Data Mining, DNA Sequencing and Polygenic Risk Scores in Familial Hypercholesterolaemia

Abstract

Identifying familial hypercholesterolaemia (FH) using data mining, genetic testing and stratifying risk has value for targeting novel lipid lowering agents. We undertook a multistep approach to do this using electronic health records. An algorithm to identify FH was applied to a database of 57,643 cardiac patients, comparing statin use and mean age of death in high and low probability groups. A further 6,267 acute coronary syndrome (ACS) patients were manually audited for missed FH diagnoses. 100 clinic patients with suspected FH were sequenced. 27 and 50 single nucleotide polymorphism (SNP) polygenic risk scores (PRS) were validated in 78 patients, who underwent CTCA and Duke scoring and then used to risk stratify FH patients. n=5 were enrolled in siRNA ANGPTL3 clinical trials. Predicted/estimated FH was 0.9%/0.4% in the automated and 0.7%/2% in the manually adjudicated populations. In high versus low FH probability, statin use was 95% versus 68%, p<0.0001 and mean age of death was 68 versus 79 years, p<0.0001. Of 100 clinic patients 21% had a pathogenic variant: 15 LDLR, 3 ApoB and 1 PCSK9 variant of uncertain significance (VUS). Duke score predicted CAD AUROC 0.66; 95% CI, 0.5 to 0.8; p =0.04 compared to PRS AUROC 0.58; 95% CI, 0.4 to 0.7; p=0.3. FH algorithms need further validation with genotyping to match epidemiological statistics. Despite adequate use of statins, probable FH resulted in an estimated 11-year loss of life. PRS can be used for risk stratification in FH patients.