04 October, 2024

Contributing authors:

Data analysis, code, and maintenance of this notebook: Justin Jia (current), Carmen Lia Murall, Raphaël Poujol, Susanne Kraemer, Arnaud N’Guessan, Sarah Otto, Art Poon, Jesse Shapiro, Fiona Brinkman, Zohaib Anwar, and Erin Gill. Input and direction by other members of Pillar 6 and CAMEO, which include: Caroline Colijn, Jorg Fritz, Morgan Langille, Paul Gordon, Julie Hussin, Jeff Joy, and William Hsiao.

Sequence collection, generation, release, and feedback on analyses: Canadian laboratories as part of the CPHLN and CanCOGeN are making these data publicly available and contribute feedback on analyses presented here. A complete list of lab authors is in this repository, and more details are below in the Acknowledgement section.

Citation

To cite Duotang in publications, please use:

Gill E.E., et al. The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology. arXiv.
doi:10.48550/arXiv.2405.04734

SARS-CoV-2 In Canada

Introduction

This notebook was built to explore Canadian SARS-CoV-2 genomic and epidemiological data with the aim of investigating viral evolution and spread. It is developed by the CAMEO team (Computational Analysis, Modelling and Evolutionary Outcomes Group) associated with the Coronavirus Variants Rapid Response Network (CoVaRR-Net) for sharing with collaborators, including public health labs. These analyses are freely available and open source, enabling code reuse by public health authorities and other researchers for their own use.

Canadian genomic and epidemiological data will be regularly pulled from various public sources (see list below) to keep these analyses up-to-date. Only representations of aggregate data will be posted here.

Important limitations

These analyses represent only a snapshot of SARS-CoV-2 evolution in Canada. Only some infections are detected by PCR testing, only some of those are sent for whole-genome sequencing, and not all sequences are posted to public facing reposittories. Furthermore, sequencing volumes and priorities have changed during the pandemic, specific variants or populations might be preferentially sequenced at certain times in certain jurisdictions. When possible, these differences in sampling strategies are mentioned but they are not always known. With the arrival of the Omicron wave, many jurisdictions across Canada reached testing and sequencing capacity mid-late December 2021 and thus switched to targeted testing of priority groups (e.g., hospitalized patients, health care workers, and people in high-risk settings). Currently, most jurisdictions are sequencing mainly hospitalized patients or outbreaks, with little population-level random sampling, underestimating case counts and viral diversity.

Thus, interpretation of these plots and comparisons between health regions should be made with caution, considering that the data may not be fully representative. These analyses are subject to frequent change given new data and updated lineage designations.

The last sample collection date is 16 September, 2024

Current SARS-CoV-2 situation

KP.3.1.1 is still dominant, and by far the most prevalent lineage. Variants growing include XEC, which is growing consistently in multiple provinces, though data is very limited. However any growth advantage versus KP.3.1.1 for any variant is still relatively small.

Variants of current interest (due to their current/potential growth advantage, mutations of potential functional significance, or spread in other countries):

  • XEC which also has a KP.3-type spike, (recombinant of KS.1.1 and KP.3.3) but some changes that appear to confer a slight advantage including immune evasion.
  • Additional variants with S:S31del (deletion also referred to as S:S31-) including KP.3.1.1 subvariants (MC.**) and KP.3 subvariants.

Plus any highly divergent variants (“saltation” lineages with a sudden increase in number of mutations) and sublineages with additional combinations of mutations identified through mutation scanning (see some references under List of Useful Tools below).

We thank the global team of those monitoring variants (such as those posting issues here: https://github.com/cov-lineages/pango-designation/issues), and other SARS-CoV-2 genome analysis tool providers (see List of Useful Tools below), which play a key role in identifying new variants of note.

Sublineages in Canada

There are 297 unique named variants currently circulating in Canada since 2024-05-09 (last 120 days). Please see Pango lineage table for number of sequences per lineage present.

