Placental DNA methylation captures shared and trait-specific genetic susceptibility across complex health conditions

The prenatal exposome and genome in predictive modelling of DNA methylation

It is found that genomic associations with cord blood DNAm are stronger and more widespread than prenatal exposures, although typically, the prenatal exposome explains additional variation in DNAm beyond genetic influences.

DNA methylation informs adult cardiovascular risk and prevention for low birthweight individuals

The Barker-consistent hypothesis-free discovery approach identified novel and known candidate genes that, with future validation, may serve as therapeutic targets, identify high-risk individuals, and support clinical trial recruitment.

DNA methylation as proxy of genetic, prenatal and perinatal psychiatric risk factors

Background. Cord blood DNA methylation profile scores (MPSs) based on genetic and pre-/perinatal risk factors for neurodevelopmental conditions (NDCs) may capture downstream biological effects and help understand how combined exposure signals contribute to NDC risk. Methods. Using data from two longitudinal birth cohorts, Generation R (N-train = 1856, N-test = 476) and ALSPAC (N-validation= 832), we developed cord blood MPSs based on genetic and pre-/perinatal NDC risk factors. We assessed individual and combined predictive performance of risk factors and MPSs for eight childhood psychiatric outcomes (four broad, four specific), measured between ages 5 and 14 years. We also evaluated if the MPSs could be combined into a composite "transmission load" MPS. Results. We validated four novel MPSs: maternal age, birthweight, and genetic liability for ADHD and schizophrenia (r range = 0.08 to 0.29) and included two previously validated MPSs: maternal smoking and gestational age (r range = 0.42 to 0.63). Jointly modeling the six MPSs with their corresponding risk factors explained on average 3.3% of variance in outcomes, higher than that explained by risk factors (1.8%) or MPSs alone (1.6%), indicating complementary sources of risk. The "transmission load" MPS did not replicate due to heterogeneous contributions of the predictors across cohorts. Conclusions. The four novel MPSs based on genetic and pre-/perinatal risk factors can serve as valuable tools for future research. Integrating genetic and prenatal risk factors with DNA methylation at birth can provide insights into their individual and joint contributions to early psychiatric risk and may improve prediction.

Genome-wide association analyses of gestational phenotypes identify context-specific genetic effects

Intergenerational epigenetic signatures of prenatal adversity and their role in emerging child psychopathology

PSD was associated with epigenetic signatures at birth, with a subset of associations persisting across early childhood and converging on cellular stress response biology, suggesting differential biological embedding of structural versus psychological dimensions of adversity.

Genomic approaches to understanding pregnancy phenotypes and birth outcomes and their links with long-term maternal and offspring health—the value of distinguishing maternal and fetal genetic effects

Pregnancy is a biologically complex period with profound implications for maternal and offspring health. Adverse pregnancy outcomes such as pre-eclampsia, gestational diabetes, preterm birth, and growth restriction contribute substantially to maternal and neonatal morbidity and mortality, while also increasing risks of late-onset disorders in both mothers and children. Understanding the genetic basis of these pregnancy phenotypes and birth outcomes is therefore critical for advancing maternal–child health research. Unlike most human traits, pregnancy phenotypes are shaped by both maternal and fetal genomes, which are correlated but exert distinct influences. This dual genetic contribution presents unique challenges for genetic studies and necessitates specialized statistical approaches. In this review, we highlight recent methodological advances in genomic research designed to disentangle maternal and fetal genetic effects. We first discuss single-variant genome-wide association approaches, including conditional analyses, haplotype-based methods, structural equation modeling, and weighted linear models, which enable partitioning of maternal and fetal contributions. We then examine multi-variant strategies such as polygenic scores and Mendelian randomization, which provide insights into causal relationships, shared genetic architectures, and long-term health consequences. Finally, we explore approaches to characterizing the genetic architecture of pregnancy phenotypes, including methods for partitioning maternal and fetal genetic contributions and genetic correlation analyses with other health-related traits. Together, these genomic approaches have begun to clarify the complex interplay between maternal and fetal genomes in shaping pregnancy outcomes and their links to later-life health in both mothers and offspring. By emphasizing methodological innovation rather than empirical findings, this review provides a framework for future studies aiming to unravel the genetic underpinnings of pregnancy phenotypes and their connections with long-term health.