Paper on within-host diversity (credits in Gerry’s tweet) is from the first wave of the pandemic, but the conclusions are timely, as the world wonders how the virus evolved and will evolve. In particular, I think the mutation spectrum is relevant to some interesting questions 🧵
This figure shows relative mutation rate: each colour is a type of mutation like C to A, with the bars showing the context of the bases to the left and right; below the line is the reverse complement (e.g. G to T for blue).Red C>U (C>T in DNA) mutations are by far the most common
In the very early history of the virus in humans it split into the A and B lineages, which (figure from zenodo.org/record/5075888…) differ from each other at two positions, in each case one is C and the other is T. If you just look at these two positions, lineage A is TC and B is CT
It’s possible that either A or B represent a common ancestor, and the other one therefore accrued two mutations, or that the common ancestor is unobserved, and each of these earliest lineages differ from that “parent” by one mutation (assuming no back mutation).
As the first figure showed, C to T is about six times more likely than T to C. So in the absence of other information, a scenario where the common ancestor was CC, and each got one mutation is ~3x as likely as one where one of the two observed lineages is ancestral.
This could be interesting in trying to precisely estimate the difference between the genetic time to most recent common ancestor of all observed SARS-CoV-2 sequences, and the earliest cases of human infection (e.g. as examined in science.sciencemag.org/content/372/65…).

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More from @jcbarret

19 Aug
Should I be worried about AY.3?

TLDR: No.

Longer answer about this lineage, the challenges of cataloguing the current diversity of #SARSCoV2, the miracle of global open science, and handy questions to ask yourself when you see a new variant report follows in a 🧵.
First off, summary and background on AY.3 and its cousins here:
AY.3 was designated by @PangoNetwork because it was growing in parts of the USA, apparently from a single (or small number of) introductions. This is done NOT because it is necessarily biologically more transmissible but to make it easier to refer to. github.com/cov-lineages/p…
Read 11 tweets
14 Jun
Monday always means new data at covid19.sanger.ac.uk, but this week also brings new features! Overall picture should come as no surprise: Delta variant growing in proportion and absolute numbers. In fact, next week is likely to be our highest ever count of one lineage🧵
First new feature is "fade areas by uncertainty", or "nebulosity view", as I like to call it. We fade the colouring of local authorities on proportion view to give a visual hint that you shouldn't over-interpret an area with 100% Delta if it only has a handful of sequences.
Related to this, we show confidence intervals on proportion tooltips and in the unstacked version of the proportion graph. Compare the following two local authorities with similar point estimates, but different amounts of data.
Read 5 tweets
8 Jun
The description of genomic analyses in this piece about the spread of the Delta variant in the UK has major inaccuracies, and hindsight bias. 🧵
theguardian.com/commentisfree/…
Early on, the author, @chrischirp, notes that Pakistan and Bangladesh were red-listed on 9 April, but India wasn't until 23 April. There is a reasonable criticism to be made here, but it has nothing to do with variants. Cases and % positivity were already on the rise in India
We know now that delay led to hundreds of importations of the Delta variant. The article says India "first reported a concerning new variant on 24 March...so why didn’t PHE immediately escalate Delta to a variant of concern in March?"
Read 11 tweets
31 May
We've pushed the weekly numbers update (thanks @theosanderson!) at covid19.sanger.ac.uk. As expected, in the most recent 2 week window, B.1.617.2 is the most common lineage in England. Still driven by local concentrations of high case numbers (L cases, R B.1.617.2):
A couple of notes on the site. We have updated the text describing ascertainment: we are no longer excluding surge tests, which make up a large fraction of all tests in key areas in recent weeks. I think this provides least biased frequencies now. Feedback welcome.
I've mentioned this before, but please be cautious interpreting proportions where we have few genomes. For example the "100%" in Dover doesn't mean much because it based on 3 genomes in the most recent 2 weeks of data:
Read 4 tweets
24 May
It's clear that B.1.617.2 has been growing faster than B.1.1.7 in UK for a few weeks. Key question for policy, not yet fully answered, is how much due to:
1. vaccine efficacy
2. intrinsic transmissibility
3. human epidemiological factors

🧵of my current thoughts on the topic:
Prelim. data show vaccines work somewhat less well against symptomatic infection w/B.1.617.2, so this makes a non-zero contribution to its recent growth. Good threads on vaccine efficacy data from PHE: &
But I think magnitude of observed VE reduction, as well as age distribution of B.1.617.2 infections, makes it unlikely to fully explain growth in UK. B.1.617.2 (blue) is a bit older than recent B.1.1.7 (green), but both more similar to each other than B.1.1.7 from December (red)
Read 16 tweets
10 May
Last week @PHE_uk designated B.1.617.2 a VOC (excellent summary from @kallmemeg below). Today we released another week's worth of surveillance genomes at covid19.sanger.ac.uk, and I'd like to walk through some of the features & findings. 🧵

First, headline result, as reported by others, is that B.1.617.2 has hit 6% in England in our 2-week rolling average (>10% in the most recent week). Other VOCs & VUIs steady.
It is hard to know exactly what this means for transmissibility. There were hundreds of imported cases in a short span, and while those are not counted here, they arrived in both a quantity and at a moment of loosening restriction that we have not seen before for other variants.
Read 7 tweets

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