RNAi–Based Functional Profiling of Loci from Blood Lipid
Genome-Wide Association Studies Identifies Genes with
Peter Blattmann1,2, Christian Schuberth2,3, Rainer Pepperkok1,2*, Heiko Runz2,3*
1Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany, 2Molecular Medicine Partnership Unit (MMPU), European Molecular
Biology Laboratory, Heidelberg, Germany, 3Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany
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.
Citation: Blattmann P, Schuberth C, Pepperkok R, Runz H (2013) RNAi–Based Functional Profiling of Loci from Blood Lipid Genome-Wide Association Studies
Identifies Genes with Cholesterol-Regulatory Function. PLoS Genet 9(2): e1003338. doi:10.1371/journal.pgen.1003338
Editor: Mark I. McCarthy, University of Oxford, United Kingdom
Received November 15, 2012; Accepted January 7, 2013; Published February 28, 2013
Copyright: ? 2013 Blattmann et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the EMBL International PhD Program (to PB) and the Transatlantic Networks of Excellence in Cardiovascular Research
Program of the Fondation Leducq (grant 10CVD03 to RP and HR). Support of RP by the Systems Microscopy network of excellence (FP7/2007-2013-258068) and
Nationales Genomforschungsnetz-Plus consortium IG-CSG (01GS0865) is acknowledged. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com (RP); Heiko.Runz@med.uni-heidelberg.de (HR)
To date more than 120 genomic loci have been tightly linked to
variation in blood lipid levels (low-density lipoprotein (LDL), high-
density lipoprotein (HDL), total cholesterol (TC), triglycerides
(TG)), susceptibility to coronary artery disease (CAD) and/or
myocardial infarction (MI) in more than 23 published large-scale
GWAS [1–23]. These loci contain 15 out of 18 genes in which
variants cause monogenic lipid disorders and further genes with
previously defined roles in lipid metabolism , supporting the
assumption that GWAS enrich for genes with a functional
importance on the associated trait [17,24–26]. For the majority
of associated loci, however, genes with a function in regulating
blood lipid levels or with relevance to CAD/MI have yet to be
Several recent examples show that non-coding variants within
associated loci affect the expression of nearby genes, suggesting
that cis-regulatory effects on functionally relevant proteins
constitute a major trait determinant [17,24–28]. On the level of
gene transcripts, such dominant-negative regulatory effects can be
closely mimicked by RNAi. RNAi also permits to evaluate the
functional consequences of gene knockdown in vitro and in vivo 
and has previously enabled us to unravel regulators of cholesterol
metabolism from a subset of sterol-regulated genes . This was
performed using a strategy that relies on the knockdown of
candidate genes in tissue culture cells using siRNA-arrays [30–32]
and the quantification of how this impacts on two major
determinants of blood lipid levels: cellular levels of free cholesterol
(FC) and the efficiency of LDL-uptake into cells .
Here we applied this technology with the aim to identify
candidate genes within trait-associated loci with a conserved lipid-
regulatory function in cells. For this, we functionally analyzed 56
of the 64 genomic loci that were reported until 2009 as associated
with lipid traits and/or CAD/MI for genes with a role in cellular
cholesterol homeostasis (Figure 1A; Table S1; see Materials and
Methods for details). For 38 of the 56 loci all protein-coding genes
within 650 kb of the respective lead SNPs were analyzed, with up
to 16 genes at the 19p12 locus. The 18 remaining loci were
represented by candidate genes close to the lead SNPs (Table S2).
We followed a two-step screening-approach: First, a core gene set
of 109 genes was analyzed (‘‘GWAS1’’). Promising loci from this
gene set were then complemented by additional genes and
experimentally re-evaluated (‘‘GWAS2’’) (see Materials and
PLOS Genetics | www.plosgenetics.org1 February 2013 | Volume 9 | Issue 2 | e1003338
Methods). In total, we profiled 133 candidate genes out of which
93 genes had not previously been functionally linked to lipid
metabolism (Table S3). Each gene was profiled with 3–5
independent siRNAs, resulting in a total of 534 gene-specific
siRNAs tested (Table S4). Uptake of fluorescently-labeled LDL
and free perinuclear cholesterol (FC) within siRNA-transfected
cells was determined using high-content automated microscopy as
described  (see Figure S1 and Materials and Methods for how
specificity of filipin to reliably detect free cholesterol was assured).