Below is an interactive visualization showing frequencies of ciruclating lineages, sub-divided by major sub-lineages, currently circulating in Canada. A table of lineage frequencies can be downloaded by clicking on the (Frequency Table Download) button.

Tips: Click and drag to zoom, double click to reset. Clicking on an item in the legend will hide it, double clicking an item in legend will hide everything else but that item.

Last 120 days

Last 120 days sublineages starting from 2024-05-09 (Frequency Table Download)

BA.1

BA.1 sublineages (Frequency Table Download)

BA.2

BA.2 sublineages (Frequency Table Download)

BA.4

BA.4 sublineages (Frequency Table Download)

BA.5

BA.5 sublineages (Frequency Table Download)

Recombinants

Recombinants sublineages (Frequency Table Download)

## [1] -9

Selection on recent variants

Here we examine the relative rate of spread of the different sublineages of SARS-CoV-2 currently circulating in Canada. Specifically, we determine if a new or emerging lineage has a selective advantage (s), and by how much, against a previously common reference lineage (broad scale (and in the Fastest Growing Lineages section): JN.1* and at the fine scale, against JN.1; see methods for more details about selection and how it is estimated).

Currently, the major group of SARS-CoV-2 lineages circulating are BA.2.86* variants, particularly JN.1 and its descendants. Thus, at the broad scale, we are currently track the frequencies of JN.1* descendants other than KP.3*, KP.3* descendants, XBB descendants and other BA.2 lineages (mainly BA.2.86 lineages not in JN.1*).

Left plot: y-axis is the proportion of these sub-lineages over time. Right plot: y-axis describes the logit function, log(freq(KP.3, KP.Others, Others)/freq(JN.1*)), which gives a straight line whose slope is the selection coefficient if selection is constant over time (see methods).

For comparison, Alpha had a selective advantage of s ~ 6%-11% per day over preexisting SARS-CoV-2 lineages, and Delta had a selective advantage of about 10% per day over Alpha.

Caveat: These selection analyses must be interpreted with caution due to the potential for non-representative sampling, lags in reporting, and spatial heterogeneity in prevalence of different sublineages across Canada. Provinces that do not have at least 20 sequences of a lineage during this time frame are not displayed.

Canada

Canada

BC

British Columbia

AB

Alberta

SK

Saskatchawan

MB

Manitoba

ON

Ontario

QC

Quebec

NS

Nova Scotia

NB

New Brunswick

NL

Newfoundland and Labrador

NULL

Fastest growing lineages

Here we show the selection estimates and their 95% confidence intervals for SARS-CoV-2 lineages with more than 10 sequences in present in a region since 2024-05-09, and with enough data to estimate the confidence interval. Each selection estimate measures the growth rate relative to JN.1 stricto (i.e., sequences designated as JN.1 and not its descendants). Plots showing the change in variant frequency over time in Canada as a whole are given below for lineages with more than 50 sequences. For Canada-wide plot, a dot with a circle border indicates lineages with a positive selection coefficient in multiple provinces. The most prevelant lineage in the last two weeks is highlighted in grey. A table of the selection estimates is available for download below.

Growth advantage of 0-5% corresponds to doubling times of more than two weeks, with 5-10% reflecting one to two week doubling times and over 10% representing significant growth of less than one week doubling time. Note that estimating selection of sub-variants with low sequence counts (points with less than 100 counts) is prone to error, such as mistaking one-time super spreader events or pulses of sequence data from one region as selection. Estimates with lower sequence counts in one region should be considered as very preliminary.

Plot (stricto)

This plot highlights single lineages that are growing fastest.

Canada

Plot single lineages in Canada *

BC

Plot single lineages in British Columbia

AB

Plot single lineages in Alberta

SK

Plot single lineages in Saskatchawan

MB

Plot single lineages in Manitoba

ON

Plot single lineages in Ontario

QC

Plot single lineages in Quebec

NS

Plot single lineages in Nova Scotia

NB

Plot single lineages in New Brunswick

NL

Plot single lineages in Newfoundland and Labrador

Plot (non stricto)

This plot highlights the groups of related lineages that are growing fastest (e.g., JN.1* is the monophyletic clade that includes JN.1.7 and all other JN.1 sublineages, excluding recombinants.

