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During the Hungarian Conquest in the 10th century CE, the early medieval Magyars, a group of mounted warriors from Eastern Europe, settled in the Carpathian Basin. They likely introduced the Hungarian language to this new settlement area, during an event documented by both written sources and archaeological evidence. Previous archaeogenetic research identified the newcomers as migrants from the Eurasian steppe. However, genome-wide ancient DNA from putative source populations has not been available to test alternative theories of their precise source. We generated genome-wide ancient DNA data for 131 individuals from candidate archaeological contexts in the Circum-Uralic region in present-day Russia. Our results tightly link the Magyars to people of the Early Medieval Karayakupovo archaeological horizon on both the European and Asian sides of the southern Urals. Our analyes show that ancestors of the people of the Karayakupovo archaeological horizon were established in the Southern Urals by the Iron Age and that their descendants persisted locally in the Volga-Kama region until at least the 14th century.
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Population history-focused DNA and ancient DNA (aDNA) research in Africa has dramatically increased in the past decade, enabling increasingly fine-scale investigations into the continent's past. However, while international interest in human genomics research in Africa grows, major structural barriers limit the ability of African scholars to lead and engage in such research and impede local communities from partnering with researchers and benefitting from research outcomes. Because conversations about research on African people and their past are often held outside Africa and exclude African voices, an important step for African DNA and aDNA research is moving these conversations to the continent. In May 2023 we held the DNAirobi workshop in Nairobi, Kenya and here we synthesize what emerged most prominently in our discussions. We propose an ideal vision for population history-focused DNA and aDNA research in Africa in ten years' time and acknowledge that to realize this future, we need to chart a path connecting a series of "landmarks" that represent points of consensus in our discussions. These include effective communication across multiple audiences, reframed relationships and capacity building, and action toward structural changes that support science and beyond. We concluded there is no single path to creating an equitable and self-sustaining research ecosystem, but rather many possible routes linking these landmarks. Here we share our diverse perspectives as geneticists, anthropologists, archaeologists, museum curators, and educators to articulate challenges and opportunities for African DNA and aDNA research and share an initial map toward a more inclusive and equitable future.
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ADN Antiguo , Genética de Población , Humanos , ADN Antiguo/análisis , África , Genómica , Población Negra/genéticaRESUMEN
The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation. We included 2288 serial patients admitted to the Medical ICU at an urban academic medical center, who underwent invasive mechanical ventilation, with at least one intubated CXR, and a documented extubation attempt. The last CXR before extubation for each patient was taken and split 79/21 for training/testing sets, then transfer learning with k-fold cross-validation was used on a pre-trained ResNet50 deep learning architecture. The top three models were ensembled to form a final classifier. The Grad-CAM technique was used to visualize image regions driving predictions. The model achieved an AUC of 0.66, AUPRC of 0.94, sensitivity of 0.62, and specificity of 0.60. The model performance was improved compared to the Rapid Shallow Breathing Index (AUC 0.61) and the only identified previous study in this domain (AUC 0.55), but significant room for improvement and experimentation remains.
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Soqotra, an island situated at the mouth of the Gulf of Aden in the northwest Indian Ocean between Africa and Arabia, is home to ~60,000 people subsisting through fishing and semi-nomadic pastoralism who speak a Modern South Arabian language. Most of what is known about Soqotri history derives from writings of foreign travellers who provided little detail about local people, and the geographic origins and genetic affinities of early Soqotri people has not yet been investigated directly. Here we report genome-wide data from 39 individuals who lived between ~650 and 1750 CE at six locations across the island and document strong genetic connections between Soqotra and the similarly isolated Hadramawt region of coastal South Arabia that likely reflects a source for the peopling of Soqotra. Medieval Soqotri can be modelled as deriving ~86% of their ancestry from a population such as that found in the Hadramawt today, with the remaining ~14% best proxied by an Iranian-related source with up to 2% ancestry from the Indian sub-continent, possibly reflecting genetic exchanges that occurred along with archaeologically documented trade from these regions. In contrast to all other genotyped populations of the Arabian Peninsula, genome-level analysis of the medieval Soqotri is consistent with no sub-Saharan African admixture dating to the Holocene. The deep ancestry of people from medieval Soqotra and the Hadramawt is also unique in deriving less from early Holocene Levantine farmers and more from groups such as Late Pleistocene hunter-gatherers from the Levant (Natufians) than other mainland Arabians. This attests to migrations by early farmers having less impact in southernmost Arabia and Soqotra and provides compelling evidence that there has not been complete population replacement between the Pleistocene and Holocene throughout the Arabian Peninsula. Medieval Soqotra harboured a small population that showed qualitatively different marriage practices from modern Soqotri, with first-cousin unions occurring significantly less frequently than today.
