Investigating food intake in relation to number of children
library(TwoSampleMR)
d <- make_dat("ukb-b-11348", "ieu-b-4760")
## API: public: http://gwas-api.mrcieu.ac.uk/
## Extracting data for 32 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 Bread intake || id:ukb-b-11348 (ukb-b-11348) and Number of children || id:ieu-b-4760 (ieu-b-4760)
d_mr<-mr(d)
## Analysing 'ukb-b-11348' on 'ieu-b-4760'
d_mr
## id.exposure id.outcome outcome
## 1 ukb-b-11348 ieu-b-4760 Number of children || id:ieu-b-4760
## 2 ukb-b-11348 ieu-b-4760 Number of children || id:ieu-b-4760
## 3 ukb-b-11348 ieu-b-4760 Number of children || id:ieu-b-4760
## 4 ukb-b-11348 ieu-b-4760 Number of children || id:ieu-b-4760
## 5 ukb-b-11348 ieu-b-4760 Number of children || id:ieu-b-4760
## exposure method nsnp b
## 1 Bread intake || id:ukb-b-11348 MR Egger 55 -0.10468282
## 2 Bread intake || id:ukb-b-11348 Weighted median 55 -0.05566808
## 3 Bread intake || id:ukb-b-11348 Inverse variance weighted 55 -0.08829137
## 4 Bread intake || id:ukb-b-11348 Simple mode 55 -0.13830742
## 5 Bread intake || id:ukb-b-11348 Weighted mode 55 -0.05340018
## se pval
## 1 0.11613581 0.3714628040
## 2 0.03142472 0.0764819095
## 3 0.02601999 0.0006907797
## 4 0.07632455 0.0755329405
## 5 0.06042423 0.3807465958
mr_scatter_plot(d_mr,d)
## $`ukb-b-11348.ieu-b-4760`
##
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
## id.exposure id.outcome
## 1 ukb-b-11348 ieu-b-4760
d <- make_dat("ieu-b-4760", "ukb-b-11348")
## Warning in .fun(piece, ...): Duplicated SNPs present in exposure data for phenotype 'Number of children || id:ieu-b-4760. Just keeping the first instance:
## rs6800021
## rs6782190
## rs4870063
## rs10270358
## rs201945769
## rs2360806
## rs72687493
## rs62054570
## rs2957316
## Extracting data for 9 SNP(s) from 1 GWAS(s)
## Finding proxies for 1 SNPs in outcome ukb-b-11348
## Extracting data for 1 SNP(s) from 1 GWAS(s)
## Harmonising Number of children || id:ieu-b-4760 (ieu-b-4760) and Bread intake || id:ukb-b-11348 (ukb-b-11348)
d_mr<-mr(d)
## Analysing 'ieu-b-4760' on 'ukb-b-11348'
d_mr
## id.exposure id.outcome outcome
## 1 ieu-b-4760 ukb-b-11348 Bread intake || id:ukb-b-11348
## 2 ieu-b-4760 ukb-b-11348 Bread intake || id:ukb-b-11348
## 3 ieu-b-4760 ukb-b-11348 Bread intake || id:ukb-b-11348
## 4 ieu-b-4760 ukb-b-11348 Bread intake || id:ukb-b-11348
## 5 ieu-b-4760 ukb-b-11348 Bread intake || id:ukb-b-11348
## exposure method nsnp b
## 1 Number of children || id:ieu-b-4760 MR Egger 8 -0.8185228
## 2 Number of children || id:ieu-b-4760 Weighted median 8 -0.1452872
## 3 Number of children || id:ieu-b-4760 Inverse variance weighted 8 -0.2992869
## 4 Number of children || id:ieu-b-4760 Simple mode 8 -0.1202853
## 5 Number of children || id:ieu-b-4760 Weighted mode 8 -0.1116956
## se pval
## 1 0.67959966 0.273777906
## 2 0.08885562 0.102029612
## 3 0.11451056 0.008958909
## 4 0.15062358 0.450772470
## 5 0.11806504 0.375625839
mr_scatter_plot(d_mr,d)
## $`ieu-b-4760.ukb-b-11348`
##
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
## id.exposure id.outcome
## 1 ieu-b-4760 ukb-b-11348
Bread intake seems to be reverse causal.
