Genetic Insights Into Perinatal Outcomes of Maternal Antihypertensive Therapy During Pregnancy.

Importance: Limited information exists regarding the impact of pharmacotherapy in pregnancy due to ethical concerns of unintended fetal harm. Yet, maternal prescriptive drug use for chronic conditions such as hypertension is common. Objective: To investigate potential causal relationships between perturbing maternal genetic variants influencing antihypertensive drug targets and perinatal outcomes among offspring using mendelian randomization (MR). Design, Setting, and Participants: This 2-sample MR study used individual-level single-nucleotide variation (SNV) outcome data from mother-father-offspring trios with complete genetic and phenotypic information from the Norwegian Mother, Father and Child Cohort Study (MoBa) and summary-level SNV exposure data from UK Biobank participants sourced from the Integrative Epidemiology Unit OpenGWAS project. Pregnant individuals were recruited across Norway during their routine ultrasonography examination at 18 weeks' gestation between June 1999 and December 2008, and mothers, fathers, and offspring were followed up after birth. Novel genetic instruments for maternal antihypertensive drug targets that act via systolic blood pressure (SBP) were derived from individual-level data analyzed in January 2018. Two-sample multivariable MR analysis of these maternal drug targets and offspring outcomes were performed between January 2023 and April 2024. Exposures: Maternal genetic variants associated with drug targets for treatments of hypertension, as specified in the National Health Service dictionary of medicines and devices. Main Outcomes and Measures: Offspring outcomes were Apgar score at 1 minute and 5 minutes, offspring developmental score at 6 months, birth length, birth weight z score, gestational age, head circumference, and congenital malformation. Maternal hypertensive disorders of pregnancy were a positive control. Results: The MoBa sample contained 29 849 family trios, with a mean (SD) maternal age of 30.2 (18.6) years and a mean (SD) paternal age of 32.8 (13.1) years; 51.1% of offspring were male. Seven independent SNVs were identified as influencing maternal SBP via the antihypertensive drug target instruments. For higher levels of maternal SBP acting through the CACNB2 calcium channel blocker target, the estimated change in gestational age was 3.99 days (95% CI, 0.02-7.96 days) per 10-mm Hg decrease in SBP. There was no evidence of differential risk for measured perinatal outcomes from maternal SBP acting through drug targets for multiple hypertensive subclasses, such as between the ADRB1 ß-adrenoceptor-blocking target and risk of congenital malformation (estimated odds ratio, 0.28 [95% CI, 0.02-4.71] per 10-mm Hg decrease in SBP). Maternal and paternal SBP acting through the EDNRA vasodilator antihypertensive target did not have a potential causal effect on birth weight z score, with respective ß estimates of 0.71 (95% CI, -0.09 to 1.51) and 0.72 (95% CI, -0.08 to 1.53) per 10-mm Hg decrease in SBP. Conclusions and Relevance: The findings provided little evidence to indicate that perturbation of maternal genetic variants for SBP that influence antihypertensive drug targets had potential causal relationships with measures of perinatal development and health within this study. These findings may be triangulated with existing literature to guide physicians and mothers in decisions about antihypertensive use during pregnancy.

The role of accelerometer-derived sleep traits on glycated haemoglobin and glucose levels: a Mendelian randomization study.

Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806). Cross-trait linkage disequilibrium score regression was applied to determine genetic correlations across accelerometer-derived, self-reported sleep traits, and HbA1c/glucose. We found no causal effect of any accelerometer-derived sleep trait on HbA1c or glucose. Similar MR results for self-reported sleep traits in the UKB sub-sample with accelerometer-derived measures suggested our results were not explained by selection bias. Phenotypic and genetic correlation analyses suggested complex relationships between self-reported and accelerometer-derived traits indicating that they may reflect different types of exposure. These findings suggested accelerometer-derived sleep traits do not affect HbA1c. Accelerometer-derived measures of sleep duration and quality might not simply be 'objective' measures of self-reported sleep duration and insomnia, but rather captured different sleep characteristics.

How to estimate heritability: a guide for genetic epidemiologists.

Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of methods that use large samples of (unrelated or related) genotyped individuals. Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability, i.e. the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals [linkage disequilibrium score regression, genomic relatedness restricted maximum-likelihood (GREML) estimation] and family-based genomic designs (sibling regression, GREML-kinship, trio-genome-wide complex trait analysis, maternal-genome-wide complex trait analysis, relatedness disequilibrium regression). We describe how heritability is estimated for each method and the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared with samples of related individuals. Overall, this article is intended to help the reader determine the circumstances when each method would be appropriate and why.

Comparison of intergenerational instrumental variable analyses of body mass index and mortality in UK Biobank.