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ADN , Genética de Población , Humanos , África , Arabia , Irán , Genoma HumanoRESUMEN
Importance: Increased intracranial pressure (ICP) is associated with adverse neurological outcomes, but needs invasive monitoring. Objective: Development and validation of an AI approach for detecting increased ICP (aICP) using only non-invasive extracranial physiological waveform data. Design: Retrospective diagnostic study of AI-assisted detection of increased ICP. We developed an AI model using exclusively extracranial waveforms, externally validated it and assessed associations with clinical outcomes. Setting: MIMIC-III Waveform Database (2000-2013), a database derived from patients admitted to an ICU in an academic Boston hospital, was used for development of the aICP model, and to report association with neurologic outcomes. Data from Mount Sinai Hospital (2020-2022) in New York City was used for external validation. Participants: Patients were included if they were older than 18 years, and were monitored with electrocardiograms, arterial blood pressure, respiratory impedance plethysmography and pulse oximetry. Patients who additionally had intracranial pressure monitoring were used for development (N=157) and external validation (N=56). Patients without intracranial monitors were used for association with outcomes (N=1694). Exposures: Extracranial waveforms including electrocardiogram, arterial blood pressure, plethysmography and SpO2. Main Outcomes and Measures: Intracranial pressure > 15 mmHg. Measures were Area under receiver operating characteristic curves (AUROCs), sensitivity, specificity, and accuracy at threshold of 0.5. We calculated odds ratios and p-values for phenotype association. Results: The AUROC was 0.91 (95% CI, 0.90-0.91) on testing and 0.80 (95% CI, 0.80-0.80) on external validation. aICP had accuracy, sensitivity, and specificity of 73.8% (95% CI, 72.0%-75.6%), 99.5% (95% CI 99.3%-99.6%), and 76.9% (95% CI, 74.0-79.8%) on external validation. A ten-percentile increment was associated with stroke (OR=2.12; 95% CI, 1.27-3.13), brain malignancy (OR=1.68; 95% CI, 1.09-2.60), subdural hemorrhage (OR=1.66; 95% CI, 1.07-2.57), intracerebral hemorrhage (OR=1.18; 95% CI, 1.07-1.32), and procedures like percutaneous brain biopsy (OR=1.58; 95% CI, 1.15-2.18) and craniotomy (OR = 1.43; 95% CI, 1.12-1.84; P < 0.05 for all). Conclusions and Relevance: aICP provides accurate, non-invasive estimation of increased ICP, and is associated with neurological outcomes and neurosurgical procedures in patients without intracranial monitoring.
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All published methods for learning about demographic history make the simplifying assumption that the genome evolves neutrally, and do not seek to account for the effects of natural selection on patterns of variation. This is a major concern, as ample work has demonstrated the pervasive effects of natural selection and in particular background selection (BGS) on patterns of genetic variation in diverse species. Simulations and theoretical work have shown that methods to infer changes in effective population size over time (Ne(t)) become increasingly inaccurate as the strength of linked selection increases. Here, we introduce an extension to the Pairwise Sequentially Markovian Coalescent (PSMC) algorithm, PSMC+, which explicitly co-models demographic history and natural selection. We benchmark our method using forward-in-time simulations with BGS and find that our approach improves the accuracy of effective population size inference. Leveraging a high resolution map of BGS in humans, we infer considerable changes in the magnitude of inferred effective population size relative to previous reports. Finally, we separately infer Ne(t) on the X chromosome and on the autosomes in diverse great apes without making a correction for selection, and find that the inferred ratio fluctuates substantially through time in a way that differs across species, showing that uncorrected selection may be an important driver of signals of genetic difference on the X chromosome and autosomes.
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More than two hundred papers have reported genome-wide data from ancient humans. While the raw data for the vast majority are fully publicly available testifying to the commitment of the paleogenomics community to open data, formats for both raw data and meta-data differ. There is thus a need for uniform curation and a centralized, version-controlled compendium that researchers can download, analyze, and reference. Since 2019, we have been maintaining the Allen Ancient DNA Resource (AADR), which aims to provide an up-to-date, curated version of the world's published ancient human DNA data, represented at more than a million single nucleotide polymorphisms (SNPs) at which almost all ancient individuals have been assayed. The AADR has gone through six public releases at the time of writing and review of this manuscript, and crossed the threshold of >10,000 individuals with published genome-wide ancient DNA data at the end of 2022. This note is intended as a citable descriptor of the AADR.
