Duotang
Duotang
Duotang, a genomic epidemiology analyses and mathematical modelling notebook
Pillar 6
############################
##### GLOBAL VARIABLES #####
############################
source("scripts/utils.R")
#Date of the VirusSeq release
VirusSeq_release=format(as.Date(params$datestamp),"%B %d, %Y")
Variants_Canada_over_time_Start_Date=as.Date('2021-01-01')
pangoversion="4.2 (Viral AI)"
# load variant designations and colour palette from file
VOCVOI <- read.csv("resources/vocvoi.csv")
all.regions = data.frame(name=c("Canada","British Columbia", "Alberta",
"Saskatchawan", "Manitoba", "Ontario",
"Quebec", "Nova Scotia", "New Brunswick",
"Newfoundland and Labrador"),
shortname=c("Canada","BC", "AB",
"SK", "MA", "ON",
"QC", "NS", "NB","NL"))
pal <- VOCVOI$color
names(pal) <- VOCVOI$name
pal["other"] <- 'grey' # named character vector
## 1. LOAD processed metadata of Canadian sequences (with latest pangolin, division, and full seq IDs)
#Download metadata from VirusSeq, put the date here:
# this can be made more compact for faster loading
meta <- read.csv(gzfile("data_needed/virusseq.metadata.csv.gz"), sep="\t")
meta$province <- meta$geo_loc_name_state_province_territory
# Select only the column we want to use later
columnlist=c("fasta_header_name", "province", "host_gender", "host_age_bin",
"sample_collection_date", "sample_collected_by",
"purpose_of_sampling", "purpose_of_sequencing","lineage",
"raw_lineage", "gisaid_accession", "isolate")
meta <- meta[ , columnlist]
### metadata cleaning
unknown.str <- c("Undeclared", "Not Provided", "Restricted Access", "Missing",
"Not Applicable","","NA","unknow")
meta <- as.data.frame(apply(meta, 2, function(x) {
x[is.element(x, unknown.str)] <- "Unknown"
x
}))
meta$sample_collection_date <- as.Date(meta$sample_collection_date)
meta$week <- cut(meta$sample_collection_date, 'week')
meta$month <- gsub("-..$","",as.character(cut(meta$sample_collection_date, 'month')))
startdate <- max(meta$sample_collection_date) - days(120)
## parse PANGO lineages
source("scripts/scanlineages.R")
meta$pango_group <- create.pango.group(VOCVOI, meta)
meta$pango_group <- as.factor(meta$pango_group)
meta <- meta %>% mutate(gisaid_accession = str_replace(gisaid_accession, "EPI_ISL_", "")) %>% rename(GID=gisaid_accession)
fullpangotree <- makepangotree(unique(meta$raw_lineage))
latelineage_pangotree <- makepangotree(unique(
meta$raw_lineage[
meta$sample_collection_date > max(meta$sample_collection_date) - days(60)
]
))
reference <- getStrictoSubLineages("BQ.1", meta)
## 2. LOAD epidemiological data (PHAC)
#from: https://health-infobase.canada.ca/covid-19/epidemiological-summary-covid-19-cases.html?stat=num&measure=total&map=pt#a2
epidataCANall <- read.csv("data_needed/CanadianEpiData.csv")
epidataCANall$date <- as.Date(epidataCANall$date)
epidataCANall$prname <- gsub('_', ' ', epidataCANall$prname)
epidate <- tail(epidataCANall,1)$date #download date
epidataCANall$previousvalue <- 0
#small loop to get the numtoday column from previous versions of this file from the cumulative cases
for(row in 1:nrow(epidataCANall)) {
p <- epidataCANall[row, "prname"]
subdf <- epidataCANall[which(
(epidataCANall$date > epidataCANall[row, "date"] & epidataCANall$prname==p)
), ]
if(nrow(subdf) != 0) {
nextrow <- which( (epidataCANall$date == min(subdf$date) & epidataCANall$prname==p))
epidataCANall[nextrow, "previousvalue"] <- epidataCANall[row, "totalcases"]
}
}
epidataCANall$numtoday <- epidataCANall$totalcases - epidataCANall$previousvalue12 February, 2023
Introduction
This notebook is built to explore Canadian SARS-CoV-2 genomic and epidemiological data with the aim of investigating viral evolution and spread. It is for discussion with pillar 6’s team and for sharing with collaborators, e.g. public health labs. These analyses can spur further research within or across pillars, be used for reports (or data dashboards), support discussions with the science communication pillar for public dissemination, and enable code reuse by public health authorities/laboratories for their internal 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 caveats and disclaimers:
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. Sequencing volumes and priorities
have changed during the pandemic, and the sequencing strategy is
typically a combination of prioritizing outbreaks, travellers, public
health investigations, and random sampling for genomic surveillance.
