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. 2022 Oct 24;23:225. doi: 10.1186/s13059-022-02793-w

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Schematic overview of the analysis plan to evaluate DNAm algorithm performance. DNAm analyses are conducted using DNAm EPIC array samples in JHS. JHS includes a significant number of technical replicate pairs thereby allowing for a careful investigation of how the removal of unwanted technical variation impacts DNAm algorithm performance across 101 data processing pipelines. JHS has also collected information on disease-related phenotypes, including mortality status after follow-up. This allowed us to assess how the removal of technical variation in DNAm predictor estimates by a data processing pipeline impacts downstream phenotypic association analyses