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ADN Antiguo , Genoma Humano , Genómica , Humanos , PaleontologíaRESUMEN
OBJECTIVES: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations. DESIGN: Single-center prospective pragmatic nonrandomized clustered clinical trial. SETTING: Academic tertiary care medical center. PATIENTS: Adult patients admitted to four medical-surgical units. Assignment to intervention or control arms was determined by initial unit admission. INTERVENTIONS: Real-time alerts stratified according to predicted likelihood of deterioration sent either to the primary team or directly to the rapid response team (RRT). Clinical care and interventions were at the providers' discretion. For the control units, alerts were generated but not sent, and standard RRT activation criteria were used. MEASUREMENTS AND MAIN RESULTS: The primary outcome was the rate of escalation per 1000 patient bed days. Secondary outcomes included the frequency of orders for fluids, medications, and diagnostic tests, and combined in-hospital and 30-day mortality. Propensity score modeling with stabilized inverse probability of treatment weight (IPTW) was used to account for differences between groups. Data from 2740 patients enrolled between July 2019 and March 2020 were analyzed (1488 intervention, 1252 control). Average age was 66.3 years and 1428 participants (52%) were female. The rate of escalation was 12.3 vs. 11.3 per 1000 patient bed days (difference, 1.0; 95% CI, -2.8 to 4.7) and IPTW adjusted incidence rate ratio 1.43 (95% CI, 1.16-1.78; p < 0.001). Patients in the intervention group were more likely to receive cardiovascular medication orders (16.1% vs. 11.3%; 4.7%; 95% CI, 2.1-7.4%) and IPTW adjusted relative risk (RR) (1.74; 95% CI, 1.39-2.18; p < 0.001). Combined in-hospital and 30-day-mortality was lower in the intervention group (7% vs. 9.3%; -2.4%; 95% CI, -4.5% to -0.2%) and IPTW adjusted RR (0.76; 95% CI, 0.58-0.99; p = 0.045). CONCLUSIONS: Real-time machine learning alerts do not reduce the rate of escalation but may reduce mortality.
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Deterioro Clínico , Aprendizaje Automático , Humanos , Femenino , Masculino , Estudios Prospectivos , Persona de Mediana Edad , Anciano , Equipo Hospitalario de Respuesta Rápida/organización & administración , Equipo Hospitalario de Respuesta Rápida/estadística & datos numéricos , Mortalidad HospitalariaRESUMEN
BACKGROUND: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. METHODS: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days. RESULTS: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. CONCLUSION: MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.
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Aprendizaje Automático , Desnutrición , Tamizaje Masivo , Evaluación Nutricional , Humanos , Estudios Retrospectivos , Desnutrición/diagnóstico , Persona de Mediana Edad , Masculino , Femenino , Ciudad de Nueva York , Anciano , Medición de Riesgo/métodos , Tamizaje Masivo/métodos , Adulto , Hospitalización , Anciano de 80 o más AñosRESUMEN
Long DNA segments shared between two individuals, known as identity-by-descent (IBD), reveal recent genealogical connections. Here we introduce ancIBD, a method for identifying IBD segments in ancient human DNA (aDNA) using a hidden Markov model and imputed genotype probabilities. We demonstrate that ancIBD accurately identifies IBD segments >8 cM for aDNA data with an average depth of >0.25× for whole-genome sequencing or >1× for 1240k single nucleotide polymorphism capture data. Applying ancIBD to 4,248 ancient Eurasian individuals, we identify relatives up to the sixth degree and genealogical connections between archaeological groups. Notably, we reveal long IBD sharing between Corded Ware and Yamnaya groups, indicating that the Yamnaya herders of the Pontic-Caspian Steppe and the Steppe-related ancestry in various European Corded Ware groups share substantial co-ancestry within only a few hundred years. These results show that detecting IBD segments can generate powerful insights into the growing aDNA record, both on a small scale relevant to life stories and on a large scale relevant to major cultural-historical events.
