2026-27 Project (Futema & Walsh)

Dissecting the genetic modifiers of familial hypercholesterolaemia and coronary heart disease using whole-genome sequencing

SUPERVISORY TEAM

Supervisor

Dr Marta Futema at City St George’s
School of Health & Medical Sciences, Department of Medicine
Email: mfutema@sgul.ac.uk

Co-Supervisor

Dr Roddy Walsh at City St George’s
School of Health & Medical Sciences, Department of Medicine
Email: rwalsh@sgul.ac.uk

PROJECT SUMMARY

Project Summary

Coronary heart disease (CHD) remains the leading cause of death worldwide, and new prevention and treatment strategies are urgently needed. Familial Hypercholesterolaemia (FH) is a genetic condition that greatly increases CHD risk, yet some carriers of FH-causing variants remain unexpectedly healthy. This project will harness whole-genome sequencing from 500,000 UK Biobank participants, linked with health records, biomarkers, and lifestyle data, to uncover the genetic and biological mechanisms that protect against high cholesterol and CHD. By combining rare variant analysis, polygenic risk scores, and ancestry-stratified modelling, the project offers a unique opportunity to identify novel drug targets and precision prevention strategies. Students will gain cutting-edge training in statistical genetics, bioinformatics, and health data science, supported by world-leading expertise in cardiovascular genomics.

Project Key Words

genomics, cardiovascular health, polygenic risk scores

MRC LID Themes

Health Data Science

Skills

MRC Core Skills

Quantitative skills

Skills we expect a student to develop/acquire whilst pursuing this project:

During this project, the student will acquire a broad set of quantitative skills, including expertise in statistical genetics and bioinformatics, such as analysing whole-genome sequencing data, variant annotation, gene burden testing, and polygenic risk scoring. They will develop advanced computational and statistical methods to integrate diverse data sources, from omics and linked health records to biomarkers and lifestyle data, with a focus on ancestry-stratified and causal inference modelling. Alongside these technical competencies, the student will gain experience in health data science through the handling and analysis of large-scale, multi-ancestry datasets in secure environments, as well as developing translational insight into how genetic discoveries inform precision medicine and novel drug target identification. The project will also provide opportunities to strengthen critical thinking, scientific writing, and communication skills within a multidisciplinary research environment.

Routes

Which route/s are available with this project?

1+4 = Yes
+4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

City St Georges – MSc Genomic Medicine
LSHTM – MSc Epidemiology
LSHTM – MSc Health Data Science

Full-time/Part-time Study

Is this project available for full-time study? Yes
Is this project available for part-time study? Yes

Location & Travel

Students funded through MRC LID are expected to work on site at their primary institution. At a minimum, all students must meet the institutional research degree regulations and expectations about onsite working and under this scheme they may be expected to work onsite (in-person) more frequently. Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training for research degree study and training. Other travel expectations and opportunities highlighted by the supervisory team are noted below.

Day-to-day work (primary location) for the duration of this research degree project will be at: City St George’s – Tooting campus, London

Travel requirements for this project: None.

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

Minimum standard institutional eligibility criteria for doctoral study at City St George’s
Applicants should hold, or expect to obtain, a first-class or upper second-class undergraduate degree in a relevant subject such as genetics, bioinformatics, statistics, mathematics, computational biology, or related biomedical sciences.
A Master’s degree in genomic medicine, statistical genetics, epidemiology, health data science, medical statistics, or bioinformatics (or equivalent experience) would be advantageous but is not essential.
Strong quantitative skills and an interest in applying advanced statistical and computational methods to large-scale genomic and health data are essential, and prior experience with programming (e.g., R, Python, or similar) would be beneficial.

Other useful information

Potential Industrial CASE (iCASE) conversion? = No

PROJECT IN MORE DETAIL

Scientific description of this research project

Project Objectives
Familial Hypercholesterolaemia (FH) is a monogenic condition characterised by elevated LDL-cholesterol (LDL-C) and increased risk of premature coronary heart disease (CHD). It is caused by deleterious variants in LDLR, APOB, APOE, or PCSK9. Rare variants in these genes can also have opposite effects, lowering LDL-C-for example, gain-of-function variants in PCSK9 cause FH, whereas loss-of-function variants lower LDL-C and reduce CHD risk. Such discoveries have enabled PCSK9-targeted therapies. The clinical penetrance of FH-causing variants is variable and modified by common variants, typically summarised in a polygenic score (PGS). A high LDL-C PGS can mimic rare variants and increase CHD risk, whereas the protective effects of PGS at the lower end of the distribution are less understood. Penetrance can also be influenced by intermediate-effect variants (IEVs), which are often overlooked by conventional rare variant analysis or genome-wide association studies. Our exome sequencing analysis of ~150,000 UK Biobank (UKB) participants identified FH-causing variants in 1 in 288 individuals, with 60% showing LDL-C below the diagnostic threshold (4.9 mmol/L). This project will use whole-genome sequencing (WGS) from 500,000 UKB participants to:

Identify FH-causing variants and correlate them with LDL-C, biomarkers, and clinical outcomes, and compare the spectrum of FH mutations across the ancestries, which remain understudied outside European groups.
Characterise carriers with unexpectedly low LDL-C despite FH-causing variants.
Test whether protective rare, intermediate-effect, or low LDL-C polygenic variants explain this resilience, and if genetic ancestry is having an effect. We hypothesise that protective genetic architecture underlies resilience in FH carriers, with implications for CHD prevention across diverse populations and novel drug target discovery.

Techniques
WGS data will be analysed to identify rare variants in FH genes, classified according to standardised guidelines. A validated LDL-C PGS will be calculated, including a trans-ancestry version to evaluate polygenic effects across ancestry groups. FH-variant carriers will be stratified by LDL-C levels. “Cases” will be defined as carriers with LDL-C below the fourth decile of the distribution. Controls will include (1) FH carriers with LDL-C above the diagnostic threshold and (2) non-FH participants, matched by age, sex, and PGS. Gene burden analyses will detect enrichment of lipid-lowering variants in cases. Complementary analyses will focus on individuals with high LDL-C PGS but unexpectedly low LDL-C, compared to matched controls, to detect novel protective mechanisms. Analyses will incorporate ancestry, biomarker, medication, and lifestyle data

Availability of Databases and Specialist Materials
UK Biobank WGS, biomarker, and linked health data are available (Project ID: 180759). Variant annotation pipelines and PGS tools, including trans-ancestry models, are established within the supervisory team’s expertise and collaborations.

Potential Risks and Mitigation Strategies

Risk: Limited numbers of protective-variant carriers. Mitigation: Replication in 100,000 Genomes Project and All of Us WGS datasets.
Risk: Confounding by ancestry, medication, or lifestyle. Mitigation: Stratification by ancestry, adjustment for covariates, and trans-ancestry PGS.

This project leverages unique resources, robust preliminary findings, and supervisory expertise in cardiovascular genetics. Students will gain training in statistical genetics, bioinformatics, and epidemiology within a multidisciplinary environment. The framework provides structure while leaving room to refine hypotheses, explore ancestry-specific findings, and expand analytical approaches toward precision medicine.

Additional information from the supervisory team

The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.

Futema & Walsh Recording

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