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. 2024 May 29;15:4433. doi: 10.1038/s41467-024-48654-x

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© The Author(s) 2024

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/.

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Fig. 3 — a For the genetic variants selected in Non-additive GenoBoost models across the twelve disease outcomes in UK Biobank (x-axis), the fractions of the inferred inheritance mode are shown (y-axis). b We compared the inferred inheritance mode from GenoBoost (y-axis) and the ones from GWAS summary statistics (x-axis) and showed the results as a colored confusion matrix. Add: additive. Dom: dominant. Rec: recessive. c, d Comparison of GenoBoost scores and GWAS p-values, focusing on psoriasis and genetic variants in an intergenic region in chromosome 11. For each genetic variant selected in the Non-additive GenoBoost model within the 3 Mbp window (x-axis), we show the predictive utility of the variant in Non-additive GenoBoost (Supplementary Methods) (c). We also show the statistical significance from GWAS for the variant under additive, recessive, and heterozygous-only regression models (d). The statistical significance is the nominal p-values of the slope of the logistic regression from two-sided tests using up to n = 215,768 samples. Four genetic variants with the largest predictive utilities in GenoBoost are highlighted and colored based on the inferred mode of genetic inheritance. Source data are provided as a Source Data file.