 40 SNPs in outcome ukb-b-1209

## Extracting data for 40 SNP(s) from 1 GWAS(s)

## Harmonising Haematocrit percentage || id:ukb-d-30030_irnt (ukb-d-30030_irnt) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

## Removing the following SNPs for incompatible alleles:
## rs3213473, rs8070692

d_mr<-mr(d)

## Analysing 'ukb-d-30030_irnt' on 'ukb-b-1209'

d_mr

##        id.exposure id.outcome                                outcome
## 1 ukb-d-30030_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 2 ukb-d-30030_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 3 ukb-d-30030_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 4 ukb-d-30030_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 5 ukb-d-30030_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
##                                        exposure                    method nsnp
## 1 Haematocrit percentage || id:ukb-d-30030_irnt                  MR Egger  248
## 2 Haematocrit percentage || id:ukb-d-30030_irnt           Weighted median  248
## 3 Haematocrit percentage || id:ukb-d-30030_irnt Inverse variance weighted  248
## 4 Haematocrit percentage || id:ukb-d-30030_irnt               Simple mode  248
## 5 Haematocrit percentage || id:ukb-d-30030_irnt             Weighted mode  248
##             b         se       pval
## 1 0.031784985 0.02974151 0.28624884
## 2 0.006949927 0.01992786 0.72727375
## 3 0.029349175 0.01477759 0.04702674
## 4 0.030342067 0.04835436 0.53091519
## 5 0.017409940 0.02679122 0.51640109

mr_scatter_plot(d_mr,d)

## $`ukb-d-30030_irnt.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##        id.exposure id.outcome
## 1 ukb-d-30030_irnt ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-d-30030_irnt")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and Haematocrit percentage || id:ukb-d-30030_irnt (ukb-d-30030_irnt)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-d-30030_irnt'

d_mr

##   id.exposure       id.outcome                                       outcome
## 1  ukb-b-1209 ukb-d-30030_irnt Haematocrit percentage || id:ukb-d-30030_irnt
## 2  ukb-b-1209 ukb-d-30030_irnt Haematocrit percentage || id:ukb-d-30030_irnt
## 3  ukb-b-1209 ukb-d-30030_irnt Haematocrit percentage || id:ukb-d-30030_irnt
## 4  ukb-b-1209 ukb-d-30030_irnt Haematocrit percentage || id:ukb-d-30030_irnt
## 5  ukb-b-1209 ukb-d-30030_irnt Haematocrit percentage || id:ukb-d-30030_irnt
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##              b         se      pval
## 1  0.870493308 1.40225099 0.5501381
## 2 -0.016668027 0.04767576 0.7266297
## 3  0.098154470 0.16300117 0.5470608
## 4  0.001336421 0.05218731 0.9800737
## 5 -0.003931260 0.04623504 0.9339176

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-d-30030_irnt`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure       id.outcome
## 1  ukb-b-1209 ukb-d-30030_irnt

High light scatter reticulocyte count

d <- make_dat("ukb-d-30300_irnt", "ukb-b-1209")

## Extracting data for 309 SNP(s) from 1 GWAS(s)

## Finding proxies for 48 SNPs in outcome ukb-b-1209

## Extracting data for 48 SNP(s) from 1 GWAS(s)

## Harmonising High light scatter reticulocyte count || id:ukb-d-30300_irnt (ukb-d-30300_irnt) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

d_mr<-mr(d)

## Analysing 'ukb-d-30300_irnt' on 'ukb-b-1209'

d_mr

##        id.exposure id.outcome                                outcome
## 1 ukb-d-30300_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 2 ukb-d-30300_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 3 ukb-d-30300_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 4 ukb-d-30300_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 5 ukb-d-30300_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
##                                                       exposure
## 1 High light scatter reticulocyte count || id:ukb-d-30300_irnt
## 2 High light scatter reticulocyte count || id:ukb-d-30300_irnt
## 3 High light scatter reticulocyte count || id:ukb-d-30300_irnt
## 4 High light scatter reticulocyte count || id:ukb-d-30300_irnt
## 5 High light scatter reticulocyte count || id:ukb-d-30300_irnt
##                      method nsnp          b          se       pval
## 1                  MR Egger  276 0.02064066 0.015916763 0.19579537
## 2           Weighted median  276 0.04213895 0.013970898 0.00255969
## 3 Inverse variance weighted  276 0.02318584 0.009036425 0.01029321
## 4               Simple mode  276 0.03601520 0.031719798 0.25718927
## 5             Weighted mode  276 0.03214569 0.015629953 0.04066106

mr_scatter_plot(d_mr,d)

