DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies

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  • dc.contributor.author Mieth, Bettina
  • dc.contributor.author Rozier, Alexandre
  • dc.contributor.author Rodriguez, Juan Antonio
  • dc.contributor.author Höhne, Marina M.C.
  • dc.contributor.author Görnitz, Nico
  • dc.contributor.author Müller, Klaus-Robert
  • dc.date.accessioned 2021-12-02T06:48:46Z
  • dc.date.available 2021-12-02T06:48:46Z
  • dc.date.issued 2021
  • dc.description.abstract Deep learning has revolutionized data science in many fields by greatly improving prediction performances in comparison to conventional approaches. Recently, explainable artificial intelligence has emerged as an area of research that goes beyond pure prediction improvement by extracting knowledge from deep learning methodologies through the interpretation of their results. We investigate such explanations to explore the genetic architectures of phenotypes in genome-wide association studies. Instead of testing each position in the genome individually, the novel three-step algorithm, called DeepCOMBI, first trains a neural network for the classification of subjects into their respective phenotypes. Second, it explains the classifiers' decisions by applying layer-wise relevance propagation as one example from the pool of explanation techniques. The resulting importance scores are eventually used to determine a subset of the most relevant locations for multiple hypothesis testing in the third step. The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 study. Verification of the latter is achieved by validating all findings with independent studies published up until 2020. DeepCOMBI is shown to outperform ordinary raw P-value thresholding and other baseline methods. Two novel disease associations (rs10889923 for hypertension, rs4769283 for type 1 diabetes) were identified.
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Mieth B, Rozier A, Rodriguez JA, Höhne MMC, Görnitz N, Müller KR. DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies. NAR Genom Bioinform. 2021;3(3):lqab065. DOI: 10.1093/nargab/lqab065
  • dc.identifier.doi http://dx.doi.org/10.1093/nargab/lqab065
  • dc.identifier.issn 2631-9268
  • dc.identifier.uri http://hdl.handle.net/10230/49130
  • dc.language.iso eng
  • dc.publisher Oxford University Press
  • dc.relation.ispartof NAR Genom Bioinform. 2021;3(3):lqab065
  • dc.rights © The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.title DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies
  • dc.type info:eu-repo/semantics/article
  • dc.type.version info:eu-repo/semantics/publishedVersion

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