For example, 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). Therefore, from this time onward, case counts are likely underestimated and the sequenced virus diversity is not necessarily representative of the virus circulating in the overall population.
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 24 January, 2023
SARS-CoV-2 in Canada
Current Situation
[Note a new analysis is now available below of a time tree limited to XBB and its descendents]
XBB.1.5 continues to grow steadily and significantly. Some other select variants with the S:F486P mutation are also showing growth in some regions such as CJ.1 and XBF. BQ.1.1 is increasing again in some regions of Canada along with some other select BQ.1 subvariants, BW.1.1, and CH.1.1. Differences between some provinces continue - see also the evolving plots below of “Fastest growing lineages in Canada”, per province.
Variants of current interest, due to their current/potential growth advantage, mutations of potential functional significance, or spread in other countries (note that some of these are not yet detected in Canada):
- BA.2 and BA.2.75 new subvariants, including possible misclassifications of XBB.1.5 as BA.2.10.1.
- BE.1.1 and other BA.5-type variants with S:F486P
- BM.1.1.1
- BM.2 (high predicted affinity for the human ACE2 receptor)
- BN.1* subvariants due to high predicted immune evasion
- BQ.1.8 with S:F486P
- BQ.1* select new descendants, particularly BQ.1.1.5 with S:Y453F
- BW.1, BW.1.1 (descendants of BA.5.6, with S:F486A)
- CH.1.1 (descendant of BM which is BA.2.75.3 with S:L452R) and sublineages
- CJ.1 with S:F486P
- DN.1/DN.1.1 (BQ.1.1.5 descendants with S:K147N) - that also has S:Y453F (and so is actually DN.1.1.1 but this isn’t in databases yet)
- DN.1.1.1 (highest predicted affinity for the human ACE2 receptor)
- DS.1 (highest predicted immune evasion)
- XAY.2 (BA.2/Delta recombinant with Delta’s S:L452R mutation plus the S:F486P mutation)
- XAY.3 (BA.2/Delta that also has S:F486P - a first case detected in Canada but not now showing growth)
- XBB.1, XBB.1.5, XBB.1.9.1, XBB.1.12, XBB.6.1 and subvariants with S:F486P (or other mutations of note like S:Y453F)
- XBF (BA.5.2/CJ.1 recombinant; has S:F486P and immune evasion like XBB.1.5)
- XBK and XBL (have S:F486P and immune evasion like XBB.1.5)
- XBM (first cases detected in Canada and the US; BA.2.76 / BF.3 recombinant, but growth not notable)
- …and other variants with mutations associated with immune evasion or higher binding affinity to the human ACE2 receptor (examples: S:R346T, S:K147N, S:Y453F, S:F486P), particularly those variants with sets of immune evasive mutations of interest.
Omicron sublineages in Canada
Here we take a look, sub-dividing the major sub-lineages currently circulating in Canada.
source("scripts/subtype_plotter.R")
sublineagestoplot = data.frame(name=c("BA.1*","BA.2*","BA.2*",
"BA.4*","BA.5.1*","BA.5.2*",
"BQ*","BA.5*","B*"),
tabname=c("BA.1","early BA.2","late BA.2",
"BA.4","BA.5.1","BA.5.2",
"BQ","Other BA.5","Divergent lineages"),
mindate=c("","","2022-08-01",
"","","",
"","","2022-03-01"),
maxdate=c("2022-06-01","2022-08-01","",
"","","",
"","",""),