## $`ukb-d-30300_irnt.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##        id.exposure id.outcome
## 1 ukb-d-30300_irnt ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-d-30300_irnt")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and High light scatter reticulocyte count || id:ukb-d-30300_irnt (ukb-d-30300_irnt)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-d-30300_irnt'

d_mr

##   id.exposure       id.outcome
## 1  ukb-b-1209 ukb-d-30300_irnt
## 2  ukb-b-1209 ukb-d-30300_irnt
## 3  ukb-b-1209 ukb-d-30300_irnt
## 4  ukb-b-1209 ukb-d-30300_irnt
## 5  ukb-b-1209 ukb-d-30300_irnt
##                                                        outcome
## 1 High light scatter reticulocyte count || id:ukb-d-30300_irnt
## 2 High light scatter reticulocyte count || id:ukb-d-30300_irnt
## 3 High light scatter reticulocyte count || id:ukb-d-30300_irnt
## 4 High light scatter reticulocyte count || id:ukb-d-30300_irnt
## 5 High light scatter reticulocyte count || id:ukb-d-30300_irnt
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##           b         se       pval
## 1 0.6986455 1.30870165 0.60637970
## 2 0.1481042 0.06208131 0.01704904
## 3 0.1241741 0.15120119 0.41150348
## 4 0.2023406 0.08187325 0.03302623
## 5 0.1777461 0.07946185 0.04926487

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-d-30300_irnt`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure       id.outcome
## 1  ukb-b-1209 ukb-d-30300_irnt

High light scatter reticulocyte percentage

d <- make_dat("ukb-d-30290_irnt", "ukb-b-1209")

## Extracting data for 317 SNP(s) from 1 GWAS(s)

## Finding proxies for 51 SNPs in outcome ukb-b-1209

## Extracting data for 51 SNP(s) from 1 GWAS(s)

## Harmonising High light scatter reticulocyte percentage || id:ukb-d-30290_irnt (ukb-d-30290_irnt) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

## Removing the following SNPs for incompatible alleles:
## rs7094843

d_mr<-mr(d)

## Analysing 'ukb-d-30290_irnt' on 'ukb-b-1209'

d_mr

##        id.exposure id.outcome                                outcome
## 1 ukb-d-30290_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 2 ukb-d-30290_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 3 ukb-d-30290_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 4 ukb-d-30290_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 5 ukb-d-30290_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
##                                                            exposure
## 1 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
## 2 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
## 3 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
## 4 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
## 5 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
##                      method nsnp          b          se       pval
## 1                  MR Egger  281 0.01148302 0.015410542 0.45681387
## 2           Weighted median  281 0.03074898 0.013566631 0.02341982
## 3 Inverse variance weighted  281 0.02073865 0.008811926 0.01859888
## 4               Simple mode  281 0.03074832 0.030487587 0.31406027
## 5             Weighted mode  281 0.03074832 0.014044079 0.02939208

mr_scatter_plot(d_mr,d)

## $`ukb-d-30290_irnt.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##        id.exposure id.outcome
## 1 ukb-d-30290_irnt ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-d-30290_irnt")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and High light scatter reticulocyte percentage || id:ukb-d-30290_irnt (ukb-d-30290_irnt)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-d-30290_irnt'

d_mr

##   id.exposure       id.outcome
## 1  ukb-b-1209 ukb-d-30290_irnt
## 2  ukb-b-1209 ukb-d-30290_irnt
## 3  ukb-b-1209 ukb-d-30290_irnt
## 4  ukb-b-1209 ukb-d-30290_irnt
## 5  ukb-b-1209 ukb-d-30290_irnt
##                                                             outcome
## 1 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
## 2 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
## 3 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
## 4 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
## 5 High light scatter reticulocyte percentage || id:ukb-d-30290_irnt
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##           b         se        pval
## 1 0.3664361 1.05653744 0.736692445
## 2 0.1709796 0.05951172 0.004065422
## 3 0.1158979 0.12114385 0.338720412
## 4 0.2042057 0.08210461 0.032147299
## 5 0.1893218 0.07604260 0.032007731

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-d-30290_irnt`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure       id.outcome
## 1  ukb-b-1209 ukb-d-30290_irnt

Reticulocyte count

d <- make_dat("ukb-d-30250_irnt", "ukb-b-1209")

## Extracting data for 291 SNP(s) from 1 GWAS(s)

## Finding proxies for 42 SNPs in outcome ukb-b-1209

## Extracting data for 42 SNP(s) from 1 GWAS(s)

## Harmonising Reticulocyte count || id:ukb-d-30250_irnt (ukb-d-30250_irnt) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

d_mr<-mr(d)

## Analysing 'ukb-d-30250_irnt' on 'ukb-b-1209'

d_mr

##        id.exposure id.outcome                                outcome
## 1 ukb-d-30250_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 2 ukb-d-30250_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 3 ukb-d-30250_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 4 ukb-d-30250_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 5 ukb-d-30250_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
##                                    exposure                    method nsnp
## 1 Reticulocyte count || id:ukb-d-30250_irnt                  MR Egger  260
## 2 Reticulocyte count || id:ukb-d-30250_irnt           Weighted median  260
## 3 Reticulocyte count || id:ukb-d-30250_irnt Inverse variance weighted  260
## 4 Reticulocyte count || id:ukb-d-30250_irnt               Simple mode  260
## 5 Reticulocyte count || id:ukb-d-30250_irnt             Weighted mode  260
##            b         se       pval
## 1 0.01507018 0.01745333 0.38868897
## 2 0.02925432 0.01333873 0.02829400
## 3 0.02012462 0.01005854 0.04541934
## 4 0.03936700 0.02960267 0.18473886
## 5 0.02956527 0.01596349 0.06515577

mr_scatter_plot(d_mr,d)

## $`ukb-d-30250_irnt.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##        id.exposure id.outcome
## 1 ukb-d-30250_irnt ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-d-30250_irnt")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and Reticulocyte count || id:ukb-d-30250_irnt (ukb-d-30250_irnt)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-d-30250_irnt'

d_mr

##   id.exposure       id.outcome                                   outcome
## 1  ukb-b-1209 ukb-d-30250_irnt Reticulocyte count || id:ukb-d-30250_irnt
## 2  ukb-b-1209 ukb-d-30250_irnt Reticulocyte count || id:ukb-d-30250_irnt
## 3  ukb-b-1209 ukb-d-30250_irnt Reticulocyte count || id:ukb-d-30250_irnt
## 4  ukb-b-1209 ukb-d-30250_irnt Reticulocyte count || id:ukb-d-30250_irnt
## 5  ukb-b-1209 ukb-d-30250_irnt Reticulocyte count || id:ukb-d-30250_irnt
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##           b         se       pval
## 1 0.9106141 1.34066445 0.51408155
## 2 0.1180731 0.05989538 0.04868711
## 3 0.1258117 0.15617049 0.42047085
## 4 0.2289023 0.07583321 0.01292860
## 5 0.1767687 0.08031803 0.05236485

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-d-30250_irnt`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure       id.outcome
## 1  ukb-b-1209 ukb-d-30250_irnt

SHBG

d <- make_dat("ukb-d-30830_irnt", "ukb-b-1209")

## Extracting data for 264 SNP(s) from 1 GWAS(s)

## Finding proxies for 32 SNPs in outcome ukb-b-1209

## Extracting data for 32 SNP(s) from 1 GWAS(s)

## Harmonising SHBG || id:ukb-d-30830_irnt (ukb-d-30830_irnt) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

d_mr<-mr(d)

## Analysing 'ukb-d-30830_irnt' on 'ukb-b-1209'

d_mr

##        id.exposure id.outcome                                outcome
## 1 ukb-d-30830_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 2 ukb-d-30830_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 3 ukb-d-30830_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 4 ukb-d-30830_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
## 5 ukb-d-30830_irnt ukb-b-1209 Number of live births || id:ukb-b-1209
##                      exposure                    method nsnp           b
## 1 SHBG || id:ukb-d-30830_irnt                  MR Egger  245 -0.02183420
## 2 SHBG || id:ukb-d-30830_irnt           Weighted median  245 -0.01236439
## 3 SHBG || id:ukb-d-30830_irnt Inverse variance weighted  245 -0.01624450
## 4 SHBG || id:ukb-d-30830_irnt               Simple mode  245 -0.07691019
## 5 SHBG || id:ukb-d-30830_irnt             Weighted mode  245 -0.01957090
##           se       pval
## 1 0.01578065 0.16774882
## 2 0.01281869 0.33476544
## 3 0.01013313 0.10891110
## 4 0.03192404 0.01673088
## 5 0.01132389 0.08520252

mr_scatter_plot(d_mr,d)

