PubMed HealthSearch

PubMed · 41168876

Investigating the interplay between prematurity and genetic variation in the context of rare developmental disorders.

Abstract

BACKGROUND: Rare damaging genetic variation accounts for a substantial proportion of the risk of rare developmental disorders (DDs), but common genetic variants as well as environmental factors, including prematurity, also contribute. Little is known about the interplay between prematurity and genetic variation in influencing phenotypic outcomes in DDs, nor about how genetic factors may contribute to risk of preterm birth in DDs. METHODS: We leveraged phenotypic and genetic data from 21,712 patients with DDs recruited for clinical sequencing, 16% of whom were born prematurely. Using multivariable regression models, we compared phenotypic features and the prevalence of diagnostic genetic variation in specific genes between preterm and term individuals with DDs. We tested whether the fraction of cases attributable to de novo mutations differed between term and preterm probands. Additionally, we assessed whether associations between common variant contributions to education-related traits and prematurity are explained by direct genetic effects. RESULTS: Prematurity was associated with more severe clinical phenotypes among these DD patients, including more affected organ systems and more delayed developmental milestones. Prematurity and the presence of a monogenic diagnosis contributed additively to severity. We found that genes associated with fetal anomalies were enriched for diagnostic mutations among preterm individuals (p = 7.83 × 10-5). We also demonstrated an exome-wide enrichment of de novo mutations (DNMs) in both term and preterm probands; the fraction of cases explained by DNMs in known DD-associated genes was higher in term than preterm cases (25% versus 20%) but DNMs in as-yet-undiscovered genes likely contribute approximately equally to both groups (14% versus 13%). Finally, we showed that the positive association between polygenic predisposition to education-related traits and gestational duration is likely to be the result of genetically influenced parental traits or confounders, rather than direct genetic effects in the child, and that a monogenic diagnosis modifies this association. CONCLUSIONS: Our findings emphasise the importance of considering environmental factors like prematurity in understanding outcomes in DDs suspected to have a genetic component, and motivate further exploration of the role that genetic variation plays in influencing prematurity.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Olivia Wootton, Patrick Campbell, Sarah Richardson, Sarah J Lindsay, Qin Qin Huang, Erwan Delage, Sana Amanat, Hilary S Wong, Helen V Firth, Matthew E Hurles, Michael A Simpson, Elizabeth J Radford, Hilary C Martin. 2025-10-30. Investigating the interplay between prematurity and genetic variation in the context of rare developmental disorders.. https://doi.org/10.1186/s13073-025-01560-3

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans