Causal inference for multiple risk factors and diseases from genomics data

article Causal inference for multiple risk factors and diseases from genomics data published yes Nick N Machnik author 3591A0AA-F248-11E8-B48F-1D18A9856A870000-0001-6617-9742 Seyed Mahdi Mahmoudi author b9f6d5ef-7774-11eb-a47f-df2c75c02ee7 Malgorzata Borczyk author Ilse Krätschmer author 30d4014e-7753-11eb-b44b-db6d61112e730000-0002-5636-9259 Markus J. Bauer author Matthew Richard Robinson author E5D42276-F5DA-11E9-8E24-6303E66974250000-0001-8982-8813 MaRo department Mendelian randomization, the standard instrument variable causal-inference approach for genomics, can be severely biased by inadequate selection of appropriate instrument variables and horizontal pleiotropy. We introduce Causal Inference GWAS, a graphical approach that selects genetic instruments from summary statistics controlling for linkage and pleiotropy, accommodates rare variants and binary outcomes, distinguishes direct from indirect risk factors in high dimensions, and flags latent confounding. Applied to 9 risk factors, 4 metabolic disease outcomes, and 8.4M variants across 458,747 UK Biobank individuals, our method runs in 20 minutes, identifying only 696 genome-wide valid instruments. We replicate nearly all previously reported risk factor-outcome paths, but find that most are indistinguishable from unmeasured confounding. This suggests robust biobank causal inference will require longitudinal and family data to resolve temporal precedence from reverse causation. Our approach offers a principled first step toward screening genuine causal signal from phenotypic correlation in biobank data. https://research-explorer.ista.ac.at/download/23043/23070/2026_NatureComm_Machnik.pdf application/pdfno Springer Nature2026 eng Nature Communications 2041-172310.1038/s41467-026-76877-7 17 https://research-explorer.ista.ac.at/record/18648 Machnik, Nick N, Seyed Mahdi Mahmoudi, Malgorzata Borczyk, Ilse Krätschmer, Markus J. Bauer, and Matthew Richard Robinson. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” Nature Communications. Springer Nature, 2026. <a href="https://doi.org/10.1038/s41467-026-76877-7">https://doi.org/10.1038/s41467-026-76877-7</a>. Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” Nature Communications, vol. 17, 10153, Springer Nature, 2026, doi:<a href="https://doi.org/10.1038/s41467-026-76877-7">10.1038/s41467-026-76877-7</a>. Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. 2026. Causal inference for multiple risk factors and diseases from genomics data. Nature Communications. 17, 10153. N. N. Machnik, S. M. Mahmoudi, M. Borczyk, I. Krätschmer, M. J. Bauer, and M. R. Robinson, “Causal inference for multiple risk factors and diseases from genomics data,” Nature Communications, vol. 17. Springer Nature, 2026. Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J., & Robinson, M. R. (2026). Causal inference for multiple risk factors and diseases from genomics data. Nature Communications. Springer Nature. <a href="https://doi.org/10.1038/s41467-026-76877-7">https://doi.org/10.1038/s41467-026-76877-7</a> Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. Causal inference for multiple risk factors and diseases from genomics data. Nature Communications. 2026;17. doi:<a href="https://doi.org/10.1038/s41467-026-76877-7">10.1038/s41467-026-76877-7</a> N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R. Robinson, Nature Communications 17 (2026). 230432026-10-04T22:01:53Z2026-10-07T09:54:59Z