## $`ukb-d-30830_irnt.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##        id.exposure id.outcome
## 1 ukb-d-30830_irnt ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-d-30830_irnt")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and SHBG || id:ukb-d-30830_irnt (ukb-d-30830_irnt)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-d-30830_irnt'

d_mr

##   id.exposure       id.outcome                     outcome
## 1  ukb-b-1209 ukb-d-30830_irnt SHBG || id:ukb-d-30830_irnt
## 2  ukb-b-1209 ukb-d-30830_irnt SHBG || id:ukb-d-30830_irnt
## 3  ukb-b-1209 ukb-d-30830_irnt SHBG || id:ukb-d-30830_irnt
## 4  ukb-b-1209 ukb-d-30830_irnt SHBG || id:ukb-d-30830_irnt
## 5  ukb-b-1209 ukb-d-30830_irnt SHBG || id:ukb-d-30830_irnt
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##              b         se      pval
## 1  0.985035624 1.12267895 0.4030841
## 2  0.008228861 0.04985392 0.8688972
## 3  0.065401566 0.13310798 0.6231844
## 4 -0.014283100 0.06423917 0.8285225
## 5 -0.023394469 0.05645786 0.6873512

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-d-30830_irnt`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure       id.outcome
## 1  ukb-b-1209 ukb-d-30830_irnt

HbA1C

d <- make_dat("ieu-b-103", "ukb-b-1209")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising HbA1C || id:ieu-b-103 (ieu-b-103) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

d_mr<-mr(d)

## Analysing 'ieu-b-103' on 'ukb-b-1209'

d_mr

##   id.exposure id.outcome                                outcome
## 1   ieu-b-103 ukb-b-1209 Number of live births || id:ukb-b-1209
## 2   ieu-b-103 ukb-b-1209 Number of live births || id:ukb-b-1209
## 3   ieu-b-103 ukb-b-1209 Number of live births || id:ukb-b-1209
## 4   ieu-b-103 ukb-b-1209 Number of live births || id:ukb-b-1209
## 5   ieu-b-103 ukb-b-1209 Number of live births || id:ukb-b-1209
##                exposure                    method nsnp             b         se
## 1 HbA1C || id:ieu-b-103                  MR Egger   11 -0.0360081476 0.07451164
## 2 HbA1C || id:ieu-b-103           Weighted median   11 -0.0217637839 0.04274781
## 3 HbA1C || id:ieu-b-103 Inverse variance weighted   11 -0.0005167553 0.03212884
## 4 HbA1C || id:ieu-b-103               Simple mode   11 -0.0224482353 0.06431296
## 5 HbA1C || id:ieu-b-103             Weighted mode   11 -0.0184438080 0.05141735
##        pval
## 1 0.6404535
## 2 0.6106678
## 3 0.9871675
## 4 0.7342921
## 5 0.7272711

mr_scatter_plot(d_mr,d)

## $`ieu-b-103.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1   ieu-b-103 ukb-b-1209

d <- make_dat("ukb-b-1209", "ieu-b-103")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Finding proxies for 9 SNPs in outcome ieu-b-103

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

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and HbA1C || id:ieu-b-103 (ieu-b-103)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ieu-b-103'

d_mr

##   id.exposure id.outcome               outcome
## 1  ukb-b-1209  ieu-b-103 HbA1C || id:ieu-b-103
## 2  ukb-b-1209  ieu-b-103 HbA1C || id:ieu-b-103
## 3  ukb-b-1209  ieu-b-103 HbA1C || id:ieu-b-103
## 4  ukb-b-1209  ieu-b-103 HbA1C || id:ieu-b-103
## 5  ukb-b-1209  ieu-b-103 HbA1C || id:ieu-b-103
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger    7
## 2 Number of live births || id:ukb-b-1209           Weighted median    7
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted    7
## 4 Number of live births || id:ukb-b-1209               Simple mode    7
## 5 Number of live births || id:ukb-b-1209             Weighted mode    7
##             b         se      pval
## 1 -0.04329789 0.80108977 0.9589894
## 2  0.12153017 0.08523904 0.1539385
## 3  0.05912888 0.08595905 0.4915329
## 4  0.14185955 0.11844730 0.2762121
## 5  0.14185955 0.11441524 0.2613148

