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. 2024 Aug 5;56(8):1592–1596. doi: 10.1038/s41588-024-01844-1

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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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Extended Data Fig. 1 — We tested 10 million common (alternate allele frequency [AAF] > 1%) variants, derived from array genotyping followed by HRC imputation, comparing 18,334 individuals who reported a positive test for influenza (cases) against 276,295 individuals who did not report a positive test for influenza (controls). a, Quantile-quantile plot showing observed P-values for individual variants (y-axis) against P-values expected by chance given multiple testing (x-axis). The genomic inflation factor (λ_GC) of this analysis was 1.05, whereas the intercept from LD-score regression was 1.04. b, Manhattan plot showing association (−log₁₀ P-value) with imputed variants. The dotted grey line demarcates the genome-wide significance threshold of P = 5 × 10⁻⁸. c,d, Genetic ancestry-specific results for the 3q27.3/ST6GAL1 (c) and the 21q22.2/B3GALT5 (d) variants. Unadjusted P-values derived from Firth-regression (two-sided test) implemented in REGENIE⁹. Error bars represent the 95% confidence interval around the odds ratio (data point).