Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Maarten van Iterson; Erik W van Zwet; Bastiaan T Heijmans; Peter a.C ’t Hoen; Joyce B J van Meurs; Rick Jansen; Lude Franke; Dorret I. Boomsma; René Pool; Jenny van Dongen; Jouke J. Hottenga; Marleen J. Van Greevenbroek; Coen D A Stehouwer; Carla J H van der Kallen; Casper G Schalkwijk; Cisca Wijmenga; Sasha Zhernakova; Ettje F Tigchelaar; P. Eline Slagboom; Marian Beekman; Joris Deelen; Diana van Heemst; Jan H Veldink; Leonard H van den Berg; Cornelia M van Duijn; Bert A. Hofman; Aaron Isaacs; André G Uitterlinden; P Mila Jhamai; Michael Verbiest; H. Eka D. Suchiman; Marijn Verkerk; Ruud van der Breggen; Jeroen van Rooij; Nico Lakenberg; Hailiang Mei; Michiel van Galen; Jan Bot; Dasha V. Zhernakova; Peter van 't Hof; Patrick Deelen; Irene Nooren; Matthijs Moed; Martijn Vermaat; René Luijk; Marc Jan Bonder; Freerk van Dijk; Wibowo Arindrarto; Szymon M Kielbasa; Morris a. Swertz; Peter Bram 't Hoen

doi:10.1186/s13059-016-1131-9

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Maarten van Iterson^*
, Erik W van Zwet
, Bastiaan T Heijmans
, Peter a.C ’t Hoen
, Joyce B J van Meurs
, Rick Jansen
, Lude Franke
, Dorret I. Boomsma
, René Pool
, Jenny van Dongen
, Jouke J. Hottenga
, Marleen J. Van Greevenbroek
, Coen D A Stehouwer
, Carla J H van der Kallen
, Casper G Schalkwijk
, Cisca Wijmenga
, Sasha Zhernakova
, Ettje F Tigchelaar
, P. Eline Slagboom
, Marian Beekman

Joris Deelen, Diana van Heemst, Jan H Veldink, Leonard H van den Berg, Cornelia M van Duijn, Bert A. Hofman, Aaron Isaacs, André G Uitterlinden, P Mila Jhamai, Michael Verbiest, H. Eka D. Suchiman, Marijn Verkerk, Ruud van der Breggen, Jeroen van Rooij, Nico Lakenberg, Hailiang Mei, Michiel van Galen, Jan Bot, Dasha V. Zhernakova, Peter van 't Hof, Patrick Deelen, Irene Nooren, Matthijs Moed, Martijn Vermaat, René Luijk, Marc Jan Bonder, Freerk van Dijk, Wibowo Arindrarto, Szymon M Kielbasa, Morris a. Swertz, Peter Bram 't Hoen

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.

Original language	English
Article number	19
Journal	Genome Biology
Volume	18
Issue number	1
DOIs	https://doi.org/10.1186/s13059-016-1131-9
Publication status	Published - 27 Jan 2017

Funding

This work was done within the framework of the Biobank-Based Integrative Omics Studies (BIOS) Consortium funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007).

Funders	Funder number
Dutch Government
BBMRI-NL
NWO	184.021.007

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bias
Empirical null distribution
Epigenome- and transcriptome-wide association studies
Gibbs sampler
Inflation
Meta-analysis

Cohort Studies

Netherlands Twin Register (NTR)

Access to Document

10.1186/s13059-016-1131-9

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van Iterson, M., van Zwet, E. W., Heijmans, B. T., ’t Hoen, P. A. C., van Meurs, J. B. J., Jansen, R., Franke, L., Boomsma, D. I., Pool, R., van Dongen, J., Hottenga, J. J., Van Greevenbroek, M. J., Stehouwer, C. D. A., van der Kallen, C. J. H., Schalkwijk, C. G., Wijmenga, C., Zhernakova, S., Tigchelaar, E. F., Eline Slagboom, P., ... 't Hoen, P. B. (2017). Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution. Genome Biology, 18(1), Article 19. https://doi.org/10.1186/s13059-016-1131-9

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abstract = "We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.",

keywords = "Bias, Empirical null distribution, Epigenome- and transcriptome-wide association studies, Gibbs sampler, Inflation, Meta-analysis",

author = "\{van Iterson\}, Maarten and \{van Zwet\}, \{Erik W\} and Heijmans, \{Bastiaan T\} and \{{\textquoteright}t Hoen\}, \{Peter a.C\} and \{van Meurs\}, \{Joyce B J\} and Rick Jansen and Lude Franke and Boomsma, \{Dorret I.\} and Ren{\'e} Pool and \{van Dongen\}, Jenny and Hottenga, \{Jouke J.\} and \{Van Greevenbroek\}, \{Marleen J.\} and Stehouwer, \{Coen D A\} and \{van der Kallen\}, \{Carla J H\} and Schalkwijk, \{Casper G\} and Cisca Wijmenga and Sasha Zhernakova and Tigchelaar, \{Ettje F\} and \{Eline Slagboom\}, P. and Marian Beekman and Joris Deelen and \{van Heemst\}, Diana and Veldink, \{Jan H\} and \{van den Berg\}, \{Leonard H\} and \{van Duijn\}, \{Cornelia M\} and Hofman, \{Bert A.\} and Aaron Isaacs and Uitterlinden, \{Andr{\'e} G\} and Jhamai, \{P Mila\} and Michael Verbiest and Suchiman, \{H. Eka D.\} and Marijn Verkerk and \{van der Breggen\}, Ruud and \{van Rooij\}, Jeroen and Nico Lakenberg and Hailiang Mei and \{van Galen\}, Michiel and Jan Bot and Zhernakova, \{Dasha V.\} and \{van 't Hof\}, Peter and Patrick Deelen and Irene Nooren and Matthijs Moed and Martijn Vermaat and Ren{\'e} Luijk and Bonder, \{Marc Jan\} and \{van Dijk\}, Freerk and Wibowo Arindrarto and Kielbasa, \{Szymon M\} and Swertz, \{Morris a.\} and \{'t Hoen\}, \{Peter Bram\}",

