u/NotBradPitt9

r/UFOSandUNKNOWN r/indiasocial r/UFOs r/uttarpradesh r/alltheleft r/23andme r/donthelpjustfilm r/IRS r/homeowners r/MeidasTouch r/CrazyFreakingWeather r/ChatGPTPro r/dystopia r/wisdomteeth r/AskVenezuela r/NoOneIsLooking r/AskEurope r/deflock_CT r/illustrativeDNA r/therewasanattempt r/IndianFails r/Wallstreetsilver r/ww3memes r/newjersey r/suppressed_news r/JewishDNA r/skinwalkerranch r/Lenovo r/Bashar_Essassani r/DepropagandizedNews r/IndiaChronicle r/TACO_Tuesday r/AfricanDNAresults r/InterdimensionalNHI r/HornAfricanAncestry r/FlockSurveillance r/HealthTech r/northdakota r/EyesOnIce r/lehighvalley r/HOA r/AIDangers r/RandomVideos r/thatescalatedquickly r/ProgressiveHQ r/FloodWatch r/AlternateHistoryHub r/50501EugeneOR r/POTS r/UnderReportedNews r/crazyfuckingvidoes r/ObscurePatentDangers r/Assyria r/nononoyes r/RandomShit_ISaw r/bicyclewhatever r/UFOReligion r/justincaseyoumissedit r/Irony r/BadDriversIreland r/interestingasfuck r/ACAB r/skyscrapers r/TerrifyingAsFuck r/healthcare r/Palestinian_DNA r/SouthAsianAncestry r/Damnthatsinteresting r/maybemaybemaybemaybe r/JewsOfConscience r/science r/HighStrangeness r/QuebecIntl r/meiaum r/ICE_Raids r/ICE_Watch r/pmp r/news2 r/CriticalMineralBulls r/jerseycity r/BlackGenealogy r/conservation r/NYStateOfMind r/Heartfailure r/ThatsInsane r/PrepperIntel r/neolithicfarmers r/SouthernIndia r/UAP r/ChatGPT r/antimisdisinfoproject r/cringereels r/conspiracy_commons r/MapPorn r/TikTokCringe r/iranfirst r/AncestryDNA r/philly r/BlackPeopleofReddit r/wallstreetbets r/antitrump r/aliens r/NewsKnow r/ratsinthecage r/DNAAncestry r/Ohio r/bestconspiracymemes r/AtlasOfMystery r/ThePeoplesPress r/StockMarket r/millenials

▲ 1 r/Lenovo

Lenovo Thinkpad screen glitching

My screen has been going all black while intermittently flashing / glitching twice in the last two days. Would you guys recommend replacing the screen or should I just get a new laptop. I’m thinking to replace the screen or maybe one of the wires leading into the screen if this continues. Any recommendations on where I can get parts?

The relevant info is below:

Lenovo ThinkPad T14 Gen 2i (Type 20W1, model 20W1S1CUOO), with an Intel Core 15-1145G7, Intel Iris Xe graphics, 16 GB RAM, touchscreen, and Windows 11 Pro.

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u/NotBradPitt9 — 1 month ago

▲ 1.5k r/ProgressiveHQ+1 crossposts

Open corruption

u/NotBradPitt9 — 1 month ago

▲ 30 r/NYStateOfMind

Interesting coincidence?

u/NotBradPitt9 — 1 month ago

▲ 4 r/DNAAncestry

Qpadm: Iberia (Spain, Portugal, Balearics, Canary Islands)

Source populations:
Italy_Imperial_oEast.SG
Spain_IA_Celt.DG
Tunisia_Berber.SG

GENERAL PATTERN

Most Iberian groups are modeled as primarily Spain_IA_Celt.DG, with a secondary Italy_Imperial_oEast.SG contribution and usually a smaller Tunisia_Berber.SG contribution.

The highest Tunisia_Berber.SG proportions in this set are:
Spanish_Canaries.DG = 22.0%
Spanish_Ricote_Morisco.DG = 17.9%
Portuguese.DG = 10.2%
Spanish_Galicia.DG = 9.3%

The three Basque runs are 2-way models using only Italy_Imperial_oEast.SG and Spain_IA_Celt.DG.

All portrayed runs: 713 blocks; 1,150,639 SNPs.

RUN SUMMARIES

Spanish_Valencian_Community.HO
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 22.1 / 72.3 / 5.5%
p = 0.140

Spanish_Castilla-La_Mancha.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 21.3 / 72.0 / 6.7%
p = 0.202

Spanish_Balearic_Islands.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 25.4 / 70.6 / 4.0%
p = 0.827

Spanish_Extremadura.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 22.6 / 70.7 / 6.7%
p = 0.263

Spanish_Cantabria.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 18.9 / 76.4 / 4.7%
p = 0.824

Spanish_Castile_and_Leon.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 21.4 / 71.1 / 7.6%
p = 0.341

Spanish_Valencia.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 19.7 / 76.8 / 3.5%
p = 0.751

Spanish_Aragon.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 20.7 / 76.3 / 3.0%
p = 0.446

Spanish_Galicia.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 18.5 / 72.2 / 9.3%
p = 0.353

Spanish_Andalusia.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 26.1 / 66.9 / 6.9%
p = 0.914

Spanish_Murcia.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 23.0 / 69.9 / 7.1%
p = 0.535

Spanish_Canaries.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 15.1 / 62.9 / 22.0%
p = 0.908

Spanish_Balearic_Islands.HO
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 25.8 / 68.9 / 5.3%
p = 0.204

Spanish_Catalonia.HO
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 21.2 / 75.5 / 3.3%
p = 0.253

Catalan.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 19.9 / 78.0 / 2.0%
p = 0.571

Spanish_Ricote_Morisco.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 36.4 / 45.7 / 17.9%
p = 0.103

Spanish_Balearic_Islands_oChueta.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 40.6 / 54.5 / 4.9%
p = 0.607

Basque_Spanish.HO
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG = 11.3 / 88.7%
p = 0.268

Basque_French.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG = 9.8 / 90.2%
p = 0.369

Basque_Spain.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG = 6.8 / 93.2%
p = 0.249

Portuguese.DG
Italy_Imperial_oEast.SG / Spain_IA_Celt.DG / Tunisia_Berber.SG = 17.0 / 72.8 / 10.2%
p = 0.947

OUTGROUPS USED

The qpAdm runs use slightly different first outgroups depending on the batch/target.

Core outgroups used in the 3-way Iberian runs:
Villaricos_Maghreb.AG
Morocco_Azrou_EpiP.SG
Spain_Aragon_LBA.AG
France_Aude_IA.SG
Spain_C.DG
Armenia_EBA_KuraAraxes.DG
Turkey_Central_CamlibelTarlasi_C.AG
Turkey_BlackSea_Amasya_EBA.DG
Greece_LBA.AG
Czechia_EBA_Unetice.AG

Additional first outgroup used depending on the run:
Australian.DG
Papuan.DG
Ju_hoan_North.DG

Basque 2-way runs used:
Papuan.DG or Australian.DG
Spain_Aragon_LBA.AG
France_Aude_IA.SG
Spain_C.DG
Armenia_EBA_KuraAraxes.DG
Turkey_Central_CamlibelTarlasi_C.AG
Turkey_BlackSea_Amasya_EBA.DG
Greece_LBA.AG
Czechia_EBA_Unetice.AG

Note:
The Basque 2-way models do not include Villaricos_Maghreb.AG or Morocco_Azrou_EpiP.SG among their outgroups in the supplied runs.

u/NotBradPitt9 — 1 month ago

▲ 1.1k r/Irony+1 crossposts

By a foreign leader to shame the British people

u/Grammar-Warden — 1 month ago

▲ 1 r/DNAAncestry

Study: (Ghost Archaic, Super Archaic found in modern humans) Recovering signatures of archaic hominin introgression using ancestral recombination graphs

TLDR: Researchers found Super Archaic and Ghost Archaic DNA at trace amounts in modern humans along with Neanderthal and Denisovan DNA. The Ghost Archaic ancestry split ~830,000 years ago from the ancestors of modern humans. The Super Archaic split 900,000-1.4 million years ago from the ancestors of modern humans. The Ghost Archaic is found in all people since it introgressed into modern humans while we were in Africa. The Super Archaic is only found in Denisovan DNA segments which peak in Oceanian populations, and found at low levels in parts of Asia.

https://www.science.org/doi/10.1126/science.aef8874

We introduce TRACE (Tracking Archaic Contributions Via ARG Estimation), a method for identifying footprints of archaic ancestry in modern human genomes by leveraging features of ARGs constructed from contemporary genomes alone, requiring neither an archaic reference genome nor an unadmixed outgroup population. We validate TRACE by performing extensive simulations and show that it has high precision and low false discovery rate across a range of demographic scenarios and parameters. Applied to data from the 1000 Genomes Project, TRACE recovers the known signatures of Neanderthal introgression in all non-Africans and Denisovan introgression in Asians and Oceanians, with population-level patterns consistent with prior estimates.
Beyond these established signals, TRACE identifies extensive segments of ghost archaic ancestry—from a previously uncharacterized hominin lineage—in both African and non-African populations. We find ghost ancestry persists within Neanderthal and Denisovan ancestry deserts, genomic regions previously interpreted as depleted of archaic introgression and thought to be specific to Homo sapiens. This challenges the prevailing interpretation of these regions and suggests that selection acting against introgressed ancestry has not been uniform across all archaic sources. In Oceanian populations, TRACE detects a substantial enrichment of unusually deep coalescent lineages within Denisovan ancestry segments compared with Neanderthal segments, consistent with a model of super-archaic gene flow into modern humans through Denisovans. Together, these results point to multiple layers of introgression—both direct and mediated through other archaic groups—that have shaped modern human genomes.

Most non-Africans living today possess 1 to 2% Neanderthal ancestry, whereas Asians and Oceanians harbor ~0.1 to 5% Denisovan ancestry

Genetic analyses of present-day African populations have identified highly divergent haplotypes that cannot be explained by known demographic histories, hinting at “ghost” introgression (17–29, 31). At deeper timescales, analysis of the Altai Denisovan genome suggests that they harbor ancestry from a deeply divergent population, referred to as “super-archaic”, that split from modern humans around 0.9 to 1.4 million years ago (Mya)

In non-African populations, TRACE identifies 0.8 to 1% Neanderthal ancestry per individual in Europeans, East Asians, and South Asians (Fig. 2A). We recovered minimal Denisovan ancestry in Europeans (0.03%), with higher levels in East and South Asians (0.10% each), consistent with published results

In both West and East Africans, TRACE detects less than 0.1% combined Neanderthal and Denisovan ancestry per individual.

