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. 2020 Jan 13;10:151. doi: 10.1038/s41598-019-56310-4

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© The Author(s) 2020

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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The workflow of the step-wise analysis plans is shown. The framework illustrates how we explored different ways of evaluating the diagnostic performance beyond the one-locus model (using parametric linear model). We proposed to use Random Forest classification algorithm to identify the optimal subset of predictors in the context of multi-locus effects. It also includes how we investigated the relationships among the genes that harbor different tissue-specific asthma-associated DNAm signatures.