PDF(Pubmed)
Abstract：
COVID-19 has posed unforeseen circumstances and throttled major economies worldwide. India has witnessed two waves affecting around 31 million people representing 16% of the cases globally. To date, the epidemic waves have not been comprehensively investigated to understand pandemic progress in India.
Here, we aim for pan Indian cross-sectional evolutionary analysis since inception of SARS-CoV-2.
High quality genomes, along with their collection date till 26th July 2021, were downloaded. Whole genome-based phylogeny was obtained. Further, the mutational analysis was performed using SARS-CoV-2 first reported from Wuhan (NC_045512.2) as reference.
Based on reported cases and mutation rates, we could divide the Indian epidemic into seven phases. The average mutation rate for the pre-first wave was <11, which elevated to 17 in the first wave and doubled in the second wave (∼34). In accordance with mutation rate, VOCs and VOIs started appearing in the first wave (1.5%), which dominated the second (∼96%) and post-second wave (100%). Nation-wide mutational analysis depicted >0.5 million mutation events with four major mutations in >19,300 genomes, including two mutations in coding (spike (D614G), and NSP 12b (P314L) of rdrp), one silent mutation (NSP3 F106F) and one extragenic mutation (5\' UTR 241).
Whole genome-based phylogeny could demarcate post-first wave isolates from previous ones by point of diversification leading to incidences of VOCs and VOIs in India. Such analysis is crucial in the timely management of pandemic.
Here, we aim for pan Indian cross-sectional evolutionary analysis since inception of SARS-CoV-2.
High quality genomes, along with their collection date till 26th July 2021, were downloaded. Whole genome-based phylogeny was obtained. Further, the mutational analysis was performed using SARS-CoV-2 first reported from Wuhan (NC_045512.2) as reference.
Based on reported cases and mutation rates, we could divide the Indian epidemic into seven phases. The average mutation rate for the pre-first wave was <11, which elevated to 17 in the first wave and doubled in the second wave (∼34). In accordance with mutation rate, VOCs and VOIs started appearing in the first wave (1.5%), which dominated the second (∼96%) and post-second wave (100%). Nation-wide mutational analysis depicted >0.5 million mutation events with four major mutations in >19,300 genomes, including two mutations in coding (spike (D614G), and NSP 12b (P314L) of rdrp), one silent mutation (NSP3 F106F) and one extragenic mutation (5\' UTR 241).
Whole genome-based phylogeny could demarcate post-first wave isolates from previous ones by point of diversification leading to incidences of VOCs and VOIs in India. Such analysis is crucial in the timely management of pandemic.
摘要：
COVID-19造成了不可预见的情况，并扼杀了全球主要经济体。印度目睹了两次浪潮，影响了约3100万人，占全球病例的16％。迄今为止,尚未对流行病浪潮进行全面调查，以了解印度的流行病进展。
这里,我们的目标是自SARS-CoV-2问世以来进行泛印度横截面进化分析。
高质量的基因组，连同他们的收藏日期到2021年7月26日，都被下载了。获得了基于全基因组的系统发育。Further,使用武汉首次报道的SARS-CoV-2（NC_045512.2）作为参考进行突变分析。
根据报告的病例和突变率，我们可以将印度疫情分为七个阶段。前第一波的平均突变率<11，在第一波中上升到17，在第二波中增加了一倍（~34）。根据突变率，VOCs和VOIs开始出现在第一波(1.5%)，在第二波(96%)和第二波(100%)后占主导地位。全国范围的突变分析描绘了>50万个突变事件，在>19,300个基因组中具有四个主要突变，包括两个编码突变(尖峰(D614G)，和Rdrp的NSP12b（P314L），一个沉默突变（NSP3F106F）和一个基因外突变（5'UTR241）。
基于全基因组的系统发育可以通过多样化点将第一波后的分离株与以前的分离株区分开来，从而导致印度VOCs和VOI的发生。这种分析对于及时管理大流行至关重要。
这里,我们的目标是自SARS-CoV-2问世以来进行泛印度横截面进化分析。
高质量的基因组，连同他们的收藏日期到2021年7月26日，都被下载了。获得了基于全基因组的系统发育。Further,使用武汉首次报道的SARS-CoV-2（NC_045512.2）作为参考进行突变分析。
根据报告的病例和突变率，我们可以将印度疫情分为七个阶段。前第一波的平均突变率<11，在第一波中上升到17，在第二波中增加了一倍（~34）。根据突变率，VOCs和VOIs开始出现在第一波(1.5%)，在第二波(96%)和第二波(100%)后占主导地位。全国范围的突变分析描绘了>50万个突变事件，在>19,300个基因组中具有四个主要突变，包括两个编码突变(尖峰(D614G)，和Rdrp的NSP12b（P314L），一个沉默突变（NSP3F106F）和一个基因外突变（5'UTR241）。
基于全基因组的系统发育可以通过多样化点将第一波后的分离株与以前的分离株区分开来，从而导致印度VOCs和VOI的发生。这种分析对于及时管理大流行至关重要。
