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 candidate for introgression with a 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.
- It is a huge deal in South Asians
- It is found in high frequency in Papuans too…
- Non-trivial fractions in Near Eastern groups as well as Europeans
- 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)
Update: In SE Asia a lot of the frequency seems likely attributable to South Asia or Negrito ancestry. Borneo Austronesians have low frequencies of the risk allele. Burmese at 0.075% can be just due to South Asian. Looks like there is some correlation between ASI/AASI ancestry and the risk allele.
CLST | MAF | MAC | NCHROBS |
CochinJews | 0.63 | 5 | 8 |
Dusadh | 0.56 | 9 | 16 |
Dolgans | 0.50 | 1 | 2 |
Kurmi | 0.50 | 1 | 2 |
Lambadi | 0.50 | 1 | 2 |
Malayan | 0.50 | 2 | 4 |
Meena | 0.50 | 1 | 2 |
Meghawal | 0.50 | 1 | 2 |
Nihali | 0.50 | 2 | 4 |
NorthKannadi | 0.50 | 9 | 18 |
SouthIndian | 0.50 | 1 | 2 |
Tharus | 0.50 | 2 | 4 |
Bengali_2 | 0.42 | 5 | 12 |
CentralIndian | 0.38 | 3 | 8 |
MumbaiJews | 0.38 | 3 | 8 |
Paniya | 0.38 | 3 | 8 |
Sakilli | 0.38 | 3 | 8 |
Bengali | 0.37 | 61 | 164 |
BrahminsNorth | 0.36 | 5 | 14 |
Papuan | 0.35 | 12 | 34 |
LibyaJew | 0.33 | 4 | 12 |
AlgeriaJew | 0.30 | 3 | 10 |
Altaians | 0.30 | 3 | 10 |
Muslim | 0.30 | 3 | 10 |
Velamas | 0.30 | 6 | 20 |
Punjabi | 0.30 | 57 | 192 |
Telegu | 0.29 | 59 | 204 |
Udumurt | 0.28 | 9 | 32 |
Dharkars | 0.27 | 6 | 22 |
Gujrati | 0.27 | 55 | 206 |
Tamil | 0.26 | 53 | 204 |
BrahminsTN | 0.25 | 1 | 4 |
DalitTN | 0.25 | 1 | 4 |
Gond | 0.25 | 2 | 8 |
Kanjars | 0.25 | 3 | 12 |
Kurumba | 0.25 | 2 | 8 |
UzbekistaniJews | 0.25 | 1 | 4 |
Belorussian | 0.23 | 7 | 30 |
Chamar | 0.23 | 6 | 26 |
Hungarians | 0.23 | 9 | 40 |
Kshatriya | 0.21 | 3 | 14 |
Kumyks | 0.21 | 6 | 28 |
Italian_S | 0.20 | 4 | 20 |
Turkmen | 0.20 | 9 | 46 |
Kol | 0.19 | 7 | 36 |
German | 0.19 | 5 | 26 |
Kallar | 0.19 | 3 | 16 |
Cambodians | 0.18 | 4 | 22 |
Sindhi | 0.18 | 9 | 50 |
Abkhazian | 0.17 | 1 | 6 |
FranceJew | 0.17 | 2 | 12 |
Hakkipikki | 0.17 | 1 | 6 |
Kosovar | 0.17 | 3 | 18 |
Kurds | 0.17 | 2 | 12 |
Lebanese | 0.17 | 1 | 6 |
Tabassaran | 0.17 | 1 | 6 |
Brahui | 0.16 | 8 | 50 |
Burusho | 0.16 | 8 | 50 |
Bedouin | 0.16 | 15 | 96 |
ItalyJew | 0.15 | 3 | 20 |
Yemenese | 0.15 | 3 | 20 |
Montenegran | 0.14 | 4 | 28 |
