sampling bias and incorrect rooting make phylogenetic network tracing of sars cov 2 CORD-Papers-2022-06-02 (Version 1)

Title: Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable
Published: 2020-06-09
Journal: Proc Natl Acad Sci U S A
DOI: 10.1073/pnas.2007295117
DOI_URL: http://doi.org/10.1073/pnas.2007295117
Author Name: Mavian Carla
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mavian_carla
Author Name: Pond Sergei Kosakovsky
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pond_sergei_kosakovsky
Author Name: Marini Simone
Author link: https://covid19-data.nist.gov/pid/rest/local/author/marini_simone
Author Name: Magalis Brittany Rife
Author link: https://covid19-data.nist.gov/pid/rest/local/author/magalis_brittany_rife
Author Name: Vandamme Anne Mieke
Author link: https://covid19-data.nist.gov/pid/rest/local/author/vandamme_anne_mieke
Author Name: Dellicour Simon
Author link: https://covid19-data.nist.gov/pid/rest/local/author/dellicour_simon
Author Name: Scarpino Samuel V
Author link: https://covid19-data.nist.gov/pid/rest/local/author/scarpino_samuel_v
Author Name: Houldcroft Charlotte
Author link: https://covid19-data.nist.gov/pid/rest/local/author/houldcroft_charlotte
Author Name: Villabona Arenas Julian
Author link: https://covid19-data.nist.gov/pid/rest/local/author/villabona_arenas_julian
Author Name: Paisie Taylor K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/paisie_taylor_k
Author Name: Trovo Ndia S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/trovo_ndia_s
Author Name: Boucher Christina
Author link: https://covid19-data.nist.gov/pid/rest/local/author/boucher_christina
Author Name: Zhang Yun
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_yun
Author Name: Scheuermann Richard H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/scheuermann_richard_h
Author Name: Gascuel Olivier
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gascuel_olivier
Author Name: Lam Tommy Tsan Yuk
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lam_tommy_tsan_yuk
Author Name: Suchard Marc A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/suchard_marc_a
Author Name: Abecasis Ana
Author link: https://covid19-data.nist.gov/pid/rest/local/author/abecasis_ana
Author Name: Wilkinson Eduan
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wilkinson_eduan
Author Name: de Oliveira Tulio
Author link: https://covid19-data.nist.gov/pid/rest/local/author/de_oliveira_tulio
Author Name: Bento Ana I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bento_ana_i
Author Name: Schmidt Heiko A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/schmidt_heiko_a
Author Name: Martin Darren
Author link: https://covid19-data.nist.gov/pid/rest/local/author/martin_darren
Author Name: Hadfield James
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hadfield_james
Author Name: Faria Nuno
Author link: https://covid19-data.nist.gov/pid/rest/local/author/faria_nuno
Author Name: Grubaugh Nathan D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/grubaugh_nathan_d
Author Name: Neher Richard A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/neher_richard_a
Author Name: Baele Guy
Author link: https://covid19-data.nist.gov/pid/rest/local/author/baele_guy
Author Name: Lemey Philippe
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lemey_philippe
Author Name: Stadler Tanja
Author link: https://covid19-data.nist.gov/pid/rest/local/author/stadler_tanja
Author Name: Albert Jan
Author link: https://covid19-data.nist.gov/pid/rest/local/author/albert_jan
Author Name: Crandall Keith A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/crandall_keith_a
Author Name: Leitner Thomas
Author link: https://covid19-data.nist.gov/pid/rest/local/author/leitner_thomas
Author Name: Stamatakis Alexandros
Author link: https://covid19-data.nist.gov/pid/rest/local/author/stamatakis_alexandros
Author Name: Prosperi Mattia
Author link: https://covid19-data.nist.gov/pid/rest/local/author/prosperi_mattia
Author Name: Salemi Marco
Author link: https://covid19-data.nist.gov/pid/rest/local/author/salemi_marco
sha: 3b3794fc48e257c10d1130a51f9fef688c28c215
license: cc-by
license_url: https://creativecommons.org/licenses/by/4.0/
source_x: Medline; PMC
source_x_url: https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 32381734
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/32381734
pmcid: PMC7293693
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293693
url: https://doi.org/10.1073/pnas.2007295117 https://www.ncbi.nlm.nih.gov/pubmed/32381734/
has_full_text: TRUE