We note that TRACE recovers less total Neanderthal and Denisovan ancestry compared with some previous studies (33, 54, 55), owing to the lower recall with inferred ARGs

TRACE revealed “ghost” archaic ancestry—introgression from an archaic lineage more distantly related to Neanderthals and Denisovans—in all modern human populations studied. We detected 0.5 to 1.1% of ghost ancestry on average across populations (Fig. 2A). Most ghost ancestry segments found in non-Africans are shared with sub-Saharan Africans, while both East and West Africans harbor a greater diversity of distinct ghost segments consistent with the reduction in genetic diversity in non-Africans caused by the OOA bottleneck (Fig. 2B and figs. S23 and S24). Ghost segments exhibit deep divergence in marginal trees and show nearly identical genetic affinity to both sequenced Neanderthal and Denisovan genomes, indicating that they originated from an unsequenced lineage equally related to both archaic groups (Fig. 2C and figs. S17 to S19). The average coalescence time between the ghost and modern human segments, inferred from introgressed segments, is approximately 0.83 Mya (95% CI: 0.83 to 0.84 Mya, table S6).

Several additional lines of evidence support our results of ghost ancestry in modern humans. First, we find that genomic regions harboring ghost ancestry exhibit elevated heterozygosity levels, a pattern also observed for Neanderthal and Denisovan segments (fig. S25). The elevation in heterozygosity is consistent with a model of introgression from a deeply divergent lineage (56) and, notably, suggests that the ghost ancestry signal is not an artifact of ARG inference. Second, across all tested populations, ghost ancestry segments are shorter than Neanderthal and Denisovan segments, reflecting a more ancient introgression event predating Neanderthal gene flow (fig. S26).

While Neanderthal and Denisovan segments show the expected “U-shape” (17), ghost segments display distinct SFS and cSFS patterns that are consistent with a model of pre–OOA ghost introgression in simulations (fig. S29 and S30).
Together, these findings suggest that an unknown archaic population, which diverged more than 500,000 years ago, introgressed into the common ancestors of all modern humans prior to the OOA migration, resulting in similar patterns of ghost ancestry in non-Africans and Africans.

Across all ghost haplotypes identified in all populations (sub-Saharan Africans and non-Africans) by TRACE, we recovered 1548.75 Mbp or 71.54% of the accessible genome (Fig. 3, supplementary text section S6). Broadly, the genome-wide patterns of ghost ancestry segments resemble those expected from hybridization between deeply divergent lineages (57–59).

we identified 1932 peaks of ghost ancestry (average length 61 kbp, SD 64 kbp) (supplementary text section S6.2, Fig. 3, and table S13). This result contrasts with the smaller number of 1155 and 160 peaks of Neanderthal and Denisovan ancestry, respectively (average length 84.97 and 76.54 kbp). We find that ghost ancestry peaks are overrepresented in sub-Saharan African populations relative to non-African populations, which is consistent with the lower genetic diversity in non-Africans (figs. S33 and S34). Several genes intersect with high-frequency peaks of ghost ancestry, such as CSMD1 and RBFOX1, with an overall functional enrichment for immune and metabolic complexes, including the major histocompatibility and lipoprotein complexes (P < 10−5 using binomial test; figs. S35 and S36).

a fraction of super-archaic ancestry may have been inherited through Denisovan gene flow and may persist within Denisovan-introgressed segments in present-day Oceanians

In Oceanian individuals, TRACE inferred an average of 0.73% Neanderthal, 0.66% Denisovan, and 0.33% ghost ancestry (Fig. 4A), lower than estimates from previous studies.

ghost segments in Oceanians overlapped with those detected in 1000G populations (fig. S50), consistent with a shared origin in modern humans.

we infer that the super-archaic segments embedded within Denisovan ancestry tracts range between 20 to 83 kbp (average: 37.6 kbp) and contribute approximately 0.3% of the total detected Denisovan ancestry in Oceanians (table S18 and fig. S51). This estimate constitutes a very conservative lower bound on the true fraction of super-archaic ancestry

we estimate the coalescence time between super-archaic and modern human lineages to be approximately 1.77 Mya (95% CI: 1.69 to 1.83 Mya), consistent with earlier reports (2, 32).

We evaluated the potential functional effects of super-archaic segments recovered from Oceanian genomes by characterizing common gene annotations and patterns of functional enrichment (Fig. 4B). Several genic regions harbor high proportions (>70%) of super-archaic tracts such as RNF39, PPP1R11, and POLR1Hwithin the major histocompatibility complex (MHC) (Fig. 4B and fig. S53) and CYP24A1, which is a part of the cytochrome P450 family and a critical regulator of vitamin D degradation in humans (fig. S54)

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u/NotBradPitt9 — 1 month ago

