Food intake
Clare Horscroft
05/08/2021
Investigating food intake in relation to number of children fathered
library(TwoSampleMR)Bread intake
d <- make_dat("ukb-b-11348", "ukb-b-2227")## API: public: http://gwas-api.mrcieu.ac.uk/## Extracting data for 32 SNP(s) from 1 GWAS(s)## Harmonising Bread intake || id:ukb-b-11348 (ukb-b-11348) and Number of children fathered || id:ukb-b-2227 (ukb-b-2227)d_mr<-mr(d)## Analysing 'ukb-b-11348' on 'ukb-b-2227'd_mr## id.exposure id.outcome outcome
## 1 ukb-b-11348 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## 2 ukb-b-11348 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## 3 ukb-b-11348 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## 4 ukb-b-11348 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## 5 ukb-b-11348 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## exposure method nsnp b
## 1 Bread intake || id:ukb-b-11348 MR Egger 32 -0.06319662
## 2 Bread intake || id:ukb-b-11348 Weighted median 32 -0.06407991
## 3 Bread intake || id:ukb-b-11348 Inverse variance weighted 32 -0.09555541
## 4 Bread intake || id:ukb-b-11348 Simple mode 32 -0.07342946
## 5 Bread intake || id:ukb-b-11348 Weighted mode 32 -0.03780563
## se pval
## 1 0.17049883 0.713497323
## 2 0.04860818 0.187404985
## 3 0.03682832 0.009469612
## 4 0.10051798 0.470563331
## 5 0.08251970 0.650046833mr_scatter_plot(d_mr,d)## $`ukb-b-11348.ukb-b-2227`
##
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
## id.exposure id.outcome
## 1 ukb-b-11348 ukb-b-2227d <- make_dat("ukb-b-2227", "ukb-b-11348")## Extracting data for 3 SNP(s) from 1 GWAS(s)## Harmonising Number of children fathered || id:ukb-b-2227 (ukb-b-2227) and Bread intake || id:ukb-b-11348 (ukb-b-11348)d_mr<-mr(d)## Analysing 'ukb-b-2227' on 'ukb-b-11348'd_mr## id.exposure id.outcome outcome
## 1 ukb-b-2227 ukb-b-11348 Bread intake || id:ukb-b-11348
## 2 ukb-b-2227 ukb-b-11348 Bread intake || id:ukb-b-11348
## 3 ukb-b-2227 ukb-b-11348 Bread intake || id:ukb-b-11348
## 4 ukb-b-2227 ukb-b-11348 Bread intake || id:ukb-b-11348
## 5 ukb-b-2227 ukb-b-11348 Bread intake || id:ukb-b-11348
## exposure method nsnp
## 1 Number of children fathered || id:ukb-b-2227 MR Egger 3
## 2 Number of children fathered || id:ukb-b-2227 Weighted median 3
## 3 Number of children fathered || id:ukb-b-2227 Inverse variance weighted 3
## 4 Number of children fathered || id:ukb-b-2227 Simple mode 3
## 5 Number of children fathered || id:ukb-b-2227 Weighted mode 3
## b se pval
## 1 -2.7896517 3.68752609 0.58769019
## 2 -0.1545200 0.10926370 0.15730514
## 3 -0.3337554 0.17860644 0.06166964
## 4 -0.1303726 0.12533795 0.40749660
## 5 -0.1204296 0.09947084 0.34966725mr_scatter_plot(d_mr,d)## $`ukb-b-2227.ukb-b-11348`
##
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
## id.exposure id.outcome
## 1 ukb-b-2227 ukb-b-11348Hot drink temperature
d <- make_dat("ukb-b-14203", "ukb-b-2227")## Extracting data for 71 SNP(s) from 1 GWAS(s)## Harmonising Hot drink temperature || id:ukb-b-14203 (ukb-b-14203) and Number of children fathered || id:ukb-b-2227 (ukb-b-2227)## Removing the following SNPs for incompatible alleles:
## rs2952894, rs3132487d_mr<-mr(d)## Analysing 'ukb-b-14203' on 'ukb-b-2227'd_mr## id.exposure id.outcome outcome
## 1 ukb-b-14203 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## 2 ukb-b-14203 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## 3 ukb-b-14203 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## 4 ukb-b-14203 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## 5 ukb-b-14203 ukb-b-2227 Number of children fathered || id:ukb-b-2227
## exposure method nsnp
## 1 Hot drink temperature || id:ukb-b-14203 MR Egger 71
## 2 Hot drink temperature || id:ukb-b-14203 Weighted median 71
## 3 Hot drink temperature || id:ukb-b-14203 Inverse variance weighted 71
## 4 Hot drink temperature || id:ukb-b-14203 Simple mode 71
## 5 Hot drink temperature || id:ukb-b-14203 Weighted mode 71
## b se pval
## 1 -0.2567030 0.26349075 0.33334105
## 2 -0.1466158 0.05929618 0.01341340
## 3 -0.1964629 0.06096581 0.00127073
## 4 -0.1308256 0.14569108 0.37228202
## 5 -0.1525435 0.12641979 0.23163196mr_scatter_plot(d_mr,d)## $`ukb-b-14203.ukb-b-2227`
##
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
## id.exposure id.outcome
## 1 ukb-b-14203 ukb-b-2227d <- make_dat("ukb-b-2227", "ukb-b-14203")## Extracting data for 3 SNP(s) from 1 GWAS(s)## Harmonising Number of children fathered || id:ukb-b-2227 (ukb-b-2227) and Hot drink temperature || id:ukb-b-14203 (ukb-b-14203)d_mr<-mr(d)## Analysing 'ukb-b-2227' on 'ukb-b-14203'd_mr## id.exposure id.outcome outcome
## 1 ukb-b-2227 ukb-b-14203 Hot drink temperature || id:ukb-b-14203
## 2 ukb-b-2227 ukb-b-14203 Hot drink temperature || id:ukb-b-14203
## 3 ukb-b-2227 ukb-b-14203 Hot drink temperature || id:ukb-b-14203
## 4 ukb-b-2227 ukb-b-14203 Hot drink temperature || id:ukb-b-14203
## 5 ukb-b-2227 ukb-b-14203 Hot drink temperature || id:ukb-b-14203
## exposure method nsnp
## 1 Number of children fathered || id:ukb-b-2227 MR Egger 3
## 2 Number of children fathered || id:ukb-b-2227 Weighted median 3
## 3 Number of children fathered || id:ukb-b-2227 Inverse variance weighted 3
## 4 Number of children fathered || id:ukb-b-2227 Simple mode 3
## 5 Number of children fathered || id:ukb-b-2227 Weighted mode 3
## b se pval
## 1 -2.45429263 3.74620871 0.63077303
## 2 -0.11543478 0.06513341 0.07634772
## 3 -0.28918397 0.17441718 0.09731712
## 4 -0.09548624 0.07248582 0.31840756
## 5 -0.07807898 0.05305878 0.27898474mr_scatter_plot(d_mr,d)## $`ukb-b-2227.ukb-b-14203`
##
## attr(,"split_type")
## [1] "data.frame"
## attr(,"split_labels")
## id.exposure id.outcome
## 1 ukb-b-2227 ukb-b-14203These intake measures seem to be somewhat related to number of children, but it isn’t massively strong and there’s some evidence of reverse causality or confounding.