High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next-generation sequencing

Abstract

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance, and determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding, and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for the detection of SARS-CoV-2, with an additional advantage of enabling the genetic epidemiology of SARS-CoV-2.

Fig 1. Schematic summary of the analysis in this study.

The methodology adopted in the sampling, library preparation, sequencing and analysis involving custom based pipeline and COVIDSeq pipeline employed in this study.

Fig 2. Concordance among replicate samples considered in the analysis.

A) Total number of read counts B) The coverage percentage among replicates. R1-Replicate 1, and R2- Replicate 2.

Fig 3. The line plot for the mean coverage of SARS-CoV-2 genome.

The mean coverage for the 98 amplicons across the SARS-CoV-2 genome.

Fig 4. Variant number per genome and their annotation.

(A) Distribution of variants in the genomes with ≥ 99% coverage (B) Summary of the variant annotations.

Fig 5. Phylogenetic distribution of Indian SARS-CoV-2 genomes.

A) Phylogenetic trees generated by Nextstrain. 469 COVIDseq genomes reported from this study are highlighted. The 469 genomes cluster under clade A2a, I/A3i and B4, with A2a being the dominant clade. B) The proportion of clades and PANGOLIN lineages representing the Indian genomes. B.1 and B.1.113 are the dominant lineages in COVIDseq genomes whereas other Indian genomes show a dominance of B.6 and B.1 lineages.

Fig 6. Phylogenetic distribution of PANGOLIN lineages in COVIDseq genomes.

The distribution of lineages assigned by PANGOLIN in 469 COVIDseq genomes with the Wuhan/WH01 (EPI_ISL_406798) as reference.

Fig 7. The coverage plot across the SARS-CoV-2 genome.

The coverage plot constructed using Integrative Genome Viewer (IGV) for samples that were negative on RT-PCR assays but detected by DRAGEN COVIDSeq Pipeline.