▲ 3 r/DNAAncestry

G25: Italian (Iron Age) samples compared to modern samples

Italy_Etruscan_Monteriggioni_IA:EV7A__BC_600__Cov_21.86%,0.10927,0.145221,0.039598,-0.007429,0.054164,-0.00502,0.00611,0.009461,0.036201,0.046106,-0.002761,0.010191,-0.014123,-0.008257,0.001221,-0.00305,-0.00665,0.011909,0.008547,0.004377,-0.00287,0.010758,0.002465,-0.014098,-0.002634
Italy_Etruscan_Monteriggioni_IA:EV16D1__BC_600__Cov_10.57%,0.104717,0.158423,0.032055,-0.011305,0.055395,-0.007809,0.00376,-0.000462,0.033951,0.046835,0.008119,0.008842,-0.02334,-0.019267,-0.00095,-0.007292,-0.00678,-0.011909,-0.006536,0.00075,-0.000998,-0.006677,0.005546,-0.002651,-0.002155
Italy_Pesaro_LateAntiquity:PF1__AD_600__Cov_9.85%,0.046667,0.149283,0.001886,-0.059109,0.038469,-0.023427,-0.035722,0.003692,0.025156,0.01549,0.008119,-0.003447,0.003865,0.003303,0.0076,-0.016971,-0.012126,-0.006461,-0.010684,-0.012756,-0.024332,-0.00507,0.011216,-0.004217,0.007664
Italy_Pesaro_LateAntiquity:PF11__AD_600__Cov_17.10%,0.113823,0.153345,0.004148,-0.021641,0.034468,-0.010319,-0.010575,0.002077,0.007567,0.035354,-0.002761,-0.006294,-0.002825,-0.007982,-0.002986,-0.000796,-0.001173,0.00114,-0.00088,-0.001126,-0.006863,0,0.000246,0.004579,-0.003473
Italy_Pesaro_LateAntiquity:PF19__AD_600__Cov_15.19%,0.094473,0.150298,0.004525,-0.032623,0.025851,-0.013666,0.003995,-0.001154,0.016975,0.024055,-0.001137,0.004796,-0.010555,-0.010322,-0.017779,0.009679,0.018906,-0.001394,0.001885,0.005127,0.002246,0.001237,0.006039,0.006507,-0.010298
Italy_Pesaro_LateAntiquity:PF24__AD_600__Cov_29.11%,0.117238,0.136081,0.007165,-0.030039,0.017849,-0.013387,0.00564,-0.006461,0.011862,0.032985,0.005359,0.009591,-0.009663,-0.01913,-0.00665,-0.001326,-0.003651,0.005321,0.002765,-0.014882,0.00262,-0.000371,0.003081,0.003133,-0.003712
Italy_Pesaro_LateAntiquity:PF28__AD_600__Cov_24.81%,0.105855,0.145221,-0.028661,-0.070414,0.007694,-0.023985,-0.0094,0.000692,-0.007567,0.031891,0.00406,0.004196,-0.023191,-0.004129,-0.031216,0.010209,0.015646,0.003674,0.003268,-0.000875,-0.000499,-0.004204,0.000493,-0.003976,-0.005748
Italy_Pesaro_LateAntiquity:PF36__AD_600__Cov_10.61%,0.104717,0.129988,-0.000377,-0.026809,0.014156,-0.006693,0.012691,-0.010384,-0.001023,0.033349,0.004547,0.004496,-0.011596,-0.004954,-0.000679,0.011933,0.005867,-0.011782,0.003771,-0.004002,0.001622,0.002102,-0.007025,0.00723,-0.006466
Italy_Picene_Novilara_IA:PN20__BC_625__Cov_15.30%,0.118376,0.149283,0.031301,-0.023579,0.045547,-0.003347,0.011281,0.010615,0.016157,0.036812,0.005196,0.013938,-0.015461,-0.022295,-0.002714,0.006232,-0.000782,0.008868,0.019357,-0.025012,0.014974,-0.006059,0.002465,-0.01699,0.002994
Italy_Picene_Novilara_IA:PN24__BC_675__Cov_22.18%,0.119514,0.161469,0.042615,0.002261,0.0437,-0.001394,-0.00517,0,0.002863,0.03426,0.006496,0.008842,-0.017839,-0.010597,0.003664,0.002254,0.009127,0.005068,0.004399,-0.003377,-0.000374,0.005564,-0.009244,0.002289,-0.010897
Italy_Picene_Novilara_IA:PN35__BC_725__Cov_22.42%,0.119514,0.142174,0.036581,0.003876,0.030159,-0.001673,0.00235,-0.011076,0.011249,0.029522,-0.011205,-0.007044,-0.01665,-0.011836,-0.006786,0.000398,0.002217,0.000127,0.009804,-0.001501,0.004742,0.004946,0.004067,0.010363,-0.012813
Italy_Picene_Novilara_IA:PN41__BC_625__Cov_19.22%,0.1161,0.139128,0.029793,-0.007752,0.02739,-0.014781,-0.00329,0.010153,-0.002454,0.043372,-0.004709,0.02248,-0.023191,-0.015551,-0.005157,-0.003182,0.006389,-0.007601,0.008673,-0.003377,-0.006988,-0.006677,0.002342,-0.000482,-0.011735
Italy_Picene_Novilara_IA:PN42__BC_700__Cov_13.96%,0.136588,0.126941,0.037712,0.008721,0.042469,0.002789,-0.011751,0.011076,0.003681,0.016037,-0.000325,0.005245,-0.014569,-0.023258,0.002307,0.006364,0.011213,0,0.001006,-0.017383,-0.002121,0.005688,-0.001479,-0.002651,-0.00946
Italy_Picene_Novilara_IA:PN44__BC_650__Cov_21.60%,0.120652,0.137096,0.034318,-0.005814,0.031083,-0.003347,-0.003525,0.007615,0.00859,0.022233,-0.008769,0.007044,-0.013082,-0.002064,0.005836,0.012198,0.02373,-0.008235,0.012696,-0.009379,-0.011105,0.00371,-0.016515,0.006145,-0.001078
Italy_Picene_Novilara_IA:PN50__BC_650__Cov_21.27%,0.121791,0.150298,0.027153,0.003876,0.045547,0.003068,0.000705,0.000462,0.018816,0.026424,-0.004872,-0.002997,-0.035381,0.00578,0.003529,0.001193,-0.002999,0.003041,-0.004525,-0.007003,0.007237,0.004204,-0.003328,0.005543,-0.006586
Italy_Picene_Novilara_IA:PN51__BC_675__Cov_11.58%,0.120652,0.141159,0.02753,-0.015181,0.029544,-0.00502,-0.00047,0.012692,0.022702,0.039181,0.003897,0.017684,-0.012636,-0.013349,0.002036,-0.003447,0.022296,-0.008995,-0.004274,-0.001876,-0.010107,0.007914,0.007148,-0.006145,0.00012
Italy_Picene_Novilara_IA:PN78__BC_700__Cov_23.76%,0.122929,0.146236,0.037335,0.00323,0.044008,0.005578,0.00235,-0.001615,0.004909,0.018588,-0.001461,0.005245,-0.013974,-0.013487,0.008143,0.002121,0.000913,-0.007221,0.014078,-0.001501,-0.001123,0.006306,-0.00493,-0.001687,0.000599
Italy_Picene_Novilara_IA:PN79__BC_675__Cov_23.59%,0.124067,0.141159,0.037712,0.005168,0.042469,0.014502,-0.001175,-0.003231,0.009408,0.020775,-0.001299,0.009741,-0.01219,0.000413,0.0076,-0.00769,-0.001825,-0.011022,0.012821,0.001,0.002745,-0.00507,0.004314,0.005543,-0.001197
Italy_Picene_Novilara_IA:PN85__BC_625__Cov_27.04%,0.126344,0.155376,0.046009,0.001938,0.029236,0.000837,0.00611,0.002308,0.006136,0.025878,-0.006496,0.008992,-0.020812,0.000688,-0.013843,-0.01127,-0.000782,0.005828,0.007542,-0.00963,0.002745,0.007914,0.00037,-0.005663,0.005029
Italy_Picene_Novilara_IA:PN87__BC_650__Cov_15.50%,0.113823,0.139128,0.024513,0.010013,0.028005,0.001952,0.00282,-0.003,-0.002454,0.018588,0.001786,0.014537,-0.018583,-0.004542,-0.005022,-0.00769,0.004694,0.002787,-0.002514,-0.011631,-0.012478,0.006925,0.005546,-0.001325,-0.013771
Italy_Picene_Novilara_IA:PN90__BC_675__Cov_19.88%,0.1161,0.150298,0.02753,0.002584,0.037853,-0.004462,0.005405,-0.003461,0.006545,0.032074,0.00065,0.018434,-0.012933,-0.029038,-0.006515,0.005038,0.014473,0.005954,0.007039,0.003377,-0.018218,0.009645,-0.003697,-0.013014,-0.007065
Italy_Picene_Novilara_IA:PN91__BC_625__Cov_19.60%,0.113823,0.140143,0.029038,0.00323,0.032621,-0.000558,0.00235,0.003461,0.012271,0.035172,0.006496,-0.008692,-0.013677,-0.004542,-0.006379,-0.012331,-0.004563,0.007095,0.006034,-0.008004,-0.016097,-0.000247,0.006039,0.005061,0
Italy_Picene_Novilara_IA:PN101__BC_650__Cov_22.21%,0.119514,0.14319,0.032055,0.003876,0.048317,-0.013108,0.00141,0.002538,0.009817,0.031709,-0.001786,0.011839,-0.028692,-0.009909,-0.010179,0.002254,0.005607,0.000127,0.004777,-0.019384,-0.00836,0.003091,0.010106,0.006386,-0.003473
Italy_Picene_Novilara_IA:PN105__BC_650__Cov_12.49%,0.119514,0.170609,0.042992,-0.00646,0.034776,-0.013108,0.00094,-0.011307,0.019225,0.035901,0.000812,-0.002997,-0.027354,-0.013212,-0.005022,-0.030363,-0.000522,0.01875,-0.002514,-0.011881,-0.00574,0.006059,-0.007272,0.006266,0.00455
Italy_Picene_Novilara_IA:PN125__BC_625__Cov_14.66%,0.121791,0.153345,0.044877,0.000969,0.037238,-0.010877,-0.00094,0.002308,0.023316,0.032256,0.002273,0.002098,-0.004906,-0.009358,-0.000814,0.008751,0.004824,0.006461,0.019106,-0.003877,0.000749,-0.000247,0.005669,0.00012,-0.002515
Italy_Picene_Novilara_IA:PN135__BC_725__Cov_16.74%,0.117238,0.144205,0.037335,0.008075,0.042469,-0.003904,0.00188,0.006692,0.012885,0.014761,0.007307,0.006444,-0.017988,0.005918,-0.007465,0.001458,0.016559,0.005068,0.007416,-0.004627,-0.001248,-0.008779,-0.003821,0.003856,-0.012813
Italy_Picene_Novilara_IA:PN138__BC_700__Cov_26.85%,0.122929,0.144205,0.034695,-0.010336,0.028621,0.00502,-0.003995,0.004846,0.01268,0.029887,-0.003248,0.006894,-0.015461,-0.011285,-0.004207,0.001989,0.002999,-0.001647,0.005405,0.002876,0.000624,0.004328,-0.005176,-0.002771,-0.006706
Italy_Picene_Novilara_IA:PN141__BC_700__Cov_15.02%,0.135449,0.142174,0.031678,-0.003553,0.045239,-0.01506,0.00329,0.005077,0.010431,0.037358,0.006008,0.001948,-0.014123,-0.002615,-0.0019,-0.003845,0.009779,0.00228,0.008547,-0.014007,0.002371,0.005317,-0.014297,-0.000964,-0.006826
Italy_Picene_Novilara_IA:PN146__BC_700__Cov_17.54%,0.120652,0.153345,0.032432,-0.005168,0.055703,-0.00502,-0.015041,0.004384,0.01309,0.031891,-0.000487,0.008692,-0.021407,-0.017065,0.000136,-0.000398,0.004824,0.000507,0.006034,-0.006003,0.001373,0.007172,-0.000863,-0.007109,0.00491
Italy_Picene_Novilara_IA:PN157__BC_700__Cov_21.37%,0.118376,0.151314,0.037712,0.00969,0.035391,-0.010598,-0.00329,0.001385,0.009817,0.030433,0.009256,0.014987,-0.020961,-0.013487,-0.001357,-0.002784,0.007693,-0.006588,0.000754,-0.006878,0.001248,0.000618,-0.005793,-0.003615,0.003592
Italy_Picene_Novilara_IA:PN158__BC_700__Cov_11.73%,0.127482,0.125926,0.038843,0,0.0397,-0.006693,0.002115,0.003231,0.005113,0.032985,-0.005521,0.015137,-0.020664,-0.011836,-0.000271,0.002784,0.008214,0.003421,0.020237,-0.009004,0.001747,0.002968,0.003204,0.001566,0.000479
Italy_Picene_Novilara_IA:PN162__BC_700__Cov_25.84%,0.126344,0.156392,0.04714,0.008075,0.038469,-0.004741,-0.00235,0.005538,0.011044,0.029887,-0.000487,0.008992,-0.02334,-0.001927,-0.002579,0.001193,0.01369,0.002154,-0.002765,-0.006503,0.00025,0.006677,-0.011585,-0.00494,0.009939
Italy_Picene_Novilara_IA:PN172__BC_700__Cov_12.59%,0.125205,0.138112,0.050534,0.010013,0.03693,-0.005299,-0.008225,-0.000692,0.02127,0.022415,-0.005359,-0.006145,-0.006838,-0.015689,0.005157,0.01432,0.007302,-0.009122,0.004525,-0.01113,-0.00836,0.00136,-0.007518,0.00735,0.013412
Italy_Picene_Novilara_IA:PN174__BC_650__Cov_9.97%,0.124067,0.133034,0.033564,0.002907,0.048624,-0.014781,-0.00658,-0.011769,0.008999,0.02606,0.008769,-0.015436,-0.015461,0.007294,0.009636,-0.018828,-0.022556,0.003801,0.007542,0.01113,-0.004492,-0.003957,0.003821,-0.004097,-0.008382
Italy_Picene_Novilara_IA:PN177__BC_675__Cov_12.00%,0.118376,0.151314,0.032809,-0.018411,0.02739,-0.01255,0.001175,-0.003,0.020861,0.024602,0.009419,-0.009591,-0.024232,-0.007156,0.000543,-0.002254,-0.017863,0.008615,-0.002263,-0.007504,-0.000873,-0.001113,0.01479,0.008073,-0.002036
Italy_Picene_Novilara_IA:PN179__BC_675__Cov_10.95%,0.125205,0.157407,0.020365,-0.007429,0.03416,-0.003626,0.000235,-0.009923,0.005522,0.039545,0.006496,0.016036,-0.000297,-0.034956,-0.016558,0.011668,0.017602,0.000507,0.01081,0.001126,-0.008485,0.007666,-0.005916,0.007832,-0.00467
Italy_Picene_Novilara_IA:PN180__BC_650__Cov_13.10%,0.125205,0.149283,0.035449,0.012274,0.046778,0.008088,-0.00611,0.011769,0.011862,0.038816,0.004222,0.005245,-0.019772,0.001101,0.001086,-0.016309,-0.00678,0.000127,-0.01169,-0.003252,-0.000873,-0.000495,-0.005053,-0.002048,0.000958
Italy_Picene_Sirolo/Numana_IA:PNU76__BC_600__Cov_11.71%,0.124067,0.15436,0.050157,-0.013889,0.046778,0.000837,-0.00094,-0.004154,0.032315,0.030251,0.003248,0.003297,-0.009663,-0.010046,0.004343,-0.008088,-0.013299,0.012035,-0.01169,-0.01038,-0.007736,-0.003462,0.002958,-0.000964,0
Italy_Picene_Sirolo/Numana_IA:PSD2__BC_600__Cov_10.30%,0.125205,0.14319,0.041106,0.000646,0.032314,-0.003904,-0.011751,0.009692,0.012885,0.030433,-0.001624,-0.001049,-0.008325,-0.014863,-0.010179,-0.002519,0.019688,0.004941,-0.000754,0.017884,-0.007362,0.012736,0.010106,0.008796,0.00012

u/NotBradPitt9 — 1 month ago

▲ 323 r/TikTokCringe

What do the Brits think of this? Very odd interview

u/NotBradPitt9 — 1 month ago

▲ 7 r/DNAAncestry

Qpadm: [Iron Age Breakdown] Mesopotamia, Arabian, Iran and Caucasus groups

TLDR: An Iron Age Qpadm genetic breakdown of groups from Mesopotamia and regions around it. I did a couple dozen runs per group, and here are the best of the runs. Note that the Iraqi Arab North was sourced from someone’s Iron Age Qpadm results here on Reddit, since I couldn’t find any other samples for Iraqi Arabs within the AADR / HO dataset.

Yemeni_Desert2.HO (n=15)

69.9% Syria_TellQarassa_Umayyad.SG
30.1% Dinka.DG

p-value: 0.116
Chi-square / dof: 11.558 / 7
SNPs: 579,720
SE: 0.758% / 0.758%
Z-scores: 92.1 / 39.7

PASS. Both components are extremely strongly supported.