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ADN Antiguo , Genoma Humano , Humanos , Genotipo , Genoma Humano/genética , Polimorfismo de Nucleótido Simple/genéticaRESUMEN
Individuals sharing recent ancestors are likely to co-inherit large identical-by-descent (IBD) genomic regions. The distribution of these IBD segments in a population may be used to reconstruct past demographic events such as effective population size variation, but accurate IBD detection is difficult in ancient DNA data and in underrepresented populations with limited reference data. In this work, we introduce an accurate method for inferring effective population size variation during the past ~2000 years in both modern and ancient DNA data, called HapNe. HapNe infers recent population size fluctuations using either IBD sharing (HapNe-IBD) or linkage disequilibrium (HapNe-LD), which does not require phasing and can be computed in low coverage data, including data sets with heterogeneous sampling times. HapNe shows improved accuracy in a range of simulated demographic scenarios compared to currently available methods for IBD-based and LD-based inference of recent effective population size, while requiring fewer computational resources. We apply HapNe to several modern populations from the 1,000 Genomes Project, the UK Biobank, the Allen Ancient DNA Resource, and recently published samples from Iron Age Britain, detecting multiple instances of recent effective population size variation across these groups.
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ADN Antiguo , Genómica , Humanos , Haplotipos/genética , Densidad de Población , Desequilibrio de Ligamiento , Genética de Población , Polimorfismo de Nucleótido SimpleRESUMEN
Before the colonial period, California harboured more language variation than all of Europe, and linguistic and archaeological analyses have led to many hypotheses to explain this diversity1. We report genome-wide data from 79 ancient individuals from California and 40 ancient individuals from Northern Mexico dating to 7,400-200 years before present (BP). Our analyses document long-term genetic continuity between people living on the Northern Channel Islands of California and the adjacent Santa Barbara mainland coast from 7,400 years BP to modern Chumash groups represented by individuals who lived around 200 years BP. The distinctive genetic lineages that characterize present-day and ancient people from Northwest Mexico increased in frequency in Southern and Central California by 5,200 years BP, providing evidence for northward migrations that are candidates for spreading Uto-Aztecan languages before the dispersal of maize agriculture from Mexico2-4. Individuals from Baja California share more alleles with the earliest individual from Central California in the dataset than with later individuals from Central California, potentially reflecting an earlier linguistic substrate, whose impact on local ancestry was diluted by later migrations from inland regions1,5. After 1,600 years BP, ancient individuals from the Channel Islands lived in communities with effective sizes similar to those in pre-agricultural Caribbean and Patagonia, and smaller than those on the California mainland and in sampled regions of Mexico.
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Variación Genética , Pueblos Indígenas , Humanos , Agricultura/historia , California/etnología , Región del Caribe/etnología , Etnicidad/genética , Etnicidad/historia , Europa (Continente)/etnología , Variación Genética/genética , Historia del Siglo XV , Historia del Siglo XVI , Historia del Siglo XVII , Historia del Siglo XVIII , Historia del Siglo XIX , Historia Antigua , Historia Medieval , Migración Humana/historia , Pueblos Indígenas/genética , Pueblos Indígenas/historia , Islas , Lenguaje/historia , México/etnología , Zea mays , Genoma Humano/genética , Genómica , AlelosRESUMEN
BACKGROUND: Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs IMV, machine learning algorithms may help bring more prognostic value in a timely and systematic manner. Chest radiographs (CXRs) and electronic medical records (EMRs), typically obtained early in patients admitted with COVID-19, are the keys to deciding whether they need IMV. OBJECTIVE: We aimed to evaluate the use of a machine learning model to predict the need for intubation within 24 hours by using a combination of CXR and EMR data in an end-to-end automated pipeline. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City. METHODS: CXRs were first resized, rescaled, and normalized. Then lungs were segmented from the CXRs by using a U-Net algorithm. After splitting them into a training and a test set, the training set images were augmented. The augmented images were used to train an image classifier to predict the probability of intubation with a prediction window of 24 hours by retraining a pretrained DenseNet model by using transfer learning, 10-fold cross-validation, and grid search. Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR. Variables in the EMR included clinical and laboratory data routinely collected in the inpatient setting. The final fusion model gave a prediction likelihood for the need of intubation within 24 hours as well. RESULTS: At a prediction probability threshold of 0.5, the fusion model provided 78.9% (95% CI 59%-96%) sensitivity, 83% (95% CI 76%-89%) specificity, 0.509 (95% CI 0.34-0.67) F1-score, 0.874 (95% CI 0.80-0.94) area under the receiver operating characteristic curve (AUROC), and 0.497 (95% CI 0.32-0.65) area under the precision recall curve (AUPRC) on the holdout set. Compared to the image classifier alone, which had an AUROC of 0.577 (95% CI 0.44-0.73) and an AUPRC of 0.206 (95% CI 0.08-0.38), the fusion model showed significant improvement (P<.001). The most important predictor variables were respiratory rate, C-reactive protein, oxygen saturation, and lactate dehydrogenase. The imaging probability score ranked 15th in overall feature importance. CONCLUSIONS: We show that, when linked with EMR data, an automated deep learning image classifier improved performance in identifying hospitalized patients with severe COVID-19 at risk for intubation. With additional prospective and external validation, such a model may assist risk assessment and optimize clinical decision-making in choosing the best care plan during the critical stages of COVID-19.
