Quantifying evolutionary change among viral genomes is an important clinical device to track critical adaptations geographically and temporally. We built image-based haplotype-guided evolutionary inference (ImHapE) to quantify adaptations in expanding populations of non-recombining SARS-CoV-2 genomes. By combining classic population genetic summaries with image-based deep learning methods, we show that different rates of positive selection are driving evolutionary fitness and dispersal of SARS-CoV-2 globally. A 1.35-fold increase in evolutionary fitness is observed within the UK, associated with expansion of both the B.1.177 and B.1.1.7 SARS-CoV-2 lineages.

中文翻译：

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使用基于图像的单倍型比对来映射SARS-CoV-2的全局适应性

量化病毒基因组之间的进化变化是一种重要的临床设备，可以在地理和时间上跟踪关键的适应性变化。我们建立了基于图像的单倍型指导的进化推论（ImHapE），以量化非重组SARS-CoV-2基因组的扩展群体中的适应性。通过将经典的人口遗传总结与基于图像的深度学习方法相结合，我们显示出不同的积极选择率正在驱动SARS-CoV-2在全球的进化适应性和扩散。在英国，进化适应度提高了1.35倍，这与B.1.177和B.1.1.7 SARS-CoV-2谱系的扩展有关。