Yemeni_Highlands.HO (n=14)

90.6% Syria_TellQarassa_Umayyad.SG
9.4% Dinka.DG

p-value: 0.172
Chi-square / dof: 10.299 / 7
SNPs: 579,720
SE: 0.857% / 0.857%
Z-scores: ~106 / 11.0

PASS. Both components are extremely strongly supported.

Yemeni_Desert.HO (n=7)

91.5% Syria_TellQarassa_Umayyad.SG
8.6% Dinka.DG

p-value: 0.0502
Chi-square / dof: 14.057 / 7
SNPs: 579,720
SE: 0.862% / 0.862%
Z-scores: ~106 / 9.92

PASS. Both components are extremely strongly supported. The overall p-value is very close to the conventional 0.05 cutoff, however, so the overall model is borderline despite the strongly supported coefficients.

Jew_Yemenite.HO (n=1)

94.3% Syria_TellQarassa_Umayyad.SG
5.7% Dinka.DG

p-value: 0.205
Chi-square / dof: 9.712 / 7
SNPs: 579,720
SE: 1.24% / 1.24%
Z-scores: 76.3 / 4.60

PASS. Both components are statistically supported.

Saudi.HO (n=5)

94.5% Syria_TellQarassa_Umayyad.SG
5.5% Dinka.DG

p-value: 0.571
Chi-square / dof: 6.681 / 8
SNPs: 579,720
SE: 0.764% / 0.764%
Z-scores: ~124 / 7.21

PASS with an excellent fit. Both components are very strongly supported.

Assyrian.HO (n=10)

74.9% Iran_DinkhaTepe_BA_IA_1.AG
25.1% Georgia_Digomi_IA.SG

p-value: 0.798
Chi-square / dof: 4.611 / 8
SNPs: 579,720
SE: 5.64% / 5.64%
Z-scores: 13.3 / 4.45

PASS with an excellent fit. Both ancestry coefficients are strongly supported. The SE is slightly above 5%, which is why the software displays its SE warning despite both Z-scores being comfortably above 2.

Iraqi Arab North (n=1)

85.4% Turkey_East_Van_Urartian.AG
14.6% Kazakhstan_Saka_IA.AG

p-value: 0.80245088
Chi-square: 3.05
SE: 2.80% / 2.80%
Z-scores: approximately 30.5 / 5.21

This is a very strong model. The attached result reports the coefficients and SE rather than Z directly; the Z-scores above are derived as coefficient / SE and show that both components are strongly supported.

Iraqi_Jew.HO (n=6)

97.8% Iran_DinkhaTepe_BA_IA1-1.SG
2.2% Kazakhstan_Southeast_Karakhanid.SG

p-value: 0.458
Chi-square / dof: 17.958 / 18
SNPs: 579,720
SE: 4.95% / 4.95%
Z-scores: 19.8 / 0.437

PASS overall. The 97.8% Dinkha Tepe coefficient is strongly supported. The 2.2% Karakhanid coefficient is not statistically supported (Z < 2), so the minor component should not be treated as dependable.

Iranian_Jew.HO (n=9)

95.8% Iran_DinkhaTepe_BA_IA1-1.SG
4.2% Kazakhstan_Southeast_Karakhanid.SG

p-value: 0.843
Chi-square / dof: 12.088 / 18
SNPs: 579,720
SE: 1.79% / 1.79%
Z-scores: 53.4 / 2.36

PASS with an excellent fit. Both components are statistically supported.

Armenian.HO (n=10)

100.0% Armenia_Aghitu_Ancient_father.or.son.I1636.AG

p-value: 0.259
Chi-square / dof: 22.513 / 19
SNPs: 579,720
SE: 0.000000
Z-score: ~402,000,000 as displayed

PASS with an excellent fit. This is a one-way model. The essentially zero SE causes the software to display an extremely large Z-score, so the numerical Z value should not be interpreted in the same way as an ordinary multi-source coefficient.

Iranian_Lor_Khorramabad.HO (n=10)

88.7% Armenia_Sarukhan_UrartianIA.AG
11.3% Kazakhstan_Southeast_Karakhanid.SG

p-value: 0.641
Chi-square / dof: 15.304 / 18
SNPs: 579,720
SE: 5.12% / 5.12%
Z-scores: 17.3 / 2.21

PASS with an excellent fit. Both coefficients have Z > 2.

Iranian_Central.HO (n=5)

85.3% Armenia_Sarukhan_UrartianIA.AG
14.7% Kazakhstan_Southeast_Karakhanid.SG

p-value: 0.397
Chi-square / dof: 18.909 / 18
SNPs: 579,720
SE: 5.59% / 5.59%
Z-scores: 15.3 / 2.62

PASS with an excellent fit. Both coefficients have Z > 2.

Iranian_Mazandarani.HO (n=10)

85.8% Armenia_Sarukhan_UrartianIA.AG
14.2% Kazakhstan_Southeast_Karakhanid.SG

p-value: 0.512
Chi-square / dof: 17.159 / 18
SNPs: 579,720
SE: 5.14% / 5.14%
Z-scores: 16.7 / 2.77

PASS with an excellent fit. Both coefficients have Z > 2.

Iranian_Cosmopolitan_Tehran.HO (n=1)

95.9% Armenia_Beniamin_BIA.SG
4.1% Kazakhstan_Southeast_Karakhanid.SG

p-value: 0.0542
Chi-square / dof: 28.547 / 18
SNPs: 579,720
SE: 4.27% / 4.27%
Z-scores: 22.5 / 0.966

PASS, although the overall p-value is close to the 0.05 cutoff. The 95.9% Armenia_Beniamin_BIA coefficient is strongly supported, while the 4.1% Karakhanid coefficient is not statistically supported (Z < 2).

Kurd_Turkey.HO (n=1)

96.7% Iran_DinkhaTepe_BA_IA1-1.SG
3.3% Mongolia_Tuv_EIA.AG

p-value: 0.314
Chi-square / dof: 20.339 / 18
SNPs: 579,720
SE: 3.24% / 3.24%
Z-scores: 29.8 / 1.02

PASS with an excellent overall fit. The Dinkha Tepe coefficient is very strongly supported, but the 3.3% Mongolia_Tuv_EIA coefficient is not statistically supported (Z < 2).

Kurd.HO (n=8)

92.8% Iran_DinkhaTepe_BA_IA1-1.SG
7.2% Mongolia_Tuv_EIA.AG

p-value: 0.649
Chi-square / dof: 15.188 / 18
SNPs: 579,720
SE: 2.53% / 2.53%
Z-scores: 36.7 / 2.86

PASS with an excellent fit. Both components are statistically supported.

Georgian.HO (n=13)

100.0% Georgia_Digomi_IA.SG

p-value: 0.576
Chi-square / dof: 9.495 / 11
SNPs: 579,720
SE: 0.000000
Z-score: ~879,000,000 as displayed

PASS with an excellent fit. This is a one-way model. As with the Armenian one-way model, the essentially zero SE produces an extremely large numerical Z-score and that value should be regarded as a numerical artifact rather than a conventional coefficient Z-score.

u/NotBradPitt9 — 1 month ago

▲ 49 r/IndianFails+1 crossposts

These face masks manufactured in India don't appear to be sterile

u/NotBradPitt9 — 1 month ago

▲ 3 r/DNAAncestry

G25: Ancient samples from Iraq and Iran genetically closest to modern groups in the region [Assyrians, Jews (Iranian, Iraqi, Caucasus), Kurdish, Yazidis, Mandaeans, Armenians, Persians]

The ancient samples used here come from several archaeological sites across Iran and northern Iraq and span almost 10,000 years, from the Pre-Pottery Neolithic to the late medieval period.

Dinkha Tepe, Iran
Dinkha Tepe is located in the Ushnu/Oshnavieh Valley of West Azerbaijan Province in northwestern Iran, southwest of Lake Urmia and close to Hasanlu. The samples span approximately 1900–900 BCE, covering the Middle/Late Bronze Age through the Iron Age.

The Dinkha Tepe individuals shown include:
I3913 – ~1892 BCE
I4341 – ~1825 BCE
I4272 – ~1824 BCE
I4274 – ~1821 BCE
I3912 – ~1793 BCE
I3911 – ~1500 BCE
I4236 – ~1500 BCE
I4340 – ~1341 BCE
I4239 – ~1307 BCE
I4238 – ~1162 BCE
I3914 – ~1078 BCE
I4273 – ~1017 BCE
I4237 – ~948 BCE
I3915 – ~927 BCE
The DinkhaTepe_BA_IA_1 and DinkhaTepe_BA_IA_2 labels should not be interpreted as two simple consecutive chronological phases, since the dates of the individuals in the two groups overlap substantially.

Tepe Hissar, Iran
Tepe Hissar is located near modern Damghan in Semnan Province, north-central Iran. It was an important settlement connecting the Iranian Plateau with Central Asia. These individuals date mainly to the third millennium BCE and can broadly be described as Late Chalcolithic/Bronze Age.
I2924 – ~2765 BCE
I2925 – ~2765 BCE
I2923 – ~2757 BCE
I2928 – ~2675 BCE
I2927 – ~2497 BCE
I2922 – ~2093 BCE
A simple description would therefore be: Tepe Hissar, Iran – Late Chalcolithic/Bronze Age, roughly 2800–2000 BCE.

Ganj Dareh, Iran
Ganj Dareh is located in Kermanshah Province in the central Zagros of western Iran. The site is famous for its very early Neolithic settlement, but the sample Iran_GanjDareh_Historic:I1955 is NOT one of the Neolithic inhabitants.
I1955 was an intrusive much later burial dated to approximately 1430–1485 CE. It can therefore be described as a late medieval, approximately 15th-century CE individual from Ganj Dareh.
Hasanlu, Iran
Tepe Hasanlu is located in West Azerbaijan Province in northwestern Iran, south of Lake Urmia near modern Naqadeh. It was a major Bronze and Iron Age settlement and is particularly famous for its destruction during the Iron Age.
The Hasanlu samples cover a very long period, from approximately 2100 to 600 BCE:
I4354 – ~2092 BCE, Middle Bronze Age
I4098 – ~1428 BCE, Late Bronze Age
I4097 – ~1354 BCE, Late Bronze/Early Iron transition
I4099 – ~1201 BCE
I6432 – ~1178 BCE
I6431 – ~1072 BCE
I6388 – ~1065 BCE
I6428 – ~1027 BCE
I4356 – ~1017 BCE
I4100 – ~961 BCE
I4353 – ~961 BCE
I6429 – ~957 BCE
I4355 – ~948 BCE
I6430 – ~900 BCE
F38 – ~883 BCE
I4232 – ~874 BCE
I4357 – ~871 BCE
I4233 – ~856 BCE
I4269 – ~803 BCE
I4280 – ~787 BCE
I4338 – ~632 BCE
Most of the later Hasanlu individuals belong to the Iron Age. I4097 is labelled as a genetic outlier in some datasets and dates to approximately the Late Bronze/Early Iron Age transition.

