kalis: a modern implementation of the Li & Stephens model for local ancestry inference in R

Aslett, Louis J. M.; Christ, Ryan R.

Abstract

Background: Approximating the recent phylogeny of N phased haplotypes at a set of variants along the genome is a core problem in modern population genomics and central to performing genome-wide screens for association, selection, introgression, and other signals. The Li & Stephens (LS) model provides a simple yet powerful hidden Markov model for inferring the recent ancestry at a given variant, represented as an N×N distance matrix based on posterior decodings. Results: We provide a high-performance engine to make these posterior decodings readily accessible with minimal pre-processing via an easy to use package kalis, in the statistical programming language R. kalis enables investigators to rapidly resolve the ancestry at loci of interest and developers to build a range of variant-specific ancestral inference pipelines on top. kalis exploits both multi-core parallelism and modern CPU vector instruction sets to enable scaling to hundreds of thousands of genomes. Conclusions: The resulting distance matrices accessible via kalis enable local ancestry, selection, and association studies in modern large scale genomic datasets.

Citation

Aslett, L. J. M., & Christ, R. R. (2024). kalis: a modern implementation of the Li & Stephens model for local ancestry inference in R. BMC Bioinformatics, 25(1), Article 86. https://doi.org/10.1186/s12859-024-05688-8

• Journal Article Type: Article
• Acceptance Date: Feb 1, 2024
• Online Publication Date: Feb 28, 2024
• Publication Date: Feb 28, 2024
• Deposit Date: Apr 17, 2024
• Publicly Available Date: Apr 17, 2024
• Journal: BMC Bioinformatics
• Electronic ISSN: 1471-2105
• Publisher: BioMed Central
• Peer Reviewed: Peer Reviewed
• Volume: 25
• Issue: 1
• Article Number: 86
• DOI: https://doi.org/10.1186/s12859-024-05688-8
• Keywords: Li & Stephens model, High performance computation, R package, Hidden Markov model, Probabilistic haplotype model, Genomics
• Public URL: https://durham-repository.worktribe.com/output/2292412