Canada

Plot single lineages in Canada

BC

Plot single lineages in British Columbia

AB

Plot single lineages in Alberta

SK

Plot single lineages in Saskatchawan

MB

Plot single lineages in Manitoba

ON

Plot single lineages in Ontario

QC

Plot single lineages in Quebec

NS

Plot single lineages in Nova Scotia

NB

Plot single lineages in New Brunswick

NL

Plot single lineages in Newfoundland and Labrador

Table of all the selection estimates

Download Table

Sublineages selection

XBB sublineages

Here we show the trends of the various XBB.* sublineages over time, relative to the frequency of JN.1 by itself (shown for sublineages with at least 50 (Canada) or 20 (provinces) cases). Proportions shown here are only among JN.1 (stricto) and the lineage illustrated. Note that these plots are not necessarily representative of trends in each province and that mixing of data from different provinces may lead to shifts in frequency that are not due to selection.

Canada

Canada

BC

British Columbia

AB

Alberta

SK

Saskatchawan

MB

Manitoba

ON

Ontario

QC

Quebec

NS

Nova Scotia

NB

New Brunswick

NL

Newfoundland and Labrador

NULL

BA.2 sublineages

Here we show the trends of the various BA.2.* sublineages over time, excluding any recombinants, relative to the frequency of JN.1 by itself (shown for sublineages with at least 50 (Canada) or 20 (provinces) cases). Proportions shown here are only among JN.1 (stricto) and the lineage illustrated. Note that these plots are not necessarily representative of trends in each province and that mixing of data from different provinces may lead to shifts in frequency that are not due to selection.

Canada

Canada

Only the three most strongly selected variants are displayed. Click here to see the rest.

BC

British Columbia

Only the three most strongly selected variants are displayed. Click here to see the rest.

AB

Alberta

Only the three most strongly selected variants are displayed. Click here to see the rest.

SK

Saskatchawan

MB

Manitoba

ON

Ontario

Only the three most strongly selected variants are displayed. Click here to see the rest.

QC

Quebec

Only the three most strongly selected variants are displayed. Click here to see the rest.

NS

Nova Scotia

NB

New Brunswick

NL

Newfoundland and Labrador

NULL

VIRUS-MVP: Mutational composition of Omicron

The image below is a screenshot from VIRUS-MVP showing a snapshot of the mutations from lineages actively circulating in Canada. Please click on the link to scan the entire genome and examine the functional impact of the mutations. More details, click on the image below or see https://virusmvp.org/covid-mvp.

covid-mvp

Variants in Canada over time

This plot shows the changing composition of sequences for all Canadian data posted to the VirusSeq Portal according to Pango lineage designation, up to 16 September, 2024. Because sampling and sequencing procedures vary by region and time, this does not necessarily reflect the true composition of SARS-CoV-2 viruses in Canada over time.

Canadian trees

Below is an interactive visualization of a subsampled phylogenetic snapshot of SARS-CoV-2 genomes from Canada. Please see methods for details.

The x-axis of the time tree represents the estimated number of years from today for which the root emerged. The x-axis of the diversity trees shows the number of mutations from the outgroup.

Hovering over a node will display a tool tip with sequence metadata, clicking on a node with the tooltip shown will copy the isolate ID to your clipboard.