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ieu-b-103`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-1209  ieu-b-103

Lungs

Forced vital capacity (FVC)

d <- make_dat("ukb-b-7953", "ukb-b-1209")

## Extracting data for 320 SNP(s) from 1 GWAS(s)

## Harmonising Forced vital capacity (FVC) || id:ukb-b-7953 (ukb-b-7953) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

## Removing the following SNPs for incompatible alleles:
## rs1040457, rs11191818, rs6441103

d_mr<-mr(d)

## Analysing 'ukb-b-7953' on 'ukb-b-1209'

d_mr

##   id.exposure id.outcome                                outcome
## 1  ukb-b-7953 ukb-b-1209 Number of live births || id:ukb-b-1209
## 2  ukb-b-7953 ukb-b-1209 Number of live births || id:ukb-b-1209
## 3  ukb-b-7953 ukb-b-1209 Number of live births || id:ukb-b-1209
## 4  ukb-b-7953 ukb-b-1209 Number of live births || id:ukb-b-1209
## 5  ukb-b-7953 ukb-b-1209 Number of live births || id:ukb-b-1209
##                                       exposure                    method nsnp
## 1 Forced vital capacity (FVC) || id:ukb-b-7953                  MR Egger  320
## 2 Forced vital capacity (FVC) || id:ukb-b-7953           Weighted median  320
## 3 Forced vital capacity (FVC) || id:ukb-b-7953 Inverse variance weighted  320
## 4 Forced vital capacity (FVC) || id:ukb-b-7953               Simple mode  320
## 5 Forced vital capacity (FVC) || id:ukb-b-7953             Weighted mode  320
##             b         se         pval
## 1 -0.10644391 0.04822378 2.800761e-02
## 2 -0.07438888 0.02357206 1.600547e-03
## 3 -0.07084253 0.01777922 6.760302e-05
## 4 -0.05286306 0.06694762 4.303373e-01
## 5 -0.05842445 0.04516561 1.967531e-01

mr_scatter_plot(d_mr,d)

## $`ukb-b-7953.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-7953 ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-b-7953")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and Forced vital capacity (FVC) || id:ukb-b-7953 (ukb-b-7953)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-b-7953'

d_mr

##   id.exposure id.outcome                                      outcome
## 1  ukb-b-1209 ukb-b-7953 Forced vital capacity (FVC) || id:ukb-b-7953
## 2  ukb-b-1209 ukb-b-7953 Forced vital capacity (FVC) || id:ukb-b-7953
## 3  ukb-b-1209 ukb-b-7953 Forced vital capacity (FVC) || id:ukb-b-7953
## 4  ukb-b-1209 ukb-b-7953 Forced vital capacity (FVC) || id:ukb-b-7953
## 5  ukb-b-1209 ukb-b-7953 Forced vital capacity (FVC) || id:ukb-b-7953
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##             b         se        pval
## 1 -1.61325898 0.89318752 0.104364208
## 2 -0.16453746 0.06248146 0.008453954
## 3 -0.21581622 0.11526785 0.061164842
## 4 -0.21892540 0.11033320 0.075341833
## 5 -0.06170674 0.12034614 0.619265344

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-b-7953`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-1209 ukb-b-7953

FVC

d <- make_dat("ieu-b-105", "ukb-b-1209")

## Extracting data for 297 SNP(s) from 1 GWAS(s)

## Finding proxies for 32 SNPs in outcome ukb-b-1209

## Extracting data for 32 SNP(s) from 1 GWAS(s)

## Harmonising FVC || id:ieu-b-105 (ieu-b-105) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

## Removing the following SNPs for incompatible alleles:
## rs11191818

d_mr<-mr(d)

