| Title: | Low-density lipoprotein cholesterol (LDL-C) polygenic risk score and whole exome sequencing to diagnose individuals affected by familial hypercholesterolaemia |
| Lead Institution: | St George's, University of London |
| Principal investigator: | Dr Marta Futema |
Application 40721
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About
Familial Hypercholesterolaemia (FH) is an inherited disease of dangerously high 'bad' cholesterol (LDL-C), which affects 1 in 250 individuals, however only 12% of the UK FH populations are currently diagnosed. Early diagnosis and treatment of FH is important in order to minimise the risk of developing premature heart problems. The high cholesterol levels in FH individuals is caused by rare mutations in one of genes which code for proteins involved in the clearance of LDL-C from blood. However a substantial proportion of affected individuals have high cholesterol because they inherited a higher than average number of common genetic variants with mild cholesterol-rising effect, which is known as the polygenic hypercholesterolaemia. To identify those with polygenic hypercholesterolaemia we have designed a polygenic risk score (PRS) and tested it on a cohort of about 3000 cases and 3000 controls. The PRS analysis showed a clear difference between the cases and controls. However, in order for the PRS to be clinically applicable we need to replicate the findings in a bigger cohort of individuals. Studying the UK Biobank will enable us to refine the PRS diagnostic cut-points that can be used to predict the risk of developing hypercholesterolaemia. This information is required, as stated in the current National Institute for Health and Care Excellence (NICE) guidelines on FH, to design the most cost-efficient way of identifying affected individuals. The UK BioBank next generation sequencing data will enable us, for the first time on such large scale, to look for rare FH mutations and then establish the most efficient cholesterol cut-offs to be used for FH diagnosis. This information will be used to design the most cost-effective screening FH programme and to tackle the disease under-diagnosis. We are planning to complete our analysis and interpret the results within 18 months from receiving all the data.3 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11524 | A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia | Jasmine Gratton (+5) | 2023 | JACC Advances |
| 9308 | Modelling a two-stage adult population screen for autosomal dominant familial hypercholesterolaemia: cross-sectional analysis within the UK Biobank | Jasmine Gratton (+8) | 2023 | BMJ Public Health |
| 11622 | Prevalence of FH-Causing Variants and Impact on LDL-C Concentration in European, South Asian, and African Ancestry Groups of the UK Biobank - Brief Report | Jasmine Gratton (+2) | 2023 | Arteriosclerosis Thrombosis and Vascular Biology |
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