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  • Machine Learning Reveals the Contribution of Rare Genetic Variants and Enhances Risk Prediction for Coronary Artery Disease in the Japanese Population. Circulation. Genomic and precision medicine Ieki, H., Zhang, S., Koyama, S., Kjellberg, M., Yoshida, H., Kurosawa, R., Matsunaga, H., Miyazawa, K., Enzan, N., Kim, C., Seo, J. S., Higasa, K., Ozaki, K., Onouchi, Y., Matsuda, K., Kamatani, Y., Terao, C., Matsuda, F., Snyder, M., Komuro, I., Ito, K. 2026: e005341

    Abstract

    GWASs (genome-wide association studies) have advanced our understanding of coronary artery disease (CAD) genetics and enabled the development of polygenic risk scores (PRSs) for estimating genetic risk based on common variant burden. However, GWASs have limitations in analyzing rare variants due to insufficient statistical power, thereby constraining PRS performance.We conducted whole-genome sequencing of 1752 Japanese patients with CAD and 3019 controls. A machine learning-based analytical framework was applied to identify and interpret rare genetic variants associated with CAD pathogenesis.This approach identified 59 CAD-related genes, including known causal genes such as LDLR and those not previously captured by GWASs. A rare variant-based risk score derived from the framework demonstrated distinct clinical characteristics compared with a conventional common variant-based PRS. The rare variant-based risk score significantly discriminated CAD cases and predicted cardiovascular mortality in an independent cohort. Furthermore, combining the rare variant-based risk score with the traditional PRS improved CAD prediction compared with the PRS alone (area under the curve, 0.66 versus 0.61; P=0.007).These findings underscore the distinct and complementary value of the rare variant-based risk score compared with the conventional PRS, highlighting the enhanced predictive power achieved through their integration. This comprehensive approach proposes broader genetic profiling, offering substantial potential for improved clinical risk stratification and personalized prevention strategies.

    View details for DOI 10.1161/CIRCGEN.125.005341

    View details for PubMedID 42237915

  • A comparison of deep multiomics profiles across ethnicity, geography, and age. Cell Barapour, N., Cao, J. Z., Wu, Y., Gupta, S., Hoopmann, M. R., Qin, R., Midha, M. K., Mireault, M., Juanes-Velasco, P., Hanson, C., Ahadi, S., Higgs, E., Baxter, D. H., Diener, C., Dagan-Rosenfeld, O., Hornburg, D., Che, S., Edfors, F., Church, S. J., Babu, M., Thota, D., Jin, C., Chou, T., Rego, S., Avina, M., McGuire, L., Li, J. W., Karathanos, T., Panyard, D. J., Acosta Parra, M. A., Roberts, A. K., Ranjit, A. K., Rangan, E., Almagro Armenteros, J. J., Ashland, M., Castillo, K. E., Traber, G., Ellenberger, M., Kellogg, R., Zhou, W., Rost, H., Kjellberg, M., Mishra, T., Kapil, C., Kusebauch, U., Patwardhan, S., Landeira-Viñuela, A., Hernandez, A. P., Thomsen, M. E., Mashkoor, M., Sutantiwanichkul, T., Dodig-Crnkovic, T., Bendes, A., Dahl, L., Gibbons, S. M., Rangan, P. V., Stensballe, A., Schwenk, J. M., Unwin, R. D., Fuentes, M., Sleno, L., Moritz, R. L., Mahal, L. K., Snyder, M. P. 2026; 189 (10): 3004-3024.e35

    Abstract

    Despite extensive research, molecular differences in human populations and the influence of ancestry, age, geography, and diet are poorly understood. We performed comprehensive multiomics profiling (including genomics, transcriptomics, proteomics, metabolomics, lipidomics, metallomics, glycomics, and microbiomics) on samples from 322 healthy individuals of European, East Asian, and South Asian ancestry across multiple continents. We identified ethnicity-associated molecular features linked to host metabolism, autoimmune disease risk, drug metabolism, and neurodegenerative pathways. We uncovered ancestry- and geography-related molecular changes affecting metabolism, immune function, microbiome composition, and biological aging. Specific genetic variants and gene expression differences were associated with lipid metabolism and immune regulation. Geography influenced biological age: East Asians showed lower biological age in their ancestral regions, whereas individuals of European ancestry exhibited lower biological age in the US/Canada than in Europe. Diet-microbiome metabolism interactions displayed ethnicity-specific patterns, many related to health. This open access resource advances understanding of ethnicity-environment interactions and supports precision medicine.

    View details for DOI 10.1016/j.cell.2026.04.032

    View details for PubMedID 42134306

  • Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis. medRxiv : the preprint server for health sciences Zhang, S., Jahanbani, F., Chander, V., Kjellberg, M., Liu, M., Glass, K. A., Iu, D. S., Ahmed, F., Li, H., Maynard, R. D., Chou, T., Cooper-Knock, J., Zhang, M. J., Thota, D., Zeineh, M., Grenier, J. K., Grimson, A., Hanson, M. R., Snyder, M. P. 2025

    Abstract

    Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogeneous, and systemic disease defined by a suite of symptoms, including unexplained persistent fatigue, post-exertional malaise (PEM), cognitive impairment, myalgia, orthostatic intolerance, and unrefreshing sleep. The disease mechanism of ME/CFS is unknown, with no effective curative treatments. In this study, we present a multi-site ME/CFS whole-genome analysis, which is powered by a novel deep learning framework, HEAL2. We show that HEAL2 not only has predictive value for ME/CFS based on personal rare variants, but also links genetic risk to various ME/CFS-associated symptoms. Model interpretation of HEAL2 identifies 115 ME/CFS-risk genes that exhibit significant intolerance to loss-of-function (LoF) mutations. Transcriptome and network analyses highlight the functional importance of these genes across a wide range of tissues and cell types, including the central nervous system (CNS) and immune cells. Patient-derived multi-omics data implicate reduced expression of ME/CFS risk genes within ME/CFS patients, including in the plasma proteome, and the transcriptomes of B and T cells, especially cytotoxic CD4 T cells, supporting their disease relevance. Pan-phenotype analysis of ME/CFS genes further reveals the genetic correlation between ME/CFS and other complex diseases and traits, including depression and long COVID-19. Overall, HEAL2 provides a candidate genetic-based diagnostic tool for ME/CFS, and our findings contribute to a comprehensive understanding of the genetic, molecular, and cellular basis of ME/CFS, yielding novel insights into therapeutic targets. Our deep learning model also offers a potent, broadly applicable framework for parallel rare variant analysis and genetic prediction for other complex diseases and traits.

    View details for DOI 10.1101/2025.04.15.25325899

    View details for PubMedID 40321247

    View details for PubMedCentralID PMC12047926