Genetic linkage causes the fate of new mutations in a population to be contingent on the genetic background on which they appear. This makes it challenging to identify how individual mutations affect fitness. To overcome this challenge, we developed marginal path likelihood (MPL), a method to infer selection from evolutionary histories that resolves genetic linkage. Validation on real and simulated data sets shows that MPL is fast and accurate, outperforming existing inference approaches. We found that resolving linkage is crucial for accurately quantifying selection in complex evolving populations, which we demonstrate through a quantitative analysis of intrahost HIV-1 evolution using multiple patient data sets. Linkage effects generated by variants that sweep rapidly through the population are particularly strong, extending far across the genome. Taken together, our results argue for the importance of resolving linkage in studies of natural selection.

遗传连锁导致群体中新突变的命运取决于它们出现的遗传背景。这使得确定个体突变如何影响健康变得具有挑战性。为了克服这一挑战，我们开发了边际路径可能性（MPL），这是一种从解决遗传连锁的进化历史中推断选择的方法。对真实数据集和模拟数据集的验证表明，MPL 快速且准确，优于现有的推理方法。我们发现，解决连锁对于准确量化复杂进化群体中的选择至关重要，我们通过使用多个患者数据集对宿主内 HIV-1 进化进行定量分析证明了这一点。快速席卷整个群体的变异所产生的连锁效应尤其强烈，远远延伸到整个基因组。总而言之，我们的结果证明了在自然选择研究中解决联系的重要性。