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July 15, 2020

A possible reason for inter-regional differences in COVID-19 prevalence?

Filed under: COVID-19,Health — Razib Khan @ 3:14 pm


There have been striking differences in COVID-19 severity/penetration by region. There are all sorts of reasons posited. This post from Derek Lowe at In The Pipeline, New Data on T Cells and the Coronavirus, suggests a possibility:

And turning to patients who have never been exposed to either SARS or the latest SARS CoV-2, this new work confirms that there are people who nonetheless have T cells that are reactive to protein antigens from the new virus. As in the earlier paper, these cells have a different pattern of reactivity compared to people who have recovered from the current pandemic (which also serves to confirm that they truly have not been infected this time around). Recognition of the nsp7 and nsp13 proteins is prominent, as well as the N protein. And when they looked at that nsp7 response, it turns out that the T cells are recognizing particular protein regions that have low homology to those found in the “common cold” coronaviruses – but do have very high homology to various animal coronaviruses.

Very interesting indeed! That would argue that there has been past zoonotic coronavirus transmission in humans, unknown viruses that apparently did not lead to serious disease, which have provided some people with a level of T-cell based protection to the current pandemic. This could potentially help to resolve another gap in our knowledge, as mentioned in that recent post: when antibody surveys come back saying that (say) 95% of a given population does not appear to have been exposed to the current virus, does that mean that all 95% of them are vulnerable – or not? I’ll reiterate the point of that post here: antibody profiling (while very important) is not the whole story, and we need to know what we’re missing.

It seems that later we will find out that perhaps the Vietnamese benefited from some immunity conferred by a previous asymptomatic coronavirus outbreak?

July 4, 2020

Using 23andMe/Ancestry/Family Tree DNA to identify risk allele for respiratory failure with COVID-19

Filed under: COVID-19,Disease — Razib Khan @ 12:33 pm

Several people have asked about the risk haplotype in the post below. If you have been genotyped on Ancestry, 23andMe, and Family Tree DNA (unless you are on 23andMe after summer of 2017) there is one SNP in high LD with the causal variant you can look up. It’s rs10490770. The risk allele is C and non-risk allele T. If you download the raw data from any of these services you can find rs10490770 with a search, and look for your genotype. (if by someone chance it is a reverse strand, the risk allele may actually be G and the non-risk allele A)

What does risk vs. non-risk mean? You can read the original paper, Genomewide Association Study of Severe Covid-19 with Respiratory Failure. They say: “We identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system…”

Here are the results. Focus on rs11385942 at locus 3p21.31 (the top one):

July 3, 2020

Neanderthal introgression at COVID-19 severity locus at high frequency in South Asians

Filed under: COVID-19,Human Evolutionary Genetics — Razib Khan @ 6:36 pm

To review, as most of you know about ~2% of the ancestry outside of Africa is attributable to ancestry form Neanderthals. The fraction is a bit higher in East Asia, a bit lower in Europe, and lower still in the Near East. That being said, a disproportionate fraction of the Neanderthal ancestry is found in non-genic regions, which implies there’s been “purification.” Negative selection. The implication being that Neanderthal variation doesn’t “work well” with the genetic background of modern humans, the main lineage of Neo-Africans.

But there are exceptions to this, whether through natural selection (the Neanderthal variant was beneficial) or drift.

Svante Paabo’s group has discovered a new newsy twist, The major genetic risk factor for severe COVID-19 is inherited from Neandertals:

A recent genetic association study (Ellinghaus et al. 2020) identified a gene cluster on chromosome 3 as a risk locus for respiratory failure in SARS-CoV-2. Recent data comprising 3,199 hospitalized COVID-19 patients and controls reproduce this and find that it is the major genetic risk factor for severe SARS-CoV-2 infection and hospitalization (COVID-19 Host Genetics Initiative). Here, we show that the risk is conferred by a genomic segment of ~50 kb that is inherited from Neandertals and occurs at a frequency of ~30% in south Asia and ~8% in Europe.

First, they need to change the title. From “The” to “a”. The study they’re piggybacking off of, Genomewide Association Study of Severe Covid-19 with Respiratory Failure, found in several thousand Spanish and Italian individuals that a particular SNP that Paabo’s group found to be embedded in a haplotype of Neanderthal origin was a risk variant. There are other genetic risk locations, probably in Africans, and non-Europeans.

Within European populations, the odds ratio of severe respiratory failure all things equal is ~1.75 if you are a heterozygote for the risk allele, and ~3.0 if you are a homozygote. This is not anything, though presumably hypertension and all the other covariations are still a massive deal.

The authors highlight the South Asian angle. But they focused on the 1000 Genomes. One of the SNPs in perfect LD with the causal variant. (1.0) is in major SNP-arrays. I computed the frequency in a range of modern and ancient (page down for the ancient) populations. You can see the pattern below.