For siRNAs analyzed in both, GWAS1 and GWAS2 screens
(n=86), findings correlated well (e.g., Pearson’s correlations for
the parameter ‘‘total cellular intensity’’ were 0.81 for DiI-LDL
uptake and 0.71 for FC) (Table S5), proposing that the results
obtained are reproducible and specific. SiRNA-mediated knock-
down of multiple known and novel regulators resulted in a
consistent increase or reduction of LDL-uptake, FC, or an altered
distribution of relevant sub-cellular organelles as signs of perturbed
cellular lipid homeostasis (Figure 1B, 1C).
For an objective and quantitative evaluation of our results we
developed an automated pipeline for multi-parametric image
analysis (Figure S2 and Materials and Methods). From each cell,
three (for LDL-uptake) or four (for FC) parameters were measured
per siRNA-transfected cell and scored according to effect size
(Figure 2A, Figure S3, Table S4 and Materials and Methods).
Knockdown of 55 (41%) of the 133 candidate genes tested
significantly affected the parameter ‘‘total cellular intensity’’ with
two independent siRNAs in at least one of the two screening
assays, suggesting these genes as functional effectors on cellular
LDL-uptake, FC or both (Table 1, Table S4). This suggested an
unexpected high number of effectors, as the typical hit rate of most
reported siRNA-screens with unbiased gene sets ranges from 1–
6% [32–36]. Even in our recent siRNA-screen on a gene set
enriched for sterol-regulated genes and known lipid regulators
using the same assays as applied here only 25% of the genes scored
as effectors . However, of the 63 siRNAs that scored as
effectors in GWAS1 and were re-analyzed in validation screens, 30
siRNAs met our stringent statistical criteria also in GWAS2,
resulting in a validation rate of 48% (Table S4). Our findings thus
strongly support the hypothesis that GWAS enrich for functional
regulators of the underlying trait or pathogenic process. They
further support previous assumptions that a large proportion of the
genes uncovered by GWAS also have a conserved role in tissue
culture cells [17,24,26].
55 genes that scored as the most pronounced functional
effectors on total LDL-signal and/or cellular FC-levels were
selected for further analyses (Tables S3, S6, S7, S8). According to
phenotypic fingerprints of the two strongest effector siRNAs/gene
(Figure S3 and Materials and Methods), 37 of these genes were
tentatively clustered into five distinct functional groups (Figure 2).
For 15 genes, direction of functional effects in both screening
assays positively correlated (Figure 2B, 2C). Knockdown of 17
genes consistently impacted on FC without obvious effects on
LDL-uptake (Figure 2E, 2F), while for 5 genes effects on FC were
inversely directed to those observed for LDL-uptake (Figure 2D).
For several known effectors, our results were consistent with a priori
knowledge on the respective genes. For instance, it was recently
elegantly demonstrated that altered expression of SORT1 at the
1p13.3 locus inversely correlates with serum LDL . Consis-
tently, one siRNA targeting SORT1 induced a strong reduction in
FC and also tended to inhibit LDL-uptake, thereby corroborating
further that SORT1 is a key-player in cellular cholesterol
homeostasis . Several examples demonstrate that this is most
certainly true also for other genes among our effectors that had not
previously been linked to lipid metabolism. For instance, two
GWAS report association of the WDR12 locus (2q33) with CAD/
MI [10,16], while a demonstration that this locus is associated with
lipid traits is so far missing. However, siRNAs targeting this gene
consistently reduced FC, making a lipid-regulatory role for
WDR12 highly likely.