Hajji Firuz, Iran
Hajji Firuz Tepe is also located in the Lake Urmia/Naqadeh region of West Azerbaijan Province, close to Hasanlu. The samples from this site belong to three very different periods.
Neolithic:
I4351 – ~5970 BCE
I2323 – ~5960 BCE
I4241 – ~5954 BCE
I4349 – ~5797 BCE
Bronze Age:
I4243 – ~2362 BCE
Iron Age:
I2327 – ~1100 BCE
Therefore HajjiFiruz_N, HajjiFiruz_BA and HajjiFiruz_IA should be treated as historically distinct populations rather than as one group.

Nemrik 9, Iraq
Nemrik 9 is located in northern Iraq, in the region of modern Iraqi Kurdistan. It was an important early Pre-Pottery Neolithic settlement.
I6445 is the genuine ancient Neolithic individual and dates broadly to approximately 9500–8000 BCE, with a representative date around 8750 BCE. It can be described as Pre-Pottery Neolithic A/early Pre-Pottery Neolithic northern Mesopotamian.
I6441 is very different. Despite appearing under an Iraq_PPNA label in the comparison, it is actually a Late Bronze Age individual from Nemrik 9, approximately 1500–1200 BCE, or around 1350 BCE.
Therefore:
I6445 = Nemrik 9 PPNA, ~8750 BCE
I6441 = Nemrik 9 Late Bronze Age, ~1350 BCE

Shahr-i Sokhta, Iran
Shahr-i Sokhta, or the "Burnt City," is located in Sistan and Baluchestan Province in southeastern Iran near the Hamun basin. It was a major Bronze Age urban center connected to trade networks linking the Iranian Plateau, Central Asia and South Asia. The settlement flourished approximately 3200–1800 BCE.

The samples shown are all Bronze Age:
BA1:
I8724 – ~2950 BCE
I8725 – ~2912 BCE
I11462 – ~2894 BCE
I11474 – ~2650 BCE
I11476 – ~2650 BCE
I11478 – ~2550 BCE
BA2:
I8726 – ~3050 BCE
I11459 – ~2749 BCE
I11456 – ~2550 BCE
I8728 – ~2550 BCE
I11466 – ~2250 BCE
The BA1 and BA2 labels should not be interpreted simply as an earlier and later chronological sequence. Their dates overlap and the groups primarily reflect archaeological/genetic classifications. Shahr-i Sokhta was also highly cosmopolitan, which helps explain the considerable genetic differences between some individuals.

In broad chronological order, the samples are:
Nemrik 9 PPNA – ~8750 BCE
Hajji Firuz Neolithic – ~6000 BCE
Shahr-i Sokhta – ~3050–2250 BCE
Tepe Hissar – ~2800–2000 BCE
Hasanlu Middle Bronze Age – ~2100 BCE
Dinkha Tepe Bronze Age – beginning ~1900 BCE
Hasanlu/Dinkha Tepe/Hajji Firuz Late Bronze and Iron Age – ~1500–600 BCE
Ganj Dareh I1955 – ~15th century CE

Dinkha Tepe, Hasanlu and Hajji Firuz are especially noteworthy because all three are located in the broader Lake Urmia region of northwestern Iran. The substantial differences between individual samples therefore reflect both the long chronological span and changing population history of the region, rather than simply representing completely unrelated geographic populations.

u/NotBradPitt9 — 1 month ago

▲ 1.2k r/TerrifyingAsFuck+1 crossposts

Video from the recent Himalayan floods

u/NotBradPitt9 — 2 months ago

▲ 1.5k r/science

Postmortem evidence of Babesia and Bartonella infections in chronically ill people

researchgate.net

u/NotBradPitt9 — 2 months ago

▲ 5 r/DNAAncestry

DNA sequencing used to confirm Bartonella and Babesia infections in people with neuropsychiatric illnesses.

TLDR: DNA sequencing was used in this study to confirm Bartonella and Babesia infections in people with neuropsychiatric illnesses. This gives us a new look on the role of these organisms in severe illnesses.

https://www.researchgate.net/publication/410845265\_Postmortem\_evidence\_of\_Babesia\_and\_Bartonella\_infections\_in\_chronically\_ill\_suicidal\_young\_people

Background: Vector-borne diseases represent an emerging global threat to public health, yet the
potential role of flea and tick-borne pathogens as a cause or cofactor in neuropsychiatric illnesses
and suicidal ideation remains relatively uninvestigated. Co-infections with vector borne Babesia and Bartonella spp., as well as infection with more than one Babesia species, may represent a more important cause of neurological and neuropsychiatric illnesses than is currently appreciated.

Methods: Postmortem blood, tissue, and urine samples from six chronically ill youths who ended or wanted to end their lives were tested for molecular evidence of Babesia, Bartonella, and Borrelia spp. using qPCR, dPCR, and enrichment blood culture.

Results: Infections with Babesia, Bartonella, or both genera were confirmed by DNA sequencing in five of six subjects. Three different Babesia spp. were detected in four subjects, of whom two were infected with more than one Babesia sp. All six had equivocal Bartonella spp. dPCR+ or enrichment culture dPCR+ results. Bartonella henselae was confirmed by DNA sequencing in two individuals. Borrelia dPCR was equivocally positive in one subject but could not be confirmed by DNA sequencing.
infections may have a causal role in chronic illness and potentially suicidality in young people. More research is needed to determine the prevalence and role of these pathogens in patients with chronic physical and neuropsychiatric symptoms and suicidal ideation.

Background

There is growing interest among patients, advocacy groups, physicians, and researchers in the role of microorganisms in chronic neurological and neuropsychiatric illnesses [1, 2, 3]. Microbes have been associated with various disorders, including schizophrenia, bipolar disorders, depressive disorders, anxiety disorders, aggressive/violent behavior, and suicidality (see [4] for a review). There are individual case reports and small case series, but few comprehensive prospective studies that define the role of specific or polymicrobial infections as cause or cofactor in neuropsychiatric disorders. With the exception of Lyme disease (Borrelia burgdorferi), a potential role for flea and tick transmitted, zoonotic vector borne bacteria or protozoa as a cause of suicidal ideation has been minimally investigated [5, 6].

Prior publications from the Intracellular Pathogens Research Laboratory (IPRL) at North Carolina State University have documented persistent infections with Babesia and Bartonella spp. in persons with chronic physical, neurological, and neuropsychiatric disorders.
Bartonella spp. are proteobacteria that are endotheliotropic—their primary niche is the endothelial cell. They enter erythrocytes to disperse throughout the body.

Bartonella spp. infections can be acquired via animal scratches or bites, fleas, and many arthropods (see [7] for a review). Babesia spp. are protozoa that are closely related to the Plasmodium spp., the cause of human malaria. Babesia spp. are highly adapted for tick transmission. In the infected host, Babesia merozoites invade erythrocytes, evade elimination by the immune system and coopt erythrocyte nutrition to fulfill metabolic needs (see [8] for a review). Both genera constitute stealth pathogens that cause minimal-to-no symptoms in reservoir hosts, but multisystem disease in domestic and wild animals and humans. Clinical outcome is determined by the virulence of the infecting Babesia spp. and host’s immune response to the parasites.

The IPRL has documented persistent Bartonella spp. infections in persons with neurological and neuropsychiatric disorders, many of whom had extensive arthropod and animal contact [9, 10, 11, 12, 13, 14, 15]. In 2019, we reported the case of a boy diagnosed with pediatric acute-onset neuropsychiatric syndrome (PANS) who was homicidal and suicidal [16]. Bartonellosis was first suspected due to unusual cutaneous stretch marks—Bartonella-associated cutaneous lesions (BACL) [17, 18]. After Bartonella henselae bacteremia was confirmed, he recovered fully with an extended course of antimicrobials. Subsequently, BACL were reported in 29 of 33 persons with neuropsychiatric symptoms who had serological and/or PCR evidence of Bartonella spp. exposure and/or infection, 24 of whom reported that the BACL developed in association with their symptoms [19].

Historically, the IPRL has focused on generating improvements in diagnostic testing modalities,
Borrelia (BBB) spp. detection [20, 21, 22, 23]. Following the development and validation of Bartonella digital droplet PCR (ddPCR) [20], Bartonella DNA was more often amplified from blood or enrichment blood cultures from patients diagnosed with schizophrenia or psychosis compared to controls [24, 25]. Following the subsequent validation of digital PCR (dPCR) tests for Babesia and Borrelia spp. [22, 23], we documented polymicrobial vector-borne infections in patients reporting neurological and neuropsychiatric symptoms, including suicidality [26, 27, 28, 29, 30]. In this study, we report postmortem evidence of Babesia and Bartonella infections in suicidal young adults and youths.

reddit.com

u/NotBradPitt9 — 2 months ago

▲ 3 r/illustrativeDNA

Qpadm: some groups from the Caucasus, Mesopotamia

TLDR: Did a bunch of runs in qpadm using the AADR/ HO dataset. These are the best of those runs, and it’s basically just to compare the ranges per group. I believe some of the CHG Caucasus Hunter Gatherer ancestry is being absorbed into the ANF. Viewing only works on the phone app, seems like desktop always has the first photo as blurry.

NORTHWEST CAUCASUS

Abazin.HO
Nat/ZNF/ANF/CHG/EHG = 13.0/18.9/27.8/23.7/16.5%
p = 0.123; χ²/dof = 2.374/1
SE = 0.0207/0.0210/0.0235/0.0258/0.0109
Z = 6.29/9.02/11.8/9.20/15.2

Circassian.HO
Nat/ZNF/ANF/CHG/EHG = 12.6/16.9/25.6/27.4/17.4%
p = 0.668; χ²/dof = 0.183/1
SE = 0.0204/0.0204/0.0220/0.0243/0.0109
Z = 6.18/8.31/11.7/11.3/16.0

ind_S_Abkhasian-1.DG
4-way model: ZNF/ANF/CHG/EHG = 24.1/39.0/27.2/9.7%
p = 0.552; χ²/dof = 1.189/2
SE = 0.0382/0.0262/0.0461/0.0211
Z = 6.32/14.9/5.89/4.59

GEORGIAN

ind_GEO-002.HO
Nat/ZNF/ANF/CHG/EHG = 15.8/16.6/32.8/25.3/9.6%
p = 0.585; χ²/dof = 1.073/2
SE = 0.0418/0.0410/0.0473/0.0481/0.0215
Z = 3.78/4.04/6.92/5.26/4.46

ind_GEO-005.HO
Nat/ZNF/ANF/CHG/EHG = 11.4/17.4/35.1/22.6/13.5%
p = 0.845; χ²/dof = 0.336/2
SE = 0.0440/0.0363/0.0439/0.0427/0.0214
Z = 2.59/4.79/8.00/5.28/6.31

ind_GEO-010.HO
Nat/ZNF/ANF/CHG/EHG = 11.4/15.8/33.8/29.0/9.9%
p = 0.490; χ²/dof = 0.477/1
SE = 0.0400/0.0403/0.0422/0.0461/0.0211
Z = 2.86/3.91/8.02/6.30/4.71

ind_GEO-015.HO
Nat/ZNF/ANF/CHG/EHG = 9.8/23.3/35.5/23.9/7.5%
p = 0.577; χ²/dof = 1.100/2
SE = 0.0411/0.0380/0.0464/0.0473/0.0207
Z = 2.38/6.13/7.66/5.05/3.62

ind_GEO-028.HO
Nat/ZNF/ANF/CHG/EHG = 10.2/17.4/32.5/26.9/13.1%
p = 0.645; χ²/dof = 0.212/1
SE = 0.0405/0.0382/0.0436/0.0452/0.0211
Z = 2.51/4.56/7.46/5.95/6.20

ind_GEO-031.HO
Nat/ZNF/ANF/CHG/EHG = 14.0/16.7/33.9/29.1/6.3%
p = 0.0608; χ²/dof = 3.515/1
SE = 0.0421/0.0374/0.0462/0.0453/0.0212
Z = 3.33/4.46/7.33/6.43/2.95

ind_GEO-032.HO
Nat/ZNF/ANF/CHG/EHG = 8.3/15.9/39.7/28.0/8.1%
p = 0.239; χ²/dof = 1.385/1
SE = 0.0412/0.0377/0.0444/0.0435/0.0193
Z = 2.02/4.22/8.93/6.44/4.21

ind_GEO-039.HO
Nat/ZNF/ANF/CHG/EHG = 13.7/14.5/23.1/41.3/7.3%
p = 0.137; χ²/dof = 2.215/1
SE = 0.0440/0.0389/0.0481/0.0500/0.0222
Z = 3.12/3.74/4.81/8.27/3.30

Georgian.HO
Nat/ZNF/ANF/CHG/EHG = 10.5/17.1/33.8/29.5/9.0%
p = 0.283; χ²/dof = 1.153/1
SE = 0.0189/0.0185/0.0204/0.0230/0.00929
Z = 5.55/9.24/16.6/12.9/9.74

ARMENIAN

ind_armenia176.HO
Nat/ZNF/ANF/CHG/EHG = 16.4/23.1/30.4/21.9/8.2%
p = 0.171; χ²/dof = 3.536/2
SE = 0.0455/0.0405/0.0464/0.0476/0.0215
Z = 3.60/5.70/6.56/4.61/3.81

ind_armenia191.HO
Nat/ZNF/ANF/CHG/EHG = 11.5/22.2/33.3/21.2/11.8%
p = 0.464; χ²/dof = 1.536/2
SE = 0.0428/0.0402/0.0441/0.0464/0.0213
Z = 2.70/5.51/7.55/4.57/5.53

ind_armenia279.HO
Nat/ZNF/ANF/CHG/EHG = 13.5/26.2/36.4/12.5/11.4%
p = 0.891; χ²/dof = 0.230/2
SE = 0.0441/0.0383/0.0485/0.0464/0.0213
Z = 3.06/6.84/7.50/2.69/5.36

ind_armenia293.HO
Nat/ZNF/ANF/CHG/EHG = 16.6/19.9/32.4/22.5/8.6%
p = 0.572; χ²/dof = 1.118/2
SE = 0.0449/0.0362/0.0506/0.0442/0.0215
Z = 3.70/5.49/6.40/5.09/4.01

ind_armenia86.HO
Nat/ZNF/ANF/CHG/EHG = 7.7/18.0/37.1/25.8/11.3%
p = 0.682; χ²/dof = 0.765/2
SE = 0.0447/0.0375/0.0454/0.0455/0.0206
Z = 1.73/4.80/8.18/5.67/5.50

ind_armenia91.HO
Nat/ZNF/ANF/CHG/EHG = 22.8/20.9/28.1/23.0/5.2%
p = 0.368; χ²/dof = 1.999/2
SE = 0.0415/0.0416/0.0478/0.0497/0.0221
Z = 5.49/5.01/5.88/4.63/2.36

ASSYRIAN / MESOPOTAMIAN

ind_Assyrian151.HO
Nat/ZNF/ANF/CHG/EHG = 19.1/26.1/32.5/16.5/5.7%
p = 0.620; χ²/dof = 0.955/2
SE = 0.0415/0.0417/0.0451/0.0486/0.0231
Z = 4.61/6.26/7.21/3.41/2.48

ind_Assyrian152.HO
Nat/ZNF/ANF/CHG/EHG = 9.4/26.4/39.6/16.9/7.7%
p = 0.385; χ²/dof = 1.911/2
SE = 0.0422/0.0389/0.0470/0.0444/0.0205
Z = 2.23/6.78/8.42/3.80/3.77

ind_Assyrian153.HO
Nat/ZNF/ANF/CHG/EHG = 20.0/20.0/30.8/19.8/9.4%
p = 0.715; χ²/dof = 0.670/2
SE = 0.0439/0.0394/0.0467/0.0466/0.0214
Z = 4.56/5.08/6.58/4.25/4.39

ind_Assyrian155.HO
Nat/ZNF/ANF/CHG/EHG = 19.9/20.4/28.4/19.6/11.7%
p = 0.821; χ²/dof = 0.395/2
SE = 0.0431/0.0375/0.0478/0.0433/0.0218
Z = 4.61/5.44/5.95/4.53/5.34

ind_Assyrian159.HO
Nat/ZNF/ANF/CHG/EHG = 14.6/25.6/33.7/21.5/4.6%
p = 0.817; χ²/dof = 0.405/2
SE = 0.0417/0.0403/0.0469/0.0471/0.0219
Z = 3.50/6.35/7.18/4.56/2.11

ind_Assyrian161.HO
Nat/ZNF/ANF/CHG/EHG = 18.4/25.8/31.2/16.9/7.7%
p = 0.820; χ²/dof = 0.398/2
SE = 0.0428/0.0412/0.0457/0.0463/0.0217
Z = 4.30/6.26/6.83/3.64/3.56

ind_Assyrian162.HO
Nat/ZNF/ANF/CHG/EHG = 18.4/32.3/29.2/13.8/6.3%
p = 0.774; χ²/dof = 0.512/2
SE = 0.0439/0.0394/0.0449/0.0443/0.0233
Z = 4.20/8.20/6.50/3.11/2.70

ind_Assyrian163.HO
Nat/ZNF/ANF/CHG/EHG = 22.2/26.2/26.6/16.7/8.2%
p = 0.193; χ²/dof = 3.289/2
SE = 0.0468/0.0421/0.0521/0.0479/0.0223
Z = 4.74/6.24/5.11/3.50/3.69

ind_Assyrian164.HO
Nat/ZNF/ANF/CHG/EHG = 19.5/29.2/34.0/12.0/5.3%
p = 0.683; χ²/dof = 0.764/2
SE = 0.0469/0.0393/0.0497/0.0466/0.0230
Z = 4.16/7.44/6.84/2.57/2.30

ind_Assyrian165.HO
Nat/ZNF/ANF/CHG/EHG = 16.7/28.2/32.4/17.1/5.5%
p = 0.622; χ²/dof = 0.951/2
SE = 0.0411/0.0390/0.0455/0.0464/0.0222
Z = 4.07/7.25/7.12/3.69/2.48

Assyrian_WGA.HO
Nat/ZNF/ANF/CHG/EHG = 17.3/22.3/32.6/20.6/7.2%
p = 0.492; χ²/dof = 1.417/2
SE = 0.0261/0.0256/0.0284/0.0304/0.0143
Z = 6.62/8.73/11.5/6.77/5.00

IRAQI JEWISH / MESOPOTAMIAN

ind_IraqiJew1417.HO
Nat/ZNF/ANF/CHG/EHG = 24.8/21.0/30.8/18.6/4.7%
p = 0.566; χ²/dof = 1.139/2
SE = 0.0479/0.0412/0.0500/0.0486/0.0223
Z = 5.18/5.11/6.16/3.84/2.12

ind_IraqiJew1430.HO
Nat/ZNF/ANF/CHG/EHG = 17.2/30.5/36.6/9.2/6.5%
p = 0.113; χ²/dof = 4.369/2
SE = 0.0522/0.0409/0.0535/0.0479/0.0213
Z = 3.29/7.46/6.85/1.93/3.04

ind_IraqiJew1771.HO
Nat/ZNF/ANF/CHG/EHG = 23.7/29.1/33.1/8.6/5.6%
p = 0.390; χ²/dof = 1.881/2
SE = 0.0468/0.0429/0.0455/0.0495/0.0225
Z = 5.05/6.78/7.27/1.73/2.47

ind_IraqiJew4061.HO
Nat/ZNF/ANF/CHG/EHG = 24.8/21.4/26.6/21.4/5.8%
p = 0.386; χ²/dof = 1.906/2
SE = 0.0431/0.0405/0.0472/0.0485/0.0224
Z = 5.75/5.27/5.65/4.42/2.58

ind_IraqiJew4241.HO
Nat/ZNF/ANF/CHG/EHG = 20.8/25.7/27.3/17.5/8.7%
p = 0.842; χ²/dof = 0.344/2
SE = 0.0425/0.0398/0.0475/0.0476/0.0218
Z = 4.89/6.47/5.75/3.68/4.00

ind_IraqiJew4291.HO
Nat/ZNF/ANF/CHG/EHG = 26.0/28.3/32.1/10.8/2.9%
p = 0.733; χ²/dof = 0.620/2
SE = 0.0448/0.0421/0.0470/0.0472/0.0226
Z = 5.80/6.72/6.82/2.29/1.28

u/NotBradPitt9 — 2 months ago

▲ 4 r/DNAAncestry

Qpadm: Baltics (Lithuanian, Latvian, Estonian), Ukrainian, Belarusian, Finnish, Norwegian

TLDR: Qpadm runs for the Baltics and adjacent regions. Information sourced from Qpadm runs by @m_izanami_ on twitter and AndreiDNA on YouTube.