  1. It is a huge deal in South Asians
  2. It is found in high frequency in Papuans too…
  3. Non-trivial fractions in Near Eastern groups as well as Europeans
  4. Found in some ancient samples

My assumption is that there is some selective benefit from this. Notice how in Northeast Asians it’s almost totally expunged. Perhaps ancient coronavirus sweeps? I don’t know.

(rs10490770 by the way)

CLSTMAFMACNCHROBS
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Dusadh0.56916
Dolgans0.5012
Kurmi0.5012
Lambadi0.5012
Malayan0.5024
Meena0.5012
Meghawal0.5012
Nihali0.5024
NorthKannadi0.50918
SouthIndian0.5012
Tharus0.5024
Bengali_20.42512
CentralIndian0.3838
MumbaiJews0.3838
Paniya0.3838
Sakilli0.3838
Bengali0.3761164
BrahminsNorth0.36514
Papuan0.351234
LibyaJew0.33412
AlgeriaJew0.30310
Altaians0.30310
Muslim0.30310
Velamas0.30620
Punjabi0.3057192
Telegu0.2959204
Udumurt0.28932
Dharkars0.27622
Gujrati0.2755206
Tamil0.2653204
BrahminsTN0.2514
DalitTN0.2514
Gond0.2528
Kanjars0.25312
Kurumba0.2528
UzbekistaniJews0.2514
Belorussian0.23730
Chamar0.23626
Hungarians0.23940
Kshatriya0.21314
Kumyks0.21628
Italian_S0.20420
Turkmen0.20946
Kol0.19736
German0.19526
Kallar0.19316
Cambodians0.18422
Sindhi0.18950
Abkhazian0.1716
FranceJew0.17212
Hakkipikki0.1716
Kosovar0.17318
Kurds0.17212
Lebanese0.1716
Tabassaran0.1716
Brahui0.16850
Burusho0.16850
Bedouin0.161596
ItalyJew0.15320
Yemenese0.15320
Montenegran0.14428
Balochi0.14750
Kalash0.14750
Druze0.141394
IraqiJews0.14322
Abhkasians0.13538
SephardicJews0.13538
AzerbaijaniJews0.13216
Chechens0.13540
Chenchus0.1318
Egyptans0.13324
GeorgianJews0.1318
Orcadian0.13432
Romanians0.13432
Syrians0.13432
Tajik0.1213106
Makrani0.12650
Palestinian0.1212102
Pole0.12434
Bashkir0.11544
Tuscan0.1125228
Azeri0.11546
Croat0.11546
Pathan0.11546
Finn0.1020198
Bosnian0.10330
Georgian0.10660
Iranians0.10440
Jordanians0.10440
MoroccanJews0.10330
Pulliyar0.10110
Russian0.1012124
AshkenazyJews0.10442
Komi0.09332
Vep0.09222
Sardinian0.09556
Tatar0.09446
Cypriots0.08224
Hazara0.08448
TunisiaJew0.08112
UtahWhite0.0816198
Turks0.08338
Uzbeks0.08338
Bulgarians0.08226
Italian_N0.08226
Adygei0.08340
Lezgins0.08340
Saudis0.08340
Ukranians0.08340
GreatBritain0.0713182
Selkups0.07114
Sicily0.07228
Balkar0.07344
Karelian0.07230
YemeniteJews0.07230
French_Basque0.06348
Nogais0.06232
Sweden0.06232
Kyrgyzians0.06234
Armenians0.06470
Colombian0.0612212
Kirghiz0.06118
Chuvash0.05238
Ethiopians0.05238
Serb0.05238
French0.05358
Spaniard0.0512238
Greek0.05240
Moroccans0.05120
Mozabite0.05360
Uygur0.05122
Gagauz0.04124
Mongol0.04124
Mexican0.045128
EthiopianJews0.04126
PuertoRican0.048208
Kalmyk0.04128
Estonians0.03130
