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. 2023 Jan 26;55(2):291–300. doi: 10.1038/s41588-022-01282-x

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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 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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Fig. 1 — TESLA uses meta-regression to model phenotypic effect estimates as functions of the PCs of genome-wide allele frequencies from each cohort. For a given gene expression prediction model generated from an eQTL dataset, we use TESLA to more accurately estimate phenotypic effects, then use them to perform TWASs and attain optimal power. We also performed fine mapping and enrichment analysis using the TESLA results (which we call eTESLA).