Integrated approaches to functionally characterize novel factors in lipoprotein metabolism

To discuss if and how the combined analysis of large-scale datasets from multiple independent sources benefits the mapping of novel genetic elements with relevance to lipoprotein metabolism and allows for conclusions on underlying molecular mechanisms.

Genome-wide association studies (GWAS) have identified numerous genomic loci associated with plasma lipid levels and cardiovascular disease. Yet, despite being highly successful in mapping novel loci the GWAS approach falls short to systematically extract functional information from genomic data. With the aim to complement GWAS for a better insight into disease mechanisms and identification of the most promising targets for drug development, a number of high-throughput functional genomics strategies have now been applied. These include computational approaches, consideration of gene-gene and gene-environment interactions, as well as unbiased gene-expression analyses in relevant tissues. For a limited number of loci, mechanistic insight has been gained through in-vitro and in-vivo studies by knockdown and overexpression of candidate genes.

The integration of GWAS data with existing functional genomics strategies has contributed to ascertain the relevance of a number of novel factors for lipoprotein biology and disease. However, technologies are warranted that provide a more systematic insight into the molecular function and pathogenic relevance of promising candidate genes.