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f-statistics have emerged as a first line of analysis for making inferences about demographic history from genome-wide data. Not only are they guaranteed to allow robust tests of the fits of proposed models of population history to data when analyzing full genome sequencing data-that is, all single nucleotide polymorphisms (SNPs) in the individuals being analyzed-but they are also guaranteed to allow robust tests of models for SNPs ascertained as polymorphic in a population that is an outgroup in a phylogenetic sense to all groups being analyzed. True "outgroup ascertainment" is in practice impossible in humans because our species has arisen from a substructured ancestral population that does not descend from a homogeneous ancestral population going back many hundreds of thousands of years into the past. However, initial studies suggested that non-outgroup-ascertainment schemes might produce robust enough results using f-statistics, and that motivated widespread fitting of models to data using non-outgroup-ascertained SNP panels such as the "Affymetrix Human Origins array" which has been genotyped on thousands of modern individuals from hundreds of populations, or the "1240k" in-solution enrichment reagent which has been the source of about 70% of published genome-wide data for ancient humans. In this study, we show that while analyses of population history using such panels work well for studies of relationships among non-African populations and one African outgroup, when co-modeling more than one sub-Saharan African and/or archaic human groups (Neanderthals and Denisovans), fitting of f-statistics to such SNP sets is expected to frequently lead to false rejection of true demographic histories, and failure to reject incorrect models. Analyzing panels of SNPs polymorphic in archaic humans, which has been suggested as a solution for the ascertainment problem, has limited statistical power and retains important biases. However, by carrying out simulations of diverse demographic histories, we show that bias in inferences based on f-statistics can be minimized by ascertaining on variants common in a union of diverse African groups; such ascertainment retains high statistical power while allowing co-analysis of archaic and modern groups.
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Pueblo Africano , Demografía , Filogenia , Polimorfismo de Nucleótido Simple , Animales , Humanos , Población Negra/genética , Mapeo Cromosómico , Genotipo , Hombre de Neandertal/genética , Polimorfismo de Nucleótido Simple/genética , Pueblo Africano/genética , Demografía/historia , Variación Biológica Poblacional/genética , Modelos Estadísticos , SesgoRESUMEN
Few African Americans have been able to trace family lineages back to ancestors who died before the 1870 United States Census, the first in which all Black people were listed by name. We analyzed 27 individuals from Maryland's Catoctin Furnace African American Cemetery (1774-1850), identifying 41,799 genetic relatives among consenting research participants in 23andMe, Inc.'s genetic database. One of the highest concentrations of close relatives is in Maryland, suggesting that descendants of the Catoctin individuals remain in the area. We find that many of the Catoctin individuals derived African ancestry from the Wolof or Kongo groups and European ancestry from Great Britain and Ireland. This study demonstrates the power of joint analysis of historical DNA and large datasets generated through direct-to-consumer ancestry testing.
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Negro o Afroamericano , Bases de Datos Genéticas , Humanos , Negro o Afroamericano/genética , Irlanda , Maryland , Estados Unidos , Análisis de Secuencia de ADNRESUMEN
Ancient DNA studies have begun to explore the possibility of identifying identical DNA segments shared between historical and living people. This research requires access to large genetic datasets to maximize the likelihood of identifying previously unknown, close genetic connections. Direct-to-consumer genetic testing companies, such as 23andMe, Inc., manage by far the largest and most diverse genetic databases that can be used for this purpose. It is therefore important to think carefully about guidelines for carrying out collaborations between researchers and such companies. Such collaborations require consideration of ethical issues, including policies for sharing ancient DNA datasets, and ensuring reproducibility of research findings when access to privately controlled genetic datasets is limited. At the same time, they introduce unique possibilities for returning results to the research participants whose data are analyzed, including those who are identified as close genetic relatives of historical individuals, thereby enabling ancient DNA research to contribute to the restoration of information about ancestral connections that were lost over time, which can be particularly meaningful for families and groups where such history has not been well documented. We explore these issues by describing our experience designing and carrying out a study searching for genetic connections between 18th- and 19th-century enslaved and free African Americans who labored at Catoctin Furnace, Maryland, and 23andMe research participants. We share our experience in the hope of helping future researchers navigate similar ethical considerations, recognizing that our perspective is part of a larger conversation about best ethical practices.
