Estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples
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Estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples. / Pipes, Lenore; Chen, Zihao; Afanaseva, Svetlana; Nielsen, Rasmus.
In: Cell Reports Methods, Vol. 2, No. 10, 100313, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples
AU - Pipes, Lenore
AU - Chen, Zihao
AU - Afanaseva, Svetlana
AU - Nielsen, Rasmus
N1 - Publisher Copyright: © 2022 The Author(s)
PY - 2022
Y1 - 2022
N2 - Wastewater surveillance has become essential for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The quantification of SARS-CoV-2 RNA in wastewater correlates with the coronavirus disease 2019 (COVID-19) caseload in a community. However, estimating the proportions of different SARS-CoV-2 haplotypes has remained technically difficult. We present a phylogenetic imputation method for improving the SARS-CoV-2 reference database and a method for estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples. The phylogenetic imputation method uses the global SARS-CoV-2 phylogeny and imputes based on the maximum of the posterior probability of each nucleotide. We show that the imputation method has error rates comparable to, or lower than, typical sequencing error rates, which substantially improves the reference database and allows for accurate inferences of haplotype composition. Our method for estimating relative proportions of haplotypes uses an initial step to remove unlikely haplotypes and an expectation maximization (EM) algorithm for obtaining maximum likelihood estimates of the proportions of different haplotypes in a sample. Using simulations with a reference database of >3 million SARS-CoV-2 genomes, we show that the estimated proportions reflect the true proportions given sufficiently high sequencing depth.
AB - Wastewater surveillance has become essential for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The quantification of SARS-CoV-2 RNA in wastewater correlates with the coronavirus disease 2019 (COVID-19) caseload in a community. However, estimating the proportions of different SARS-CoV-2 haplotypes has remained technically difficult. We present a phylogenetic imputation method for improving the SARS-CoV-2 reference database and a method for estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples. The phylogenetic imputation method uses the global SARS-CoV-2 phylogeny and imputes based on the maximum of the posterior probability of each nucleotide. We show that the imputation method has error rates comparable to, or lower than, typical sequencing error rates, which substantially improves the reference database and allows for accurate inferences of haplotype composition. Our method for estimating relative proportions of haplotypes uses an initial step to remove unlikely haplotypes and an expectation maximization (EM) algorithm for obtaining maximum likelihood estimates of the proportions of different haplotypes in a sample. Using simulations with a reference database of >3 million SARS-CoV-2 genomes, we show that the estimated proportions reflect the true proportions given sufficiently high sequencing depth.
KW - COVID-19
KW - expectation maximization
KW - imputation
KW - SARS-CoV-2
KW - wastewater surveillance
KW - wastewater-based epidemiology
U2 - 10.1016/j.crmeth.2022.100313
DO - 10.1016/j.crmeth.2022.100313
M3 - Journal article
C2 - 36159190
AN - SCOPUS:85139179174
VL - 2
JO - Cell Reports Methods
JF - Cell Reports Methods
SN - 2667-2375
IS - 10
M1 - 100313
ER -
ID: 331788323