Balochi | 0.14 | 7 | 50 |
Kalash | 0.14 | 7 | 50 |
Druze | 0.14 | 13 | 94 |
IraqiJews | 0.14 | 3 | 22 |
Abhkasians | 0.13 | 5 | 38 |
SephardicJews | 0.13 | 5 | 38 |
AzerbaijaniJews | 0.13 | 2 | 16 |
Chechens | 0.13 | 5 | 40 |
Chenchus | 0.13 | 1 | 8 |
Egyptans | 0.13 | 3 | 24 |
GeorgianJews | 0.13 | 1 | 8 |
Orcadian | 0.13 | 4 | 32 |
Romanians | 0.13 | 4 | 32 |
Syrians | 0.13 | 4 | 32 |
Tajik | 0.12 | 13 | 106 |
Makrani | 0.12 | 6 | 50 |
Palestinian | 0.12 | 12 | 102 |
Pole | 0.12 | 4 | 34 |
Bashkir | 0.11 | 5 | 44 |
Tuscan | 0.11 | 25 | 228 |
Azeri | 0.11 | 5 | 46 |
Croat | 0.11 | 5 | 46 |
Pathan | 0.11 | 5 | 46 |
Finn | 0.10 | 20 | 198 |
Bosnian | 0.10 | 3 | 30 |
Georgian | 0.10 | 6 | 60 |
Iranians | 0.10 | 4 | 40 |
Jordanians | 0.10 | 4 | 40 |
MoroccanJews | 0.10 | 3 | 30 |
Pulliyar | 0.10 | 1 | 10 |
Russian | 0.10 | 12 | 124 |
AshkenazyJews | 0.10 | 4 | 42 |
Komi | 0.09 | 3 | 32 |
Vep | 0.09 | 2 | 22 |
Sardinian | 0.09 | 5 | 56 |
Tatar | 0.09 | 4 | 46 |
Cypriots | 0.08 | 2 | 24 |
Hazara | 0.08 | 4 | 48 |
TunisiaJew | 0.08 | 1 | 12 |
UtahWhite | 0.08 | 16 | 198 |
Turks | 0.08 | 3 | 38 |
Uzbeks | 0.08 | 3 | 38 |
Bulgarians | 0.08 | 2 | 26 |
Italian_N | 0.08 | 2 | 26 |
Adygei | 0.08 | 3 | 40 |
Lezgins | 0.08 | 3 | 40 |
Saudis | 0.08 | 3 | 40 |
Ukranians | 0.08 | 3 | 40 |
GreatBritain | 0.07 | 13 | 182 |
Selkups | 0.07 | 1 | 14 |
Sicily | 0.07 | 2 | 28 |
Balkar | 0.07 | 3 | 44 |
Karelian | 0.07 | 2 | 30 |
YemeniteJews | 0.07 | 2 | 30 |
French_Basque | 0.06 | 3 | 48 |
Nogais | 0.06 | 2 | 32 |
Sweden | 0.06 | 2 | 32 |
Kyrgyzians | 0.06 | 2 | 34 |
Armenians | 0.06 | 4 | 70 |
Colombian | 0.06 | 12 | 212 |
Kirghiz | 0.06 | 1 | 18 |
Chuvash | 0.05 | 2 | 38 |
Ethiopians | 0.05 | 2 | 38 |
Serb | 0.05 | 2 | 38 |
French | 0.05 | 3 | 58 |
Spaniard | 0.05 | 12 | 238 |
Greek | 0.05 | 2 | 40 |
Moroccans | 0.05 | 1 | 20 |
Mozabite | 0.05 | 3 | 60 |
Uygur | 0.05 | 1 | 22 |
Gagauz | 0.04 | 1 | 24 |
Mongol | 0.04 | 1 | 24 |
Mexican | 0.04 | 5 | 128 |
EthiopianJews | 0.04 | 1 | 26 |
PuertoRican | 0.04 | 8 | 208 |
Kalmyk | 0.04 | 1 | 28 |
Estonians | 0.03 | 1 | 30 |
Maris | 0.03 | 1 | 30 |
Nenet | 0.03 | 1 | 32 |
Peruvian | 0.03 | 5 | 170 |
Kazakhs | 0.03 | 1 | 36 |
Ossetian | 0.03 | 1 | 36 |
Vietnamese | 0.02 | 4 | 198 |
Yakut | 0.02 | 1 | 56 |
AfricanBarbados | 0.02 | 3 | 192 |
AfricanAmerican | 0.01 | 1 | 122 |