Belarusian.HO
26.6% Lithuania_Marvele_Roman.SG
63.1% Hungary_Avar_5.AG
10.3% Russia_Davydovskoye_Antiquity.AG
P-value: 0.295
Standard errors:
Lithuania_Marvele_Roman.SG: 0.166095
Hungary_Avar_5.AG: 0.190438
Russia_Davydovskoye_Antiquity.AG: 0.060361
Z-scores:
Lithuania_Marvele_Roman.SG: 1.602
Hungary_Avar_5.AG: 3.312
Russia_Davydovskoye_Antiquity.AG: 1.710

Norwegian.HO
25.5% Scotland_IA.AG
71.2% Denmark_IA.SG
3.4% Finland_Levanluhta.AG
P-value: 0.199
Standard errors:
Scotland_IA.AG: 0.091568
Denmark_IA.SG: 0.101782
Finland_Levanluhta.AG: 0.027053
Z-scores:
Scotland_IA.AG: 2.781
Denmark_IA.SG: 6.993
Finland_Levanluhta.AG: 1.242

Finnish_East_Southeast
29.5% Finland_Levanluhta.AG
20.3% Russia_IA_Ingria.SG
33.3% Denmark_IA.SG
16.8% Hungary_Avar_5.AG
P-value: 0.857
Standard errors:
Finland_Levanluhta.AG: 0.056431
Russia_IA_Ingria.SG: 0.075072
Denmark_IA.SG: 0.168516
Hungary_Avar_5.AG: 0.167525
Z-scores:
Finland_Levanluhta.AG: 5.235
Russia_IA_Ingria.SG: 2.710
Denmark_IA.SG: 1.978
Hungary_Avar_5.AG: 1.003

Ukraine_Volhyn
95.1% Poland_Lod_IA.SG
2.4% Ukraine_IA_Scythian.SG
2.5% Denmark_IA.SG
P-value: 0.173604331
Chi-square: 6.36
Standard errors:
Poland_Lod_IA.SG: ±11.8%
Ukraine_IA_Scythian.SG: ±5.6%
Denmark_IA.SG: ±11.8%

All four models have passing overall qpAdm p-values. In terms of individual components, Belarusian.HO has only the Hungary_Avar_5.AG component clearly above Z=2; Norwegian.HO has Scotland_IA.AG and Denmark_IA.SG supported; Finnish_East_Southeast has Finland_Levanluhta.AG and Russia_IA_Ingria.SG clearly supported, while Denmark_IA.SG is just below Z=2 and Hungary_Avar_5.AG is unsupported. The two small components in Ukraine_Volhyn also have very large errors relative to their estimates, so the model is effectively dominated by Poland_Lod_IA.SG.

Latvian.DG
67.3% Lithuania_IA.SG
18.6% Poland_MA.SG
10.9% Denmark_Jutland_IA.SG
3.13% Russia_Boyasky_Mazunino_IA.SG
P-value: 0.625
Chi-square: 7.12
Degrees of freedom: 9
SNPs: 1,150,639
Blocks: 713
Standard errors:
Lithuania_IA.SG: 0.0407
Poland_MA.SG: 0.0679
Denmark_Jutland_IA.SG: 0.0411
Russia_Boyasky_Mazunino_IA.SG: 0.0120
Z-scores:
Lithuania_IA.SG: 16.6
Poland_MA.SG: 2.74
Denmark_Jutland_IA.SG: 2.66
Russia_Boyasky_Mazunino_IA.SG: 2.62

This is a strong passing model. All four ancestry components are statistically supported at Z > 2, including the relatively small 3.1% Russia_Boyasky_Mazunino_IA component.

Lithuanian.DG
52.8% Lithuania_IA.SG
36.9% Poland_MA.SG
10.2% Denmark_Jutland_IA.SG
P-value: 0.689
Chi-square: 5.62
Degrees of freedom: 8
SNPs: 1,150,639
Blocks: 713
Standard errors:
Lithuania_IA.SG: 0.0370
Poland_MA.SG: 0.0585
Denmark_Jutland_IA.SG: 0.0366
Z-scores:
Lithuania_IA.SG: 14.3
Poland_MA.SG: 6.31
Denmark_Jutland_IA.SG: 2.80
This is a very strong passing model. All three ancestry components are statistically supported, with even the smallest Denmark_Jutland_IA component comfortably above Z = 2.

Estonian.HO
43.3% Lithuania_IA.SG
27.8% Poland_MA.SG
22.6% Denmark_IA.SG
6.33% Russia_Boyasky_Mazunino_IA.SG
P-value: 0.196
Chi-square: 12.3
Degrees of freedom: 9
Standard errors:
Lithuania_IA.SG: 0.0451
Poland_MA.SG: 0.0789
Denmark_IA.SG: 0.0413
Russia_Boyasky_Mazunino_IA.SG: 0.0116
Z-scores:
Lithuania_IA.SG: 9.61
Poland_MA.SG: 3.53
Denmark_IA.SG: 5.46
Russia_Boyasky_Mazunino_IA.SG: 5.47
This is a good passing model. All four components are strongly statistically supported, including the relatively small 6.3% Russia_Boyasky_Mazunino_IA component.

Latvian.DG:
67.3% Lithuania_IA
18.6% Poland_MA
10.9% Denmark_Jutland_IA
3.1% Russia_Boyasky_Mazunino_IA
p = 0.625

Lithuanian.DG:
52.8% Lithuania_IA
36.9% Poland_MA
10.2% Denmark_Jutland_IA
p = 0.689

Estonian.HO:
43.3% Lithuania_IA
27.8% Poland_MA
22.6% Denmark_IA
6.3% Russia_Boyasky_Mazunino_IA
p = 0.196

u/NotBradPitt9 — 2 months ago

▲ 2 r/DNAAncestry

Qpadm: Egyptian (Coptic, A, B), Saudi, Jew_Yemenite

Egyptian_Coptic
20.4% Iran_Wezmeh_N.SG
22.4% Turkey_Marmara_Barcin_N.AG
49.1% Israel_Natufian.AG
8.1% Ethiopia_MotaCave_4500BP.SG
p-value: 0.752
χ²/dof: 2.66 / 5
Fit: Excellent

This is the strongest-fitting model in the set by p-value. The profile is dominated by Natufian ancestry, with substantial Barcin and Iran Wezmeh ancestry and a modest Ethiopia_MotaCave component. The overall model passes, but Turkey_Marmara_Barcin_N and Israel_Natufian have SE values above 0.05, so their exact percentages should be treated cautiously.

Saudi
__ind_saudi1434.HO
44.9% Iran_GanjDareh_N.AG
27.6% Turkey_Marmara_Barcin_N.DG
27.5% Israel_Natufian.AG
p-value: 0.318
χ²/dof: 7.026 / 6
Fit: Excellent
This is a clean 3-way model with Iran_GanjDareh as the largest component. The target looks more eastern/Iran-related than the Egyptian samples. The overall model passes, though Turkey_Marmara_Barcin_N is slightly above the preferred SE threshold.

Egyptian.HO
45.0% Israel_Natufian.AG
12.9% Turkey_Marmara_Barcin_N.AG
14.8% Iran_Wezmeh_N.SG
3.0% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
15.7% Ethiopia_MotaCave_4500BP.SG
8.7% Georgia_Kotias_Mesolithic.SG
p-value: 0.378
χ²/dof: 3.093 / 3
Fit: Excellent
This is a strong 6-way model. Natufian is again the largest component, followed by Ethiopia_MotaCave, Iran_Wezmeh, and Barcin. The only notable issue is that the Russia_YuzhniyOleniyOstrov_Mesolithic share is very small and not dependable, with Z below 2.

EgyptianA.HO
52.0% Israel_Natufian.AG
7.6% Turkey_Marmara_Barcin_N.AG
12.6% Iran_Wezmeh_N.SG
4.2% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
10.7% Ethiopia_MotaCave_4500BP.SG
13.0% Georgia_Kotias_Mesolithic.SG
p-value: 0.0862
χ²/dof: 6.589 / 3
Fit: Good
This is the weakest overall fit in the set, though it still passes. EgyptianA is the most Natufian-heavy of the Egyptian subsets. However, the Turkey_Marmara_Barcin component is poorly supported, and the Natufian component is slightly above the preferred SE threshold, so some of the exact proportions are less stable.

EgyptianB.HO
44.1% Israel_Natufian.AG
12.2% Turkey_Marmara_Barcin_N.AG
17.3% Iran_Wezmeh_N.SG
2.6% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
12.6% Ethiopia_MotaCave_4500BP.SG
11.2% Georgia_Kotias_Mesolithic.SG
p-value: 0.144
χ²/dof: 5.411 / 3
Fit: Good
This also passes, with a profile similar to Egyptian.HO but slightly more Iran_Wezmeh and Georgia_Kotias. The Russia_YuzhniyOleniyOstrov component is again very small and not dependable, with Z below 2.

Jew_Yemenite.HO
29.9% Israel_Natufian.AG
40.5% Turkey_Marmara_Barcin_N.AG
21.8% Iran_GanjDareh_N.AG
7.8% Ethiopia_MotaCave_4500BP.SG
p-value: 0.707
χ²/dof: 1.395 / 3
Fit: Excellent
This is another very strong model. Compared with the Egyptian samples, Yemeni Jews are much more Barcin-heavy and less Natufian-heavy, while still carrying a notable Iran_GanjDareh component and a small Ethiopia_MotaCave share. The overall fit is strong, but Natufian and Barcin are both slightly above the preferred SE threshold.

Overall comparison:

The Egyptian-related models are all Natufian-dominant, with varying levels of Ethiopia_MotaCave, Iran_Wezmeh, Barcin, and smaller Georgia_Kotias input. EgyptianA.HO is the most Natufian-rich at 52.0%, while Egyptian_Coptic has the best overall fit and a simpler 4-way structure. Egyptian.HO and EgyptianB.HO show similar 6-way profiles with moderate Ethiopia_MotaCave and modest Georgia_Kotias, plus a tiny and weakly supported YuzhniyOleniyOstrov component. The Saudi-linked sample (__ind_saudi1434.HO) is distinct in being dominated by Iran_GanjDareh ancestry. Jew_Yemenite.HO also differs clearly from the Egyptian profiles, showing the highest Marmara_Barcin proportion in this set and a strong Iran_GanjDareh contribution.

u/NotBradPitt9 — 2 months ago

▲ 3 r/DNAAncestry

Qpadm: [Neolithic breakdown] Levant- Jordanian, Lebanese (Christian, Muslim), Palestinian (Muslim, Christian), Samaritan, Bedouin (A and B)

BedouinB.HO (n=19)

17.2% Iran_Wezmeh_N.SG
21.3% Turkey_Marmara_Barcin_N.AG
40.8% Israel_Natufian.AG
11.6% Georgia_Kotias_Mesolithic.SG
4.0% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
5.1% Ethiopia_MotaCave_4500BP.SG
p-value: 0.239
χ²/dof: 1.386 / 1
Fit: Excellent

This is a solid 6-way passing model. All sources meet the z ≥ 2 and SE ≤ 0.05 thresholds. The profile is strongly Natufian-shifted, with moderate Iran and Barcın ancestry and a small but supported Ethiopia_Mota component.

Palestinian_Christian (n=1)

19.1% Iran_Wezmeh_N.SG
33.2% Turkey_Marmara_Barcin_N.AG
36.2% Israel_Natufian.AG
5.9% Georgia_Kotias_Mesolithic.SG
5.6% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.356
χ²/dof: 2.068 / 2
Fit: Excellent

The overall model passes. Broadly, this looks like a Natufian- and Barcın-heavy Levantine profile with small northern Mesolithic components.

Lebanese_Christian.HO (n=9)
20.7% Iran_Wezmeh_N.SG
32.2% Turkey_Marmara_Barcin_N.AG
27.8% Israel_Natufian.AG
13.6% Georgia_Kotias_Mesolithic.SG
5.6% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.547
χ²/dof: 1.208 / 2
Fit: Excellent

This is one of the strongest-fitting models in the set. All sources are supported. Compared with the more southern-shifted Levantine groups, Lebanese Christians show lower Natufian and somewhat higher Barcın and Kotias-related ancestry.