Keywords Extracted from Text Content: SARS-CoV-2's C. human bats coronavirus humans clade/ lineage Wuhan root nucleotide https://www.gisaid.org/ Nextstrain (5), but this Americans coronavirus disease 2019 SARS-CoV-2 B COVID-19 SI Appendix network solid Wuhan B-type virus
Extracted Text Content in Record: First 5000 Characters:There is obvious interest in gaining insights into the epidemiology and evolution of the virus that has recently emerged in humans as the cause of the coronavirus disease 2019 (COVID-19) pandemic. The recent paper by Forster et al. (1) analyzed 160 severe acute respiratory syndrome coronavirus (SARS-CoV-2) full genomes available (https://www.gisaid.org/) in early March 2020. The central claim is the identification of three main SARS-CoV-2 types, named A, B, and C, circulating in different proportions among Europeans and Americans (types A and C) and East Asians (type B). According to a median-joining network analysis, variant A is proposed to be the ancestral type because it links to the sequence of a coronavirus from bats, used as an outgroup to trace the ancestral origin of the human strains. The authors further suggest that the "ancestral Wuhan B-type virus is immunologically or environmentally adapted to a large section of the East Asian population, and may need to mutate to overcome resistance outside East Asia." There are several serious flaws with their findings and interpretation. First, and most obviously, the sequence identity between SARS-CoV-2 and the bat virus is only 96.2%, implying that these viral genomes (which are nearly 30,000 nucleotides long) differ by more than 1,000 mutations. Such a distant outgroup is unlikely to provide a reliable root for the network. Yet, strangely, the branch to the bat virus, in figure 1 of their paper, is only 16 or 17 mutations in length. Indeed, the network seems to be misrooted, because (see their SI Appendix, figure S4 ) a virus from Wuhan from week 0 (24 December 2019) is portrayed as a descendant of a clade of viruses collected in weeks 1 to 9 (presumably from many places outside China), which makes no evolutionary (2) or epidemiological sense (3) . As for the finding of three main SARS-CoV-2 types, we must underline that finding different lineages in different countries and regions is expected with any RNA virus experiencing founder effects (2) . According to Forster et al.'s (1) own analysis, a single synonymous mutation (nucleotide change in a gene that does not result in a modified protein) distinguishes type A from type B, while one nonsynonymous mutation (resulting in a protein with a single amino acid change) separates types A and C, and another one separates types B and C. Given SARS-CoV-2's fast evolutionary rate, random emergence of new mutations is entirely expected, even in a relatively short timeframe (4) . When a viral strain is introduced and spreads in a new population, such random mutations can be propagated without them being selected or advantageous, due to founder effects. The fact that SARS-CoV-2 sequences show some geographical clustering is not new and is nicely and interactively shown on Nextstrain (5), but this cannot be used as a proof of biological differences unless backed by solid experimental data (6) . This is particularly true for the work of Forster et al., since their findings are based on a nonrepresentative dataset of 160 genomes, with no significant correlation between prevalence of confirmed cases and number of sequenced strains per country (7, 8) . The essential role of representative sampling is well documented in the literature (9), but was not acknowledged by the authors, who, instead, claim that their "network faithfully traces routes of infections for documented cases," without taking into consideration missing viral diversity, or evaluating multiple transmission hypotheses that would be consistent with sequence data, or even providing any support on the robustness of the branching pattern in their network. Ultimately, no firm conclusion should be drawn without evaluating the probability of alternative dissemination routes. The inappropriate application and interpretation of phylogenetic methods to analyze limited and unevenly sampled datasets begs for restraint about origin, directionality, and early clade/ lineage inference of SARS-CoV-2. We feel the urgency to reframe the current debate in more rigorous scientific terms, given the dangerous implications of misunderstanding the true dispersal dynamics of SARS-CoV-2 and the COVID-19 pandemic. We are grateful to Paul Sharp, Andrew Rambaut
