kalis: A Modern Implementation of the Li & Stephens Model for Local Ancestry Inference in R

TL;DR

kalis is a high-performance R package that efficiently implements the Li & Stephens model, enabling scalable local ancestry inference and related analyses in large genomic datasets by leveraging multi-core and vectorized CPU capabilities.

Contribution

This work introduces kalis, a scalable and efficient R implementation of the Li & Stephens model for local ancestry inference in large-scale genomic data.

Findings

Enables analysis of datasets with hundreds of thousands of genomes.

Uses multi-core and CPU vector instructions for high performance.

Facilitates local ancestry, selection, and association studies.

Abstract

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 $N \times N$ distance matrix based on posterior decodings. However, existing posterior decoding implementations for the LS model cannot scale to modern datasets with tens or hundreds of thousands of genomes. This work focuses on providing a high-performance engine to compute the LS model, enabling users to rapidly develop a range of variant-specific ancestral inference pipelines on top, exposed via an easy to use package, kalis, in the statistical programming language R. kalis exploits both multi-core parallelism and modern CPU vector instruction sets to enable scaling to problem sizes that would previously have been prohibitively slow to work with. The resulting distance matrices enable local ancestry, selection, and association studies in modern large scale genomic datasets.