Dai | 0.00 | 1 | 206 |
Gambian | 0.00 | 1 | 226 |
Bantu_NE | 0.00 | 0 | 24 |
Bantu_S | 0.00 | 0 | 16 |
Bhunjia | 0.00 | 0 | 2 |
Biaka_Pygmies | 0.00 | 0 | 64 |
Buriat | 0.00 | 0 | 6 |
Chukchi | 0.00 | 0 | 4 |
Colombians | 0.00 | 0 | 2 |
Daur | 0.00 | 0 | 18 |
Dhurwa | 0.00 | 0 | 2 |
EsanNigeria | 0.00 | 0 | 198 |
Evenk | 0.00 | 0 | 12 |
Evens | 0.00 | 0 | 4 |
Han | 0.00 | 0 | 66 |
HanBeijing | 0.00 | 0 | 206 |
Han_N | 0.00 | 0 | 20 |
Han_S | 0.00 | 0 | 210 |
Hezhen | 0.00 | 0 | 18 |
IranianJews | 0.00 | 0 | 8 |
IranJew | 0.00 | 0 | 10 |
Japanese | 0.00 | 0 | 266 |
Karakalpak | 0.00 | 0 | 20 |
Karitiana | 0.00 | 0 | 48 |
Kazakh | 0.00 | 0 | 4 |
Ket | 0.00 | 0 | 4 |
Koryak | 0.00 | 0 | 4 |
Kumyk | 0.00 | 0 | 6 |
KurdJew | 0.00 | 0 | 18 |
Lahu | 0.00 | 0 | 20 |
Lithuanians | 0.00 | 0 | 20 |
Luhya | 0.00 | 0 | 198 |
Macedonian | 0.00 | 0 | 26 |
Mandenka | 0.00 | 0 | 48 |
Mawasi | 0.00 | 0 | 2 |
Maya | 0.00 | 0 | 50 |
Mbuti_Pygmies | 0.00 | 0 | 30 |
Mende | 0.00 | 0 | 170 |
Miaozu | 0.00 | 0 | 20 |
Moldavian | 0.00 | 0 | 14 |
Mordovians | 0.00 | 0 | 30 |
Naga | 0.00 | 0 | 8 |
NAN_Melanesian | 0.00 | 0 | 38 |
Naxi | 0.00 | 0 | 18 |
Nganassan | 0.00 | 0 | 4 |
Oroqen | 0.00 | 0 | 20 |
Outlier | 0.00 | 0 | 2 |
Pima | 0.00 | 0 | 50 |
Samaritians | 0.00 | 0 | 6 |
San | 0.00 | 0 | 12 |
She | 0.00 | 0 | 20 |
SouthItalian | 0.00 | 0 | 2 |
Surui | 0.00 | 0 | 42 |
SyriaJew | 0.00 | 0 | 4 |
Tu | 0.00 | 0 | 20 |
Tujia | 0.00 | 0 | 20 |
Tusvim | 0.00 | 0 | 0 |
Tuvan | 0.00 | 0 | 6 |
Xibo | 0.00 | 0 | 18 |
Yagnobi | 0.00 | 0 | 2 |
Yizu | 0.00 | 0 | 20 |
Yoruba | 0.00 | 0 | 264 |
Altai.DG | 1.00 | 2 | 2 |
Altai_published.DG | 1.00 | 2 | 2 |
BEN.SG | 1.00 | 2 | 2 |
Brazil_Botocudo.SG | 1.00 | 2 | 2 |
DenisovaNeanderthalMix.SG | 1.00 | 2 | 2 |
Hungary_ALPc_Szatmar_MN | 1.00 | 2 | 2 |
Mezmaiskaya2_Neanderthal.SG | 1.00 | 2 | 2 |
Scotland_LBA | 1.00 | 2 | 2 |
Spy_Neanderthal.SG | 1.00 | 2 | 2 |
Vindija.DG | 1.00 | 2 | 2 |
BIR.SG | 0.56 | 10 | 18 |
Cambodian.DG | 0.50 | 2 | 4 |
Estonia_Corded_Ware.SG | 0.50 | 2 | 4 |
Greek.DG | 0.50 | 2 | 4 |
Hungary_Langobard | 0.50 | 2 | 4 |
Kalash.DG | 0.50 | 2 | 4 |
Kapu.DG | 0.50 | 2 | 4 |
Kashmiri_Pandit.DG | 0.50 | 1 | 2 |
Madiga.DG | 0.50 | 2 | 4 |
ONG.SG | 0.50 | 6 | 12 |
Papuan_o.DG | 0.50 | 1 | 2 |
Polish.DG | 0.50 | 1 | 2 |
Russia_Andronovo.SG | 0.50 | 2 | 4 |