Samaritan.DG (n=1)
18.0% Iran_Wezmeh_N.SG
22.1% Turkey_Marmara_Barcin_N.AG
38.5% Israel_Natufian.AG
16.9% Georgia_Kotias_Mesolithic.SG
4.5% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.332
χ²/dof: 2.204 / 2
Fit: Excellent

The overall model passes. Turkey_Marmara_Barcin_N.AG, Israel_Natufian.AG, and Georgia_Kotias_Mesolithic.SG all have SE slightly above 0.05, and Russia_YuzhniyOleniyOstrov_Mesolithic.AG has Z = 1.79. So the broad profile is usable, but exact small-component proportions are slightly less dependable.

Lebanese_Muslim.HO (n=11)
19.1% Iran_Wezmeh_N.SG
27.2% Turkey_Marmara_Barcin_N.AG
28.3% Israel_Natufian.AG
17.1% Georgia_Kotias_Mesolithic.SG
5.9% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
2.5% Ethiopia_MotaCave_4500BP.SG
p-value: 0.238
χ²/dof: 1.392 / 1
Fit: Excellent
This is a good 6-way model with all components supported. Compared with Lebanese Christians, Lebanese Muslims show slightly more Natufian and Kotias ancestry and a small additional Ethiopia_Mota-related signal.

Palestinian.HO (n=34)
17.1% Iran_Wezmeh_N.SG
23.4% Turkey_Marmara_Barcin_N.AG
33.0% Israel_Natufian.AG
15.2% Georgia_Kotias_Mesolithic.SG
5.8% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
5.5% Ethiopia_MotaCave_4500BP.SG
p-value: 0.280
χ²/dof: 1.167 / 1
Fit: Excellent
This is a strong 6-way passing model. All sources are supported. Palestinians here show a distinctly Natufian-heavy profile, with moderate Kotias ancestry and a small but clearly supported Ethiopia_Mota-related contribution.

Jordanian.HO (n=10)
16.9% Iran_Wezmeh_N.SG
20.2% Turkey_Marmara_Barcin_N.AG
32.4% Israel_Natufian.AG
13.1% Georgia_Kotias_Mesolithic.SG
6.8% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
10.6% Ethiopia_MotaCave_4500BP.SG
p-value: 0.286
χ²/dof: 1.137 / 1
Fit: Excellent

This is a strong 6-way model with all components supported. Jordanians stand out for having the largest Ethiopia_Mota-related component in the entire set, along with relatively low Barcın ancestry.

BedouinA.HO (n=25)
15.1% Iran_Wezmeh_N.SG
18.8% Turkey_Marmara_Barcin_N.AG
37.7% Israel_Natufian.AG
14.3% Georgia_Kotias_Mesolithic.SG
5.3% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
8.8% Ethiopia_MotaCave_4500BP.SG
p-value: 0.278
χ²/dof: 1.175 / 1
Fit: Excellent
This is a strong 6-way model. All sources are statistically supported. BedouinA is one of the most Natufian-rich profiles here and also shows a relatively elevated Ethiopia_Mota-related component.

Overall comparison:
All eight models pass overall, and most are statistically clean at the component level. The two exceptions are Palestinian_Christian and Samaritan, where one or more components slightly miss the preferred z ≥ 2 and/or SE ≤ 0.05 criteria.

Broadly, the pattern is:
• Natufian ancestry is the largest component in every population except Lebanese_Christian, where Marmara_Barcın is slightly higher.

• BedouinB, BedouinA, Samaritan, and Palestinian_Christian are especially Natufian-rich.

• Lebanese_Christian shows the strongest northern/Anatolian shift, with the highest ANF share among the clearly well-supported models.

• Lebanese_Muslim and Palestinian are intermediate Levantine profiles, balancing Natufian, Barcın, and Kotias.

• Jordanian stands out for the highest Ethiopia_MotaCave_4500BP share (10.6%), followed by BedouinA (8.8%).

• Iran_Wezmeh stays fairly stable across the set, with Lebanese_Christian at the higher end.

• Russia_YuzhniyOleniyOstrov_Mesolithic is consistently small across all populations, usually around 4% to 7%.

u/NotBradPitt9 — 2 months ago

▲ 3 r/DNAAncestry

Qpadm: Assyrian, Syrian, Syrian Jew, Iraqi Jew, Kurdish (Turkey)

Kurd_Turkey.HO (n=1)
33.7% Iran_Wezmeh_N.SG
23.0% Turkey_Marmara_Barcin_N.AG
11.5% Israel_Natufian.AG
20.2% Georgia_Kotias_Mesolithic.SG
11.6% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.132
χ²/dof: 4.055 / 2
Fit: Good
This is a passing 5-way model. All sources meet the z ≥ 2 and SE ≤ 0.05 thresholds, so the proportions are statistically supported.

Jew_Iraqi.DG (n=2)
28.6% Iran_Wezmeh_N.SG
32.5% Turkey_Marmara_Barcin_N.AG
25.0% Israel_Natufian.AG
9.9% Georgia_Kotias_Mesolithic.SG
4.2% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.785
χ²/dof: 0.484 / 2
Fit: Excellent
This is the strongest-fitting model in the set by p-value. All components are supported. Compared with the others here, Iraqi Jews show especially high Natufian ancestry and relatively low Kotias and Yuzhniy Oleniy Ostrov-related ancestry.

Assyrian.HO (n=10)
26.1% Iran_Wezmeh_N.SG
31.9% Turkey_Marmara_Barcin_N.AG
17.7% Israel_Natufian.AG
17.1% Georgia_Kotias_Mesolithic.SG
7.1% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.717
χ²/dof: 0.666 / 2
Fit: Excellent
Another very strong passing 5-way model. All components are clearly supported. The profile is fairly balanced, with Barcın-related ancestry as the largest component.

Syrian_Jew (n=1)
23.9% Iran_Wezmeh_N.SG
32.5% Turkey_Marmara_Barcin_N.AG
29.9% Israel_Natufian.AG
9.3% Georgia_Kotias_Mesolithic.SG
4.5% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.448
χ²/dof: 1.607 / 2
Fit: Excellent
The overall model passes, but this one comes with an important caution. At least one source misses the preferred thresholds: Georgia_Kotias_Mesolithic.SG has Z = 1.98, Russia_YuzhniyOleniyOstrov_Mesolithic.AG has Z = 1.89, and Israel_Natufian.AG has SE = 0.0533. So while the model itself passes, some of these smaller shares are not fully dependable.

Syrian.HO (n=8)
21.6% Iran_Wezmeh_N.SG
20.6% Turkey_Marmara_Barcin_N.AG
30.8% Israel_Natufian.AG
14.4% Georgia_Kotias_Mesolithic.SG
7.7% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
4.9% Ethiopia_MotaCave_4500BP.SG
p-value: 0.152
χ²/dof: 2.048 / 1
Fit: Good
This is a passing 6-way model. All sources meet the support thresholds. Syrians in this run have the highest Natufian share in the set, along with a small but supported Ethiopia_MotaCave_4500BP-related component.

Overall comparison:
All five models pass overall. Jew_Iraqi.DG and Assyrian.HO are the strongest statistical fits, followed closely by Syrian_Jew, though Syrian_Jew has component-level support issues. Kurd_Turkey.HO has the highest Iran_Wezmeh-related share. Jew_Iraqi.DG and Syrian_Jew have the highest Barcın and Natufian-related proportions among the 5-way models, while Syrian.HO has the highest Natufian proportion overall and is the only model here with an added Ethiopia_MotaCave_4500BP component. Assyrians sit in between the Jewish and Kurdish/Syrian profiles, with a balanced mix of Iran Wezmeh, Barcın, Natufian, and Kotias ancestry.

u/NotBradPitt9 — 2 months ago

▲ 2 r/DNAAncestry

Qpadm: Georgian, Georgian Jew, Armenian, Chechen, Kurdish

Georgian.HO (n=13)
17.0% Iran_Wezmeh_N.SG
33.9% Turkey_Marmara_Barcin_N.AG
10.5% Israel_Natufian.AG
29.6% Georgia_Kotias_Mesolithic.SG
9.1% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.311
χ²/dof: 2.334 / 2
Excellent passing model. All five ancestry components are strongly statistically supported (Z > 5).

Georgian_Jew.HO (n=7)
25.6% Iran_Wezmeh_N.SG
31.1% Turkey_Marmara_Barcin_N.AG
18.5% Israel_Natufian.AG
15.6% Georgia_Kotias_Mesolithic.SG
9.2% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.0856
χ²/dof: 4.915 / 2
Passing model with all five components strongly supported. It has the lowest p-value of the five populations, but still passes. Georgian Jews show the highest Natufian proportion in this comparison.

Armenian.HO (n=10)
22.2% Iran_Wezmeh_N.SG
34.4% Turkey_Marmara_Barcin_N.AG
13.8% Israel_Natufian.AG
20.3% Georgia_Kotias_Mesolithic.SG
9.4% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.410
χ²/dof: 1.785 / 2
Excellent passing model with every ancestry component strongly supported. Armenians have the highest Barcın Neolithic proportion of the five populations.

Chechen.DG (n=1)
19.4% Iran_Wezmeh_N.SG
27.9% Turkey_Marmara_Barcin_N.AG
7.9% Israel_Natufian.AG
24.5% Georgia_Kotias_Mesolithic.SG
20.3% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.447
χ²/dof: 1.609 / 2
Excellent passing model. The Natufian component is the weakest-supported component but still passes the Z ≥ 2 threshold (Z = 2.13). The northern Mesolithic/Yuzhniy Oleniy Ostrov component is substantially higher here than in the other populations.

Kurd.HO (n=8)
31.0% Iran_Wezmeh_N.SG
27.9% Turkey_Marmara_Barcin_N.AG
15.1% Israel_Natufian.AG
15.7% Georgia_Kotias_Mesolithic.SG
10.3% Russia_YuzhniyOleniyOstrov_Mesolithic.AG
p-value: 0.727
χ²/dof: 0.638 / 2
Excellent passing model and the strongest statistical fit of the five based on p-value. Kurds have the highest Iran Wezmeh-related ancestry at 31.0%, alongside relatively reduced Kotias-related ancestry compared with Georgians and Chechens.

Overall comparison:
All five populations successfully pass the same five-way qpAdm model, and every ancestry source satisfies Z ≥ 2 and SE ≤ 0.05. Barcın Neolithic ancestry is the largest component in Georgians, Georgian Jews, and Armenians, while Iran Wezmeh becomes the largest component in Kurds. Chechens stand out for their much higher Yuzhniy Oleniy Ostrov Mesolithic ancestry (20.3%), while Georgian Jews have the highest Natufian ancestry (18.5%). Georgians have by far the highest Kotias Mesolithic ancestry (29.6%), followed by Chechens (24.5%).

u/NotBradPitt9 — 2 months ago