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April 1st, 2025
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Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India
genomics
biorxiv

GViNC: An Innovative Framework for Genome Graph Comparison reveals Hidden Patterns in the Genetic Diversity of Human Populations

Kamaraj, V.Open in Google Scholar•Gupta, A.Open in Google Scholar•Raman, K.Open in Google Scholar•Narayanan, M.Open in Google Scholar•Sinha, H.Open in Google Scholar

Background: Genome graphs are reference structures appropriate for studying genetic diversity. By emphasising the polymorphic regions in a collection of genomes, their network layout can capture and compare the genetic diversity of different populations of interest. However, there are no existing methods to characterise and compare genome graphs based on their complex structures. Results: Our study introduces an original framework GViNC: Genome graph Visualisation, Navigation and Comparison. GVINC maps linear genomic coordinates onto genome graph nodes, enabling subgraph partitioning by genomic regions, thereby helping navigate the genome graph, summarise the heterogeneity of these regions, and compare them using novel metrics. We applied GViNC to multiple pan-genomic and population-specific genome graphs constructed with the variants from the 1000 Genomes Project. We found that genomic complexity varied by ancestry and across chromosomes, with rare variants increasing genome graph variability by 10-fold and hypervariability by 50-fold. GViNC highlighted biologically significant regions, such as HLA and DEFB loci, along with several novel high-diversity regions while revealing population-specific heterogeneity patterns in areas associated with fundamental biological functions. Conclusions: The versatility and scalability of GViNC can aid researchers in extensively investigating the genetic diversity of different cohorts, populations, or species of interest.

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