Keywords Extracted from PMC Text: https://www.gisaid.org/ SARS-CoV-2 " solid COVID-19 bats 's (1 root Wuhan B-type virus Wuhan Americans Nextstrain (5), but this C. SI Appendix B nucleotide human coronavirus disease 2019 humans coronavirus SARS-CoV-2's
Extracted PMC Text Content in Record: First 5000 Characters:There is obvious interest in gaining insights into the epidemiology and evolution of the virus that has recently emerged in humans as the cause of the coronavirus disease 2019 (COVID-19) pandemic. The recent paper by Forster et al. (1) analyzed 160 severe acute respiratory syndrome coronavirus (SARS-CoV-2) full genomes available (https://www.gisaid.org/) in early March 2020. The central claim is the identification of three main SARS-CoV-2 types, named A, B, and C, circulating in different proportions among Europeans and Americans (types A and C) and East Asians (type B). According to a median-joining network analysis, variant A is proposed to be the ancestral type because it links to the sequence of a coronavirus from bats, used as an outgroup to trace the ancestral origin of the human strains. The authors further suggest that the "ancestral Wuhan B-type virus is immunologically or environmentally adapted to a large section of the East Asian population, and may need to mutate to overcome resistance outside East Asia." There are several serious flaws with their findings and interpretation. First, and most obviously, the sequence identity between SARS-CoV-2 and the bat virus is only 96.2%, implying that these viral genomes (which are nearly 30,000 nucleotides long) differ by more than 1,000 mutations. Such a distant outgroup is unlikely to provide a reliable root for the network. Yet, strangely, the branch to the bat virus, in figure 1 of their paper, is only 16 or 17 mutations in length. Indeed, the network seems to be misrooted, because (see their SI Appendix, figure S4) a virus from Wuhan from week 0 (24 December 2019) is portrayed as a descendant of a clade of viruses collected in weeks 1 to 9 (presumably from many places outside China), which makes no evolutionary (2) or epidemiological sense (3). As for the finding of three main SARS-CoV-2 types, we must underline that finding different lineages in different countries and regions is expected with any RNA virus experiencing founder effects (2). According to Forster et al.'s (1) own analysis, a single synonymous mutation (nucleotide change in a gene that does not result in a modified protein) distinguishes type A from type B, while one nonsynonymous mutation (resulting in a protein with a single amino acid change) separates types A and C, and another one separates types B and C. Given SARS-CoV-2's fast evolutionary rate, random emergence of new mutations is entirely expected, even in a relatively short timeframe (4). When a viral strain is introduced and spreads in a new population, such random mutations can be propagated without them being selected or advantageous, due to founder effects. The fact that SARS-CoV-2 sequences show some geographical clustering is not new and is nicely and interactively shown on Nextstrain (5), but this cannot be used as a proof of biological differences unless backed by solid experimental data (6). This is particularly true for the work of Forster et al., since their findings are based on a nonrepresentative dataset of 160 genomes, with no significant correlation between prevalence of confirmed cases and number of sequenced strains per country (7, 8). The essential role of representative sampling is well documented in the literature (9), but was not acknowledged by the authors, who, instead, claim that their "network faithfully traces routes of infections for documented [COVID-19] cases," without taking into consideration missing viral diversity, or evaluating multiple transmission hypotheses that would be consistent with sequence data, or even providing any support on the robustness of the branching pattern in their network. Ultimately, no firm conclusion should be drawn without evaluating the probability of alternative dissemination routes. The inappropriate application and interpretation of phylogenetic methods to analyze limited and unevenly sampled datasets begs for restraint about origin, directionality, and early clade/lineage inference of SARS-CoV-2. We feel the urgency to reframe the current debate in more rigorous scientific terms, given the dangerous implications of misunderstanding the true dispersal dynamics of SARS-CoV-2 and the COVID-19 pandemic.
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