PangenomeX: a graph convolutional network-based pangenome framework for unbiased population-scale genomic variation analysis.

ABSTRACT: In population-scale genomic variation studies based on shallow whole genome sequencing, pangenomes have become an effective tool for identifying population-specific single-nucleotide polymorphisms and indels. Extending these advantages to copy number variation (CNV), however, remains challenging due to two unresolved issues. First, current pangenome frameworks exhibit pronounced population-representation bias arising from uneven sampling across populations. As the number of samples increases, the pangenome tends to capture variations primarily from majority populations while suppressing signals from minority populations. Second, in population-scale genomic variation analyses, common but benign population-specific copy number polymorphisms (CNPs) frequently obscure pathogenic CNVs. Existing

SUBMITTER: Xue Z 

PROVIDER: S-EPMC12536880 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature