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Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies

Lussier, Alexandre A.; Zhu, Yiwen; Smith, Brooke J.; Simpkin, Andrew J.; Smith, Andrew D.A.C.; Suderman, Matthew J.; Walton, Esther; Ressler, Kerry J.; Dunn, Erin C.

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Authors

Alexandre A. Lussier

Yiwen Zhu

Brooke J. Smith

Andrew J. Simpkin

Matthew J. Suderman

Esther Walton

Kerry J. Ressler

Erin C. Dunn



Abstract

Introduction: Biomedical research has grown increasingly cooperative through the sharing of consortia-level epigenetic data. Since consortia preprocess data prior to distribution, new processing pipelines can lead to different versions of the same dataset. Similarly, analytic frameworks evolve to incorporate cutting-edge methods and best practices. However, it remains unknown how different data and analytic versions alter the results of epigenome-wide analyses, which could influence the replicability of epigenetic associations. Thus, we assessed the impact of these changes using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort.

Methods: We analyzed DNA methylation from two data versions, processed using separate preprocessing and analytic pipelines, examining associations between seven childhood adversities or prenatal smoking exposure and DNA methylation at age 7. We performed two sets of analyses: (1) epigenome-wide association studies (EWAS); (2) Structured Life Course Modeling Approach (SLCMA), a two-stage method that models time-dependent effects. SLCMA results were also compared across two analytic versions.

Results: Data version changes impacted both EWAS and SLCMA analyses, yielding different associations at conventional p-value thresholds. However, the magnitude and direction of associations was generally consistent between data versions, regardless of p-values. Differences were especially apparent in analyses of childhood adversity, while smoking associations were more consistent using significance thresholds. SLCMA analytic versions similarly altered top associations, but time-dependent effects remained concordant.

Conclusions: Alterations to data and analytic versions influenced the results of epigenome-wide analyses. Our findings highlight that magnitude and direction are better measures for replication and stability than p-value thresholds.

Journal Article Type Article
Acceptance Date Jan 4, 2022
Online Publication Date Feb 14, 2022
Publication Date 2022-10
Deposit Date Feb 20, 2022
Publicly Available Date Feb 15, 2023
Journal Epigenetics
Print ISSN 1559-2294
Electronic ISSN 1559-2308
Publisher Taylor & Francis
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1080/15592294.2022.2028072
Keywords ALSPAC, epigenetic data versions, analytic versions, updates/revised, adversity, DNA methylation, reproducibility.
Public URL https://uwe-repository.worktribe.com/output/9035917

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Licence
http://creativecommons.org/licenses/by-nc/4.0/

Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright Statement
This is an Accepted Manuscript version of the following article, accepted for publication in Epigenetics [Zhu, Y., Lussier, A. A., Smith, B. J., Simpkin, A. J., Suderman, M. J., Smith, A. D., …Dunn, E. C. (in press). Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies. Epigenetics, https://doi.org/10.1080/15592294.2022.2028072]. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.







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