Vanuatu_150BP | 0.50 | 2 | 4 |
Vanuatu_150BP_all | 0.50 | 2 | 4 |
Yadava.DG | 0.50 | 2 | 4 |
BEB.SG | 0.43 | 72 | 168 |
Punjabi.DG | 0.38 | 3 | 8 |
Austria_LBK_EN | 0.33 | 2 | 6 |
Kazakh.DG | 0.33 | 2 | 6 |
Papuan.DG | 0.33 | 10 | 30 |
Russia_Alan.SG | 0.33 | 2 | 6 |
GIH.SG | 0.30 | 64 | 210 |
UBR.SG | 0.30 | 6 | 20 |
ITU.SG | 0.29 | 58 | 202 |
STU.SG | 0.28 | 58 | 206 |
Armenian.DG | 0.25 | 1 | 4 |
BedouinB.DG | 0.25 | 1 | 4 |
Bengali.DG | 0.25 | 1 | 4 |
Bergamo.DG | 0.25 | 1 | 4 |
Burusho.DG | 0.25 | 1 | 4 |
Druze.DG | 0.25 | 1 | 4 |
England_MBA | 0.25 | 2 | 8 |
England_N | 0.25 | 2 | 8 |
Georgian.DG | 0.25 | 1 | 4 |
Iranian.DG | 0.25 | 1 | 4 |
JAR.SG | 0.25 | 2 | 8 |
Onge.DG | 0.25 | 1 | 4 |
Orcadian.DG | 0.25 | 1 | 4 |
Pathan.DG | 0.25 | 1 | 4 |
PJL.SG | 0.25 | 48 | 192 |
Relli.DG | 0.25 | 1 | 4 |
Russia_Srubnaya_Alakul.SG | 0.25 | 2 | 8 |
England_Roman.SG | 0.20 | 2 | 10 |
England_Saxon.SG | 0.20 | 2 | 10 |
Hungary_Baden_LCA | 0.20 | 2 | 10 |
ILA.SG | 0.20 | 4 | 20 |
RAJ.SG | 0.20 | 4 | 20 |
Mala.DG | 0.17 | 1 | 6 |
Palestinian.DG | 0.17 | 1 | 6 |
Sardinian.DG | 0.13 | 1 | 8 |
TSI.SG | 0.11 | 24 | 212 |
CEU.SG | 0.11 | 22 | 196 |
Germany_Early_Medieval.SG | 0.11 | 2 | 18 |
Hungary_Langobard.SG | 0.11 | 2 | 18 |
VLR.SG | 0.11 | 2 | 18 |
CLM.SG | 0.11 | 20 | 186 |
FIN.SG | 0.09 | 16 | 182 |
Kyrgyzstan_TianShanHun.SG | 0.08 | 2 | 24 |
GBR.SG | 0.07 | 12 | 178 |
MXL.SG | 0.06 | 8 | 134 |
KHV.SG | 0.04 | 8 | 200 |
PUR.SG | 0.04 | 8 | 208 |
IBS.SG | 0.04 | 8 | 210 |
PEL.SG | 0.03 | 6 | 172 |
ACB.SG | 0.02 | 4 | 190 |
ASW.SG | 0.02 | 2 | 130 |
GWD.SG | 0.01 | 2 | 226 |
More comparisons:
CLST | MAF | MAC | NCHROBS |
Balochi | 0.07143 | 2 | 28 |
Bangladeshi | 0.3766 | 58 | 154 |
Burma | 0.075 | 3 | 40 |
Burusho | 0.1458 | 7 | 48 |
Chamar | 0.375 | 6 | 16 |
Dharkars | 0.3333 | 4 | 12 |
Druze | 0.1383 | 13 | 94 |
Dusadh | 0.4 | 4 | 10 |
Dusun | 0 | 0 | 44 |
French | 0.05172 | 3 | 58 |
French_Basque | 0.0625 | 3 | 48 |
Gujurati_Patel | 0.2612 | 35 | 134 |
Han | 0 | 0 | 68 |
Han_N | 0 | 0 | 20 |
Igorot | 0 | 0 | 42 |
Luzon | 0.08333 | 2 | 24 |
Malay | 0.1 | 5 | 50 |
Miaozu | 0 | 0 | 20 |
Murut | 0.05882 | 2 | 34 |
Pathan | 0.07143 | 2 | 28 |
Sindhi | 0.2 | 8 | 40 |
Viet | 0 | 0 | 36 |
It looks like the Vietnamese mystery is solved. Past Coronavirus infection/infections.
Cambodian v Vietnam is very interesting.