Correlation analysis of the multi-parametric datasets enabled us
to hypothesize by which mechanisms some of the previously
uncharacterized effectors could possibly impact on cellular lipid
homeostasis (Figure 2, Table S5 and Materials and Methods). For
instance, a higher number of LDL-positive endosomes and a
scattering of FC-retaining organelles upon knockdown of PA-
FAH1B1 (Figure 1C) is consistent with a role for this gene in the
organization of endosomal membranes  and may be a sign of
impaired LDL-internalization and/or transport within the endo-/
lysosomal system. Effectors such as TBL2 on the other hand are
likely to exert more direct lipid-regulatory functions as knockdown
of this gene increased LDL-concentration within endosomes and
FC-load, but subcellular structures remained largely unaffected
The identification of genes with relevance for lipid traits and/or
CAD/MI from GWAS is complicated by the fact that many lead
SNPs locate to gene rich regions [7,24]. We therefore assessed
whether for selected GWAS loci our unbiased approach could
help prioritizing functional effectors among several possible
candidate genes in such loci. Indeed, in six of the 30 loci for
which more than one candidate gene/locus was functionally
analyzed, our results suggested one prominent effector gene. Most
surprisingly, in 9 of these 30 loci knockdown of more than one
gene per locus affected cellular cholesterol homeostasis (Figure 3).
For instance, of the 8 genes analyzed at the 7q11.23 locus
(Figure 3D) not only MLXIPL as the most likely candidate to
explain association with TG , but also five other genes scored as
significantly increasing FC, among them TBL2, knockdown of
which also induced the strongest observed stimulation of LDL-
Complex traits and diseases are assumed to result from
interactions between multiple genes in relevant biological
processes. Recent genome-wide association studies have
uncovered many novel genomic loci where genes with
functional significance are expected. However, functional
validation of such genes has thus far remained confined to
single gene approaches. Here, we use RNA interference
and high-content screening microscopy to profile 133
genes at 56 loci associated with blood lipid traits,
cardiovascular disease, and/or myocardial infarction for a
function in regulating cellular free cholesterol levels and
the efficiency of low-density lipoprotein uptake. Our
results suggest that a high number of trait-associated
genes have conserved cholesterol-regulatory functions in
cells, with several GWAS loci harboring more than one
gene of likely functional significance. For a number of
genes without previously known lipid-regulatory func-
tions, consequences upon siRNA knockdown positively
correlated with cellular levels of LDL receptor, a major
determinant of blood LDL levels. Moreover, GFP–tagged
fusion proteins of several candidates shifted cellular
cholesterol levels to inverse directions than knockdown,
and subcellular localization of some candidates was sterol-
dependent. Our study generates a valuable resource for
prioritization of lipid-trait/CAD/MI-associated genes for
future in-depth mechanistic analyses and introduces cell-
based RNAi as a scalable and unbiased tool for functional
follow-up of GWAS loci.
RNAi of Lipid GWAS Uncovers Cholesterol Regulators
PLOS Genetics | www.plosgenetics.org2 February 2013 | Volume 9 | Issue 2 | e1003338
in two-sided Student’s t-test (*,0.01; **,0.001; ***,0.0001) are
CAD/MI associated genes. LDLR protein levels in Hela-Kyoto
cells were assessed by Western Blot upon knockdown of 105 siRNAs
targeting 35 selected candidate genes from GWAS-loci. LDLR
levels quantified from blots were compared either directly or
normalized to housekeeping gene a-tubulin. Shown are results from
3 biological replicates (see Table S6 for numeric data). Arrows
denote siRNAs that significantly (*,0.01; **,0.001; ***,0.0001)
reduced (blue) or increased (red) LDLR protein levels above or
below thresholds ($2 standard deviations of negative controls).
Bands from lysates where no reliable results could be obtained were
crossed out and excluded from quantitative analysis.
LDLR protein levels upon RNAi of 35 lipid-trait/
encoding lipid-trait/CAD/MI associated genes. Hela-Kyoto cells
were transfected for 24 h with cDNAs expressing indicated proteins
control or sterol-depleted conditions (by culture in DMEM/2 mM
L-glutamine/0.5%BSAfor16 h followed byexposurefor 3 h to 1%
(w/v) hydroxyl-propyl-beta-cyclodextrin (HPCD) (2 sterols) .
Shown are maximal projections of confocal stacks of representative
cells. Proteins for which subcellular localization differed between
control and sterol-depleted conditions are highlighted in grey and
with arrows. Bar=10 mm.
