论文标题
卡利斯：li＆stephens模型的现代实施，用于r的本地血统推断
kalis: A Modern Implementation of the Li & Stephens Model for Local Ancestry Inference in R
论文作者
论文摘要
在基因组沿线的一组变体中近似$ n $分阶段的单倍型的最新系统发育是现代种群基因组学的核心问题，并且是对全基因组进行关联，选择，渗入和其他信号的核心。 Li＆Stephens（LS）模型提供了一个简单而强大的隐藏Markov模型，用于在给定变体中推断最近的血统，该模型基于后解码为$ n \ times n $ dange矩阵。但是，现有的LS模型的后验解码实现无法扩展到具有数十或数十万个基因组的现代数据集。 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.所得的距离矩阵使现代大型基因组数据集中的局部血统，选择和关联研究。
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.