### metadata and trees
source("scripts/tree.r")

# load trees from files
mltree <- read.tree(paste0(params$datadir,"/aligned_nonrecombinant_sample1.rtt.nwk"))
ttree <- read.tree(paste0(params$datadir,"/aligned_nonrecombinant_sample1.timetree.nwk"))
recombTTree <- read.tree(paste0(params$datadir,"/aligned_recombinant_X_sample1.timetree.nwk"))
recombMLree <- read.tree(paste0(params$datadir,"/aligned_recombinant_X_sample1.rtt.nwk"))

#stopifnot(all(sort(mltree$tip.label) == sort(ttree$tip.label)))
dateseq <- seq(ymd('2019-12-01'), ymd('2022-12-01'), by='3 month')

# tips are labeled with [fasta name]_[lineage]_[coldate]
# extracting just the first part makes it easier to link to metadata
mltree$tip.label <- reduce.tipnames(mltree$tip.label)
ttree$tip.label <- reduce.tipnames(ttree$tip.label)
recombTTree$tip.label  <- reduce.tipnames(recombTTree$tip.label)


fieldnames<- c("fasta_header_name", "province", "host_gender", "host_age_bin",
               "sample_collected_by", "purpose_of_sampling",
               "lineage", "pango_group","week", "GID")
# extract rows from metadata table that correspond to ttree 
metasub1 <- meta[meta$fasta_header_name%in% ttree$tip.label, fieldnames]
# sort rows to match tip labels in tree
metasub1 <- metasub1[match(ttree$tip.label, metasub1$fasta_header_name), ]


#omi tree metadata
#metasub_omi <- metasub1[grepl("Omicron",metasub1$pango_group ), ]

#recomb tree metadata
mmetasub_recomb <- meta[meta$fasta_header_name%in% recombTTree$tip.label, fieldnames]
mmetasub_recomb <- mmetasub_recomb[match(recombTTree$tip.label, mmetasub_recomb$fasta_header_name), ]

#scale to number of mutations
mltree$edge.length <- mltree$edge.length*29903
mltree <- ladderize(mltree, FALSE)
recombMLree$edge.length <- recombMLree$edge.length*29903
recombMLree <- ladderize(recombMLree, FALSE)

#enforce a non zero branch length so lines can be drawn in javascript
###Time Tree
ttree$edge.length[ttree$edge.length == 0] <- 1e-4
#ttree <- ladderize(ttree, FALSE)
recombTTree$edge.length[recombTTree$edge.length == 0] <- 1e-4
#recombTTree <- ladderize(recombTTree, FALSE)

hab=unique(meta$host_age_bin)
hab=hab[order(hab)]
months=unique(meta$month)
months=as.character(months[order(months)])
weeks=unique(meta$week)
weeks=as.character(weeks[order(weeks)])
presetColors=data.frame(name=c("other",
                               VOCVOI$name,
                               hab,
                               months,
                               weeks), 
                        color=c("#777777",
                                VOCVOI$color,
                                rev(hcl.colors(length(hab)-1, "Berlin")),"#777777",
                                hcl.colors(length(months), "Berlin"),
                                hcl.colors(length(weeks), "Berlin")
                                ))

#suppressWarnings({
#  res <- ace(metasub1$pango.group, ttree2, type="discrete", model="ER")
#})
#idx <- apply(res$lik.anc, 1, which.max)[2:nrow(res$lik.anc)]  # exclude root edge
#anc <- levels(as.factor(metasub1$pango.group))[idx]
source("scripts/tree.r")

timeTreeJsonObj <- DrawTree(ttree, metasub1, "timetree", presetColors, fieldnames=fieldnames)
recombTimeTreeJsonObj <-  DrawTree(recombTTree, mmetasub_recomb, "recombtimetree", presetColors, "lineage", fieldnames= fieldnames)

#diversity ML tree
diversityTreeJsonObj <- DrawTree(mltree, metasub1, "mltree", presetColors, fieldnames=fieldnames)
recombDiversityTreeJsonObj <- DrawTree(recombMLree, mmetasub_recomb, "recombmltree", presetColors, "lineage", fieldnames=fieldnames)
#write(recombDiversityTreeJsonObj, "downloads/test.json")
### omicron diversity tree
#MLtree_omi<-keep.tip(mltree, metasub_omi$fasta_header_name)

#OmicrondiversityTreeJsonObj <- DrawTree(MLtree_omi, metasub_omi, "omimltree", presetColors, fieldnames=fieldnames)

Time Tree