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Time-varying effects are common in genetic control of gestational duration.

ABSTRACT: Preterm birth is a major burden to neonatal health worldwide, determined in part by genetics. Recently, studies discovered several genes associated with this trait or its continuous equivalent - gestational duration. However, their effect timing, and thus clinical importance, is still unclear. Here, we use genotyping data of 31,000 births from the Norwegian Mother, Father and Child cohort (MoBa) to investigate different models of the genetic pregnancy "clock". We conduct genome-wide association studies using gestational duration or preterm birth, replicating known maternal associations and finding one new foetal variant. We illustrate how the interpretation of these results is complicated by the loss of power when dichotomizing. Using flexible survival models, we resolve this complexity and find that many of the known loci have time-varying effects, often stronger early in pregnancy. The overall polygenic control of birth timing appears to be shared in the term and preterm, but not very preterm periods, and exploratory results suggest involvement of the major histocompatibility complex genes in the latter. These findings show that the known gestational duration loci are clinically relevant, and should help design further experimental studies.

SUBMITTER: Juodakis J

PROVIDER: S-EPMC9934791 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Time-varying effects are common in genetic control of gestational duration.

Juodakis Julius J Ytterberg Karin K Flatley Christopher C Sole-Navais Pol P Jacobsson Bo B

medRxiv : the preprint server for health sciences 20230210

Preterm birth is a major burden to neonatal health worldwide, determined in part by genetics. Recently, studies discovered several genes associated with this trait or its continuous equivalent - gestational duration. However, their effect timing, and thus clinical importance, is still unclear. Here, we use genotyping data of 31,000 births from the Norwegian Mother, Father and Child cohort (MoBa) to investigate different models of the genetic pregnancy "clock". We conduct genome-wide association ...[more]

PMID: 36798334

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Project description:PurposeIn studies of effects of time-varying drug exposures, adequate adjustment for time-varying covariates is often necessary to properly control for confounding. However, the granularity of the available covariate data may not be sufficiently fine, for example when covariates are measured for participants only when their exposure levels change.MethodsTo illustrate the impact of choices regarding the frequency of measuring time-varying covariates, we simulated data for a large target trial and for large observational studies, varying in covariate measurement design. Covariates were measured never, on a fixed-interval basis, or each time the exposure level switched. For the analysis, it was assumed that covariates remain constant in periods of no measurement. Cumulative survival probabilities for continuous exposure and non-exposure were estimated using inverse probability weighting to adjust for time-varying confounding, with special emphasis on the difference between 5-year event risks.ResultsWith monthly covariate measurements, estimates based on observational data coincided with trial-based estimates, with 5-year risk differences being zero. Without measurement of baseline or post-baseline covariates, this risk difference was estimated to be 49% based on the available observational data. With measurements on a fixed-interval basis only, 5-year risk differences deviated from the null, to 29% for 6-monthly measurements, and with magnitude increasing up to 35% as the interval length increased. Risk difference estimates diverged from the null to as low as -18% when covariates were measured depending on exposure level switching.ConclusionOur simulations highlight the need for careful consideration of time-varying covariates in designing studies on time-varying exposures. We caution against implementing designs with long intervals between measurements. The maximum length required will depend on the rates at which treatments and covariates change, with higher rates requiring shorter measurement intervals.

Dataset Information

Time-varying effects are common in genetic control of gestational duration.

Publications

Time-varying effects are common in genetic control of gestational duration.

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