Lee Brian, Quadeer Ahmed Abdul, Sohail Muhammad Saqib, Finney Elizabeth, Ahmed Syed Faraz, McKay Matthew R, Barton John P
Department of Physics and Astronomy, University of California, Riverside, Riverside, CA, USA.
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China.
Nat Commun. 2025 Jan 7;16(1):441. doi: 10.1038/s41467-024-55593-0.
New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a generic, analytical epidemiological model to infer the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that substantially affect the transmission rate, both within and outside the Spike protein. The mutations that we infer to have the largest effects on transmission are strongly supported by experimental evidence from prior studies. Importantly, our model detects lineages with increased transmission even at low frequencies. As an example, we infer significant transmission advantages for the Alpha, Delta, and Omicron variants shortly after their appearances in regional data, when they comprised only around 1-2% of sample sequences. Our model thus facilitates the rapid identification of variants and mutations that affect transmission from genomic surveillance data.
在新冠疫情期间，新型且传播性更强的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）变种多次出现。快速识别影响传播的突变，有助于我们增进对病毒生物学的理解，并找出值得进一步研究的新变种。在此，我们开发了一种通用的分析性流行病学模型，以从基因组监测数据中推断突变对传播的影响。将我们的模型应用于多个地区的SARS-CoV-2数据，我们发现多个对传播率有显著影响的突变，这些突变在刺突蛋白内外均有出现。我们推断对传播影响最大的突变，得到了先前研究实验证据的有力支持。重要的是，我们的模型即使在低频情况下也能检测到传播增加的谱系。例如，我们推断，在阿尔法、德尔塔和奥密克戎变种在区域数据中出现后不久，当它们仅占样本序列的约1%-2%时，它们就具有显著的传播优势。因此，我们的模型有助于从基因组监测数据中快速识别影响传播的变种和突变。
