Background: Adverse pregnancy outcomes (APOs) like preeclampsia, foetal growth restriction, and pre-term delivery affect up to 30% of pregnancies, significantly impacting quality of life. These complications are difficult to detect before the appearance of clinical symptoms, and their biology is poorly understood. Genome-wide association studies (GWAS) have identified dozens of genetic loci associated with these conditions, as well as with perinatal traits like placental weight and birthweight. However, the functional consequences of these variants are not currently understood.
Previous research suggests that placental development plays a key role in perinatal health, and that genetic variants associated with APOs may disrupt placental function. However, accessing placental tissue is impractical due to the associated risk of miscarriage, which limits placental monitoring. This severely hinders our ability to functionally interpret APO-associated variants.
During pregnancy a large proportion of circulating cell-free DNA (cfDNA) in the parental bloodstream comes from the placenta. This makes cfDNA a unique, minimally invasive biomarker which could be used to monitor placental health. Thus, we propose a project that leverages cfDNA sequencing to track gene activity in the placenta throughout pregnancy. We aim to use these observations to dissect the role of genetic variants associated with perinatal traits and APOs.
Research questions:
- Can cfDNA information be used to estimate gene activity in the placenta in real time?
- Do genetic variants associated with APOs modulate gene activity in the placenta?
- Can cfDNA be used as a minimally invasive biomarker of APOs?
Project design:
With guidance from their supervisors, the student will leverage single-cell RNA sequencing (scRNA-seq) data to identify cell type-specific markers of key placental cell types, which they will use to interpret publicly available cfDNA sequencing data from healthy pregnancies and APO cases. They will do this by mapping nucleosomal footprints from cfDNA, which they will integrate with single-cell markers to infer placental gene activity. The student will lead on the design and development of computational tools to predict gene activity from nucleosomal footprints, extending previous work published by KECG. The student will next integrate cfDNA-derived gene activity metrics with GWAS associations for perinatal traits and APOs using statistical genetics approaches like heritability partitioning and SNP enrichment. Where appropriate, the student may supplement these analyses with new data generation from plasma samples from pregnancies available via the Genetic Beta Cell Research Bank (GBCRB).
A key strength of our research environment is that, through their supervisory team, the student will have access to key data sets uniquely suited to answering these research questions. These include: 1) a collection of publicly available sequencing data sets of cfDNA from pregnancy cohorts, currently being curated by KECG and comprising both APOs and healthy pregnancies; 2) GWAS summary statistics for perinatal traits generated by RF, including associations with placental weight and birthweight; 3) a longitudinal data set of gene expression across pregnancy generated by ED within the Pregnancy Outcome Prediction Study 2 (POPS2); and 4) a scRNA-seq atlas of the placenta generated by the Vento-Tormo group at the Wellcome Sanger Institute. ED’s close links with this group will enable the student to access key knowledge and expertise, fostering collaborations and enhancing the value of this project.