## Analysing 'ieu-b-105' on 'ukb-b-1209'

d_mr

##   id.exposure id.outcome                                outcome
## 1   ieu-b-105 ukb-b-1209 Number of live births || id:ukb-b-1209
## 2   ieu-b-105 ukb-b-1209 Number of live births || id:ukb-b-1209
## 3   ieu-b-105 ukb-b-1209 Number of live births || id:ukb-b-1209
## 4   ieu-b-105 ukb-b-1209 Number of live births || id:ukb-b-1209
## 5   ieu-b-105 ukb-b-1209 Number of live births || id:ukb-b-1209
##              exposure                    method nsnp           b         se
## 1 FVC || id:ieu-b-105                  MR Egger  273 -0.09068103 0.04619842
## 2 FVC || id:ieu-b-105           Weighted median  273 -0.05921830 0.02237460
## 3 FVC || id:ieu-b-105 Inverse variance weighted  273 -0.06277762 0.01728204
## 4 FVC || id:ieu-b-105               Simple mode  273 -0.11184498 0.06617059
## 5 FVC || id:ieu-b-105             Weighted mode  273 -0.05823142 0.04822735
##           pval
## 1 0.0506852276
## 2 0.0081287258
## 3 0.0002806491
## 4 0.0921253762
## 5 0.2283133184

mr_scatter_plot(d_mr,d)

## $`ieu-b-105.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1   ieu-b-105 ukb-b-1209

d <- make_dat("ukb-b-1209", "ieu-b-105")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Finding proxies for 1 SNPs in outcome ieu-b-105

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

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and FVC || id:ieu-b-105 (ieu-b-105)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ieu-b-105'

d_mr

##   id.exposure id.outcome             outcome
## 1  ukb-b-1209  ieu-b-105 FVC || id:ieu-b-105
## 2  ukb-b-1209  ieu-b-105 FVC || id:ieu-b-105
## 3  ukb-b-1209  ieu-b-105 FVC || id:ieu-b-105
## 4  ukb-b-1209  ieu-b-105 FVC || id:ieu-b-105
## 5  ukb-b-1209  ieu-b-105 FVC || id:ieu-b-105
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##             b         se       pval
## 1 -1.40124715 0.92875074 0.16563995
## 2 -0.08351365 0.06269796 0.18286037
## 3 -0.22526402 0.11693342 0.05405073
## 4 -0.07095464 0.09786099 0.48503826
## 5 -0.01817606 0.07455110 0.81230947

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ieu-b-105`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1  ukb-b-1209  ieu-b-105

Pulse

Pulse rate

d <- make_dat("ukb-b-18103", "ukb-b-1209")

## Extracting data for 293 SNP(s) from 1 GWAS(s)

## Harmonising Pulse rate, automated reading || id:ukb-b-18103 (ukb-b-18103) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

d_mr<-mr(d)

## Analysing 'ukb-b-18103' on 'ukb-b-1209'

d_mr

##   id.exposure id.outcome                                outcome
## 1 ukb-b-18103 ukb-b-1209 Number of live births || id:ukb-b-1209
## 2 ukb-b-18103 ukb-b-1209 Number of live births || id:ukb-b-1209
## 3 ukb-b-18103 ukb-b-1209 Number of live births || id:ukb-b-1209
## 4 ukb-b-18103 ukb-b-1209 Number of live births || id:ukb-b-1209
## 5 ukb-b-18103 ukb-b-1209 Number of live births || id:ukb-b-1209
##                                          exposure                    method
## 1 Pulse rate, automated reading || id:ukb-b-18103                  MR Egger
## 2 Pulse rate, automated reading || id:ukb-b-18103           Weighted median
## 3 Pulse rate, automated reading || id:ukb-b-18103 Inverse variance weighted
## 4 Pulse rate, automated reading || id:ukb-b-18103               Simple mode
## 5 Pulse rate, automated reading || id:ukb-b-18103             Weighted mode
##   nsnp          b         se       pval
## 1  293 0.04116245 0.02825156 0.14619538
## 2  293 0.03282781 0.01704770 0.05414884
## 3  293 0.01174228 0.01294136 0.36422427
## 4  293 0.01868210 0.04225559 0.65872871
## 5  293 0.04228061 0.02218145 0.05761569

mr_scatter_plot(d_mr,d)