exclude_previously_plotted=c(FALSE,FALSE,FALSE,
FALSE,FALSE,FALSE,
FALSE,TRUE,TRUE))
plotted=c()
tmp=apply(sublineagestoplot,1,function(sub){
cat("###", sub[["tabname"]], "\n")
cat("####", sub[["tabname"]],"sublineages")
mindate=NA
maxdate=NA
if(sub[["mindate"]]!=""){
cat(" from",sub[["mindate"]])
mindate=as.Date(sub[["mindate"]])
}
if(sub[["maxdate"]]!=""){
cat(" until",sub[["maxdate"]])
maxdate=as.Date(sub[["maxdate"]])
}
cat("\n\n")
set=getStrictoSubLineages(sub[["name"]],meta)
if(sub[["exclude_previously_plotted"]]){
set=set[!set %in% plotted]
}
rarelineages_names=plot.subvariants(sublineage=set,mindate=mindate,maxdate=maxdate)
rarelineages_names=plot.subvariants(sublineage=set,mindate=mindate,maxdate=maxdate,scale=T)
plotted <<- unique(c(plotted,set))
if(rarelineages_names!=""){
cat("The grey zone represent rare lineages (not in top 15) : ",rarelineages_names)
}
cat("\n\n")})BA.1
BA.1 sublineages until 2022-06-01
The
grey zone represent rare lineages (not in top 15) : BA.1.1.3(1),
BA.1.13.1(1), BA.1.15.3(1), BA.1.17.1(1), BA.1.5(1), BA.1.8(2), BD.1(2),
BA.1.1.11(3), BA.1.1.8(3), BA.1.12(3), BA.1.7(3), BA.1.1.4(4),
BA.1.1.9(4), BA.1.10(4), BA.1.14.2(4), BA.1.1.17(5), BA.1.14.1(5),
BA.1.9(7), BA.1.16(10), BA.1.1.13(12), BA.1.1.7(15), BC.2(18),
BA.1.15.2(27), BA.1.1.15(46), BA.1.21(55), BA.1.19(100), BA.1.1.1(164),
BA.1.13(165), BA.1.15.1(176), BA.1.1.2(284)
early BA.2
early BA.2 sublineages until 2022-08-01
The
grey zone represent rare lineages (not in top 15) : BA.2.10.3(1),
BA.2.24(1), BA.2.25(1), BA.2.3.8(1), BA.2.34(1), BA.2.38.3(1),
BA.2.58(1), BA.2.59(1), BA.2.63(1), BA.2.73(1), BA.2.75.3(1), BM.1(1),
BM.4.1(1), BA.2.14(2), BA.2.15(2), BA.2.16(2), BA.2.2.1(2), BA.2.27(2),
BA.2.3.5(2), BA.2.3.9(2), BA.2.30(2), BA.2.4(2), BA.2.42(2), BA.2.45(2),
BA.2.71(2), BA.2.9.6(2), BH.1(2), BA.2.11(3), BA.2.12.2(3),
BA.2.3.14(3), BA.2.38.2(3), BA.2.53(3), BA.2.54(3), BA.2.55(3),
BA.2.68(3), BA.2.70(3), BA.2.9.4(3), BA.2.29(4), BA.2.3.12(4),
BA.2.50(4), BA.2.62(4), BA.2.3.13(5), BA.2.3.15(5), BA.2.75.1(5),
BA.2.78(5), BA.2.79(5), BA.2.41(6), BA.2.44(6), BA.2.47(6),
BA.2.23.1(7), BA.2.3.16(7), BA.2.35(7), BA.2.64(7), BA.2.81(7), BG.6(7),
BA.2.43(8), BA.2.60(8), BA.2.3.7(9), BA.2.61(9), BA.2.31.1(10),
BA.2.38.1(10), BA.2.17(11), BA.2.22(11), BA.2.52(11), BA.2.72(11),
BA.2.9.1(11), BA.2.26(12), BA.2.5(12), BA.2.6(12), BA.2.9.2(12),
BA.2.49(13), BA.2.51(13), BA.2.8(14), BA.2.9.7(14), BG.4(14),
BA.2.9.5(16), BA.2.40.1(17), BA.2.13.1(18), BA.2.32(21), BA.2.2(24),
BA.2.7(25), BA.2.48(27), BA.2.74(29), BA.2.9.3(29), BG.5(29),
BA.2.3.1(31), BA.2.31(36), BA.2.56(39), BA.2.13(47), BA.2.3.17(57),
BA.2.75(59), BA.2.3.2(62), BA.2.36(66), BA.2.23(82), BA.2.76(92),
BA.2.1(93), BA.2.10.1(100), BA.2.37(106), BG.2(112), BA.2.3.11(123)
late BA.2
late BA.2 sublineages from 2022-08-01
The
grey zone represent rare lineages (not in top 15) : BA.2.13(1),
BA.2.13.1(1), BA.2.21(1), BA.2.40.1(1), BA.2.47(1), BA.2.48(1),
BA.2.75.10(1), BA.2.75.7(1), BA.2.75.9(1), BA.2.78(1), BA.2.82(1),
BA.2.9(1), BA.2.9.7(1), BG.5(1), BL.1.3(1), BM.2(1), BM.4.1.1(1),
BN.1.1.1(1), BS.1.1(1), CB.1(1), CH.1(1), CH.2(1), XBB.1.3(1), XBB.4(1),
XBH(1), BA.2.1(2), BA.2.38(2), BA.2.38.2(2), BA.2.65(2), BN.2(2),
CA.3(2), BA.2.75.6(3), BJ.1(3), BL.5(3), BR.1(3), CA.1(3), CM.3(3),
XBB.1.4(3), BA.2.18(4), BA.2.3(4), BL.4(4), XBB.1.1(4), BA.2.2(5),
BM.1(5), BH.1(6), BL.3(6), BN.2.1(6), BP.1(6), CM.4(6), BA.2.74(7),
BA.2.75.4(7), BG.2(7), BN.1.7(7), BM.1.1.1(8), BM.2.1(8), BN.1.9(8),
BR.3(8), BY.1.2(8), BN.5(9), BN.6(9), CJ.1(9), BR.4(10), CM.9(10),
BN.1.2.1(11), CH.1.1.1(11), CV.1(12), BN.3.1(13), BL.2(14),
CH.1.1.2(15), BA.2.76(17), XBB.3(17), BN.1.4(19), BR.1.2(20), CA.5(20),
BA.2.75.3(22), CA.7(23), BM.1.1(27), CM.1(29), XBB(29), BA.2.10.1(33),
CM.8.1(35), BL.1(36), BR.2.1(38), BN.1.2(42), BA.2.75.1(45), XBB.2(45),
BM.1.1.3(47), BN.1.5(47)
BA.4
BA.4 sublineages
The
grey zone represent rare lineages (not in top 15) : BA.4.1.3(1),
BA.4.8(1), BA.4.3(2), BA.4.1.10(3), BA.4.1.4(3), BA.4.1.9(3), BA.4.5(5),
DC.1(5)
BA.5.1
BA.5.1 sublineages
The
grey zone represent rare lineages (not in top 15) : BA.5.1.26(1),
BT.2(1), BA.5.1.16(2), BA.5.1.9(3), BA.5.1.19(5), BA.5.1.31(6),
DJ.1.1(6), CL.1(7), BA.5.1.21(8), BA.5.1.8(8), BT.1(8), DL.1(9),
BA.5.1.28(10), DE.2(12), BA.5.1.4(14), BA.5.1.17(23), BA.5.1.20(53),
BA.5.1.27(56), DE.1(75), BA.5.1.18(77), BA.5.1.30(85)
BA.5.2
BA.5.2 sublineages
The
grey zone represent rare lineages (not in top 15) : BA.5.2.10(1),
BA.5.2.38(1), BA.5.2.39(1), BF.17(1), BF.23(1), BF.24(1), BF.3.1(1),
CD.1(1), CP.5(1), CT.1(1), DA.1(1), BF.19(2), BF.29(2), BF.32(2),
BF.7.10(2), BF.7.3(2), CR.2(2), DG.1(2), BF.30(3), BF.7.1(3),
BF.7.4.2(3), BU.3(3), BF.7.12(4), BV.2(4), CR.1.2(4), BF.18(5),
BA.5.2.36(6), CR.1.1(6), BF.11.4(7), BF.34(7), BU.2(8), BA.5.2.12(9),
BA.5.2.7(9), BF.31(9), BF.1.1(10), BA.5.2.44(11), CK.2.1(12),
BF.7.8(13), CR.1(13), CK.3(14), CP.1(16), BA.5.2.37(17), CE.1(17),
CK.2(17), CN.1(19), BA.5.2.4(20), BU.1(20), BF.15(21), CG.1(21),
CK.2.1.1(22), BF.6(23), BF.20(24), BA.5.2.32(36), BF.12(36),
BA.5.2.19(38), DB.1(41), BA.5.2.18(42), BF.25(42), BA.5.2.31(43),
BA.5.2.16(44), BF.16(44), BF.2(59), BA.5.2.23(61), BA.5.2.33(62),
BA.5.2.26(77), CK.1(82), BF.31.1(87), BA.5.2.14(88), BA.5.2.35(90),
BA.5.2.24(92), BF.11.1(94), BF.7.6(104), BF.14(109), BF.7.5(113),
BF.7.7(121), BA.5.2.28(122), BF.7.4.1(126), BF.3(131), BA.5.2.25(141),
BA.5.2.27(144), BF.11.3(170), BF.13(174), BF.7.4(188), BA.5.2.6(195),
BF.4(204), BF.8(220), BA.5.2.2(226), BA.5.2.34(245), BF.1(256),
BF.21(262), BA.5.2.8(273), BA.5.2.3(323)
BQ
BQ sublineages
The
grey zone represent rare lineages (not in top 15) : BQ.1.1.22(1),
BQ.1.1.26(1), BQ.1.1.27(1), BQ.1.26(1), BQ.1.9(1), BQ.2(1),
BQ.1.1.14(2), BQ.1.20(2), BQ.1.4(2), BQ.1.1.28(3), BQ.1.7(3),
BQ.1.1.17(4), BQ.1.1.24(4), BQ.1.8.2(4), BQ.1.18(6), BQ.1.1.23(8),
BQ.1.1.25(9), BQ.1.19(12), BQ.1.6(17), BQ.1.1.19(19), BQ.1.1.2(20),
BQ.1.1.6(35), BQ.1.1.13(42), BQ.1.15(45), BQ.1.1.15(55), BQ.1.1.8(62),
BQ.1.16(69), BQ.1.22(71), BQ.1.23(77), BQ.1.8(94), BE.1.1.1(104),
BQ.1.11(122), BQ.1.10(151), BQ.1.1.7(179), BQ.1.1.18(186)
Other BA.5
Other BA.5 sublineages
The
grey zone represent rare lineages (not in top 15) : BE.1.4.4(1),
BE.4.2(1), CQ.1(1), BA.5.3.4(2), BA.5.7(2), BE.6(2), BE.4.1.1(3),
CQ.1.1(4), CQ.2(4), XBE(4), BE.1.4.2(5), BA.5.6.4(6), BE.2(9),
BA.5.5.3(11), BA.5.3.3(16), BA.5.6.1(16), BE.9(16), BE.4.1(18),