BACKGROUND: An increasing proportion of people have a body mass index (BMI) classified as overweight or obese and published studies disagree whether this will be beneficial or detrimental to health. We applied and evaluated two intergenerational instrumental variable methods to estimate the average causal effect of BMI on mortality in a cohort with many deaths: the parents of UK Biobank participants. METHODS: In Cox regression models, parental BMI was instrumented by offspring BMI using an 'offspring as instrument' (OAI) estimation and by offspring BMI-related genetic variants in a 'proxy-genotype Mendelian randomization' (PGMR) estimation. RESULTS: Complete-case analyses were performed in parents of 233 361 UK Biobank participants with full phenotypic, genotypic and covariate data. The PGMR method suggested that higher BMI increased mortality with hazard ratios per kg/m2 of 1.02 (95% CI: 1.01, 1.04) for mothers and 1.04 (95% CI: 1.02, 1.05) for fathers. The OAI method gave considerably higher estimates, which varied according to the parent-offspring pairing between 1.08 (95% CI: 1.06, 1.10; mother-son) and 1.23 (95% CI: 1.16, 1.29; father-daughter). CONCLUSION: Both methods supported a causal role of higher BMI increasing mortality, although caution is required regarding the immediate causal interpretation of these exact values. Evidence of instrument invalidity from measured covariates was limited for the OAI method and minimal for the PGMR method. The methods are complementary for interrogating the average putative causal effects because the biases are expected to differ between them.

Using Mendelian Randomisation to Prioritise Candidate Maternal Metabolic Traits Influencing Offspring Birthweight.

Marked physiological changes in pregnancy are essential to support foetal growth; however, evidence on the role of specific maternal metabolic traits from human studies is limited. We integrated Mendelian randomisation (MR) and metabolomics data to probe the effect of 46 maternal metabolic traits on offspring birthweight (N = 210,267). We implemented univariable two-sample MR (UVMR) to identify candidate metabolic traits affecting offspring birthweight. We then applied two-sample multivariable MR (MVMR) to jointly estimate the potential direct causal effect for each candidate maternal metabolic trait. In the main analyses, UVMR indicated that higher maternal glucose was related to higher offspring birthweight (0.328 SD difference in mean birthweight per 1 SD difference in glucose (95% CI: 0.104, 0.414)), as were maternal glutamine (0.089 (95% CI: 0.033, 0.144)) and alanine (0.137 (95% CI: 0.036, 0.239)). In additional analyses, UVMR estimates were broadly consistent when selecting instruments from an independent data source, albeit imprecise for glutamine and alanine, and were attenuated for alanine when using other UVMR methods. MVMR results supported independent effects of these metabolites, with effect estimates consistent with those seen with the UVMR results. Among the remaining 43 metabolic traits, UVMR estimates indicated a null effect for most lipid-related traits and a high degree of uncertainty for other amino acids and ketone bodies. Our findings suggest that maternal gestational glucose and glutamine are causally related to offspring birthweight.

Investigating how the accuracy of teacher expectations of pupil performance relate to socioeconomic and genetic factors.

Teacher expectations of pupil ability can influence educational progression, impacting subsequent streaming and exam level. Systematic discrepancies between teacher expectations of pupil achievement may therefore have a detrimental effect on children's education. Associations between socioeconomic and demographic factors with teacher expectation accuracy have been demonstrated, but it is not known how teacher expectations of achievement may relate to genetic factors. We investigated these relationships using nationally standardized exam results at ages 11 and 14 from a UK longitudinal cohort study. We found that teacher expectation of achievement was strongly correlated with educational test scores. Furthermore, the accuracy of teacher expectation was patterned by pupil socioeconomic background but not teacher characteristics. The accuracy of teacher expectation related to pupil's genetic liability to education as captured by a polygenic score for educational attainment. Despite correlation with the polygenic score, we found no strong evidence for genomewide SNP heritability in teacher reporting accuracy.

Pharmacoepidemiology in pregnancy: analysis protocol for an observational cohort study in the UK Clinical Practice Research Datalink.

Large numbers of women take prescription and over-the-counter medications during pregnancy. However, there is very little definitive evidence about the potential effects of these drugs on the mothers and offspring. We will investigate the risks and benefits of continuing prescriptive drug use for chronic pre-existing maternal conditions such as diabetes, hypertension and thyroid related conditions throughout pregnancy. If left untreated, these conditions are established risk factors for adverse neonatal and maternal outcomes. However, some treatments for these conditions are associated with adverse neonatal outcomes. Our primary aims are twofold. Firstly, we aim to estimate the beneficial effect on the mother of continuing treatment during pregnancy. Second, we aim to determine whether there is an associated detrimental impact on the neonate of continuation of maternal treatment during pregnancy. To establish this evidence, we will investigate the relationship between maternal drug prescriptions and adverse and beneficial offspring outcomes to provide evidence to guide clinical decisions. We will conduct a hypothesis testing observational intergenerational cohort study using data from the UK Clinical Practice Research Datalink (CPRD). We will apply four statistical methods: multivariable adjusted regression, propensity score regression, instrumental variables analysis and negative control analysis. These methods should account for potential confounding when estimating the association between the drug exposure and maternal or neonatal outcome. In this protocol we describe the aims, motivation, study design, cohort and statistical analyses of our study to aid reproducibility and transparency within research.