Evidence map›Paper›PMID 36714842›Full record

ArticlePNAS nexus2022

Subtyping of major SARS-CoV-2 variants reveals different transmission dynamics based on 10 million genomes.

Hsin-Chou Yang, Jen-Hung Wang, Chih-Ting Yang, Yin-Chun Lin, Han-Ni Hsieh, Po-Wen Chen, Hsiao-Chi Liao, Chun-Houh Chen, James C Liao

Abstract read
In one paragraph

Article in PNAS nexus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Hsin-Chou YangInstitute of Statistical Science, Academia Sinica, Academia Rd, Nangang District Taipei 115, Taiwan.ORCID https://orcid.org/0000-0001-6853-7881
Jen-Hung WangInstitute of Statistical Science, Academia Sinica, Academia Rd, Nangang District Taipei 115, Taiwan.ORCID https://orcid.org/0000-0001-5437-222X
Chih-Ting YangInstitute of Statistical Science, Academia Sinica, Academia Rd, Nangang District Taipei 115, Taiwan.ORCID https://orcid.org/0000-0003-0062-4131
Yin-Chun LinInstitute of Statistical Science, Academia Sinica, Academia Rd, Nangang District Taipei 115, Taiwan.ORCID https://orcid.org/0000-0002-8320-6965
Han-Ni HsiehInstitute of Statistical Science, Academia Sinica, Academia Rd, Nangang District Taipei 115, Taiwan.ORCID https://orcid.org/0000-0003-1740-5963
Po-Wen ChenInstitute of Statistical Science, Academia Sinica, Academia Rd, Nangang District Taipei 115, Taiwan.ORCID https://orcid.org/0000-0002-0909-1047
Hsiao-Chi LiaoInstitute of Statistical Science, Academia Sinica, Academia Rd, Nangang District Taipei 115, Taiwan.ORCID https://orcid.org/0000-0002-9586-1246
Chun-Houh ChenInstitute of Statistical Science, Academia Sinica, Academia Rd, Nangang District Taipei 115, Taiwan.ORCID https://orcid.org/0000-0003-0899-7477
James C LiaoInstitute of Biological Chemistry, Academia Sinica, Academia Rd, Nangang District Taipei 115, Taiwan.ORCID https://orcid.org/0000-0002-4580-7276

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

SARS-CoV-2 continues to evolve, causing waves of the pandemic. Up to May 2022, 10 million genome sequences have accumulated, which are classified into five major variants of concern. With the growing number of sequenced genomes, analysis of the big dataset has become increasingly challenging. Here we developed systematic approaches based on sets of correlated single nucleotide variations (SNVs) for comprehensive subtyping and pattern recognition of transmission dynamics. The approach outperformed single-SNV and spike-centric scans. Moreover, the derived subtypes elucidate the relationship of signature SNVs and transmission dynamics. We found that different subtypes of the same variant, including Delta and Omicron exhibited distinct temporal trajectories. For example, some Delta and Omicron subtypes did not spread rapidly, while others did. We identified sets of characteristic SNVs that appeared to enhance transmission or decrease efficacy of antibodies for some subtypes. We also identified a set of SNVs that appeared to suppress transmission or increase viral sensitivity to antibodies. For the Omicron variant, the dominant type in the world, we identified the subtypes with enhanced and suppressed transmission in an analysis of eight million genomes as of March 2022 and further confirmed the findings in a later analysis of ten million genomes as of May 2022. While the "enhancer" SNVs exhibited an enriched presence on the spike protein, the "suppressor" SNVs are mainly elsewhere. Disruption of the SNV correlation largely destroyed the enhancer-suppressor phenomena. These results suggest the importance of fine subtyping of variants, and point to potential complex interactions among SNVs.

Indexed as

allelic associationCOVID-19SARS-CoV-2 variantssingle nucleotide variationviral subtype

Identifiers

PMID36714842
PMCPMC9802201

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.