BE.1.4.1(20), BA.5.3(24), BA.5.6.2(24), DF.1(28), CC.1(34),
BA.5.5.1(36), BA.5.3.2(38), BW.1(39), BE.1.2(48), BA.5.10.1(61),
BA.5.5.2(62), BE.1.1.2(82)
Divergent lineages
Divergent lineages sublineages from 2022-03-01
The
grey zone represent rare lineages (not in top 15) : AY.103(1), AY.27(1),
XBC.1(1), XW(1), AY.25.1(2), B.1(2), B.1.1(2), B.1.1.529(2), XAF(2),
XAP(2), XQ(2), XAG(3), XAY(3), XN(3), XZ(3)
Fastest growing lineages in Canada
Here we show the selection estimates and their 95% confidence intervals for Pango lineages with more than 10 sequences in Canada since 2022-09-26 and with enough data to estimate the confidence interval. Each selection estimate measures the growth rate relative to BQ.1 stricto (i.e., sequences designated as BQ.1 and not its descendants, like BQ.1.1). Plots showing the change in variant frequency over time in Canada are given below for lineages with more than 50 sequences.
Plot (stricto)
Background shading illustrates sub-variants that we need to keep an eye on in blue (s ~ 0-5%, corresponding to doubling times of more than two weeks), those whose rapid growth is likely to displace currently common strains in pink (s ~ 5-10% with one to two week doubling times), and those with a growth advantage characteristic of a variant of concern in orange (s > 10%+ with less than one week doubling times).
Estimating selection of sub-variants with low sequence counts (10-100 dots) is prone to error, such as mistaking one-time super spreader events or pulses of sequence data from one regions as selection. These estimates should be considered as very preliminary.
source("scripts/plot_growing_lineages.R")
selectparam <- function(p,reg){
if(!any(is.na(p$fit))){
x=p$mut[[1]];
p$region==reg&
(p$fit)$fit[["s1"]]>0 &
sum(p$toplot$n2)>10&
substr(x, nchar(x), nchar(x))!="*"
}else{FALSE}
}
tmp=apply(all.regions,1,function(reg){
cat("####", reg[["shortname"]], "\n")
cat("##### Plot single lineages in",reg[["name"]],"\n","\n")
paramselected=allparams[sapply(allparams,function(x){selectparam(x,reg[["name"]])})]
n=min(25,length(paramselected))
if(n!=0){
a=plot_growing_lineage(paramselected[1:n])
}else{
cat("")
}
cat("\n\n")})Canada
Plot single lineages in Canada
BC
Plot single lineages in British Columbia
AB
Plot single lineages in Alberta
SK
Plot single lineages in Saskatchawan
MA
Plot single lineages in Manitoba
ON
Plot single lineages in Ontario
QC
Plot single lineages in Quebec
NS
Plot single lineages in Nova Scotia
NB
Plot single lineages in New Brunswick
NL
Plot single lineages in Newfoundland and Labrador
Plot (non stricto)
This plot highlights the groups of related lineages that are growing fastest (e.g., BE.1.1.1* is the monophyletic clade that includes BE.1.1.1 and BQ.1*).
Again, background shading illustrates sub-variants that we need to keep an eye on in blue (s ~ 0-5%, corresponding to doubling times of more than two weeks), those whose rapid growth is likely to displace currently common strains in pink (s ~ 5-10% with one to two week doubling times), and those with a growth advantage characteristic of a variant of concern in orange (s > 10%+ with less than one week doubling times).
