Exploring the lifetime effect of children on wellbeing using two-sample Mendelian randomisation

Abstract

ObjectivesTo provide Mendelian randomisation evidence of the effect of having children on parental wellbeing.DesignTwo sample Mendelian randomisation.SettingNon-clinical European ancestry participants.ParticipantsWe used the UK Biobank (460,654 male and female European ancestry participants) as a source of genotype-exposure associations, and the Social Science Genetics Consortia (SSGAC) (298,420 male and female European ancestry participants) and Within-Family Consortia (effective sample of 22,656 male and female European ancestry participants) as sources of genotype-outcome associations.InterventionsThe lifetime effect of an increase in the genetic liability to having children.Primary and secondary outcome measuresThe primary analysis was an inverse variance weighed analyses of subjective wellbeing measured in the 2016 SSGAC GWAS. Secondary outcomes included pleiotropy robust estimators applied in the SSGAC and an analysis using the Within-Family consortia GWAS.ResultsThe primary IVW estimate found evidence of a 0.153 standard deviation increase for every child a parent has (95% CI: -0.210 to 0.516). Secondary outcomes were generally slightly deflated (e.g. -0.049 [95% CI: -0.533 to 0.435] for the WFC and 0.090 [95% CI: -0.167 to 0.347] for weighted median) implying the presence of some residual confounding and pleiotropy.ConclusionsContrary to the existing literature, our results are not compatible with a measurable negative effect of number of children on the average wellbeing of a parent over their life course. However, we were unable to explore non-linearities, interactions, or time varying effects.Strengths and limitations of this study-Mendelian randomisation (MR) is a natural experiment which is theoretically robust to confounding and reverse causation.-We were able to use two negative control analyses to explore the robustness of our study to two potential sources of residual confounding (populations structure and passive gene-environment correlation).-We additionally use pleiotropy robust estimates (like MR-PRESSO, MR-Egger, weighted median, and weighed mode) to explore if our result was affected by direct effects of the genetic variants on the outcome, not mediated by the exposure.-Because we use summary data, we were unable to explore interactions, non-linear and time-varying, or time sensitive, effects.-Our study is a proof of concept for using MR to explore the causal effect of the heritable environment.

MRSamePopTest: Introducing a simple falsification test for the Two-Sample Mendelian randomisation ‘same population’ assumption.

“…Since not all consortia force each study to perform identical GWASs, it could be difficult to compare the methodology to a single study GWAS. However, our applications of this method here and elsewhere to date imply that in practice consortia which use different methods to a single study GWAS, or which do not enforce homogenous methods, do not produce heterogeneous effects from single study GWASs drawn from a comparable population [22][23][24]. We would however suggest, when possible, triangulating our proposed sensitivity analysis with other approaches, such as a comparison of the measured demographic factors.…”

Comparison of caffeine consumption behavior with plasma caffeine levels as exposures in drug-target Mendelian randomization and implications for interpreting effects on obesity

“…Likewise, the gene-environment equivalence assumption, which is required for MR estimates to translate to the effects of pharmacological interventions, is less plausible when using these behavioral phenotypes (52,53). We believe that the two issues we have highlighted here could be relevant for other pharmacological targets.…”