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. 2023 Dec 19;24:790. doi: 10.1186/s12864-023-09875-4

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© The Author(s) 2023

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 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. 6 — Use of noninvasive samples in disease-relevant analyses. a ASE for annotated stop-gain variants vs synonymous. Only sites with > 16 total counts were included. b Genes with median expression > 0.1 TPM in a tissue were intersected with the OMIM gene set. Shown is the intersection of OMIM genes captured across tissues. c Capture of common disease signals in the OpenTargets database. SES = Σ(evidence scores of disease genes expressed in a tissue)/ Σ(evidence scores of disease genes expressed in any included tissue)