RNAi-based functional profiling of loci from blood lipid genome-wide association studies identifies genes with cholesterol-regulatory function

• Verfasst von: Blattmann, Peter Nils [VerfasserIn]
• Verfasst von: Schuberth, Christian [VerfasserIn]
• Verfasst von: Pepperkok, Rainer [VerfasserIn]
• Verfasst von: Runz, Heiko [VerfasserIn]
• Titel: RNAi-based functional profiling of loci from blood lipid genome-wide association studies identifies genes with cholesterol-regulatory function
• Verf.angabe: Peter Blattmann, Christian Schuberth, Rainer Pepperkok, Heiko Runz
• E-Jahr: 2013
• Jahr: February 28, 2020
• Teil: volume:9
• Teil: year:2013
• Teil: number:2
• Fussnoten: Gesehen am 27.11.2020
• Titel Quelle: Enthalten in: Public Library of SciencePLoS Genetics
• Ort Quelle: San Francisco, Calif. : Public Library of Science, 2005
• Jahr Quelle: 2013
• Band/Heft Quelle: (2020,2) Artikel-Nummer e1003338, 13 Seiten
• ISSN Quelle: 1553-7404
• DOI: doi:10.1371/journal.pgen.1003338
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Cholesterol
• Sach-SW: Genetic loci
• Sach-SW: Genome-wide association studies
• Sach-SW: Lipid analysis
• Sach-SW: Lipids
• Sach-SW: RNA interference
• Sach-SW: Small interfering RNA
• Sach-SW: Transfection
• K10plus-PPN: 1741300878

Abstract

Genome-wide association studies (GWAS) are powerful tools to unravel genomic loci associated with common traits and complex human disease. However, GWAS only rarely reveal information on the exact genetic elements and pathogenic events underlying an association. In order to extract functional information from genomic data, strategies for systematic follow-up studies on a phenotypic level are required. Here we address these limitations by applying RNA interference (RNAi) to analyze 133 candidate genes within 56 loci identified by GWAS as associated with blood lipid levels, coronary artery disease, and/or myocardial infarction for a function in regulating cholesterol levels in cells. Knockdown of a surprisingly high number (41%) of trait-associated genes affected low-density lipoprotein (LDL) internalization and/or cellular levels of free cholesterol. Our data further show that individual GWAS loci may contain more than one gene with cholesterol-regulatory functions. Using a set of secondary assays we demonstrate for a number of genes without previously known lipid-regulatory roles (e.g. CXCL12, FAM174A, PAFAH1B1, SEZ6L, TBL2, WDR12) that knockdown correlates with altered LDL-receptor levels and/or that overexpression as GFP-tagged fusion proteins inversely modifies cellular cholesterol levels. By providing strong evidence for disease-relevant functions of lipid trait-associated genes, our study demonstrates that quantitative, cell-based RNAi is a scalable strategy for a systematic, unbiased detection of functional effectors within GWAS loci.