Tree sequences as a general-purpose tool for population genetic inference.

ABSTRACT: As population genetics data increases in size new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient, but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks (CNNs) applied to population genetic alignments. To better utilize these new data structures we propose and implement a graph convolutional network (GCN) to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format. We then compare our approach to standard CNN approaches

SUBMITTER: Whitehouse LS 

PROVIDER: S-EPMC11343121 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature