Category Archives: DNA

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DNA metabarcoding focusing on the plankton community: an effective approach to reconstruct the paleo-environment ... - Nature.com

On August 3, 1986, at about 5:00 p.m., several citizens were at Crater Lake near FM 3083 and Exxon Rd in the Conroe area when they observed a human body partially submerged in the water. Montgomery County Sheriffs Office Deputies responded, and the body was removed from the water. The body was weighted down with two cement cinder blocks attached to an electrical cord. An autopsy revealed that the unidentified male died from multiple gunshot wounds.

The body was not identified in 1986 and was described as follows: white male 20-30 years of age, about 506, 133 pounds, slight build, collar length reddish brown hair, decaying teeth, tattoos left lower arm (small devil with painted tail), left upper arm (the name Liz), right upper arm (the word Baby Dawn), left ear lobe pierced, wearing mens blue denim jeans size 30 waist, 31 length, brown short sleeve shirt, and mens white athletic type socks.

In 2015, Montgomery County Sheriffs Office Cold Case Detectives exhumed the remains to obtain DNA for entry into the Combined DNA Index System (CODIS), a local, state, and federal database of DNA profiles from convicted offenders, unsolved crime scene evidence, and missing persons, in an attempt to make an identification and facial approximation. Both of these tasks were subsequently accomplished, but neither resulted in an identification.

Due to significant advancements in DNA technology, in May 2023, Cold Case Detectives exhumed the remains a second time to obtain additional DNA for Forensic Investigative Genetic Genealogy (FIGG). The remains were taken to Othram Lab in The Woodlands, where they successfully got more DNA, and began conducting a genealogy assessment. A possible family member was identified in California for targeted DNA testing. Contact was made with the family member by local law enforcement, and a DNA sample was voluntarily obtained and submitted to Othram Lab.

In October 2023, Othram Lab issued a report confirming the familial match between the unknown human remains and the family member in California. The remains have been positively identified as Clarence Lynn Wilson, DOB: 02/18/52, with a last known address in Texas City, Texas.

At this time, the Homicide investigation into Wilsons death is still ongoing. If you know Clarence or have any information, please contact the Montgomery County Sheriffs Office Cold Case Squad at 936-760-5820 or Multi-County Crime Stoppers at 1-800-392-STOP [7867].

Source: Scott Spencer, Lieutenant, Montgomery County Sheriffs Office, Administrative Services

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Advancement in DNA Technology and Tenacity of Cold Case Detective Identifies Victim in 37-year-old Homicide - hellowoodlands.com

Description of datasets

In this study we, utilized five independent cohorts including four cancer- and one Alzheimer-related datasets. Gene expression profiling was done using RNA-seq and DNA methylation data were obtained using the Illumina Infinium HumanMethylation450 BeadChip, measuring DNA methylation levels (beta values) on about 450,000 genomic loci.

The TCGA cohorts were obtained using the TCGAbiolinks package50 (Version2.24.3). TCGA-LUSC22 and TCGA-LUAD23 had clinical and genomic data from 589 and 592 patients, respectively (Supplementary TableS2). Information on the pathological stages of the tumors was included in both datasets. We used this information to stratify the patients into distinct risk groups and compared the resulting stratification with clusters obtained from our approach.

TCGA-LIHC24 was provided by a comprehensive study that included 436 cases with clinical information available in the data. We used the Ishak fibrosis score51 and alpha-fetoprotein (AFP) level52,53,54,55,56 to stratify patients into low-, intermediate-, and high-risk groups. The employed score is described later in this section.

TCGA-L AML was provided by a thorough genomic and epigenomic study on 200 adult cases with AML25. The risk groups were defined based on cytogenetic abnormalities25,57.

In addition, we used the ROSMAP cohort provided by the longitudinal cohort studies of aging and dementia. We downloaded the ROSMAP dataset from accelerating Medicines Partnership- AD58 with Synapse IDs syn3388564 (bulk RNA-seq) and syn5850422 (DNA methylation), using the synapser (https://r-docs.synapse.org/articles/synapser.html) R package (Version0.6.61) and a custom R scripts (Version3.6.1)59.

In the TCGA cohorts, events were defined by patients death and the time to an event referred to the duration from the initial diagnosis to death time or the last follow-up. In the ROSMAP cohort, the event was clinical diagnosis of any dementia including mild cognitive impairment with or without other cognitive conditions, Alzheimers dementia with or without other cognitive conditions, and other primary causes of dementia without clinical evidence of Alzheimers dementia. The time to an event in this context referred to the age at which the first dementiarelated diagnosis was made.

To enhance the power of our network, we included cases that have either a single type of data (i.e., gene expression or DNA methylation) or both data available. In the survival analysis, we included only patients whose gene expression, DNA methylation, and survival data were available (Supplementary TableS2).

The initial step in preprocessing involves normalizing the gene expression data. This is accomplished via a logarithmic transformation in based 10 to stabilize the variance and make the data more amenable to following analyses. Because logarithm of zero is not defined, a small offset is added to the expression levels prior to applying this transformation. iNETgrate further preprocesses data in two steps: cleaning and filtering. The former step involved cleaning DNA methylation and clinical data using the wrapper function cleanAllData(). Loci with more than (50%) missing beta values were removed, while loci with less than (50%) missing values were imputed. The imputation was performed by replacing each missing value with the mean of the beta values for the corresponding locus (preprocessDnam()). The clinical data was subsequently cleaned by removing cases with missing survival time and status (prepareSurvival()). The cleaned survival data had patient information including ID, events, time, and risk based on the clinical gold standard.

The second step in the preprocessing data was filtering out genes and loci that have a weak absolute Pearson correlation with survival time and vital status. This was performed by calling electGenes() inside the cleanAllData() wrapper function. In this study, we set the absolute correlation coefficient cutoffs to 0.2 in all TCGA datasets and 0.1 in the ROSMAP dataset.

Every gene and locus that met the quality control criteria was retained for the subsequent steps. In addition, we used computeUnion() to include corresponding loci of the selected genes and corresponding genes of the selected loci in the next steps of analysis.

In iNETgrate, every node represents a gene with two features (i.e., gene expression and DNA methylation values). Therefore, we needed to calculate the DNA methylation value for each gene using computEigenloci(). This function calculated a weighted average of loci levels for their corresponding gene in the following way. When the number of loci corresponding to a gene was less than six, the first principal component (i.e., eigenloci) was calculated directly by taking a weighted average of beta values using PCA. This was the case for almost (95%) of loci in our datasets (Supplementary Fig.S1).

For the remaining (5%) of cases, in which the number of loci representing a gene was six or more, we used findCore() to determine the most connected cluster of loci for each gene. Specifically, a graph was constructed for each gene using the igraph package (Version 1.5.0). In this graph, each locus is represented by a node. We used a fast greedy algorithm60 to calculate the pairwise correlation between loci and detected communities (i.e., clusters) in the graph. Within each community, the average pairwise correlation was computed. The community with the highest average pairwise correlation was identified as a dense subset of highly co-methylated loci in the graph, and the eigenloci value was then computed based on this subset.

We constructed a network in which nodes represent genes and edges are weighted based on the absolute correlation of gene expression and DNA methylation levels for each pair of genes. This was achieved using the makeNetwork() function. The weight of the edges between genes (g_i) and (g_j) was calculated using the following equation:

$$begin{aligned} mathscr {W}(g_i,g_j)=(1-mu )|{{,textrm{cor},}}_E(g_i,g_j)|+ mu |{{,textrm{cor},}}_M(g_i,g_j)|, end{aligned}$$

(1)

Here, (mathscr {W}(g_i,g_j)) describes the integrated similarity between genes (g_i) and (g_j). The term (|{{,textrm{cor},}}_E(g_i,g_j)|) represents the absolute value of the Pearson correlation between the gene expression levels of genes (g_i) and (g_j). Similarly, (|{{,textrm{cor},}}_M(g_i,g_j)|) represents the absolute value of the Pearson correlation between the DNA methylation levels of these two genes. The hyperparameter (mu ) is an integrative factor controlling the relative contributions of gene expression and DNA methylation data in the network. When (mu =0), the network is based solely on gene expression data. Increasing the value of (mu ) emphasizes the DNA methylation data in the model, whereas (mu =1) indicates that only DNA methylation data is used in calculating the edge weights (i.e., gene similarities).

Construction of the network and identification of the modules were done by the wrapper function makeNetwork(), which first uses the pickSoftTreshold() function (RsquaredCut=0.75) from the weighted gene co-expression network analysis20(WGCNA) package (Version 1.72.1) to determine the optimal soft-thresholding power for our integrated network. Then, the blockwiseModules() function (with minModuleSize=5, the absolute value of Pearson correlation, and the default values for the rest of parameters) is utilized to execute a hierarchical clustering approach. This leads to identification of modules, where each module is a group of genes that exhibit similar patterns of expression and DNA methylation. Additionally, module zero is designed to contain outlier genes that cannot be confidently assigned to any module due to their weak or negligible correlation with other genes.

We employed PCA to compute an eigengene for every module (computEgengenes()). In order to balance the contribution of high-risk and low-risk groups, the gene expression and DNA methylation data were oversampled. Intermediate-risk cases were not included in the PCA. An eigengene is computed from a weighted average of gene expression levels ((E^e)), DNA methylation levels ((E^m)), or both ((E^{em})), using the following equations:

$$begin{aligned} E^e = alpha ^e_{_1} g^e_{_1} + alpha ^e_{_2} g^e_{_2} + cdots + alpha ^e_{_n} g^e_{_n}, end{aligned}$$

(2)

$$begin{aligned} E^m = alpha ^m_{_1} g^m_{_1} + alpha ^m_{_2} g^m_{_2} + cdots + alpha ^m_{_n} g^m_{_n}, end{aligned}$$

(3)

$$begin{aligned} E^{em} = (1-mu ) E^e + mu E^m. end{aligned}$$

(4)

Here, n is the number of genes in the module, (g^e_{_i}) is the expression level of gene i, and (g^m_{_i}) is the methylation level corresponding to gene i (i.e., eigenloci), while (alpha ^e_{_n}) and (alpha ^m_{_n}) are the corresponding weights. These weights are computed using PCA ensuring maximum variance and minimum loss of biological information. The eigengene levels are then inferred for the intermediate-risk group using the same weights obtained from PCA. It should be emphasized that regardless of which eigengenes are used, our network and the corresponding modules are consistently constructed based on both gene expression and DNA methylation data and they depend on the (mu ) hyperparameter. The resulting eigengenes are robust features, carrying useful biological information, which can be leveraged in classification, clustering, and other downstream analyses including survival analysis.

To identify the optimal subset of modules for precise prognostication of risk groups, we conducted a two-step survival analysis using analyzeSurvival(). In the first step, we performed a penalized Cox regression analysis using the least absolute shrinkage and selection operator (lasso) penalty29,30 from the glmnet R package61 (Version 4.1.7). This analysis identified the three modules that were most associated with the survival data. Second, we fitted an AFT model31 to each combination of the top three modules and determined the optimal combination that leads to the smallest p-value. p-values were based on a log-rank test with a null hypothesis that there is no difference between the two high- and low-risk groups62.

To categorize the risk groups, iNETgrate uses findAliveCutoff() that searches for a cutoff on the AFT predictions such that the difference between high- vs. low-risk groups is optimized. More specifically, for each risk group, the function iterates over all possible cutoff values leading to a recall of more than a given threshold (i.e., for low-risk: minRecall=0.2, for high-risk: minRecall=0.1 in ROSMAP and 0.05 in other datasets) and selects the cutoff value that maximizes precision.

To ensure the reliability of our integrative approach, we performed a comparative analysis by benchmarking our results against alternative methodologies including a well-known patient similarity network called SNFtool. We also compared our results vs. risk classification according to the clinical gold standards based on the intrinsic nature of the disease in each cohort.

The SNFtool first computes a similarity matrix for each data type (i.e., gene expression and DNA methylation). That is, using each data type independently, a network is constructed where nodes are patients and weights of the edges represent similarity between patients computed based on correlation. The networks (similarity matrices) are then fused to create a consensus network representing the overall similarity between patients across different data types. The resulting patient similarity network is then used to cluster patients into subgroups. We noted that the SNFtool faced some limitations in using all the DNA methylation loci due to memory exhaustion while computing the similarity matrices. We tackled this issue by filtering out loci with a relatively low variation characterized by a standard deviation of less than 0.1. Determining the appropriate cutoff for a given dataset is subjective and challenging for SNFtool users.

In lung cohorts (LUSC and LUAD), we evaluated the risk groups based on the tumor stage. Specifically, we classified stages I,IA,IB,II, and IIA as the low-risk group, stages IIIB and IV as the high-risk group, and the remaining stages as the intermediate-risk group. In the LIHC cohort, we considered a case high-risk if the AFP level was greater than 500 or the Ishak fibrosis score was six. In contrast, patients were considered low-risk if their AFP levels were smaller than 250 and their Ishak fibrosis scores were 0, 1, or 2. The remaining cases were considered intermediate-risk. In the LAML cohort, we utilized the classification system available in the clinical data that categorized cases based on cytogenetic criteria into three groups of favorable (low-risk), intermediate, and poor (high-risk). We utilized the Braak score63 to stratify the ROSMAP cohort into three risk groups. Cases with a Braak score of 0, 1, or 2 were considered low-risk, those with a Braak score of 5 or 6 were classified high-risk, while the remaining cases were grouped as intermediate-risk.

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Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package ... - Nature.com

A former Washington State Patrol Crime Lab employees DNA was found on Ruby Doss shirt and jacket after he examined the items without gloves, a forensic scientist testified Tuesday.

Its the third week of former Pasco police officer Richard Aguirres bench trial over the killing. Doss was found beaten and strangled in an industrial area off of East Sprague Avenue.

Anna Wilson, a forensic scientist at the crime lab, testified Tuesday afternoon about testing Doss jacket and blouse for DNA in October 2019.

Wilson swabbed the collar area of the jacket and blouse, along with the pussycat bow on the blouse. Then she used a device similar to a carpet cleaner to squirt water on the area and then suck it back up, she said.

That hydrates any DNA cells, which are then collected in a solution and tested.

I was specifically told to target the area that may have been touched by a perpetrator if they strangled the victim, Wilson said.

She found a mixture of DNA from three people on both items.

On the jacket, Doss DNA was 48%; William Morig, a forensic technician who examined the items in 1986, contributed 49%; and an unknown contributor had 3% of the sample.

The blouse sample was 35% Doss, 61% Morig and 4% unknown contributor.

Wilson said she saw a photo of Morig examining the clothing without gloves.

Our current standard, they were not following, Wilson said.

DNA testing was extremely new in 1986, and scientists did not understand fully how DNA transferred.

It was standard practice not to take the precautions not to leave DNA, Wilson said.

All former crime lab employees have their DNA entered into a database to be cross-referenced because of this issue, Wilson said.

The unknown samples, Wilson said, did not contain enough DNA to be entered into the national DNA database.

Aguirre was excluded as the third contributor.

A state lab employee, Jeremy Sanderson, testified about being asked to check other labs work and evaluate if the DNA profiles were enough to upload into the Combined DNA Index System (CODIS) database in 2009.

If not enough DNA markers are found in the sample, it cant be entered into CODIS, Sanderson said.

Three partial male profiles were found in Doss underwear, the waistband of her pants and testing from other parts of her body. The profiles were found in a mixture of DNA in which Doss was the predominant source. The partial profiles were too limited to enter into CODIS, he said.

They were eligible for comparison to specific DNA profiles, he said.

Michelle Galusha testified about her analysis of the condom extracts in 2002 while working at Bohdi, another private lab. She was able to develop the first full profile from the sperm portion of the DNA taken from the condom.

She was unable to develop a profile from the non-sperm portion.

Other labs went on to do similar work, as witnesses testified to Monday.

Aguirres trial is set to continue through mid-December.

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Forensic scientist testifies about evidence contamination at state lab in 1986, says knowledge of DNA has since ... - The Spokesman Review

Tracy Morgan just found out that a branch on his family tree belongs to Nas.

During the kick-off episode of the "Connect The Dots" podcast, the "SNL" alum revealed that he and the "Made You Look" hitmaker share more than just friendship; they are cousins, as reported by Today.

The comedian says the startling revelation came while appearing on a segment of the PBS docuseries "Finding Your Roots" set to air on Feb. 20, 2024 that the two are actually kin.

I turn the last page, and guess whos sitting there? Morgan said while reflecting on that moment. Nas. Me and Nas are third cousins on my moms side.

Morgan also said his bond with the rapper transcends the limelight.

Me and Esco was always tight before that, he explained. I did a show years ago on "Comedy Central" called One Mic, that was for Nas mom that just passed away. So me and Esco always been tight.

After Morgan got the news of his newfound family member, he reached out to Nas to notify him.

I called him up, and I say, Yo Esco, he said, What up Trey? Morgan recalled. And I said, I just did 'Finding Your Roots.' Me and you related.

While sharing the news, both became overtaken with emotion.

He started crying, I started crying, Morgan recalled. And I said to him, If you ever need me, Im there, Cuz. He said, Cuz, if you ever need me, Im there.

Additionally, during the show, Morgan will trace his ancestors' footsteps after they arrived in the United States from slave ships.

They went back 400 years on my fathers side and 400 years on my mothers side. I thought I was big in my life till I found out what my great, great, great grandmother did, Morgan continued. My great, great, great, great, great grandfathers name was Abraham Mack. I know the name of the slave masters who owned us, I got it right here on my phone and the slave ship.

From the The Last O.G. actors powerful experience with discovering truths within his lineage, he hopes more people will take charge and do the same.

You need to know who you come from before you leave this earth. Know who you are and where you come from. Knowledge itself. I did a lot of crying. And no matter who you are, youre gonna break down.

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Tracy Morgan Discovers He Is Related To Nas Through DNA Test - BET

The most comprehensive analysis of Indigenous Australians genomes collected to date has revealed an abundance of DNA variations some of which have never been reported anywhere else in the world paving the way for new, tailored treatments that address health inequities for Aboriginal and Torres Strait Islander peoples. A team of Australian researchers, involving scientists from The Australian National University (ANU),The University of Melbourne and the Garvan Institute of Medical Research, found DNA differences between Indigenous Australians in the Tiwi Islands and those in the Central Desert are greater than anywhere else in the world outside of Africa.

The researchers detected millions of small genetic differences and hundreds of thousands of much larger structural variants that affect segments of DNA. These variants occur naturally in different individuals of a population and make up most of the genetic differences between individuals. They may also be linked to diseases in some families. These DNA sequences show a level of genetic variation not observed anywhere else in the world outside of Africa, reflecting Aboriginal and Torres Strait Islander peoples deep cultural and linguistic diversity and long-standing connection to the Australian continent, Dr Hardip Patel, from ANU, said. Some of the DNA variations we discovered appear to be exclusively found in Indigenous Australians, while others appear to be found in just one out of the four Indigenous communities that we consulted and worked with. Previously weve had to try to utilise the DNA of non-Indigenous populations to help diagnose and treat disease among Indigenous Australians, which has proven difficult and is often less reliable. But now we have a new, more representative genomic dataset to build off. Under the leadership of the National Centre for Indigenous Genomics (NCIG) at ANU, research teams examined the DNA of up to 159 Indigenous Australians from four Aboriginal communities in the Central Desert, Far North Queensland and three islands off the coast of the Northern Territory the Tiwi Islands and Elcho Island. Its hoped the research will improve health outcomes for Indigenous Australians by enabling tailored treatments for a range of conditions including diabetes, coronary disease and cancer all of which disproportionately impact Indigenous peoples compared to the rest of the Australian population. Aboriginal people have long said you cant treat us the same because we are so different. Having scientific proof to show this is true is remarkable, ANU Associate Professor Azure Hermes, a proud Gimuy Walubara Yidinji woman and deputy director of NCIG, said.

Clinicians must realise treatment options for Indigenous Australians cant be viewed through a one-model-fits-all lens. We are not a single genetic group and cant be lumped into one category.

Professor Stephen Leslie, from The University of Melbourne, said: Genomics enables us to look back through time at aspects of human history. This history has a direct bearing on the genetic variation we see today.

As scientists we were keen to ensure that Indigenous Australians took the lead on shaping how these questions were approached and how their data was used. Working with NCIG provided the framework to enable this, for which we are very grateful.

Dr Ashley Farlow, also from The University of Melbourne, said: These genomic patterns allowed us to make predictions about the most effective ways to build genomic resources for Australian Indigenous populations in the future.

We identified more than 160,000 structural gene variants, which is more than any previous population-level, long-read study to date,"Dr Ira Deveson, from the Garvan Institute of Medical Research, said.

The research team discovered at least 300 structural variants in each individual that appear to be unique to Indigenous Australians. A genome is equivalent to an instruction manual for the body. It is a blueprint that contains all the genetic information we need to grow, develop, function and respond to the environments in which we live. Genomics medicine harnesses a populations genetic information to help individuals and communities prevent, diagnose and treat a range of complex conditions, as well as rare genetic disease. The code embedded in our genome is unique to each individual its what makes us different to other human beings. Variations within our genetic code can not only contribute to the way we look but can sometimes impact our risk of developing certain diseases, Dr Patel said. We still dont understand why Aboriginal people are more prone to certain health conditions such as kidney disease, diabetes, coronary disease, cancer and other conditions. But genomics might be an important piece of the puzzle that helps unlock some of these answers. Associate Professor Hermes said the project is also about giving Indigenous communities oversight of how their genetic information is used by science. Our goal is to work with and empower Indigenous Australians to take ownership of their genetic information and show them the power of genomics and the health benefits it can deliver, Associate Professor Hermes said. Its taken us almost eight years to get to this point and has only been made possible because of guidance by Indigenous communities, careful consultation, building relationships with communities and understanding their priorities and protocols.

The research is published in two separate papers in Nature.This work was a collaboration between ANU and a number ofinstitutions from across the country.

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DNA discovery opens door to tailored medicine for Indigenous Australians | Australian National University - ANU

When scientists attempt to transfer genetic material into an organism, they often use an electric field, a technique called "electroporation," that makes cell walls more permeable. This sophisticated form of genetic engineering is thought to be something restricted to laboratory equipment, not nature. Yet a recent study published in the journal PeerJ reveals that electric eels which produce an electric organ discharge (EOD) that can reach up to 860 volts may be able to transfer genetic material through their infamous jolts.

Researchers fromNagoya University and Kyoto University in Japan learned this by placing zebrafish larvae in the same tank as electric eels, then dousing the tank inDNA that codes for a green fluorescent protein. Afterward the scientists fed a goldfish to an electric eel, prompting it to emit pulses of up to 185 volts in the tank. (Don't worry, the fish were given anesthesia.) Within a day, some of the zebrafish larvae began to glow, indicating that the electric eel's pulses had indeed caused the fluorescent gene to be transferred into the zebrafish larvae. The fluorescence lingered for three days to a week.

While this study raises tantalizing questions, it leaves many of them unanswered. The implication of this experiment is that electric eels could directly cause gene transfers that increase biodiversity or create new species. Yet as the authors admit in the study, "this investigation represents the initial exploration of the uncharted impact of electric eel EOD, but it does not directly establish its significance within the natural environment." The researchers add that further research will be required, with corresponding author Atsuo Iida from Nagoya University telling New Scientist that he plans follow-up studies on EOD and gene transfer with smaller organisms like plankton and bacteria.

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Electric eel zaps do more than just stun they can alter the DNA of their victims, study suggests - Salon

Tell me why I snook an swab this babydna test results came in.iam not the father smh its a bitter sweat feeling cus I was coming around to it but definitely in my best interest thank you Jesus , he wrote on X Saturday morning. I cant even pretend like im not happy as hell.

Despite previously refuting the possibility of being Chrisean Jr.'s father, Blueface had previously referred to the child in question as his son, per messages he wrote on X following an incident where Rock was seen improperly cradling the childs head at a Walmart store.

On Monday morning, the 26-year-old rapper went live via Instagram after an incident where Rock had reportedly left the child behind with a friend named Marsh so that she could allegedly meet up with another man. When Blue caught wind of the situation, he appeared to have taken the infant from the woman and recounted the story to his followers, according to TMZ.

Rock gave birth to the baby boy on Sept. 3. Blueface shares two other children with rapper Jaidyn Alexis.

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Blueface Says He Took a DNA Test That Proves He's Not the Father of Chrisean Rock's Son: 'Thank You Jesus' - Complex

A comprehensive DNA analysis of the Balkan population from 1 to 1000 CE challenges the assumed Roman influence, revealing no Italian genetic traits. Instead, the study uncovers significant ancestry from Western Anatolia, Europe, and the Pontic-Kazakh Steppe, with a notable Slavic migration shaping modern Balkan genetics. Credit: SciTechDaily.com

Despite the Roman Empires extensive military and cultural influence on the nearby Balkan peninsula, a DNA analysis of individuals who lived in the region between 1 and 1000 CE found no genetic evidence of Iron Age Italian ancestry. Instead, a study published December 7 in the journal Cell revealed successive waves of migrations from Western Anatolia, central and northern Europe, and the Pontic-Kazakh Steppe during the Empires reign.

From the 7th century CE onwards (coincident with the fall of the Western Roman Empire), large numbers of people emigrated from Eastern Europe, likely related to the arrival of Slavic-speaking populations, which resulted in present-day Balkan residents having 30%60% Slavic ancestry seen in present-day Balkan peoples.

Skull of an individual of East African ancestral origin found in Viminacium, with the oil lamp featuring an eagle found in his tomb. Credit: Miodrag (Mike) Grbic

We found this genetic signal of Slavic migration all across the Balkans, says senior author and paleogenomicist Carles Lalueza-Fox of the Institute of Evolutionary Biology (IBE:CSIC-Universitat Pompeu Fabra) and Museu de Cincies Naturals de Barcelona. This could have important social and political implications given that the Balkans has had a long history of conflict associated with their perceived identities.

Most ancient DNA studies focus on pre-historybefore the written recordbut ancient DNA methods can also provide insight into more recent historical periods, especially when used in combination with historical and archeological information.

Ancient DNA can give a lot of insight into historical periods, especially for regions where historical sources are scarce or when we dont know whether sources are biased or not, says first author and population geneticist Iigo Olalde of the University of the Basque Country (UPV/EHU). For example, most historical sources from the Balkans are written from the side of the Romans because the Slavic people didnt write at that time.

This photograph shows the Mausoleum of Viminacium. Credit: Carles Lalueza-Foz

Previous studies have investigated the ancestry of people who lived in Italy and England during and after the fall of the Roman Empire, but little is known about demography and ancestry of the Balkans during this time. This region was one of the distant frontiers of the Roman Empire, which makes it interesting to study because this is clearly a place where you would expect people to come in contact with people from outside the Empire, so you can test things such as globalization, says Olalde.

To explore the population history of the Balkans and examine the influence of the rise and fall of the Roman empire, the researchers extracted DNA from 136 ancient individuals excavated from 20 different sites across the Balkansdefined as the region bounded by the Adriatic, Central Mediterranean, and Aegean Seas and the Middle and Lower Danube and Sava Rivers. These sites included large Roman cities, military fortresses, and small rural towns. The team focused on three periods: during the expansion and height of the Roman Empire (1250 CE), during the late Imperial period (circa 250550 CE), and following the Western Empires collapse (5501000 CE).

To provide cultural and historical context for the genetic data, the team collaborated with local archeologists and historians. For each grave, they documented burial type, as well as any objects buried alongside the individuals, such as coins, jewelry, pottery, tools, and weapons. The researchers also used radiocarbon dating to verify the age of 38 of the ancient individuals, which generated isotopic data that provide a window into those individuals diets.

This photograph shows a Roman aqueduct that supplied water to Viminacium, a large Roman city. Credit: Carles Lalueza-Foz

The researchers were surprised to find no evidence of Italian Iron Age ancestry in the Balkan populations during the height of the Roman Empire. Instead, they showed that there was an influx of people from Western Anatolia, another part of the Roman Empire, during that period. They also found evidence of individual migrations into the Balkans from both within and outside the Roman Empire. Notably, a 16-year-old male who was excavated from a necropolis in a large Roman city was of 100% East African ancestry. The individual was buried with an oil lamp depicting Jupiter-related eagle iconography, but isotopic analysis of his teeth indicated that he had consumed marine protein sources during his childhood and therefore had likely grown up in a distant location.

This was the only full Eastern African individual that we analyzed, and he was also a clear outlier with respect to the diet compared to the rest of the individuals buried in the same necropolis, which tells us that this individual clearly grew up outside the borders of the Roman Empire, says Lalueza-Fox.

During the late Imperial period, between 250 and 550 CE, the researchers detected migrants with mixed ancestry from Northern Europe and the Pontic-Kazakh steppe. We found that those two ancestriescentral/northern European and Sarmatian-Scythian tended to come together, which suggests that these are likely to have been multi-ethnic confederations of moving people, says senior author and population geneticist David Reich of Harvard University.

However, these sources of ancestry disappeared after 700 CE. From 600 CE, shortly after the fall of the Western Roman Empire, there was a major influx of individuals from Eastern Europe. After 700 CE, individuals in the Balkans had very similar ancestral composition to present-day groups in the region, suggesting that these migrations resulted in the last large demographic shift in the area. These migrations coincide with recorded Slavic migrations, but the DNA analysis provides insight into the scale of these migrations that is impossible to glean from historical resources.

There have been debates about how impactful these migrations were and to what extent the spread of Slavic language was largely through cultural influences or movements of people, but our study shows that these migrations had a profound demographic effect, says Reich. More than half of the ancestry of most peoples in the Balkans today comes from the Slavic migrations, with around a third Slavic ancestry even in countries like Greece where no Slavic languages are spoken today.

The team are already planning what they call version two of the study, which will take advantage of improvements in ancient DNA technologies. We are now able to sequence hundreds of individuals from the same site, so we can go to another level of resolution and start to understand more about the social interactions and kinship between the different individuals, says Olalde.

Reference: A genetic history of the Balkans from Roman frontier to Slavic migrations by Iigo Olalde, Pablo Carrin, Ilija Miki, Nadin Rohland, Swapan Mallick, Iosif Lazaridis, Matthew Mah, Miomir Kora, Sneana Golubovi, Sofija Petkovi, Nataa Miladinovi-Radmilovi, Dragana Vulovi, Timka Alihodi, Abigail Ash, Miriam Baeta, Juraj Bartk, eljka Bedi, Maja Bili, Clive Bonsall, Maja Buni, Domagoj Buani, Mario Cari, Lea ataj, Mirna Cvetko, Ivan Drni, Anita Dugonji, Ana uki, Ksenija uki, Zdenk Farka, Pavol Jelnek, Marija Jovanovic, Iva Kai, Hrvoje Kalafati, Marijana Krmpoti, Sinia Krznar, Tino Lelekovi, Marian M. de Pancorbo, Vinka Matijevi, Branka Miloevi Zaki, Anna J. Osterholtz, Julianne M. Paige, Dinko Tresi Pavii, Zrinka Premui, Petra Raji ikanji, Anita Rapan Papea, Lujana Paraman, Mirjana Sanader, Ivana Radovanovi, Mirjana Roksandic, Alena efkov, Sofia Stefanovi, Maria Teschler-Nicola, Domagoj Tonini, Brina Zagorc, Kim Callan, Francesca Candilio, Olivia Cheronet, Daniel Fernandes, Aisling Kearns, Ann Marie Lawson, Kirsten Mandl, Anna Wagner, Fatma Zalzala, Anna Zettl, eljko Tomanovi, Duan Keckarevi, Mario Novak, Kyle Harper, Michael McCormick, Ron Pinhasi, Miodrag Grbi, Carles Lalueza-Fox and David Reich, 7 December 2023, Cell. DOI: 10.1016/j.cell.2023.10.018

This research was supported by the Spanish Ministry of Science of Innovation, la Caixa Foundation, the Natural Sciences and Engineering Research Council of Canada, the Ministry of Science and Education of the Republic of Croatia, the National Institutes of Health, the John Templeton Foundation, the Allen Discovery Center, the Paul G. Allen Family Foundation, and the Howard Hughes Medical Institute.

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In the Shadow of Rome: Ancient DNA Recasts Balkan History - SciTechDaily

Scientists in McMasters Ancient DNA Centre made the discovery while examining data from previous research in what is present-day Yukon. (Photo by Tyler Murchie)

Researchers at McMaster have traced the impacts of ancient ecological transitions right down to the microbial level, finding even bacteria and single-celled organisms changed as the climate around them shifted at the end of the last ice age.

Scientists in McMasters Ancient DNA Centre made the discovery while examining data from previous research that helped construct an advanced picture of environments that existed during thePleistocene-Holocene transition about 11,000 years ago.

When examining soil samples for the ancient DNA of microbial species, the researchers observed that a clear change in the mix of bacteria-like organisms called prokaryotes had occurred more than 13,000 years ago when the Mammoth-Steppe, a monolithic and fertile grassland-tundra ecosystem, collapsed in what is present-day Yukon, Canada.

There was a complete community shift, with many organisms decreasing or increasing in abundance, some disappearing completely and new ones arriving, says Tyler Murchie, lead author of a new paper based on the findings.

Murchie completed the work while a postdoctoral researcher in McMasters Department of Anthropology and isnow a scientist at BCs Hakai Institute, a non-profit research institute specializing in long-term ecological science.

This shows that the Pleistocene-Holocene transition affected everything in the ecosystem, down to microbes in the soil. It wasnt an ecological change that just affected large animals, like woolly mammoths, and grasses and trees, he says.

The studys co-authors include McMaster Ancient DNA Lab director Hendrik Poinar. It was published in the latest issue of the journal Environmental DNA.

Murchie says some types of microbes linked to the droppings of large megafauna such as woolly mammoths vanished during the Holocene transition.

Some microbes essentially disappeared from the region because they were tightly associated with the presence of mammoths, horses and bison, he says. In the data we see that organisms called archaea associated with breaking down megafaunal waste, shifted to other species that are more associated with woody plants and wetter soils.

While the study looks at the transition from a climate epoch that ended many thousands of years ago, researchers hope the findings can also inform how we discuss climate change today.

These environmental transitions have happened throughout time in different ecosystems, and its easy to think of these shifts as having primarily impacted big animals and plants, the things we typically find in fossil records. But by looking at the microbial scale, we can see the massively interconnected and reverberating effects that occur as ecosystems find new balance, says Murchie.

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Even microbes felt the impact of ancient climate transitions, McMaster DNA researchers find - Brighter World