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Approved research

Validation and improvement of our Machine Learning approach to analyze interactions in your genotyping/imputation data for better Familial Hypercholesterolemia diagnostics

Principal Investigator: Dr Marco Schmidt
Approved Research ID: 36226
Approval date: December 23rd 2019

Lay summary has developed a novel genome analysis tool specifically designed for the identification of gene-gene-interactions. This may allow more insights from genetically complex diseases. We applied our method before to find genes causing Familial Hypercholesterolemia (FH). FH is a genetic disorder that leads to high blood cholesterol levels, eventually to heart attacks in young age and, from there, to a reduced life expectancy. FH is caused by not a single gene, but by several genes in a complex interaction. Our rationale with the UK Biobank data is to confirm our previous findings in a broader population. Finally, our aim is to provide a highly accurate genetic diagnostic that predicts FH patients so that existing therapy of cholesterol-lowering drugs can start earlier. Heart attacks in young age should be avoided so that patients' life expectancy can be expanded.