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Moving from GWAS hits to functional variants

Published on January 31, 2024   50 min

A selection of talks on Cardiovascular & Metabolic

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0:00
My name is Steve Humphries. I'm the emeritus professor of Cardiovascular Genetics at University College in London. I'm going to be talking today about moving from Genome Wide Association Study Hits, that is GWAS Hits to identifying the functional variants that underlie those hits.
0:21
First off, we'll talk about GWAS hits for cardiovascular disease, that is CVD, and then focus on GWAS hits in lipid genes. Now, not all problems are solved by using the GWAS technology. I'll illustrate this using a study where we examine the phenotype of carotid intimal medial thickness, that's CIMT and a big study I've been involved in called iMprOVE. I'll demonstrate the use of bioinformatic tools and present the results of the molecular approaches that we've used in this work.
0:54
Now when we started trying to find genes involved in cardiovascular disease, we used what we call a candidate gene approach. We identified a gene that has the potential to be involved in CHD, coronary heart disease, or cardiovascular disease, because we knew what the gene coded for, and we knew that that protein was important in some of the pathophysiology of heart disease; for example, in determining lipid levels in the blood. We looked at the association of a single nucleotide polymorphism, that is a SNP, with an intermediate trait, for example, with cholesterol levels in the blood or with cardiovascular disease in a prospective study. This was usually a frequency comparison in a large group of cases with cardiovascular disease versus a large group of controls who didn't have the disease. This is how we were doing these studies to try to find genes involved in CVD before June 2007, which was when, of course, we had the benefit of the whole genome project and we knew now the sequence of the entire human genome. Here's the timeline and what happened in June 2007 was because of this advancing technology. Instead of doing a single SNP in a case-control design, we were able to use genome-wide association scans. We were able to use the power of the whole genome.
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Moving from GWAS hits to functional variants

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