Graph Neural Networks for Immune Gene–Disease Association Discovery Using Long-Read Sequencing and Population Genomics Data - Bevin Little; Vaibhav Bose; Jack Korris; Dominik C. Cook (International Journal of Clinical and Translational Medicine, 2026)
The growing availability of long-read sequencing data and large-scale population genomics resources presents unprecedented opportunities for characterizing the highly polymorphic immune gene regions that underlie susceptibility to infectious and autoimmune diseases. However, the complexity of immune gene families, including the major histocompatibility complex and killer-cell immunoglobulin-like receptors, demands computational frameworks capable of integrating heterogeneous genomic signals through structured relational learning. This paper proposes a graph neural network approach for immune gene–disease association discovery that leverages long-read sequencing calls and population-level variation data within a unified graph representation. We discuss the architectural trade-offs between inductive and transductive learning paradigms, the scalability of message-passing schemes over genome-scale interaction graphs, and the integration of multi-omic layers such as expression quantitative trait loci and epigenetic marks. A central emphasis is placed on the system-level design choices that govern model robustness, including graph construction from phased haplotypes, handling of missing data in rare alleles, and the incorporation of clinical covariates to reduce confounding. Infrastructure considerations for deploying such models across distributed computing environments are examined, along with strategies for ensuring fairness when training data are drawn from ancestrally diverse cohorts. The paper also addresses policy and governance challenges related to data privacy, consent for long-read repositories, and the ethical deployment of predictive models in clinical decision support. By situating graph neural networks within the broader socio-technical infrastructure of immune genomics, we aim to provide a roadmap for future research that balances analytical power with responsible translation.