year = "2017",

month = jan,

day = "27",

doi = "10.1186/s13059-016-1131-9",

language = "English",

volume = "18",

journal = "Genome Biology",

issn = "1474-7596",

publisher = "Springer Verlag",

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van Iterson, M, van Zwet, EW, Heijmans, BT, ’t Hoen, PAC, van Meurs, JBJ, Jansen, R, Franke, L, Boomsma, DI , Pool, R , van Dongen, J, Hottenga, JJ, Van Greevenbroek, MJ, Stehouwer, CDA, van der Kallen, CJH, Schalkwijk, CG, Wijmenga, C, Zhernakova, S, Tigchelaar, EF, Eline Slagboom, P, Beekman, M, Deelen, J, van Heemst, D, Veldink, JH, van den Berg, LH, van Duijn, CM, Hofman, BA, Isaacs, A, Uitterlinden, AG, Jhamai, PM, Verbiest, M, Suchiman, HED, Verkerk, M, van der Breggen, R, van Rooij, J, Lakenberg, N, Mei, H, van Galen, M, Bot, J, Zhernakova, DV, van 't Hof, P, Deelen, P, Nooren, I, Moed, M, Vermaat, M, Luijk, R, Bonder, MJ, van Dijk, F, Arindrarto, W, Kielbasa, SM, Swertz, MA & 't Hoen, PB 2017, 'Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution', Genome Biology, vol. 18, no. 1, 19. https://doi.org/10.1186/s13059-016-1131-9

TY - JOUR

T1 - Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

AU - van Iterson, Maarten

AU - van Zwet, Erik W

AU - Heijmans, Bastiaan T

AU - ’t Hoen, Peter a.C

AU - van Meurs, Joyce B J

AU - Jansen, Rick

AU - Franke, Lude

AU - Boomsma, Dorret I.

AU - Pool, René

AU - van Dongen, Jenny

AU - Hottenga, Jouke J.

AU - Van Greevenbroek, Marleen J.

AU - Stehouwer, Coen D A

AU - van der Kallen, Carla J H

AU - Schalkwijk, Casper G

AU - Wijmenga, Cisca

AU - Zhernakova, Sasha

AU - Tigchelaar, Ettje F

AU - Eline Slagboom, P.

AU - Beekman, Marian

AU - Deelen, Joris

AU - van Heemst, Diana

AU - Veldink, Jan H

AU - van den Berg, Leonard H

AU - van Duijn, Cornelia M

AU - Hofman, Bert A.

AU - Isaacs, Aaron

AU - Uitterlinden, André G

AU - Jhamai, P Mila

AU - Verbiest, Michael

AU - Suchiman, H. Eka D.

AU - Verkerk, Marijn

AU - van der Breggen, Ruud

AU - van Rooij, Jeroen

AU - Lakenberg, Nico

AU - Mei, Hailiang

AU - van Galen, Michiel

AU - Bot, Jan

AU - Zhernakova, Dasha V.

AU - van 't Hof, Peter

AU - Deelen, Patrick

AU - Nooren, Irene

AU - Moed, Matthijs

AU - Vermaat, Martijn

AU - Luijk, René

AU - Bonder, Marc Jan

AU - van Dijk, Freerk

AU - Arindrarto, Wibowo

AU - Kielbasa, Szymon M

AU - Swertz, Morris a.

AU - 't Hoen, Peter Bram

PY - 2017/1/27

Y1 - 2017/1/27

N2 - We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.

AB - We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.

KW - Bias

KW - Empirical null distribution

KW - Epigenome- and transcriptome-wide association studies

KW - Gibbs sampler

KW - Inflation

KW - Meta-analysis

UR - https://www.scopus.com/pages/publications/85011072560

UR - https://www.scopus.com/pages/publications/85011072560#tab=citedBy

U2 - 10.1186/s13059-016-1131-9

DO - 10.1186/s13059-016-1131-9

M3 - Article

AN - SCOPUS:85011072560

SN - 1474-7596

VL - 18

JO - Genome Biology

JF - Genome Biology

IS - 1

M1 - 19

ER -

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Abstract

Funding

UN SDGs

Keywords

Cohort Studies

Access to Document

Persistent URL (handle)

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Additional file 3 of Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Additional file 5 of Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Additional file 7 of Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Cite this

Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Abstract

Funding

UN SDGs

Keywords

Cohort Studies

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Datasets

Additional file 3 of Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Additional file 5 of Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Additional file 7 of Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

Cite this