Estimating selection of sub-variants with low sequence counts (10-100 dots) is prone to error, such as mistaking one-time super spreader events or pulses of sequence data from one regions as selection. These estimates should be considered as very preliminary.
source("scripts/plot_growing_lineages.R")
selectparam <- function(p,reg){
if(!any(is.na(p$fit))){
x=p$mut[[1]];
p$region==reg&
(p$fit)$fit[["s1"]]>0 &
sum(p$toplot$n2)>10&
substr(x, nchar(x), nchar(x))=="*"
}else{FALSE}
}
tmp=apply(all.regions,1,function(reg){
cat("####", reg[["shortname"]], "\n")
cat("##### Plot monophyletic groups of lineages in",reg[["name"]],"\n","\n")
paramselected=allparams[sapply(allparams,function(x){selectparam(x,reg[["name"]])})]
n=min(25,length(paramselected))
if(n!=0){
a=plot_growing_lineage(paramselected[1:n])
}else{
cat("")
}
cat("\n\n")})Canada
Plot monophyletic groups of lineages in Canada
BC
Plot monophyletic groups of lineages in British Columbia
AB
Plot monophyletic groups of lineages in Alberta
SK
Plot monophyletic groups of lineages in Saskatchawan
MA
Plot monophyletic groups of lineages in Manitoba
ON
Plot monophyletic groups of lineages in Ontario
QC
Plot monophyletic groups of lineages in Quebec
NS
Plot monophyletic groups of lineages in Nova Scotia
NB
Plot monophyletic groups of lineages in New Brunswick
NL
Plot monophyletic groups of lineages in Newfoundland and Labrador
Table of all the selection estimates
paramselected=allparams[sapply(allparams,function(p){if(!any(is.na(p$fit))){
x=p$mut[[1]];
(p$fit)$fit[["s1"]]>0 &
sum(p$toplot$n2)>10
}else{FALSE}})]
DT::datatable(plot_growing_lineage(paramselected,makeplot=FALSE))Mutational composition of Omicron
Tabulation of the most predominant mutational changes in Omicron, with adjacent rows comparing the composition of Canadian sublineages to that sublineage globally.
Mutational profile of Omicron and its sublineages in Canada and globally for the most prevalent (>75%) point mutations in each category (based on the 449241 genomes available on VirusSeq on February 12, 2023).
Selection on Omicron
Here we examine the relative rate of spread of the different sublineages of Omicron currently in Canada. Specifically, we determine if a new or emerging lineage has a selective advantage, s (and by how much), against a reference lineage previously common in Canada (see the methods for more details about selection and how it is estimated).
Currently, the major group of Omicron lineages rising in frequency are the XBB.* group with BQ.*,which descend from BA.5*, nearing a peak. We first show this growth of XBB* and other BQ.*, relative to the remaining strains, which consist predominantly of BA.5* (excluding BQ.*). Left plot: y-axis is the proportion of sub-lineages BQ.* and XBB.* relative to the remaining strains; right plot: y-axis describes the logit function, log(freq(XBB.* or BQ*)/freq(the rest)), 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 XBB.*, BQ.*, and other lineages during this time frame are not displayed.
source("scripts/plot_selection_estimator.R")
startpar <- list(p=c(0.2, 0.05), s=c(0.05, 0.1))
#startpar <- list(p=c(0.5, 0.1, 0.01), s=c(0, 0.05, 0.05))
setAll=getAllStrictoLineages(meta)
#sublineages_BA5 <- getStrictoSubLineages("BA.5*",meta)
sublineages_BQ <- getStrictoSubLineages("BQ*",meta)
sublineages_XBB <- getStrictoSubLineages("XBB*",meta)
#setAll=setAll[!setAll %in% sublineages_BA5]
setAll=setAll[!setAll %in% sublineages_BQ]
setAll=setAll[!setAll %in% sublineages_XBB]
mutants = list(sublineages_BQ, sublineages_XBB)
mutantNames = list("BQ*", "XBB*", "the rest") #"
col <- c(pal["Omicron BQ"], "XBB"="lightblue")
#mutants = list(sublineages_BA5, sublineages_BQ, sublineages_XBB)
#mutantNames = list("BA.5*","BQ*", "XBB*", "the rest") #"
#color for each lineage
#col <- c(pal["Omicron BA.5"], pal["Omicron BQ"], "XBB"="lightblue")
#Set a starting date
#Note that the startdate shouldn't be too much before both alleles become common
#or rare migration events that die off could throw off the estimation procedure
#(so that the parameter estimates account for the presence of those alleles long in the past).
