AI as a research tool for aDNA

**Jovialis** · 29-03-23, 18:57

EDIT: Just get Chat GPT plus and get on the plug in wait list!
I bought a license for the Pro version.
Here's a advanced AI-generated summary of the following study. Though I feel basic provides more clarity, as a summary:
Kinship practices in Early Iron Age South-east Europe: genetic and isotopic analysis of burials from the Dolge njive barrow cemetery, Dolenjska, Slovenia
Introduction

The Early Iron Age in most of Europe was marked by a series of social changes. In some cases, these developments appear to have been related to the increasing intensity of contact and exchange among the communities around the head of the Adriatic and with the urbanizing societies of the wider Mediterranean world. These changes are marked in eastern Slovenia by the emergence of new centres of population in the form of large hillforts associated with extensive barrow cemeteries and, in some cases, evidence for ironworking like Dolenjska, which is the subject of this article. The transition from cremation to inhumation is significant, and the new hillforts and associated cemeteries attest to the emergence of extended social hierarchies that exploited the production and trade of commodities such as iron and salt.
It is suggested that these barrows were intended for the burial of family or lineage groups. This is hard to prove with traditional archaeological techniques. As part of the HERA-funded ENTRANS project, osteological and isotope analyses were applied to sites across the region. Some of these sites, like the Dolge njive barrow cemetery, yielded results that suggest human mobility and family structure during the Iron Age.

The Dolge njive barrow cemetery

The Dolge njive cemetery is one of many distributed around the large (12.68ha) hillfort at Veliki Vinji vrh. It is one of the largest Dolenjska mortuary complexes. The estimated total of 145 barrows - comprising four main groups ascending to the north-western entrance of the hillfort - suggests a total of about 145 graves, with a poor standard of documentation and recording. Modern excavations confirm that skeletal remains in the area are typically very poorly preserved.
The Dolge njive cemetery is located between deep incisions in the land to the east of the hillfort. In 2002, excavations before the construction of the motorway revealed three Bronze Age barrows. Two were found on the site of the Bronze Age cremation platforms, while the third was located a short distance to the east. The Dolge njive cemetery is thought to have been located on one of the possible routes to the hillfort.
In two of the three Dolge njive barrows (2 and 3) examined, at least one grave contained an inhumation, each accompanied by spearheads. Barrow 1 was better preserved and contained the remains of six graves, including three that contained extended, supine inhumations. Two of the burials (Burial 1 and Burial 3) lay closer to the perimeter of the barrow. This latter grave, however, cut the edge of Burial 3 and thus cannot be primary. Although central graves in the region tend to be the earliest within each barrow, exceptions are known where they belong to later or even to the latest phases of a barrow's use (Križ 2019).
Dating suggests that the people buried in Barrow 1 were buried during the early-to-mid seventh century BC. Intercutting with earlier burial sites and a slight degree of overlap indicates that the later graves were laid out to respect the earlier burials. Grave goods and typology suggest that the graves are from the Sticňa (I) phase of the Dolenjska Early Iron Age chronology. Combining the dates of the grave goods and the stratigraphic and aDNA evidence, we conclude that the bodies were probably deposited over a relatively short period.

Osteological analysis

The Dolge njive barrows exhibit signs of cortical exfoliation and root etching that precludes osteological analysis of age and sex. The remains of the barrows exhibit varying degrees of preservation, with loose teeth. They are all assumed to be of young (20-35 years old) and middle-aged adults (36-50 years old). The remains do not exhibit any pathological alterations, which is unsurprising given their poor preservation.

Ancient DNA analysis

We successfully analyzed DNA from all nine Dolge njive individuals. By looking at the ratio of Y chromosome sequences to both the X and Y chromosome sequence we can determine genetic sex, maternal and paternal lineages, including the population genetics of the group.
In regards to the mitochondrial lineages of Barrow 1, six out of seven samples belong to the H1e5 haplogroup. The seventh sample has the H haplogroup. The males from Barrows 2 and 3 carry the H5a6 and H1ba haplogroups, respectively, while all males carry the R1b Y chromosome haplogroup, which is one of the major Y-chromosome haplogroups in Europe following the Late Neolithic/Bronze Age transition. Haak et al. (2015) note that the mitochondrial DNA haplogroups of the males at this site are not only consistent with a steppe-associated ancestry but also consistent with the Y-chromosome haplogroups in that.
This software was used to explore potential genetic relationships among the Dolge njive individuals. Within Barrow 1, all seven individuals are close relatives. Burial 5 represents the father of the individuals in Burials 1, 3a, 3b, and 4: three brothers and a sister. The young adult female from Burial 2 is a second-degree relative of these siblings and their father, and therefore most likely to be their grandmother. The young adult male from Burial 6 shares the same mitochondrial haplogroup as the four siblings, so he is their maternal-cousin, father's half-sibling, or great-uncle. The individuals interred in Barrows 2 and 3 are not close relatives, either of each other or of the family group buried in Barrow 1.

Multi-isotope analysis

Individuals were sampled in Barrow 1 to explore evidence of diet and mobility. The Barrow 1 site was chosen because it is the most thoroughly studied site in the region. We investigated multiple isotopes from the bones and teeth of the individuals in order to explore evidence for lifetime variability. In addition, the nature of the assemblage gives a rare opportunity to examine variation in isotopes within a familial group.
The δ 13 C and δ 15 N isotope ratios of the individuals range from −16.6‰ to −13.6‰ and 7.9‰ to 9.5‰, respectively. The values of different elements that show little variation. The Δ δ 13 C CARB-COL values consistently exceed 4‰, reflecting a diet high in C 4 carbohydrates-probably millet. These results are consistent with previous, albeit limited, isotopic analyses of human and archaeobotanical remains of this period in Slovenia.
The strontium isotope ratios and δ 18 O CARB isotope ratios are variable and consistent with variable local geology.
It appears that the father before him, Burial 5, consumed a different diet than the other four individuals at his site. He is the only one with a highly elevated strontium concentration, likely due to a diet with a larger proportion of lower-trophic-level foods. This is indicated by his δ 15 N values, which are the lowest of the group. The tooth enamel composition reflects the Sr, O and C CARB isotopic compositions of diet during early childhood of these individuals.

Sex identification from aDNA, osteology and grave goods

There has been much criticism regarding traditional methods of determining sex based on grave goods, especially without osteological identification. However, Teržan (1985) has proposed that grave goods are a reliable guide to an individual's biological sex. Thus, it's appropriate to consider the sex estimates provided by osteological analysis and aDNA analysis here.
The regional artefact typology makes it possible to attribute gender to, and therefore to infer biological sex of, six individuals from the three Dolge njive barrows, based on the presence of gender specific items as described in Table 1. With poor preservation of the skeletal remains, only tentative gender estimations were possible. Three out of five cases of these tentative attributions prove incorrect when compared with the DNA results.

AMS dating

Six AMS radiocarbon dates were obtained from the skeletal remains of the people found at the Viking settlement of Barrow 1 (8th-7th centuries AD). The results form a consistent date line in the period c. 800-540 BC. The radiocarbon calibration problems associated with the Hallstatt plateau, however, are such that the AMS dates remain highly imprecise and are not readily amenable to further resolution through Bayesian analysis. The dates are, however, consistent with the typological dates for the grave goods, which suggest deposition in the first half of or mid 7th century BC. Figure 5, a) Carbon isotope ratios from individuals in Barrow 1, b) nitrogen isotope ratios from individuals in Barrow 1 and c) carbonate oxygen isotope ratios against strontium isotope ratios obtained from the tooth enamel. Analytical precision based on instrumental error of ±0.2‰ (too small to be visible on the chart).

Discussion

The presence of four full siblings and the implied existence of a fifth suggests a monogamous family structure but it is possible that any half-siblings were buried elsewhere. The composition of Burrow 1 in Barrow, Alaska, appears to be one of intergenerational ties and not of matrilineal descent. Because of this, the group of Burrow 1 cannot be characterized as one of matrilineal descent. There is evidence that the biological father and his children, who form the "core family" within the barrow, are the only members of the family that intermit the rest of the group. Moreover, the two female relatives are the only core members of the family that are absent from the barrow. They are the only link that connects the "core family" to the non-first-degree members of the family, making them central to any scenario of matriliny. The genetic evidence does not support any specific kinship structure drawn from the ethnographic literature, despite the close biological relationships between all occupants of Barrow 1. The presence of a maternal relative and a grandchild from a different patrilineage suggests that this does not represent a straightforwardly patrilineal system.
To be fair, biological relatedness doesn't equate to social relatedness, which can be constituted very differently. For example, in a society where many would have died young, children may be raised by foster parents or even their grandparents. The granddaughter and possible cousin from Barrow 1 (Burials 2 and 6) were additional dependents of the senior male (Burial 5), acquiring their membership of the patriline through adoption or fosterage. The Barrow 1 site has a messily patrilineal system. The mother of the young woman in the burial is not found. It is possible that this woman was returned to her natal group for burial. But the fifth sibling is not found, meaning that the woman and her father were not buried in the same pit. It is possible that, while societies may espouse certain idealized kinship structures, these need not always be rigidly adhered to in practice.
The biological relationships between the individuals in Barrow 1, combined with the stratigraphic and osteological evidence, suggest a short use-life for the barrow. The father is identified osteologically as a middle adult (approximately 35-50 years old) while the others (where age can be estimated) died as young adults. The time that elapsed between the deaths of father and children is likely to have been short, perhaps no more than a decade or so. The internal stratigraphy of Barrow 1 demonstrates that the father's grave (Burial 5) was cut by that of one of the sons' (Burial 4) and by that of the likely cousin's (Burial 6) (Figure 3b). The degree of intercutting is minimal and the layout suggests that the latter two graves were laid out to respect that of the father, whose grave was probably marked above ground. Burial 3, the double grave containing two brothers (presumably buried at the same time), is cut by that of their sister's (Burial 1) and closely respects the grave of their niece (Burial 2). The central placement of Burial 1, combined with its stratigraphic position, suggests that it represents a conscious 'closing' of the monument.
The Dolge njiva site has implications for the interpretation of the numerous other small barrows found in small groups or within more extensive barrow cemeteries throughout the region. At Kapiteljska njiva, it's apparent that most of the 67 barrows at the site contain 10 or fewer graves and are likely to have been used for only a short duration - like the Dolge njiva site. It's unclear why such barrows should be so short-lived, rather than containing multiple generations of the same kinship group. One possibility is that this society was highly mobile, with kinship groups fissioning and moving away frequently, leading to the establishment of new barrows. Other barrows in the region, however, were used for several centuries, despite containing much larger numbers of burials as seen at Preloge.
The death rate for males is 7:2 compared to females in the Dolge njive burials. Although based on a small sample, this does not suggest that the death rates for males and females are equal in the Dolge njive burials. These values may indicate that factors other than kinship - such as biological sex, social position, or the time of death - played a role in determining the composition of the sample of burials.

Conclusions

The genetic results do not provide a definitive answer as to how kinship was organized in the Barrow Cliffs. While links through the maternal line appear to have been important, the composition of the burials suggest neither a matrilineal nor bilateral kinship structure. Evidence for a straightforwardly patrilineal system is also weak, although the composition of the burials may nonetheless be the result of a more flexible patrilineal system, which included the adoption or fosterage of cognatic relatives.
The study of Dolge njive reveals a shift in burial practices in early Iron Age society in Southeast Europe. The change in burial pattern from flat cremation cemeteries to barrows and the burial of multiple individuals suggests that the dead were being seen as warriors, but the gendered objects in the graves balances out the importance of warriors. The data reveals that the importance of male and female descent in determining the composition of the cemetery population. Kinship structures in European prehistory are likely to be complex and reducible to patrilineal and matrilineal descent-based principles. These kinships are built on both male and female lines, providing wider and potentially more robust social networks than those based on patrilineal or matrilineal principles only.

**Jovialis** · 29-03-23, 19:09

Genome-wide patterns of selection in 230 ancient Eurasians

Insight into population transformations

To learn about the genetic affinities of the archaeological cultures for which genome-wide data are reported for the first time here, we studied either 1,055,209 autosomal SNPs when analysing 230 ancient individuals alone, or 592,169 SNPs when co-analysing them with 2,345 present-day individuals genotyped on the Human Origins array 4 . We removed 13 samples either as outliers in ancestry relative to others of the same archaeologically determined culture, or first-degree relatives.
We performed a whole-genome-wide ancient DNA analysis on 26 individuals from the Anatolian Neolithic period. We were able to analyze this much data because of our success at obtaining DNA from the interior ear region of the petrous bone, which increases the amount of DNA obtained by up to two orders of magnitude. The PCA and ADMIXTURE analysis shows that the Anatolian Neolithic do not resemble present-day Near-Eastern populations but cluster with early European farmers. The high frequency of haplogroup G2a (47%; n = 15) on the Y-chromosome makes it likely that the Anatolian Neolithic and EEF were related. The low F ST value between the Anatolian Neolithic and EEF (0.005 -0.016) is also indicative of an ancient shared ancestry. These results support the hypothesis 7 of a common ancestral population of EEF before their dispersal along distinct inland/central European and coastal/Mediterranean routes. The EEF are more similar to Europe as predicted by their PCA scores (Fig. 1b) and have more Western hunter-gatherer admixture than their Anatolian Neolithic relatives (Extended Data Table 2). They have 7-11% more Western hunter-gatherer ancestry than the Anatolian Neolithic (Extended Data Fig. 2, Supplementary Information section 2).
The Iberian Chalcolithic individuals from El Mirador cave are genetically similar to Middle Neolithic Iberians and have more WHG ancestry than their Early Neolithic predecessors. They do not have a significantly different proportion of WHG ancestry than Middle Neolithic Iberians. Chalcolithic Iberians have no evidence of steppe ancestry and arrived later than central Europeans. This suggests that the steppe-related ancestry that is ubiquitous across Europe only arrived in central Europe later.
To understand changes in population in the Eurasian steppe region, we analyzed a time transect of 37 samples spanning ~5600-1500 BC and including the Eastern hunter-gatherer (EHG), Eneolithic, Yamnaya, Poltavka, Potapovka, and Srubnaya cultures. Admixture between populations of Near Eastern ancestry and the EHG started as early as the Eneolithic, with some individuals resembling the EHG and others resembling the Yamnaya. The Yamnaya from Samara and Kalmykia, the Afanasievo people from the Altai, and the Poltavka Middle Bronze Age people from Samara are all genetically homogeneous and form a tight 'Bronze Age steppe' cluster in PCA. They share predominantly R1b Y chromosomes and have ~48-58% ancestry from an Armenian-like Near Eastern source without additional Anatolian Neolithic or EEF ancestry. After the Poltavka period, population change occurred in Samara. The Late Bronze Age Srubnaya have ~17% Anatolian Neolithic or EEF ancestry. An ancestry from Eastern Europe is thought to have migrated to the steppe during the Late Bronze Age. However, the Srubnaya possess an ancestry from an even more eastern source, suggesting that migrations originating as far west as central Europe may not have had an important impact on the steppe. Evidence that migrations originating as far west as central Europe may not have had an impact comes from the fact that the Srubnaya possess exclusively (n = 6) R1a Y chromosomes, which is rare in central/ western Europeans and absent in all ancient central Europeans studied so far.

Twelve signals of selection

This study used 1,084,781 SNPs in 617 samples to study selection in present-day Europeans. They examined three ancient populations in six present-day European populations, using the data set to predict present-day frequencies. They found a pattern in which present-day Europeans can be modelled as a mixture of three ancient populations. To evaluate the accuracy of their predictions, they tested every SNP. They corrected for test statistic inflation by applying a genomic control correction analogous to that used to correct for population structure in genome-wide association studies. Of approximately one million non-monomorphic autosomal SNPs, the ~50,000 in the set of potentially functional SNPs were more inconsistent with the model than neutral SNPs. Using a conservative significance threshold of P = 5.0 × 10 −8 , and a genomic control correction of 1.38, they identified 12 loci containing at least three SNPs achieving genomewide significance within 1 Mb of the most associated SNP.
Recent research shows that the lactase persistence allele for Europeans is only 4,000 years old and its earliest appearance is in a Bell Beaker sample dating to around 2140-2450 years ago in central Europe. Variations at the FADS1 and DHCR7 loci are thought to be involved in fatty acid metabolism and are part of independent selection on chromosome 11. A selection signal in a non-European population is also found at the NADSYN1 and DHCR7 loci. The selected allele is found at the SNP rs4988235 near FADS1 and DHCR7 and is associated with decreased triglyceride levels.
One signal is associated with coeliac disease. It occurs at the ergothioneine transporter SLC22A4 and may have been selected to protect against ergothioneine deficiency in agricultural diets. Common variants at this locus are associated with increased risk for ulcerative colitis, coeliac disease, and irritable bowel disease. A specific variant (rs1050152, L503F) that was thought to be the target reached high frequency only recently. Another signal associated with coeliac disease shows a similar pattern.
The second largest signal among the genome-wide scans is at the derived allele of rs16891982 in SLC45A2, which contributes to light skin pigmentation and is almost fixed in present-day Europeans but occurred at much lower frequency in ancient populations. In contrast, the derived allele of SLC24A5 that is the other major determinant of light skin pigmentation in modern Europe appears fixed in the Anatolian Neolithic, suggesting that its rapid increase in frequency to around 0.9 in Early Neolithic Europe was due to migration. Another pigmentation signal is at GRM5, where SNPs are associated with pigmentation possibly through a regulatory effect on nearby TYR 27. They also find evidence of selection for the derived allele of rs12913832 at HERC2/OCA2, which is at 100% frequency in the European huntergatherers they analyzed, and is the primary determinant of light eye colour in present-day Europeans. The range of frequencies in modern populations is within the ancient populations. The frequency increases with higher latitude, suggesting a complex pattern of environmental selection.
The TLR1-TLR6-TLR10 gene cluster is a known target of selection in Europe. There is also a strong signal of selection at the major histocompatibility complex on chromosome 6. The strongest signal is at rs2269424 near the genes PPT2 and EGFL8. There are at least six other apparently independent signals in the MHC (Extended Data Fig. 3). This could be the result of multiple sweeps, balancing selection, or increased drift as a result of background selection reducing effective population size in this gene-rich region.
A study involving six Scandinavian hunter-gatherers from Motala in Sweden finds that 3 of the 6 samples have the derived allele of rs3827760 in the EDAR gene, which affects tooth morphology and hair thickness. The derived allele is likely an East Asian-specific allele. The study also noted another surprise - that, unlike closely related WHGs, the Motala samples have predominantly derived pigmentation alleles at SLC45A2 and SLC24A5.

Evidence of selection on height

In this paper, they test for selection on complex traits. The best-documented example of this in humans is height. To test for this signal in their data, they use a statistic to test whether the effects of trait-affecting alleles are both highly correlated and more differentiated, than random alleles. They predict genetic heights for each population and apply the test to all populations together, as well as to pairs of populations. From 180 height-associated SNPs, they observe a significant signal of directional selection on height. They also find signals in Iberian Neolithic (Neolithic Iberia) and Chalcolithic samples in comparison to Anatolian Neolithic (Anatolia) and Central European Early and Middle Neolithic (Central Europe). First, we detect a signal for increased height in steppe populations in modern Europe. The South-North gradient in height across Europe is likely due to both increased steppe ancestry in northern populations and selection for decreased height in Neolithic migrants to southern Europe. Second, we did not observe any other significant signals of polygenetic selection in five other complex traits we tested: body mass index, waist-to-hip ratio, type 2 diabetes, inflammatory bowel disease, and lipid levels.

Future studies of selection with ancient DNA

Results from a study using ancient DNA in order to explore selection in the petrous bone suggest multiple selection signals in Europeans. Multiple regions in the genome, including pigmentation, diet, and immunity, show evidence of selection. This is an important step towards understanding how Europeans evolved over time.

Early farmers

This study took advantage of the fact that certain samples didn't have enough coverage to make reliable diploid calls. They estimate the likelihood of allele frequencies in each population by looking at the "counts of sequences covering each SNP.". Suppose a particular site has M samples with sequence level data and N samples with full diploid genotype calls, where in sample i, out of 2N total chromosomes, X copies of the reference allele were observed for a particular reference population (N + 1) through (N + M). For each of the other populations, out of T i total sequences, R i sequence-level copies of the reference allele were observed. Then, the likelihood of the reference allele being observed, p, given the data is.
( ) − is the binomial probability distribution and ε. is a small probability of error, which we set to 0.001. We write ( ).
To estimate allele frequencies, for example in Fig. 3 or for the polygenic selection test, we maximized this likelihood numerically for each population.
The authors use a linear combination of allele frequencies in B ancient populations to model the allele frequencies in A modern populations. This test proved effective in identifying selection.
The log-likelihood of the allele frequencies equals the sum of the log-likelihoods for each population.
To detect deviations in allele frequency from expectation, we test the null hypothesis that allele frequencies are a function of population (CEU, GBR, IBS and TSI) against the alternative that they are unconstrained. We numerically maximize this likelihood in both the constrained and unconstrained model and use the fact that twice the difference in log-likelihood is approximately χ A 2 distributed to compute a test statistic and P value. They define ancient source populations by the 'Selection group 1' label in Extended Data Table 1 and Supplementary Table 1 and use the 1000 Genomes CEU, GBR, IBS and TSI as the present-day populations. They remove SNPs that are monomorphic in all four of these modern populations as well as in 1000 Genomes Yoruba (YRI). They do not use FIN as one of the modern populations, because they do not fit this three-population model well. They estimated the proportions of (HG, EF, SA) to be CEU = (0.196, 0.257, 0.547), GBR = (0.362, 0.229, 0.409), IBS = (0, 0.686. Although it identifies likely signals of selection, this test cannot provide much information about the strength or date of selection. It is limited in that even if the ancestral populations are close to the real ancestral populations, selection must have occurred after the first admixture event - regardless of what populations are in the model. One limitation of this test is that the test statistic is inflated due to unmodelled ancestry or additional drift. A remedy for this would be to divide all the test statistics by a constant, λ, chosen to match the median of the null distribution. They estimated λ = 1.38 and used this correction throughout.
To estimate power, we sampled allele counts from a large, unbiased sample.
They simulate a Wright-Fisher trajectory with selection for 50, 100 or 200 generations, starting at the observed frequency. They then take the final frequency from this simulation and replace the actual observations in that population. They count the proportion of simulations that give a genome-wide significant result after GC correction. They resample sequence counts for the observed distribution of the observed distribution for each population to simulate the effect of increasing sample size.
We investigated how the genomic control correction responded when we simulated small amounts of admixture from a highly diverged population (Yoruba).

METHODS

No statistical methods were used to decide how big the sample size would be. They did not use randomization and the investigators were not blind to it during the experiments or at the end. Ancient DNA analysis. They screened 433 new-generation sequencing libraries from 270 samples, and treated them with UDG to take out errors of ancient DNA.
We performed in-solution enrichment for a targeted set of 1,237,207 SNPs using previously reported protocols. This targeted set merges 394,577 SNPs first reported in ref. 7 (390k capture), and 842,630 SNPs first reported in ref. 44 (840k capture). For 67 samples for which we previously reported data, there was pre-existing 390k capture data. We performed 840k capture and merged the resulting sequences with previously generated 390k data. For the remaining samples, we pooled the 390k and 840k reagents together to produce a single enrichment reagent and sequenced concentrates up to the point where we estimate that it is economically inefficient to continue sequencing. We iteratively sequenced more and more from each sample until we estimated that the expected increase in the number of targeted SNPs hit at least once would be less than about one for every 100 new read pairs generated. After sequencing, we filtered out samples with <30,000 targeted SNPs covered at least once, with evidence of contamination based on mitochondrial DNA polymorphism, atypical X to Y ratio, or high rate of heterozygosity on chromosome X.
Two data sets were used for population history analysis. The 'HO' data set is made up of 592,169 SNPs taken from the intersection of the SNP targets and the Human Origins SNP array 4. This data set is used for co-analysis of present-day and ancient samples. The 'HOIll' data set is made up of 1,055,209 SNPs. These additional sites on the Illumina genotype array 48 are used for analyses only involving the ancient samples.
This example of principal components analysis is based on a set of 777 West Eurasian individuals, which is included in the smartpca49 analysis. The ancient samples were projected with the option 'lsqproject: YES'. The method used for admixture analysis is PLINK 1.9, which was used with the parameters '-indep-pairwise 200 25 0.4'. Cross-validation was used to find the best value for K = 17 for the figure in Extended Data Fig. 2f.
They use ADMIXTOOLS to compute f-statistics, determining standard errors with a block jackknife and default parameters. They used the "inbreed" option when computing f 3 -statistics. They estimate the F ST genetic distances between populations on the HO data set with at least two individuals in smartpca also using the "inbreed: YES" option.
They investigate how robust the test is to misspecification of the mixture matrix C. They first estimate ancestral proportions as in Supplementary Information section 9 of ref. 7, using a method that fits mixture proportions on a 'test' population as a mixture of n 'reference' populations by using f 4 -statistics of the form f 4 (test or ref, O 1 ; O 2 , O 3 ) that exploit allele frequency correlations of the test or reference populations with triples of outgroup populations. They used a set of 15 world outgroup populations. They determined sex by examining the ratio of aligned reads to the sex chromosomes. They assigned Y-chromosome haplogroups to males using version 9.1.129 of the nomenclature of the International Society of Genetic Genealogy (http://www.isogg.org), restricting analysis using samtools to sites with map quality and base quality of at least 30, and excluding two bases at the ends of each sequenced fragment. They also used 1000 Genomes YRI) into a randomly chosen modern population. They determined genome inflation factor for the test population for various levels of admixture. Finally, they analyzed how robust the test was to misspecification of the mixture matrix C by re-running the power simulations using a matrix C′ = xC + (1 − x)R.
This study finds that by explicitly modeling the ancestries of modern populations, one can detect if there is evidence for polygenic selection of a trait. They implemented a test for this and found that when it was applied to GWAS findings (both to all populations combined and to selected pairs of populations), the result was stronger. For height, they restricted their list of GWAS associations to 169 SNPs where there was at least two chromosomes in all tested populations. Using the likelihood described in the paper, they used the MLE to compute frequencies in the different populations. For each test they sampled SNPs and matched them in 20 bins, computed the test statistic Q X and converted these to Z scores, signed according to the direction of the genetic effects. Theoretically Q X is distributed as a χ 2, but in practice it is over-dispersed. Therefore, they report bootstrap P values computed by sampling 10,000 sets of frequency-matched SNPs.
To estimate population-level genetic height in Fig. 4a, we assumed a uniform prior on [0,1] for the frequency of all height-associated alleles, and then sampled from the posterior joint frequency distribution of the alleles, assuming they were independent, using a Metropolis-Hastings sampler with a N(0,0.001) proposal density. We then multiplied the sampled allele frequencies by the effect sizes to get a distribution of genetic height. We caution that the true cost is more than that of sequencing alone. The figure above plots the number of raw sequence reads against the mean coverage of analyzed SNPs after removal of duplicates, comparing the 163 samples for which capture data are newly reported in this study, against the 102 samples analyzed by shotgun sequencing in ref. 5.

Extended Data

Extended Data Figure 5:. In this figure, the six Motala samples and 20 randomly chosen CHB and CEU samples are compared for the derived, selected EDAR allele of rs3827760. Each row is a sample and each column is a SNP. Grey means homozygous for the major allele. Pink denotes heterozygous and red indicates homozygous for the other allele. Three of the Motala samples are heterozygous for rs3827760 and the derived allele lies on the same haplotype background as in present-day East Asians. The only other ancient samples with evidence of the derived EDAR allele are two Afanasievo samples and one Scythian dating to 400-200 bc.
Extended Data Figure 6 compares the power of the selection scan for different selection coefficients (s). It gives a visual representation of estimated power as a function of selection coefficient for a SNP that is selected in all populations for either 50, 100 or 200 generations. It also shows the effect of increasing sample size on estimated power. It then compares the power of a SNP selected for 100 generations versus a SNP selected for 200 generations. Finally, it shows how a mis-specification of mixture proportions can affect power to detect selection on a SNP selected for 100 generations with a selection coefficient of 0.02.

**Jovialis** · 29-03-23, 21:07

The pharaphasing feature is a bit disappointing because it is vauge. However, I am delighted that it cites multiple sources within the data base from which it gives the answer. Keep in mind, I have only uploaded the Harvard papers (not the supplements yet.) and a hand full of others that peak my interest. I still need to expand the data base. Right now there are only 272 studies from 1998-2023:

Summary: "what is the genetic history of Italy from the Neolithic to the Bronze Age?"

The genetic history of Italy is complex, with a shift in ancestry from the Copper Age to the Bronze Age. This shift is likely the result of a mixture of ancestries, including those from the Caucasus and Iran.
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1-s2.0-S0888754322001501-main (1).pdf

Notably, a substantial genetic affinity was highlighted between Peloponnesian individuals from the Neolithic and the Bronze Age, whose signatures are also recorded in Italy. Recent genomic studies [17,34,37] detected a contribution ultimately derived from the Caucasus (CHG) in modern Southern Italians and likely brought to Italy as early as the Bronze Age, although their demographic dynamics are still unknown. Overall, these results are in agreement with the detection of a small proportion of Iranian-related ancestry in Sicilian Middle Bronze Age samples [18], which could be tentatively linked to the spread of the Mycenaean culture [63]. Interestingly, our results modelled the source of this contribution as a mixture of AN and Iran Neolithic ancestries [14]. The latter was found consistently across Southern Italy and the Peloponnese, confirming similar again common genetic sources ancestral profiles shared between these two regions (Fig. 2C, D). Furthermore, our comparison between ancient and modern samples, showing an overall similarity between Southern Italian and Iron Age Individuals, may suggest that the CHG/Iran_Neolithic signature reached the East side of the Adriatic Sea multiple times, or possibly as a continuous gene flow.
1-s2.0-S0888754322001501-main (1).pdf

Ancient genetic profiles were preliminarily investigated through PCA by projecting ancient genotypes onto the first two eigenvectors inferred on modern individuals (Fig. 2A). Hunter Gatherers from West (WHG) and East (EHG) Europe cluster on the right side of the PCA delineating a West to East cline along the second PC, with Italian HGs being at one end of this cluster, possibly as the results of genetic drift after their arrival in the Peninsula ~ 17 kya [8]. Most of the individuals enriched in "Neolithic ancestry" are placed close to the genetic variability of present-day Sardinians, with the formation of two different groups characterised by high Anatolian Neolithic ancestry, as previously observed [40,41]. One group of samples is composed mainly by Sardinians from the Neolithic, Copper and Bronze Age together with Iberians from the Neolithic and Copper Age. This cluster also included few North/Central Italians from Copper and Bronze Age and British Isles populations from Neolithic period. The other group is mostly formed by Neolithic individuals from Anatolia, Central Italy, Sicily and Greece. Notably, modern-day Sardinians do not completely overlap with either of the two groups, confirming the lack of genetic continuity in the island. If the two groups reflect the so-called "Mediterranean" and "Danubian" route of farming expansion [42,43], these observations may indicate a major contribution of the latter in Sardinia, with later demographic events forming the genetic variability observed in the island nowadays. However, the observation that the PCA grouping is somewhat correlated to the magnitude of HG contribution, may also support the hypothesis of different admixture scenarios in different Neolithic groups. In detail, a Neolithic individual from Greece is close to European Early Neolithic samples and forms a cluster including Anatolians and two Peloponnesians from the Neolithic Age.
nihms-1551077.pdf

We collected data from 11 Iron Age individuals dating from 900 to 200 BCE (including the Republican period). This group shows a clear ancestry shift from the Copper Age, interpreted by ADMIXTURE as the addition of a Steppe-related ancestry component and an increase in the Neolithic Iranian component (Figs. 2B and 3B). Using qpAdm, we modeled the genetic shift by an introduction of ~30 to 40% ancestry from Bronze and Iron Age nomadic populations from the Pontic-Caspian Steppe (table S15), similar to many Bronze Age populations in Europe (10,13,14,19,22). The presence of Steppe-related ancestry in Iron Age Italy could have happened through genetic exchange with intermediary populations (5,23). Additionally, multiple source populations could have contributed, simultaneously or subsequently, to the ancestry transition before Iron Age. By 900 BCE at the latest, the inhabitants of central Italy had begun to approximate the genetics of modern Mediterranean populations.
8_25_2022_Manuscript3_HistoricalPeriod_1.pdf

This study is a part of a comprehensive archaeogenetic analysis of the genetic history of the populations of the Southern Arc, spanning a trio of papers. For a description of the full dataset and analysis framework and characterization of the population history of the Chalcolithic and Bronze Age periods, see (1). For analysis of the population history of the Neolithic, see (2). The present paper focuses on peoples for which there is also information from written texts. A main theme is to test the extent to which textual insights are supported or not supported by the genetic data and furthermore to explore what complementary information genetics can provide. When we reference ancient literature, we use standard abbreviations for locating passages in online repositories of texts, such as the Perseus Digital Library (3). Our study begins at the end of the Bronze Age and traces the region's history through the first millennium BCE, through the Roman Empire and up to the present, a time span of >3000 years.
nature23310_1_0.pdf

Geographical structure may have prevented the spread of the 'northern' ancestry from the mainland to Crete, contributing to genetic differentiation. Such a structure may, in principle, be long-standing, even before the advent of the Neolithic in the seventh millennium bc. Alternatively, both 'northern' and 'eastern' ancestry may have arrived in the Aegean at any time between the Early Neolithic and the Late Bronze Age. Wider geographical and temporal sampling of pre-Bronze Age populations of the Aegean may better trace the advent of 'northern' and 'eastern' ancestry in the region. However, sampled Neolithic samples from Greece, down to the Final Neolithic ~ 4100 bc (ref.
8_25_2022_Manuscript3_HistoricalPeriod_1.pdf

To contextualize the transformations in the Bronze Age Aegean, it is critical to characterize the pre-Bronze Age genetic landscape (Fig. 1). We begin with the Neolithic inhabitants (4, 6, 7), estimating proportions of ancestry using a five-source model that we developed for Southern Arc Holocene populations (1), which includes as proxies for the sources Caucasus hunter-gatherers (9), Eastern European hunter-gatherers (5, 10), Levantine Pre-Pottery Neolithic (11), Balkan huntergatherers from the Iron Gates in Serbia (7), and Northwestern Anatolian Neolithic from Barcın (5). We infer that not only Neolithic Greeks from the Peloponnese (7) but also those from Northern Greece (6) had~8 to 10% Caucasus hunter-gatherer-related ancestry (Fig. 1C). We find small amounts of Caucasus hunter-gatherer-related ancestry in Southeastern Europe and Neolithic populations in general, which is different from the pattern in Central/Western Europe where there is none (1). This provides proof of multiple streams of migration from different Anatolian Neolithic populations into Europe.
1-s2.0-S0888754322001501-main (1).pdf

Modern Italians, with the exception of Sardinians, are very different from the Mesolithic, Neolithic and Bronze Age individuals from the same area, with some resemblance only in Iron Age samples [44]. A notable exception is the fact that the Iron Age Southern Italians here investigated do not overlap with the genetic variation observed in modern-day individuals from the same area, in line with previous observations [35]. Interestingly, three out of the five Neolithic Peloponnesians, together with the totality of Minoans and Mycenaeans included in our dataset, plot towards the genetic variability of people currently inhabiting Southern Peloponnese (Maniots and Tsakonians) and Southern Italian Fig. 1. Admixture and genetic relatedness in modern South-Eastern Europeans. A) ADMIXTURE plot for K = 9 (lowest CV error) and 34 populations. Populations are ordered according to their geographical longitude. B) Barplots represent the proportion of individuals assigned to a specific cluster (pie in the inset) by the mclust clustering approach (see Fig. S4). Bars are placed on the population geographic origin. regions (Sicily, Calabria and Apulia) (Fig. 2B). Modern Southern Italians are closer to Southern European Neolithic and Bronze Age samples (Neolithic Peloponnesians and Minoans) than most modern Peloponnesian groups, with the exception of the Deep Mani and Taygetos individuals (Fig. 2B).
SEA_published.pdf

