Efficient epistasis inference via higher-order covariance matrix factorization

被引：0

作者：

Shimagaki, Kai S. ^{[1
,2
]}

Barton, John P. ^{[1
,2
]}

机构：

[1] Univ Pittsburgh, Sch Med, Dept Computat & Syst Biol, Pittsburgh, PA 15213 USA

[2] Univ Pittsburgh, Dept Phys & Astron, Pittsburgh, PA 15213 USA

来源：

GENETICS | 2025年

基金：

美国国家卫生研究院;

关键词：

Bayesian inference; selection; epistasis; linkage; diffusion; higher-order statistics; viral evolution; TIME-SERIES DATA; NATURAL-SELECTION; BENEFICIAL MUTATIONS; CELL RESPONSES; ALLELE AGE; FITNESS; ADAPTATION; EVOLUTION; CONTACTS; ESCAPE;

D O I：

10.1093/genetics/iyaf118

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Epistasis can profoundly influence evolutionary dynamics. Temporal genetic data, consisting of sequences sampled repeatedly from a population over time, provides a unique resource to understand how epistasis shapes evolution. However, detecting epistatic interactions from sequence data is technically challenging. Existing methods for identifying epistasis are computationally demanding, limiting their applicability to real-world data. Here, we present a novel computational method for inferring epistasis that substantially reduces computational costs without sacrificing accuracy. We validated our approach in simulations and applied it to study HIV-1 evolution over multiple years in a data set of 16 individuals. There we observed a strong excess of negative epistatic interactions between beneficial mutations, especially mutations involved in immune escape. Our method is general and could be used to characterize epistasis in other large data sets.

引用

页数：12

共 89 条

[1] Selective escape from CD8+ T-cell responses represents a major driving force of human immunodeficiency virus type 1 (HIV-1) sequence diversity and reveals constraints on HIV-1 evolution [J].