## $`ukb-b-18103.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1 ukb-b-18103 ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-b-18103")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and Pulse rate, automated reading || id:ukb-b-18103 (ukb-b-18103)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-b-18103'

d_mr

##   id.exposure  id.outcome                                         outcome
## 1  ukb-b-1209 ukb-b-18103 Pulse rate, automated reading || id:ukb-b-18103
## 2  ukb-b-1209 ukb-b-18103 Pulse rate, automated reading || id:ukb-b-18103
## 3  ukb-b-1209 ukb-b-18103 Pulse rate, automated reading || id:ukb-b-18103
## 4  ukb-b-1209 ukb-b-18103 Pulse rate, automated reading || id:ukb-b-18103
## 5  ukb-b-1209 ukb-b-18103 Pulse rate, automated reading || id:ukb-b-18103
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##               b         se      pval
## 1 -0.4155922489 1.36876290 0.7683147
## 2 -0.0005493082 0.06063303 0.9927716
## 3 -0.0834749691 0.15689720 0.5947010
## 4  0.0376905014 0.09026773 0.6851058
## 5 -0.0026633221 0.06724868 0.9691883

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-b-18103`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure  id.outcome
## 1  ukb-b-1209 ukb-b-18103

Strength

Hand grip strength

d <- make_dat("ukb-b-10215", "ukb-b-1209")

## Extracting data for 176 SNP(s) from 1 GWAS(s)

## Harmonising Hand grip strength (right) || id:ukb-b-10215 (ukb-b-10215) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

d_mr<-mr(d)

## Analysing 'ukb-b-10215' on 'ukb-b-1209'

d_mr

##   id.exposure id.outcome                                outcome
## 1 ukb-b-10215 ukb-b-1209 Number of live births || id:ukb-b-1209
## 2 ukb-b-10215 ukb-b-1209 Number of live births || id:ukb-b-1209
## 3 ukb-b-10215 ukb-b-1209 Number of live births || id:ukb-b-1209
## 4 ukb-b-10215 ukb-b-1209 Number of live births || id:ukb-b-1209
## 5 ukb-b-10215 ukb-b-1209 Number of live births || id:ukb-b-1209
##                                       exposure                    method nsnp
## 1 Hand grip strength (right) || id:ukb-b-10215                  MR Egger  176
## 2 Hand grip strength (right) || id:ukb-b-10215           Weighted median  176
## 3 Hand grip strength (right) || id:ukb-b-10215 Inverse variance weighted  176
## 4 Hand grip strength (right) || id:ukb-b-10215               Simple mode  176
## 5 Hand grip strength (right) || id:ukb-b-10215             Weighted mode  176
##            b         se         pval
## 1 -0.2647562 0.12125227 0.0303365064
## 2 -0.1317512 0.03726111 0.0004064036
## 3 -0.1109240 0.03421484 0.0011870062
## 4 -0.2640180 0.12218755 0.0320733126
## 5 -0.1905276 0.09649378 0.0498974152

mr_scatter_plot(d_mr,d)

## $`ukb-b-10215.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure id.outcome
## 1 ukb-b-10215 ukb-b-1209

d <- make_dat("ukb-b-1209", "ukb-b-10215")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and Hand grip strength (right) || id:ukb-b-10215 (ukb-b-10215)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ukb-b-10215'

d_mr

##   id.exposure  id.outcome                                      outcome
## 1  ukb-b-1209 ukb-b-10215 Hand grip strength (right) || id:ukb-b-10215
## 2  ukb-b-1209 ukb-b-10215 Hand grip strength (right) || id:ukb-b-10215
## 3  ukb-b-1209 ukb-b-10215 Hand grip strength (right) || id:ukb-b-10215
## 4  ukb-b-1209 ukb-b-10215 Hand grip strength (right) || id:ukb-b-10215
## 5  ukb-b-1209 ukb-b-10215 Hand grip strength (right) || id:ukb-b-10215
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##              b         se       pval
## 1 -1.153241451 0.53224357 0.05842139
## 2  0.018671610 0.04177219 0.65488475
## 3 -0.053651785 0.07402055 0.46856073
## 4 -0.003208585 0.06693701 0.96271235
## 5 -0.009831269 0.05569824 0.86341720

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ukb-b-10215`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure  id.outcome
## 1  ukb-b-1209 ukb-b-10215

Bone density

Total body bone mineral density

d <- make_dat("ebi-a-GCST005348", "ukb-b-1209")