Maris0.03130
Nenet0.03132
Peruvian0.035170
Kazakhs0.03136
Ossetian0.03136
Vietnamese0.024198
Yakut0.02156
AfricanBarbados0.023192
AfricanAmerican0.011122
Dai0.001206
Gambian0.001226
Bantu_NE0.00024
Bantu_S0.00016
Bhunjia0.0002
Biaka_Pygmies0.00064
Buriat0.0006
Chukchi0.0004
Colombians0.0002
Daur0.00018
Dhurwa0.0002
EsanNigeria0.000198
Evenk0.00012
Evens0.0004
Han0.00066
HanBeijing0.000206
Han_N0.00020
Han_S0.000210
Hezhen0.00018
IranianJews0.0008
IranJew0.00010
Japanese0.000266
Karakalpak0.00020
Karitiana0.00048
Kazakh0.0004
Ket0.0004
Koryak0.0004
Kumyk0.0006
KurdJew0.00018
Lahu0.00020
Lithuanians0.00020
Luhya0.000198
Macedonian0.00026
Mandenka0.00048
Mawasi0.0002
Maya0.00050
Mbuti_Pygmies0.00030
Mende0.000170
Miaozu0.00020
Moldavian0.00014
Mordovians0.00030
Naga0.0008
NAN_Melanesian0.00038
Naxi0.00018
Nganassan0.0004
Oroqen0.00020
Outlier0.0002
Pima0.00050
Samaritians0.0006
San0.00012
She0.00020
SouthItalian0.0002
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SyriaJew0.0004
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Tujia0.00020
Tusvim0.0000
Tuvan0.0006
Xibo0.00018
Yagnobi0.0002
Yizu0.00020
Yoruba0.000264
Altai.DG1.0022
Altai_published.DG1.0022
BEN.SG1.0022
Brazil_Botocudo.SG1.0022
DenisovaNeanderthalMix.SG1.0022
Hungary_ALPc_Szatmar_MN1.0022
Mezmaiskaya2_Neanderthal.SG1.0022
Scotland_LBA1.0022
Spy_Neanderthal.SG1.0022
Vindija.DG1.0022
BIR.SG0.561018
Cambodian.DG0.5024
Estonia_Corded_Ware.SG0.5024
Greek.DG0.5024
Hungary_Langobard0.5024
Kalash.DG0.5024
Kapu.DG0.5024
Kashmiri_Pandit.DG0.5012
Madiga.DG0.5024
ONG.SG0.50612
Papuan_o.DG0.5012
Polish.DG0.5012
Russia_Andronovo.SG0.5024
Vanuatu_150BP0.5024
Vanuatu_150BP_all0.5024
Yadava.DG0.5024
BEB.SG0.4372168
Punjabi.DG0.3838
Austria_LBK_EN0.3326
Kazakh.DG0.3326
Papuan.DG0.331030
Russia_Alan.SG0.3326
GIH.SG0.3064210
UBR.SG0.30620
ITU.SG0.2958202
STU.SG0.2858206
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BedouinB.DG0.2514
Bengali.DG0.2514
Bergamo.DG0.2514
Burusho.DG0.2514
Druze.DG0.2514
England_MBA0.2528
England_N0.2528
Georgian.DG0.2514
Iranian.DG0.2514
JAR.SG0.2528
Onge.DG0.2514
Orcadian.DG0.2514
Pathan.DG0.2514
PJL.SG0.2548192
Relli.DG0.2514
Russia_Srubnaya_Alakul.SG0.2528
England_Roman.SG0.20210
England_Saxon.SG0.20210
Hungary_Baden_LCA0.20210
ILA.SG0.20420
RAJ.SG0.20420
Mala.DG0.1716
Palestinian.DG0.1716
Sardinian.DG0.1318
TSI.SG0.1124212
CEU.SG0.1122196
Germany_Early_Medieval.SG0.11218
Hungary_Langobard.SG0.11218
VLR.SG0.11218
CLM.SG0.1120186
FIN.SG0.0916182
Kyrgyzstan_TianShanHun.SG0.08224
GBR.SG0.0712178
MXL.SG0.068134
KHV.SG0.048200
PUR.SG0.048208
IBS.SG0.048210
PEL.SG0.036172
ACB.SG0.024190
ASW.SG0.022130
GWD.SG0.012226

