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A polygenic biomarker to identify patients with severe hypercholesterolemia of polygenic origin

BACKGROUND: Severe hypercholesterolemia (HC, LDL‐C > 4.9 mmol/L) affects over 30 million people worldwide. In this study, we validated a new polygenic risk score (PRS) for LDL‐C. METHODS: Summary statistics from the Global Lipid Genome Consortium and genotype data from two large populations were...

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Detalles Bibliográficos
Autores principales: Leal, Luis G., Hoggart, Clive, Jarvelin, Marjo‐Riitta, Herzig, Karl‐Heinz, Sternberg, Michael J. E., David, Alessia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284038/
https://www.ncbi.nlm.nih.gov/pubmed/32307928
http://dx.doi.org/10.1002/mgg3.1248
Descripción
Sumario:BACKGROUND: Severe hypercholesterolemia (HC, LDL‐C > 4.9 mmol/L) affects over 30 million people worldwide. In this study, we validated a new polygenic risk score (PRS) for LDL‐C. METHODS: Summary statistics from the Global Lipid Genome Consortium and genotype data from two large populations were used. RESULTS: A 36‐SNP PRS was generated using data for 2,197 white Americans. In a replication cohort of 4,787 Finns, the PRS was strongly associated with the LDL‐C trait and explained 8% of its variability (p = 10(–41)). After risk categorization, the risk of having HC was higher in the high‐ versus low‐risk group (RR = 4.17, p < 1 × 10(−7)). Compared to a 12‐SNP LDL‐C raising score (currently used in the United Kingdom), the PRS explained more LDL‐C variability (8% vs. 6%). Among Finns with severe HC, 53% (66/124) versus 44% (55/124) were classified as high risk by the PRS and LDL‐C raising score, respectively. Moreover, 54% of individuals with severe HC defined as low risk by the LDL‐C raising score were reclassified to intermediate or high risk by the new PRS. CONCLUSION: The new PRS has a better predictive role in identifying HC of polygenic origin compared to the currently available method and can better stratify patients into diagnostic and therapeutic algorithms.