Shimagaki Kai S, Barton John P
Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.
Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Genetics. 2025 Jun 20. doi: 10.1093/genetics/iyaf118.
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.
上位性可深刻影响进化动态。时间遗传数据由随时间从种群中反复采样的序列组成，为理解上位性如何塑造进化提供了独特资源。然而，从序列数据中检测上位性相互作用在技术上具有挑战性。现有的识别上位性的方法对计算要求很高，限制了它们对实际数据的适用性。在此，我们提出一种用于推断上位性的新型计算方法，该方法在不牺牲准确性的情况下大幅降低了计算成本。我们在模拟中验证了我们的方法，并将其应用于研究16名个体的数据集多年来的HIV-1进化。在那里，我们观察到有益突变之间，尤其是参与免疫逃逸的突变之间，存在大量负上位性相互作用。我们的方法具有通用性，可用于表征其他大数据集中的上位性。
