netview p: a network visualization tool to unravel complex population structure using genome-wide SNPs

Area of Science:

• Population genetics
• Bioinformatics
• Computational biology

Background:

• Understanding complex genetic structures in wild and captive populations is crucial for conservation and management.
• Traditional methods may not fully capture intricate population dynamics and relationships.
• Network-based approaches offer a novel perspective for analyzing genetic data.

Purpose of the Study:

• To introduce NetView P, a novel software tool for constructing and visualizing population networks.
• To provide a user-friendly pipeline for analyzing genome-wide SNP data using network analysis.
• To demonstrate the utility of NetView P across various simulated and real-world population datasets.

Main Methods:

• NetView P employs mutual k-nearest neighbors thresholds on genome-wide single nucleotide polymorphisms (SNPs) to build population networks.
• The software integrates data quality control with network construction.
• It is implemented in Python, ensuring cross-platform compatibility and open-source accessibility.

Main Results:

• Population networks effectively visualized large- and fine-scale genetic structure within and between populations.
• The tool successfully identified family-level structure and relationships in analyzed datasets.
• NetView P efficiently processed datasets ranging from hundreds to hundreds of thousands of SNPs.

Conclusions:

• NetView P provides a valuable addition to existing population genetics analysis tools.
• The software enables intuitive visualization of complex genetic structures and relationships.
• It offers a powerful and accessible network-based approach for population genetic studies.