Our findings also have implications for genetic transformations linked to later cultural and linguistic shifts in Southeast Asia and beyond. We observe substantial genetic turnover between the Neolithic period and Bronze Age in Vietnam, likely reflecting a new influx of migrants from China (24). Late Neolithic to Bronze Age Myanmar individuals from Oakaie also do not possess an Austroasiatic genetic signature, in their case being closer to populations speaking Sino-Tibetan languages (including present-day Myanmar), pointing to an independent East Asian origin. Outside of mainland Southeast Asia, we document admixture events involving Austroasiatic-related lineages in India (where Austroasiatic languages continue to be spoken) and in Borneo and Sumatra (where all languages today are Austronesian). In the latter case, the shared ancestry with Nicobarese (in addition to separate Papuan-related and Austronesian-associated components) supports previous genetic results and archaeological hints of an early Austroasiatic-associated Neolithic expansion to western Indonesia (25,26). Overall, Southeast Asia shares common themes with Europe, Oceania, and sub-Saharan Africa, where ancient DNA studies of farming expansions and language shifts have revealed similar instances of genetic turnover associated with archaeologically attested transitions in culture.
20171540.full__0.pdf

The newly reported GAC individuals fell within a cluster comprising most Early and Middle Neolithic individuals (figure 3a and electronic supplementary material, figure S4). As previously observed [20], a clear separation is apparent between hunter-gatherers and samples of more recent periods, with the Bronze Age individuals at the top of the plot, the Late Neolithic samples in a central position and the Early and Middle Neolithic samples at the bottom. We found again a Europe-Near East cline along the principal component 1 in modern populations, and the clustering of early farmers across Europe with present-day Sardinians [18,20,35] (electronic supplementary material, figure S5). We also computed a matrix of genetic distances between pairs of individuals in the AP dataset, considering for each pair of individuals only the shared SNPs. The MDS plot confirms the pattern shown by PCA, again showing three well-differentiated clusters corresponding to the Palaeolithic hunter-gatherers, to the samples spanning from the Late Copper Age to the Bronze Age, and to Middle and Early Neolithic people, including those from the GAC (electronic supplementary material, figure S6).
msac014.pdf

Genetic Origin of Daunians . doi:10.1093/molbev/msac014 MBE haplogroup during the Bronze Age in the Italian Peninsula and on the islands Sardinia and Sicily (Allentoft et al. 2015;Haak et al. 2015;Antonio et al. 2019;Fernandes et al. 2020;Saupe et al. 2021), we found the Ychr lineages I1-M253, I2d-M223, and J2b-M241. The haplogroup I2d-M223 was one of the main Y chromosome lineages in Western Europe until the Late Neolithic whereas J2b-M241 first appears in the Bronze Age (Allentoft et al. 2015;Mathieson et al. 2015;Schuenemann et al. 2017;Fernandes et al. 2020;Marcus et al. 2020). We found one Early Mediaeval individual (SGR001) belonging to haplogroup I1-M253, which is common in Northern Europe and previously also detected in a 6th Century Langobard burial from North Italy (Amorim et al. 2018).

**Jovialis** · 29-03-23, 21:08

I am essentially building an AI-powered learning tool prioritized for research in population genetics, and aDNA.

**Jovialis** · 29-03-23, 21:46

The advanced summaries cost credits which are re-supplied at the beginning of every month, but I find the basic to be sufficient, and even more helpful sometimes.

**Jovialis** · 29-03-23, 21:56

Here is an example of a basic long summary:
The diverse genetic origins of a Classical period Greek army

Untitled

Trade and colonization caused an unprecedented increase in Mediterranean human mobility in the first millennium BCE.
We provide insight into the demographic dynamics of ancient warfare by reporting genome-wide data.
ancient DNA j archaeology j history j Classical world j ancient warfare.
Violent conflict is a common theme of ancient written accounts.
It is important to test alternative hypotheses about the role of military activity in population interactions.
There is extensive archaeological evidence for links between prehistoric Sicily and the Eastern Mediterranean.
Phoenician trading posts on the western coasts of Sicily began in the ninth century BCE.
Inhabitants of the Greek colony of Emp uries in Iberia living in the fifth century BCE have clear genetic linkages.
Himera was a colony founded by Ionian and Dorian Greeks around 648 BCE.
Himera was also likely inhabited by indigenous Sicilians, Punic people, and Etruscans.

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Significance

Study of genome-wide data from 54 individuals from eighthto fifth-century Sicily.
We gain insights into composition of Classical Greek armies (ca. fifth c. BCE) and the populace of a Greek colony.
The presence of mercenaries in Greek armies fighting in the Mediterranean is absent from historical texts.
Archaic and Classical Greek armies are often understood as being composed of hoplites.
The role of mercenaries in Greek armies is frequently downplayed by ancient historians.
Punic armies, such as Carthage, often used mercenary armies.
Genome-wide data from 33 individuals associated with the Battles of Himera and from Himera's civilian population.
21 individuals from two nearby settlements associated with the indigenous Sicani culture of Sicily.
Genome-wide data provide additional data points for evaluating the role of ancient conflict.

