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Hattab et al. Genome Biology  (2017) 18:24 
DOI 10.1186/s13059-017-1148-8CORRESPONDENCE Open AccessCorrecting for cell-type effects in DNA
methylation studies: reference-based
method outperforms latent variable
approaches in empirical studies
Mohammad W. Hattab1, Andrey A. Shabalin1, Shaunna L. Clark1, Min Zhao1, Gaurav Kumar1, Robin F. Chan1,
Lin Ying Xie1, Rick Jansen2, Laura K. M. Han2, Patrik K. E. Magnusson3, Gerard van Grootheest2,
Christina M. Hultman3, Brenda W. J. H. Penninx2, Karolina A. Aberg1 and Edwin J. C. G. van den Oord1*
Please see related Correspondence article: https://genomebiology.biomedcentral.com/articles/10/1186/s13059-017-1149-7 and related Research
article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0935-yAbstract
Based on an extensive simulation study, McGregor
and colleagues recently recommended the use of
surrogate variable analysis (SVA) to control for the
confounding effects of cell-type heterogeneity in DNA
methylation association studies in scenarios where no
cell-type proportions are available. As their
recommendation was mainly based on simulated
data, we sought to replicate findings in two large-
scale empirical studies. In our empirical data, SVA did
not fully correct for cell-type effects, its performance
was somewhat unstable, and it carried a risk of
missing true signals caused by removing variation that
might be linked to actual disease processes. By
contrast, a reference-based correction method
performed well and did not show these limitations. A
disadvantage of this approach is that if reference
methylomes are not (publicly) available, they will need
to be generated once for a small set of samples.
However, given the notable risk we observed for cell-
type confounding, we argue that, to avoid
introducing false-positive findings into the literature, it
could be well worth making this investment.* Correspondence: ejvandenoord@vcu.edu
1Center for Biomarker Research and Precision Medicine, Virginia
Commonwealth University, Richmond, VA, USA
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This artic
International License (http://creativecommons
reproduction in any medium, provided you g
the Creative Commons license, and indicate if
(http://creativecommons.org/publicdomain/zeCorrespondence
Tissues often consist of multiple cell types that show differ-
ent methylation patterns. In association studies, these dif-
ferences can cause spurious findings when the relative
abundance of the cell types is related to the outcome of
interest. The inclusion of cell-type proportions as covariates
will prevent such false positives. To avoid performing cell
counts on all subjects in the study, these proportions can
be estimated by using a small set of reference methylomes
obtained using DNA from sorted cells [1]. However, refer-
ence methylomes might not always be (publicly) available
and or be difficult to generate. In these scenarios, latent
variables obtained by a decomposition of the methylation
data can be used as a proxy for cell-type proportions.
McGregor et al. [2] performed an extensive simulation
study comparing one reference-based and seven latent vari-
able methods. Although not always the best method, the
reference-based method performed well. For scenarios
where no reference is available, the authors recommended
the use of surrogate variable analysis (SVA) [3], which per-
formed adequately in all simulation scenarios.
As the recommendation by McGregor and colleagues [2]
was based mainly on simulated data, we studied SVA in
two large-scale empirical studies. The first involved 1149
Dutch subjects (825 cases with depression and 324 con-
trols) aged 18–65 years [4] and the second 1448 Swedish
subjects (774 schizophrenia cases and 674 controls) aged
25–92 years [5, 6]. Using whole-blood samples from six US
subjects, cell populations were isolated by positive selection
using EasySep™ kits (Stemcell Technologies), which applyle is distributed under the terms of the Creative Commons Attribution 4.0
.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
ive appropriate credit to the original author(s) and the source, provide a link to
changes were made. The Creative Commons Public Domain Dedication waiver
ro/1.0/) applies to the data made available in this article, unless otherwise stated.
Hattab et al. Genome Biology  (2017) 18:24 Page 2 of 3magnetic nanoparticles coated with antibodies against a
particular surface antigen (CD molecules). Specifically, we
used CD3, CD19, CD20, CD14, and CD15 to isolate all
common cell types in blood. All methylation data were
generated using methyl-CG binding domain sequencing
(MBD-seq) [7, 8], but the schizophrenia study was con-
ducted on an older sequencing platform with a slightly dif-
ferent laboratory protocol. We used a permutation test to
examine whether our top methylome-wide association
study (MWAS) results were enriched for sites showing sig-
nificant methylation differences among cell types. The
MBD-seq procedure assays almost all 28 million common
CpGs in the human genome. As the SVA package could
not process all sites simultaneously, it was performed on
12 randomly selected subsets of 100,000 CpG sites.
Table 1 indicates that, if no cell-type correction is applied,
MWAS findings show a greater than sixfold enrichment of
CpG sites exhibiting cell-type differences in methylation.
This was consistent with the significant case-control differ-
ences in estimated cell-type proportions (across cell types/
studies, the median P value was 8.0 × 10–5) and stresses the
need to control for this confounder. The enrichment disap-
pears when using the reference-based method. By contrast,
significant enrichment remained after SVA correction in all
studied scenarios. The performance of SVA was associatedTable 1 Comparison of reference-based and latent variable cell-type
Depression MWAS study
Enrich.
ratio
Enrich.