## Extracting data for 85 SNP(s) from 1 GWAS(s)

## Finding proxies for 3 SNPs in outcome ukb-b-1209

## Extracting data for 3 SNP(s) from 1 GWAS(s)

## Harmonising Total body bone mineral density || id:ebi-a-GCST005348 (ebi-a-GCST005348) and Number of live births || id:ukb-b-1209 (ukb-b-1209)

d_mr<-mr(d)

## Analysing 'ebi-a-GCST005348' on 'ukb-b-1209'

d_mr

##        id.exposure id.outcome                                outcome
## 1 ebi-a-GCST005348 ukb-b-1209 Number of live births || id:ukb-b-1209
## 2 ebi-a-GCST005348 ukb-b-1209 Number of live births || id:ukb-b-1209
## 3 ebi-a-GCST005348 ukb-b-1209 Number of live births || id:ukb-b-1209
## 4 ebi-a-GCST005348 ukb-b-1209 Number of live births || id:ukb-b-1209
## 5 ebi-a-GCST005348 ukb-b-1209 Number of live births || id:ukb-b-1209
##                                                 exposure
## 1 Total body bone mineral density || id:ebi-a-GCST005348
## 2 Total body bone mineral density || id:ebi-a-GCST005348
## 3 Total body bone mineral density || id:ebi-a-GCST005348
## 4 Total body bone mineral density || id:ebi-a-GCST005348
## 5 Total body bone mineral density || id:ebi-a-GCST005348
##                      method nsnp            b         se       pval
## 1                  MR Egger   84  0.003225476 0.02754580 0.90707108
## 2           Weighted median   84 -0.015969713 0.01275387 0.21051669
## 3 Inverse variance weighted   84 -0.017608849 0.01036540 0.08935483
## 4               Simple mode   84 -0.052906711 0.02620723 0.04673818
## 5             Weighted mode   84 -0.009777860 0.01887360 0.60578724

mr_scatter_plot(d_mr,d)

## $`ebi-a-GCST005348.ukb-b-1209`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##        id.exposure id.outcome
## 1 ebi-a-GCST005348 ukb-b-1209

d <- make_dat("ukb-b-1209", "ebi-a-GCST005348")

## Extracting data for 11 SNP(s) from 1 GWAS(s)

## Harmonising Number of live births || id:ukb-b-1209 (ukb-b-1209) and Total body bone mineral density || id:ebi-a-GCST005348 (ebi-a-GCST005348)

d_mr<-mr(d)

## Analysing 'ukb-b-1209' on 'ebi-a-GCST005348'

d_mr

##   id.exposure       id.outcome
## 1  ukb-b-1209 ebi-a-GCST005348
## 2  ukb-b-1209 ebi-a-GCST005348
## 3  ukb-b-1209 ebi-a-GCST005348
## 4  ukb-b-1209 ebi-a-GCST005348
## 5  ukb-b-1209 ebi-a-GCST005348
##                                                  outcome
## 1 Total body bone mineral density || id:ebi-a-GCST005348
## 2 Total body bone mineral density || id:ebi-a-GCST005348
## 3 Total body bone mineral density || id:ebi-a-GCST005348
## 4 Total body bone mineral density || id:ebi-a-GCST005348
## 5 Total body bone mineral density || id:ebi-a-GCST005348
##                                 exposure                    method nsnp
## 1 Number of live births || id:ukb-b-1209                  MR Egger   11
## 2 Number of live births || id:ukb-b-1209           Weighted median   11
## 3 Number of live births || id:ukb-b-1209 Inverse variance weighted   11
## 4 Number of live births || id:ukb-b-1209               Simple mode   11
## 5 Number of live births || id:ukb-b-1209             Weighted mode   11
##              b        se      pval
## 1  0.263941202 3.0953652 0.9339136
## 2 -0.007184613 0.1329225 0.9568944
## 3  0.017238821 0.3448040 0.9601256
## 4  0.062419379 0.1598324 0.7043346
## 5  0.074075051 0.1443745 0.6190414

mr_scatter_plot(d_mr,d)

## $`ukb-b-1209.ebi-a-GCST005348`

## 
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
##   id.exposure       id.outcome
## 1  ukb-b-1209 ebi-a-GCST005348

Most health measures seem to be noisy. Health measures that may be related to number of children: Hand grip strength, FVC, and Age started wearing glasses or contact lenses. Overall health also seems to be related, but there may be reverse causality too.