PGpath: recommending population-adapted linear reference genomes from pangenome subpaths

Large-scale population sequencing requires reference genomes that accurately represent the genetic background of target populations. Although high-quality linear references such as T2T-CHM13 have improved the completeness of human genome representation, a single reference genome remains insufficient to capture genetic diversity across populations. Pangenome graphs provide a richer representation of multi-source genomic variation, but translating pangenome diversity into practical reference genomes remains challenging. First, the direct application of pangenome graphs in routine large-scale sequencing analysis is limited by graph complexity and poor compatibility with existing linear-reference workflows. Second, existing pangenome-driven personalized references mainly target individual samples rather than populations, and current path recommendation strategies may overlook local source consistency during branch-node selection. In this study, we propose PGpath, a pangenome-based method for recommending population-adapted linear reference genomes. PGpath generates a reference from a selected pangenome subpath, preserving pangenome diversity while retaining a linear format compatible with standard downstream pipelines. In this study, we characterize the shared genetic background of samples using population-level k-mer frequency features and reformulate reference recommendation as branch-node prediction in the pangenome graph. To encourage local source consistency, we develop a topology-constrained multi-task neural network incorporating source-consistency relationships between adjacent branch nodes. Experiments on Human Pangenome Reference Consortium (HPRC) population datasets and HG002 variant detection benchmarks showed that PGpath-derived references improved read mapping performance and reduced downstream variant detection errors compared with commonly used references. PGpath provides a practical strategy for translating pangenome diversity into population-adapted linear references for large-scale genomic analysis.