RNAi–Based Functional Profiling of Loci from Blood Lipid Genome-Wide Association Studies Identifies Genes with Cholesterol-Regulatory Function

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.

A genome‐wide association study of multiple longitudinal traits with related subjects

“…The FAM174A gene has been recently recognized as one of six new candidate genes for its regulatory role in cholesterol homeostasis. Re-localization of the protein, FAM174A, to alternative organelles under reduced cholesterol levels resembled a key feature of other known regulators of cellular cholesterol homeostasis (Blattmann et al, 2013). This SNP is found to be strongly significant in the simultaneous test with the smallest p –value of 2.22 × 10 −16 and is also confirmed to be significantly associated with the LDL trait with a p –value of 4.44 × 10 −15 .…”

“… rs3776779 , rs7067794 , and rs41377151 are rare variants where their minor allele frequencies adjusted for the relationship among subjects are less than 0.01. † Reference(s) for significant association between a given SNP and/or candidate gene(s) and correlated trait(s) either not listed under the column ‘Trait’ or not investigated in this study. ‡ Reference(s) for confirmatory findings of significant association between a given SNP and/or candidate gene(s) and the given trait(s). 1 Andreotti et al (2009), 2 Blattmann et al (2013), 3 Browne et al (2014), 4 Chen et al (2012), 5 Hegele et al (2009), 6 Hodoglugil et al (2010), 7 Kleber et al (2010), 8 Kozian et al (2010), 9 Lieb et al (2015), 10 Ma et al (2010a), 11 Ma et al (2010b), 12 Mohlke et al (2008), 13 Muendlein et al (2009), 14 Piccolo et al (2009), 15 Rafiq et al (2012), 16 Roslin et al (2009), 17 Suchindran et al (2010), 18 Wallace et al (2008), and 19 Wang et al (2014) …”

Usefulness of genome-wide association studies to identify novel genetic variants underlying the plasma lipoprotein metabolism as risk factors for CAD

“…A remarkable large-scale functional study was performed by Blattman et al to functionally analyse 133 candidate genes in close proximity to 56 loci that had previously been reported to be strongly associated with lipids and CAD-related factors, such as atherosclerosis and MI, using the RNA interference (RNAi) approach. 30 The knockdowns revealed that more than 40% of these genes were involved in LDL-C metabolism. Furthermore, these authors demonstrated previously unknown lipid regulatory roles for several genes (e.g., FAM174A, CXCL12, TBL2, PAFAH1B1, and SEZ6L).…”

Cardiovascular RNA Interference Therapy

“…143 RNAibased functional profiling was applied to 56 loci found by genome-wide association studies to be associated with coronary artery disease and MI. RNAi was used to analyze 133 candidate genes in these genomic regions showed that individual genome-wide association studies loci may contain >1 gene with cholesterol-regulatory functions.…”