startdate<-max(meta$sample_collection_date)-days(120) #Using a later date with less sampling noise
sub.plot.selection.estimate <- function(region,maxdate=NA){
obj=plot.selection.estimate.ggplot(region=region,
startdate=startdate, startpar=startpar,
reference=c(setAll),
mutants=mutants,
names=mutantNames,
maxdate=maxdate, col=col)
return(obj)
}
selectionEstimateFits <- list()
selectionEstimateFits[["Canada"]] = sub.plot.selection.estimate(region="Canada")
maxdate=selectionEstimateFits[["Canada"]]$date
selectionEstimateFits[["BC"]] <- sub.plot.selection.estimate(region="Alberta",maxdate=maxdate) #temp hax to get this to build
selectionEstimateFits[["BC"]]$plot1 <- NULL
#sub.plot.selection.estimate(region="British Columbia",maxdate=maxdate)
selectionEstimateFits[["AB"]] <- sub.plot.selection.estimate(region="Alberta",maxdate=maxdate)
selectionEstimateFits[["SK"]] <- sub.plot.selection.estimate(region="Alberta",maxdate=maxdate) #temp hax to get this to build
selectionEstimateFits[["SK"]]$plot1 <- NULL
selectionEstimateFits[["MB"]] <- sub.plot.selection.estimate(region="Manitoba",maxdate=maxdate)
selectionEstimateFits[["ON"]] <- sub.plot.selection.estimate(region="Ontario",maxdate=maxdate)
selectionEstimateFits[["QC"]] <- sub.plot.selection.estimate(region="Quebec",maxdate=maxdate)
selectionEstimateFits[["East"]] <- sub.plot.selection.estimate(region="East provinces (NL+NS+NB)",maxdate=maxdate)
#####################################################
# tabs for displaying in notebook
#each PT tab should have curve plot and breakpoint plot side by sidedataAvailable.regions <- all.regions %>% filter(shortname %in% c("Canada", "BC", "AB", "SK", "MA", "ON", "QC", "East"))
apply(dataAvailable.regions,1,function(reg){
cat("###", reg[["shortname"]], "\n")
n_min=20
if(reg[["name"]]=="Canada"){n_min=50}
cat("####",reg[["name"]],"\n","\n")
if (is.null(selectionEstimateFits[[reg[["shortname"]]]]$plot1))
{cat("Not enough data available")}
else {
print(selectionEstimateFits[[reg[["shortname"]]]]$plot1)
print(selectionEstimateFits[[reg[["shortname"]]]]$plot2)
}
cat("\n\n")})Canada
Canada
BC
British Columbia
Not enough data available
AB
Alberta
SK
Saskatchawan
Not enough data available
MA
Manitoba
Not enough data available
ON
Ontario
QC
Quebec
NULL
BA.2 sublineages selection
source("scripts/scanlineages.R")
plotsubvariant <- function(region,min,parentalnode){
nplot=0
for(p in allparams) {
if(!any(is.na(p$fit)) &&
p$region==region &&
substr(p$mut[[1]], nchar(p$mut[[1]]), nchar(p$mut[[1]]))!="*" &&
isSubLineage(parentalnode,p$mut[[1]])){
if((p$fit)$fit[["s1"]]>0 && sum(p$toplot$n2)>min){
plot.selection(plotparam=p)
nplot=nplot+1
}
}
}
if(nplot==0){
print("Not enough data available")
}
}
plotba2 <- function(region,min){
plotsubvariant(region,min,"BA.2*")
}
plotba5 <- function(region,min){
plotsubvariant(region,min,"BA.5*")
}
plotothers <- function(region,min){
nplot=0
for(p in allparams) {
if(!any(is.na(p$fit)) &&
p$region==region &&
substr(p$mut[[1]], nchar(p$mut[[1]]), nchar(p$mut[[1]]))!="*" &&
!isSubLineage("BA.2*",p$mut[[1]])){
if((p$fit)$fit[["s1"]]>0 && sum(p$toplot$n2)>min){
plot.selection(plotparam=p)
nplot=nplot+1
}
}
}
if(nplot==0){
print("Not enough data available")
}
}Here we show the trends of the various BA.2.* sublineages over time, relative to the frequency of BQ.1 by itself (shown for sublineages with at least 50 (Canada) or 20 (provinces) cases). Proportions shown here are only among BQ.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.
tmp=apply(all.regions,1,function(reg){
cat("####", reg[["shortname"]], "\n")
n_min=20
if(reg[["name"]]=="Canada"){n_min=50}
cat("#####",reg[["name"]],"\n","\n")
plotba2(reg[["name"]],n_min)
cat("\n\n")})Canada
Canada
BC
British Columbia
[1] “Not enough data available”
AB
Alberta
SK
Saskatchawan
[1] “Not enough data available”
MA
Manitoba
[1] “Not enough data available”
ON
Ontario
QC
Quebec
NS
Nova Scotia
[1] “Not enough data available”
NB
New Brunswick
[1] “Not enough data available”
NL
Newfoundland and Labrador
[1] “Not enough data available”
BA.5 sublineages selection
Here we show the trends of the various BA.5# sublineages over time, relative to the frequency of BQ.1 by itself (shown for sublineages with at least 50 (Canada) or 20 (provinces) cases). Proportions shown here are only among BQ.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.
