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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

Tree Sequences as a General-Purpose Tool for Population Genetic Inference.

Logan S Whitehouse¹, Dylan D Ray¹, Daniel R Schrider¹

¹Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.

Molecular Biology and Evolution

|October 26, 2024

Summary

New graph convolutional networks can directly analyze tree sequence data for population genetics. This approach matches or exceeds existing methods for tasks like recombination rate estimation and selection detection.

Keywords:

ancestral recombination graphs demographic inference introgression recombination rate estimation selective sweeps tree sequences

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Area of Science:

Population genetics
Bioinformatics
Computational biology

Background:

Increasing population genetic data necessitate efficient storage and analysis methods.
Tree sequences offer computational and storage efficiency but are incompatible with existing neural network inference methods.
Current methods require converting tree sequences to alignment formats, adding an intermediate step.

Purpose of the Study:

To develop a graph convolutional network (GCN) for direct analysis of tree sequence data.
To enable neural network applications on tree sequences without format conversion.
To evaluate the GCN's performance on standard population genetic inference tasks.

Main Methods:

Implementation of a graph convolutional network (GCN) architecture.
Direct learning from tree sequence topology and node data.
Benchmarking GCN against convolutional neural networks (CNNs) on established tasks.

Main Results:

The GCN successfully learned directly from tree sequence data.
The GCN achieved accuracies comparable to or exceeding CNN-based methods.
Performance was evaluated on recombination rate estimation, selection detection, introgression detection, and demographic inference.

Conclusions:

Graph convolutional networks provide a viable method for direct analysis of tree sequence data in population genetics.
This approach streamlines inference processes and maintains high accuracy.
The GCN method offers a foundation for future population genetic inference using tree sequences.