d <- make_dat("ukb-d-1468_5", "ieu-b-4760")
## Extracting data for 11 SNP(s) from 1 GWAS(s)
## Finding proxies for 3 SNPs in outcome ieu-b-4760
## Extracting data for 3 SNP(s) from 1 GWAS(s)
## Harmonising Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5 (ukb-d-1468_5) and Number of children || id:ieu-b-4760 (ieu-b-4760)
d_mr<-mr(d)
## Analysing 'ukb-d-1468_5' on 'ieu-b-4760'
d_mr
## id.exposure id.outcome outcome
## 1 ukb-d-1468_5 ieu-b-4760 Number of children || id:ieu-b-4760
## 2 ukb-d-1468_5 ieu-b-4760 Number of children || id:ieu-b-4760
## 3 ukb-d-1468_5 ieu-b-4760 Number of children || id:ieu-b-4760
## 4 ukb-d-1468_5 ieu-b-4760 Number of children || id:ieu-b-4760
## 5 ukb-d-1468_5 ieu-b-4760 Number of children || id:ieu-b-4760
## exposure
## 1 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## 2 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## 3 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## 4 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## 5 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## method nsnp b se pval
## 1 MR Egger 17 0.80463799 0.51728698 0.140671052
## 2 Weighted median 17 0.11710895 0.10913689 0.283250301
## 3 Inverse variance weighted 17 0.24882086 0.07902116 0.001639492
## 4 Simple mode 17 0.02827124 0.20121811 0.890018192
## 5 Weighted mode 17 0.03804201 0.19621045 0.848707046
mr_scatter_plot(d_mr,d)
## $`ukb-d-1468_5.ieu-b-4760`
##
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
## id.exposure id.outcome
## 1 ukb-d-1468_5 ieu-b-4760
d <- make_dat("ieu-b-4760", "ukb-d-1468_5")
## Warning in .fun(piece, ...): Duplicated SNPs present in exposure data for phenotype 'Number of children || id:ieu-b-4760. Just keeping the first instance:
## rs6800021
## rs6782190
## rs4870063
## rs10270358
## rs201945769
## rs2360806
## rs72687493
## rs62054570
## rs2957316
## Extracting data for 9 SNP(s) from 1 GWAS(s)
## Finding proxies for 1 SNPs in outcome ukb-d-1468_5
## Extracting data for 1 SNP(s) from 1 GWAS(s)
## Harmonising Number of children || id:ieu-b-4760 (ieu-b-4760) and Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5 (ukb-d-1468_5)
d_mr<-mr(d)
## Analysing 'ieu-b-4760' on 'ukb-d-1468_5'
d_mr
## id.exposure id.outcome
## 1 ieu-b-4760 ukb-d-1468_5
## 2 ieu-b-4760 ukb-d-1468_5
## 3 ieu-b-4760 ukb-d-1468_5
## 4 ieu-b-4760 ukb-d-1468_5
## 5 ieu-b-4760 ukb-d-1468_5
## outcome
## 1 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## 2 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## 3 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## 4 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## 5 Cereal type: Other (e.g. Cornflakes, Frosties) || id:ukb-d-1468_5
## 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 -0.50080117 0.35235237 0.2050559
## 2 -0.03785676 0.04396574 0.3892097
## 3 0.04098139 0.06708972 0.5413025
## 4 -0.03622771 0.05859199 0.5559501
## 5 -0.06002203 0.04278901 0.2034552
mr_scatter_plot(d_mr,d)
## $`ieu-b-4760.ukb-d-1468_5`
##
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
## id.exposure id.outcome
## 1 ieu-b-4760 ukb-d-1468_5
Food intake seems to be somewhat related to number of children, but it isn’t massively strong and there’s some evidence of reverse causality.