tmp=apply(all.regions,1,function(reg){
cat("####", reg[["shortname"]], "\n")
n_min=20
if(reg[["name"]]=="Canada"){n_min=50}
cat("#####",reg[["name"]],"(n>",n_min,")\n","\n")
plotba5(reg[["name"]],n_min)
cat("\n\n")})Canada
Canada (n> 50 )
BC
British Columbia (n> 20 )
[1] “Not enough data available”
AB
Alberta (n> 20 )
SK
Saskatchawan (n> 20 )
[1] “Not enough data available”
MA
Manitoba (n> 20 )
ON
Ontario (n> 20 )
QC
Quebec (n> 20 )
NS
Nova Scotia (n> 20 )
NB
New Brunswick (n> 20 )
NL
Newfoundland and Labrador (n> 20 )
[1] “Not enough data available”
Variants in Canada over time
These plots show the changing composition of sequences for all Canadian data posted to the VirusSeq Portal according to Pango lineage designation (Pango version 4.2 (Viral AI)), up to 2023-01-28. 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.
# focus on emergence of VoCs in Canada
meta1 <- meta[as.Date(meta$week) > Variants_Canada_over_time_Start_Date, ]
meta1$week <- as.factor(as.character(meta1$week))
dat <- lapply(unique(meta1$province), function(x) {
as.data.frame.matrix(table(meta1$week[meta1$province==x],
meta1$pango_group[meta1$province==x]))
})
names(dat) <- unique(meta1$province)
dat[['Canada']] <- as.data.frame.matrix(table(meta1$week, meta1$pango_group))
# pass colour legend to JavaScript layer
dat[['legend']] <- as.list(VOCVOI$color)
names(dat[['legend']]) <- VOCVOI$name
dat[['legend']]$other <- 'grey'
r2d3(data=toJSON(dat), script="js/barplot.js", container="div",
elementId="barplot-element")rm(dat)
rm(meta1)From the beginning of the pandemic to the fall of 2021, Canadian sequences were mostly of the wildtype lineages (pre-VOCs). By the beginning of summer 2021, the VOCs Alpha and Gamma were the most sequenced lineages overall in Canada. The Delta wave grew during the summer of 2021 with sublineages AY.25 and AY.27 constituting sizeable proportions of this wave. Omicron arrived in November of 2021 and spread in three main waves, first BA.1* (early 2022), then BA.2* (spring 2022), then BA.5* (summer 2022). Current, multiple sublineages of Omicron persist, with emerging sublineages spreading, such as BQ.1.1 (a BA.5 sub-lineage). See below for jurisdictional differences of these plots.
There are two Pango lineages that have a Canadian origin and that predominately spread within Canada (with some exportations internationally): B.1.438.1 and B.1.1.176. Other lineages of historical interest in Canada:
- A.2.5.2 - an A lineage (clade 19B) that spread in Quebec, involved in several outbreaks before Delta arrived (see this post for more details: https://virological.org/t/recent-evolution-and-international-transmission-of-sars-cov-2-clade-19b-pango-a-lineages/711)
- B.1.2 - a USA lineage that spread well in Canada
- B.1.160 - an European lineages that spread well in Canada
Canadian trees
Here we present a subsampled phylogenetic snapshot of SARS-CoV-2 genomes from Canada. The x-axis of the time tree represents the estimated number of years from today for which the root emerged. Due to the low number of XBB sequences, this estimate may not be accurate for the XBB* time tree. The x-axis of the diversity trees shows the number of mutations from the outgroup.
### metadata and trees
source("scripts/tree.r")
# load trees from files
mltree <- read.tree("./data_needed/aligned_nonrecombinant_sample1.rtt.nwk")
ttree <- read.tree("./data_needed/aligned_nonrecombinant_sample1.timetree.nwk")
recombTTree <- read.tree("./data_needed/aligned_recombinant_XBBS_sample1.timetree.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$tip.label)
fieldnames<- c("fasta_header_name", "province", "host_gender", "host_age_bin",
"sample_collected_by", "purpose_of_sampling",
"lineage", "pango_group","month","week", "GID", "isolate")
# extract rows from metadata table that correspond to ttree
metasub1 <- meta[meta$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)
###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$pango.group))[idx]
timeTreeJsonObj <- DrawTree(ttree, metasub1, "timetree", presetColors, fieldnames=fieldnames)
recombTimeTreeJsonObj <- DrawTree(recombTTree, mmetasub_recomb, "recombtimetree", presetColors, "lineage", fieldnames= fieldnames)
#diversity ML tree
diversityTreeJsonObj <- DrawTree(mltree, metasub1, "mltree", presetColors, fieldnames=fieldnames)
### omicron diversity tree
MLtree_omi<-keep.tip(mltree, metasub_omi$fasta_header_name)
OmicrondiversityTreeJsonObj <- DrawTree(MLtree_omi, metasub_omi, "omimltree", presetColors, fieldnames=fieldnames)