A risk factor for COVID-19 in South Asians

Filed under: COVID-19,Genetics — Razib Khan @ 4:59 pm

The major genetic risk factor for severe COVID-19 is inherited from Neandertals:

A recent genetic association study (Ellinghaus et al. 2020) identified a gene cluster on chromosome 3 as a risk locus for respiratory failure in SARS-CoV-2. Recent data comprising 3,199 hospitalized COVID-19 patients and controls reproduce this and find that it is the major genetic risk factor for severe SARS-CoV-2 infection and hospitalization (COVID-19 Host Genetics Initiative). Here, we show that the risk is conferred by a genomic segment of ~50 kb that is inherited from Neandertals and occurs at a frequency of ~30% in south Asia and ~8% in Europe.

The highest frequency is in the 1000 Genomes Bangladesh sample. 60%. In a study of Europeans all things equal the risk allele at this locus increases odds of respiratory failure by a factor of 1.75. This isn’t really the major factor; age and hypertension, all the things you know, matter more. But, it’s not trivial either to increase risk by 1.75.

If you are on 23andMe and got tested before the summer of 2017, the older chips has a marker for the locus that’s informative (in LD with the haplotype). This link should take you there. I’m TT homozygote. Modern human. A C is for Neanderthals.

May 20, 2020

Late spring in the age of coronavirus

Filed under: COVID-19,Health — Razib Khan @ 10:33 pm

I haven’t posted on COVID-19 in a while. What’s there to say? The last month or so has been a great muddle. We soldier on, without purpose or direction. At least here in the United States of America. In regards to the pandemic, we’re in, all I can say is that I feel a sense of listless ennui. But perhaps I should say something, just for historical purposes of tracking where we’re at for this weblog?

On March 23th, T. A. Frank mentioned me in Vanity Faire as being a COVID-hawk. You can search this weblog and note I was relatively sanguine at the end of January, but we began to stockpile in early February. By the middle of February, I was alarmed. On February 19th news broke that Covid-19 was spreading Iran, and to be frank I flipped out.

Between February 20th and March 10th, there was a slow and gradual shift in thinking. But the real switch was flipped between March 10th and March 15th, as broad swaths of the culture moved into a high state of alarmism. It was curious seeing scientists who I followed who were fixated on Richard Dawkins in February joining the alarm about Covid-19. When they’d give a thought many (though not as many privately!) were reassuring.

They shouldn’t have been.

Some considerations and observations:

The COVID-doves: early in the pandemic there were critics who were accusing me of alarmism. This was March, so who knew? I asked for some numbers. One individual said that at most there would be 20,000 deaths. We are around 100,000 now. Over time the initial wave of skeptics faded away because the numbers were too high.

But, the second round of skeptics emerged. The interesting thing here though is that the second wave of skeptics was more focused on the opportunity costs of the lock-down. The key problem I have with this wave of COVID-doves is that I wish they would just admit that 250,000 miserable deaths may be the price we have to pay. Perhaps. We just need to put the numbers on the table and remember that the deaths seem quite unpleasant and protracted.

I am on friendly terms with many COVID-doves. I disagree with them, but I have friends and many who are liberals too, and I disagree with them. In fact, in an ideal world, I would be convinced by their arguments, and become a COVID-dove. I am not convinced by their arguments. Yet.

There is a broader class of COVID-skeptic which is, to be frank, unhinged, conspiratorial, and a promoter of misinformation. This is a serious problem.

The COVID-hysterics: another class of individuals are those who are hysterical about the impact of COVID. They want a two-year-long lockdown. They believe that the governor of Georgia has blood on his hands. They believe that COVID could kill anyone! Any skepticism or cost-vs.-benefit thinking is anathema to the COVID-hysteric.

The data is clear now that COVID-19 is particularly dangerous for older people. But the number of media profiles of young women who die of COVID-19 is quite high. There is, to my mind, a clear attempt by the media to make it seem like everyone is at risk. In fact, for people in their 20s and younger the seasonal flu seems to be more risk. The spate of stories about Kawasaki disease and children is, in my opinion, part of the issue. To convince COVID-skeptics those who wish people to take this pandemic seriously need to not exaggerate, or they’ll lose all credibility.

The IFR: I now believe that the infection fatality rate in the United States is around 0.75%. This is, as the above comment should make clear, not unconditional. For the young, it is quite low. For the aged, it is much higher. But when estimating how many Americans may die of COVID-19, this is the number that I think is reasonable. Perhaps higher. Perhaps lower. But this it the ballpark. If 50% of Americans become infected, that’s 1.2 million or so deaths. The IFR, like the R, is not a fixed parameter. Perhaps the virus will change. Perhaps our therapeutics will get better. But we go to war with the parameters we have, not the ones we want.

The uncertainty: There is still a great deal of uncertainty as we proceed forward. We know some things (e.g., no, children are not at high risk of death), but not enough. I have stopped paying attention to whether the weather impacts COVID-19. I think it does, but more in the range of 25-50% changes in the R, not an order of magnitude. There are lots of small things that are having impacts that we don’t know. And there are likely stochastic factors as well. We look through the mirror darkly.

Perhaps COVID-19 will fade away. Burn itself out. But that’s hope. A guess. We have no idea. We’re still not clear why the outbreak in New York City was so much worse on the West coast of the USA. Why Southeast Asia has been left relatively unscathed.

Pre-COVID-19 times

The quarantine: The major lacunae in the Western response has been quarantine-containment. The lockdown has, on the whole, not taken COVID-19 positive people, and put them in some sort of quarantine. It doesn’t look like it will happen.

That means COVID-19 is endemic. For now.

Where are we? It looks like as we move into fall the number of American deaths will be in the low 100,000s. This is a victory, after a fashion. My family is still self-quarantining. We have no date when we’re not going to keep doing this, at least for the foreseeable future. My children have grandparents that they want to see. What are we supposed to do? But the day will come when we go back out into the world…

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