P value
Number of
SVs
No cell-type correction 6.04 <0.001 –
Reference-based correction 1.08 0.084 –
SVA subset 1 1.11 0.001 84
SVA subset 2 1.26 0.001 83
SVA subset 3 1.85 0.004 19
SVA subset 4 1.28 <0.001 83
SVA subset 5 3.05 0.001 14
SVA subset 6 1.30 0.001 81
SVA subset 7 1.07 <0.001 78
SVA subset 8 1.28 0.004 79
SVA subset 9 1.13 0.003 84
SVA subset 10 1.07 0.003 80
SVA subset 11 1.06 0.006 21
SVA subset 12 1.17 0.004 84
We used a permutation test to examine whether our top methylome-wide associati
tion differences among cell types. Our test preserved the correlation structure of th
by a single random number in each permutation. We examined multiple cut-offs (1
selected the most significant combination. We accounted for this “multiple testing”
cell-type correction” model includes laboratory technical covariates, age, and sex. T
correction” model adds estimates of cell-type proportions, and the SVA models add
methylation differences between cell types among the top MWAS finding relative t
“Enrich. P value” is the probability under this null hypothesis, as determined throug
SVA; “Increase r2” is additional variance explained by SVs in case-control status com
sex, and cell-type estimates. SV surrogate variable, SVA surrogate variable analysiswith the number of surrogate variables (SVs), which varied
considerably across the 12 randomly selected CpG subsets
within each study. However, even when as many as 84 SVs
were included, SVA failed to control for more-subtle cell-
type effects. To enable a simultaneous analysis of all sites,
analyses were repeated using principal component analysis
(PCA) [9], which also corrects for cell types by using latent
variables. However, this did not improve results.
The use of a reference-based method ensures that only
variation linked to differences in cell-type proportions is
eliminated. SVA can eliminate any general source of
variation in the methylation data. This carries the risk of
missing true signals when some SVs capture part of the
disease processes (e.g., a pathway). Table 1 reports add-
itional variance explained by SVs in case-control status
compared with a multiple-regression model that in-
cluded technical covariates, age/sex, and cell-type pro-
portions. Depending on the number of SVs, the
additional variance ranged from 1 to 9%. This illustrates
the risk of SVA potentially eliminating true signals in a
MWAS. To mitigate this risk, one could avoid regressing
out SVs associated with the case-control status. How-
ever, as cell-type proportions are related to both case-
control status and SVs, such a modified analysis might
be even less effective in controlling for cell-type effects.corrections in two empirical DNA methylation studies
Schizophrenia MWAS study
Increase r2 Enrich.
ratio
Enrich.
P value
Number of
SVs
Increase r2
– 6.13 <0.001 – –
0.0% 1.02 0.029 – 0.0%
8.1% 6.54 0.001 5 0.8%
8.7% 7.24 <0.001 10 3.3%
2.0% 6.45 0.001 5 0.9%
9.3% 7.01 0.001 12 2.8%
1.9% 6.79 0.002 6 1.2%
7.8% 6.59 <0.001 6 0.9%
9.2% 6.42 <0.001 4 0.7%
9.2% 6.48 <0.001 4 0.7%
9.2% 7.46 0.001 10 2.7%
8.4% 6.71 0.003 8 1.7%
2.7% 6.48 <0.001 8 1.3%
7.4% 6.49 0.001 5 0.8%
on study (MWAS) results were enriched for sites showing significant methyla-
e data by shifting the CpG coordinates of the case-control and cell-type MWAS
, 5, and 10%) to define “top results” in the case-control and cell-type data and
by also selecting the most significant finding in each permutation. The “No
he other models include these same covariates where the “Reference-based
latent variables. “Enrich. ratio” is the ratio of the number of CpGs showing
o the number expected under the null hypotheses assuming no enrichment;
h permutations; “Number of SVs” is the number of latent variables selected by
pared with a multiple regression model that included technical covariates, age/
Hattab et al. Genome Biology  (2017) 18:24 Page 3 of 3With empirical data, SVA did not adequately correct
for cell-type effects, had somewhat unstable perform-
ance, and carried a risk of missing true disease signals.
The PCA suggested that these limitations might not be
specific to SVA but are inherent to the use of latent
variables—that is, whereas these corrections assume that
cell-type heterogeneity impacts many sites, cell-type
effects seem more subtle and cannot be fully captured
by just the main latent variables. For this reason, we ex-
pect our findings to generalize to methylation platforms
other than MBD-seq. By contrast, the reference-based
method was superior in all respects. If reference methy-
lomes are not (publicly) available for a given tissue and
methylation assay, they will need to be generated once
for a small set of samples. However, given the notable
risk we observed for cell-type confounding, to avoid
introducing false-positive findings into the literature it
could be well worth making this investment.
Abbreviations
MBD-seq: Methyl-CG binding domain sequencing; MWAS: Methylome-wide
association study; PCA: Principal component analysis; SV: Surrogate variable;
SVA: Surrogate variable analysis
Acknowledgments
This research was supported by the National Institute of Mental Health
(grants R03MH102723 to KAA and R01MH104576, R01MH099110, and
RC2MH089996 to EJCGvdO). MWH received salary support from the National
Institute on Drug Abuse (2R25DA026119).
Availability of data and materials
Data available from the Dryad Digital Repository: http://datadryad.org/
resource/doi:10.5061/dryad.bv376. The Swedish MWAS data are available
from dbGAP (study accession phs000608.v1.p1).
Authors’ contributions
MWH, AAS, KAA, and EJCGvdO designed the experiments. KAA oversaw the
laboratory work where MZ performed cell sorting and LY and RFC
contributed to sequencing. MWH, AAS, SLC, GK, and EJCGvdO analyzed the
data. GvG, PKEM, RJ, and LKMH curated the phenotype information. BWJHP
and CMH provided clinical input on the samples. EJCGvdO and MWH
prepared the manuscript. All authors discussed the results and contributed
to editing the paper. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Author details
1Center for Biomarker Research and Precision Medicine, Virginia
Commonwealth University, Richmond, VA, USA. 2Department of Psychiatry,
VU University Medical Center/GGZ inGeest, Amsterdam, The Netherlands.
3Department of Medical Epidemiology and Biostatistics, Karolinska Institutet,
SE-171 77, Stockholm, Sweden.
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