TY - JOUR
T1 - Correcting for cell-type effects in DNA methylation studies
T2 - Reference-based method outperforms latent variable approaches in empirical studies
AU - Hattab, Mohammad W.
AU - Shabalin, Andrey A.
AU - Clark, Shaunna L.
AU - Zhao, Min
AU - Kumar, Gaurav
AU - Chan, Robin F.
AU - Xie, Lin Ying
AU - Jansen, Rick
AU - Han, Laura K.M.
AU - Magnusson, Patrik K.E.
AU - van Grootheest, Gerard
AU - Hultman, Christina M.
AU - Penninx, Brenda W.J.H.
AU - Aberg, Karolina A.
AU - van den Oord, Edwin J.C.G.
PY - 2017/1/30
Y1 - 2017/1/30
N2 - 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. Please see related Correspondence article: https://genomebiology.biomedcentral.com/articles/10/1186/s13059-017-1149-7and related Research article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0935-y
AB - 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. Please see related Correspondence article: https://genomebiology.biomedcentral.com/articles/10/1186/s13059-017-1149-7and related Research article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0935-y
UR - http://www.scopus.com/inward/record.url?scp=85011097316&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s13059-017-1148-8
DO - https://doi.org/10.1186/s13059-017-1148-8
M3 - Letter
C2 - 28137292
SN - 1474-7596
VL - 18
JO - Genome Biology
JF - Genome Biology
IS - 1
M1 - 24
ER -