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Comunicación , ADN Antiguo , Humanos , Reproducibilidad de los Resultados , ADN/genética , Bases de Datos GenéticasRESUMEN
The establishment of agrarian economy in Eneolithic East Europe is associated with the Pre-Cucuteni-Cucuteni-Trypillia complex (PCCTC). PCCTC farmers interacted with Eneolithic forager-pastoralist groups of the North Pontic steppe as PCCTC extended from the Carpathian foothills to the Dnipro Valley beginning in the late 5th millennium BCE. While the cultural interaction between the two groups is evident through the Cucuteni C pottery style that carries steppe influence, the extent of biological interactions between Trypillian farmers and the steppe remains unclear. Here we report the analysis of artefacts from the late 5th millennium Trypillian settlement at the Kolomiytsiv Yar Tract (KYT) archaeological complex in central Ukraine, focusing on a human bone fragment found in the Trypillian context at KYT. Diet stable isotope ratios obtained from the bone fragment suggest the diet of the KYT individual to be within the range of forager-pastoralists of the North Pontic area. Strontium isotope ratios of the KYT individual are consistent with having originated from contexts of the Serednii Stih (Sredny Stog) culture sites of the Middle Dnipro Valley. Genetic analysis of the KYT individual indicates ancestry derived from a proto-Yamna population such as Serednii Stih. Overall, the KYT archaeological site presents evidence of interactions between Trypillians and Eneolithic Pontic steppe inhabitants of the Serednii Stih horizon and suggests a potential for gene flow between the two groups as early as the beginning of the 4th millennium BCE.
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Arqueología , Agricultores , Humanos , Ucrania , Artefactos , AmbienteRESUMEN
More than two hundred papers have reported genome-wide data from ancient humans. While the raw data for the vast majority are fully publicly available testifying to the commitment of the paleogenomics community to open data, formats for both raw data and meta-data differ. There is thus a need for uniform curation and a centralized, version-controlled compendium that researchers can download, analyze, and reference. Since 2019, we have been maintaining the Allen Ancient DNA Resource (AADR), which aims to provide an up-to-date, curated version of the world's published ancient human DNA data, represented at more than a million single nucleotide polymorphisms (SNPs) at which almost all ancient individuals have been assayed. The AADR has gone through six public releases since it first was made available and crossed the threshold of >10,000 ancient individuals with genome-wide data at the end of 2022. This note is intended as a citable description of the AADR.
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Density separation is a process routinely used to segregate minerals, organic matter, and even microplastics, from soils and sediments. Here we apply density separation to archaeological bone powders before DNA extraction to increase endogenous DNA recovery relative to a standard control extraction of the same powders. Using nontoxic heavy liquid solutions, we separated powders from the petrous bones of 10 individuals of similar archaeological preservation into eight density intervals (2.15 to 2.45 g/cm3, in 0.05 increments). We found that the 2.30 to 2.35 g/cm3 and 2.35 to 2.40 g/cm3 intervals yielded up to 5.28-fold more endogenous unique DNA than the corresponding standard extraction (and up to 8.53-fold before duplicate read removal), while maintaining signals of ancient DNA authenticity and not reducing library complexity. Although small 0.05 g/cm3 intervals may maximally optimize yields, a single separation to remove materials with a density above 2.40 g/cm3 yielded up to 2.57-fold more endogenous DNA on average, which enables the simultaneous separation of samples that vary in preservation or in the type of material analyzed. While requiring no new ancient DNA laboratory equipment and fewer than 30 min of extra laboratory work, the implementation of density separation before DNA extraction can substantially boost endogenous DNA yields without decreasing library complexity. Although subsequent studies are required, we present theoretical and practical foundations that may prove useful when applied to other ancient DNA substrates such as teeth, other bones, and sediments.