Subcellular localization of 29 GFP–tagged proteins
association to blood lipid levels and/or CAD/MI.
Overview of genes analyzed and the GWAS that show
Loci and lead-SNPs used to select genes for RNAi screens. GWAS
in italics appeared after start of the study. For 38 of the 56 loci all
protein-coding genes within 650 kb of the respective lead SNPs
were analyzed (locus in bold). The 18 remaining loci were
represented by candidate genes close to the lead SNPs.
Selection of genes based on SNPs from lipid GWAS.
knowledge on molecular function. Comprehensive GWAS gene
set analyzed in this study, listed according to HGNC Symbol and
Ensembl Gene ID. It has been looked up whether the genes have
been previously linked to lipid metabolism using GO annotation
(search terms: cellular lipid metabolism, cellular response to
cholesterol, cholesterol*, lipid*, lipoprotein*, triglyceride*, high-
density*, low-density*, very-low-density*), whether they have been
linked to a monogenic lipid disorder , or whether they scored as
hits in the indicated previous genome-wide RNAi screens
[32,45,49]. Gene functions were adapted from www.genecards.org.
Genes analyzed in RNAi screens and a priori
targeted by 3–5 different siRNAs which are shown with siRNA ID
(Applied Biosystems), sequence and number of targeted out of total
protein coding transcripts. SiRNAs targeting untranslated gene
regions (UTR) are indicated. For mapping of siRNA sequences to
Results from GWAS RNAi screen. Each gene was
the reference genome, see Materials and Methods. Italics, siRNAs
that did not show perfect mapping with reference sequence
(asterisks give explanations). Quantiative data for indicated
SiRNAs with mean total cellular signal intesities (‘‘total’’) below or
above thresholds (LDL GWAS1 [20.50, 0.81], LDL GWAS2
[20.55, 1.02], FC GWAS1 [21.11, 1.70], FC GWAS2 [21.06,
1.05]) are highligthed in blue (decrease) or red (increase).
GWAS1 versus GWAS2 RNAi screens.
Pearson’s correlations between analyzed parameters in
genes. Effects .2 standard deviations of untreated and mock
(mRNA) or untreated and control siRNA (protein) treated cells are
depicted in bold. * p,0.01, ** p,0.001, *** p,0.0001.
Regulation of LDLR upon knockdown of 35 different
Results for all siRNAs followed up in secondary assays. For
GWAS1/2 siRNA-screening deviation of total cellular intensities is
given (for other parameters see Table S4). LDLR mRNA and
protein levels are gven as fold-change relative to control siRNA (for
full results see Figures S4, S5 and Table S6). For localization and
functional analysis of GFP-cDNAs, enzymatic determination of
cellular cholesterol, FC quantification from HuH7 cells and
definition of statistical thresholds, see Materials and Methods.
Results of secondary assays for the respective siRNAs.
levels. For 29 genes where knockdown with $1 siRNA increased or
reduced free cholesterol (FC) levels, consequences upon overex-
pression of a representative cDNA (carboxy-terminally linked to
EGFP) on FC were analyzed (see Materials and Methods). Filipin
staining was performed 24 h upon cDNA-transfection and fraction
of GFP-positive cells 6 SEM was determined. Ratios of FC in GFP-
positive relative to non-expressing cells (within the identical dish)
and ratios of FC in GFP-positive relative to mock-transfected cells
are given as means 6 SEM (n=3–4 experiments). Effects were
considered as significant (increase, red; decrease, blue) when a two-
tailed Student’s t-test resulted in p-values ,0.01 in 2 (*) or 3–4 (***)
Results of candidategene overexpression on cellular FC
We are grateful to Miriam Reiss and Brigitte Joggerst for excellent
technical assistance. Christian Tischer and the Advanced Light Microscopy
Facility are acknowledged for support in image acquisition and analysis,
Jean-Karim He ´riche ´ for help with siRNA mapping, Beate Neumann and
Laurence Ettwiller for helpful discussions on the manuscript, and Olympus
Biosystems for continuous support of the ALMF at EMBL.
Conceived and designed the experiments: PB RP HR. Performed the
experiments: PB CS. Analyzed the data: PB CS. Wrote the paper: PB HR
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