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Results

We generated double-and single-stranded next-generation sequencing libraries (45,46) using partial uracil-DNA glycosylase (UDG) treatment and without such treatment for two samples.
We enriched them for ∼1.2 million single-nucleotide polymorphisms (SNPs) (47, 48) (Dataset S1).
Filtering for standard quality criteria and using a cutoff of at least 10,000 covered SNPs, we retain a dataset of 16 individuals.
We computed principal components (PCs) on the newly generated HO data, together with 985 previously published present-day individuals from 64 diverse West Eurasian and North African populations.
Modern Western Eurasians form two parallel clines separated on the first PC, which distinguishes European populations from populations of the Near East and Caucasus.
The Civilian Population of Himera and Surrounding Regions.
We find that the IA Sicilians (Sicily_IA) form a homogenous cluster distinct from most of the individuals excavated at Himera.
Using qpAdm, this group can be modeled as an admixture of four sources that distantly contributed to the genetic composition of Europeans.
This indicates that the population might not have been completely continuous.
The individual buried at Himera's East necropolis attests to the incorporation of local people into the populace of the colony.
Due to the limited number of samples, we cannot test whether such a significant stratification existed.
Diverse Ancestry among Himera's Soldiers of the 480 BCE Battle.
Most Himerans associated with the battles can be found clustering on the PCA closely with individuals from Greece_LBA.
The population of Himera may have been influenced by a large-scale influx of Dorians from Agrigento after a political takeover by the Agrigentine tyrant, Theron.
Among the soldiers of the 480 BCE battle, we find nine individuals that carry genetic ancestry not consistent with the first group.
We tested ancestry models with qpAdm.
Sicily_Himera_480BCE_2 consists of two outlying individuals (I10946/W1771 and I10950/W814) that fall on the PCA intermediate between the main cluster and central European individuals.
A genetic origin in the Balkans is suggested by their Y chromosomes.
Two individuals (I10943/W0396 and I10949/W0403) fall with modern northeastern European groups and eastern Baltic populations of the first millennium BCE.
One low-coverage individual, I17870/W0336, falls intermediate between Sicily_Himera_480BCE_2 and Sicily_Himera_ 480BCE_3.
Two individuals fall with individuals from IA nomadic contexts in the Eurasian Steppe.
Their mitochondrial DNA (mtDNA) haplogroups suggest east Eurasian genetic roots.
One outlier (I10951/W0653; Sicily_Himera_480BCE_5) falls with modern Caucasus populations and intermediate to ancient Steppe and Caucasus individuals on the PCA.
A single one-way model with a group closely related to Armenia_MBA as the source fit the data.
Outgroup-f 3 statistics with ancient groups reflect the same general patterns of genetic affinities.
The groups can all be modeled as deriving differing proportions of ancestry.

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Integration of Isotopic Evidence Supports Mercenary Presence

in the 480 BCE Battle.
.
Genetic and isotopic information provide complementary information about human mobility and life history.
We correlated the diversity in genetic histories with strontium and oxygen isotope data gathered from the teeth of Himeran individuals.
For all the individuals from the civilian sample, all the individuals from 409 BCE graves, and most of the individuals in the 480 BCE graves determined to be Aegean related, the strontium isotopic ratios are consistent with local origins.
All 480 BCE soldiers with primary genetic affinities with central Europe, northeastern Europe, the Eurasian steppe, and Armenia are isotopically nonlocal.
Findings suggest that soldiers from 480 BCE had more diverse origins.
This supports the interpretation that the Greek army in 480 BCE included mercenaries.
Both Greek colonies and other polities hired mercenaries.
Soldiers from 480 BCE, whose genetic diversity exceeds that of the civilian sample at Himera, therefore likely did not reflect a draft exclusively from the Himeran polis.
The combination of genetic and isotope data suggest they were born and raised even further afield than Sicily.
There are patterns in genetic affinities among the seven mass graves associated with the battle of 480 BCE.
All soldiers who fall outside the Aegean genetic cluster are interred in mass graves Nos. 1-4.
Mass graves Nos. 1-4 and Nos. 5-7 also are spatially segregated.
Individuals in mass graves Nos. 5-7 are significantly older than individuals interred in mass graves Nos. 1-4.
These individuals also fall within the Aegean genetic cluster.
Constructions of ethnicity in the ancient Greek world were chiefly a matter of common descent and ancestral homelands.
Survivors incorporated information on the social origins of the deceased as part of their social logic for respectful treatment of the dead.

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Discussion

These results highlight how colonies facilitated migration, as reflected by heterogeneous ancestry deriving from geographically disparate locations.
Results show how armed conflict served as a contact mechanism among diverse ancient populations in classical antiquity.
Mercenaries were among the most long-distance travelers of the Greek world, bringing persons of widely divergent cultural and genetic backgrounds face to face.
Historically contextualized genetic and isotopic evidence of foreign soldiers at Himera is another indicator of the heterogeneity and evolving complexity characterizing IA and post-IA societies.
Identifying diverse origins of Classical Greek mercenaries.

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Materials and Methods

We extracted tooth and bone powder from samples excavated at Himera at the University of Vienna and Harvard Medical School.
We produced either dual-barcoded, double-stranded, high-throughput sequencing DNA libraries (45,106,107) or dual-indexed, single-stranded libraries (108) with custom oligonucleotide sequences.
We enriched the amplified libraries using two consecutive rounds of hybridization capture.
We sequenced reads by trimming adapters and barcodes with SeqPrep and merging paired-end reads that overlap in at least 15 bases.
We filtered out sequences that overlapped known insertion/deletion polymorphisms and that have a mapping quality below 10 and a base quality below 20.
We obtained pseudohaploid SNP calls by randomly selecting a single sequence.
Analyses.
We assessed ancient DNA authenticity in each library by confirming that the rate of cytosine-to-thymine substitution in the first nucleotide occurred at a level consistent with genuine ancient DNA.
We used contamMix to test for contamination based on polymorphism in the mtDNA.
We merged the dataset above with 3,291 modern-day individuals from 109 worldwide populations genotyped on the HO array.
We used the smartpca function of EIGENSOFT (121) to perform PCA using default parameters.
We performed clustering using unsupervised ADMIXTURE (50) for k = 2 to 15.

**Jovialis** · 30-03-23, 17:19

The best way I could describe using this software is comparable to a PCA, in that using too much information breaks it (Creates errors, and inaccuracies). However, if you are looking at specific studies that are all on a similar topic, you are likely to get extremely informative and accurate results.

For example, if you dedicate a folder to have just studies on a particular topic like Ancient Greeks. It will be a very useful tool

But if you just search all studies at once, you are bound to get some issues in terms of accuracy.

Just like the way you can't expect to project every single DNA sample on a single PCA; you can't expect to get a highly accurate AI for using all aDNA studies. The topic, although niche within and of itself, is far too broad. Thus, AI at least at this point, needs some assistance from people familiar in the topic. Thus if I could arrange studies that match each other, I could start asking it very particular questions about aDNA.

**Jovialis** · 30-03-23, 20:06

Genetic Origins of the Minoans and Mycenaeans

Genei AI Advanced Summary:

Genetic data from 19 individuals including Minoans from Crete, Mycenaeans from mainland Greece, and their eastern neighbours from southwest Anatolia show that Minoans and Mycenaeans: were genetically similar - having at least three-quarters of their ancestry from the first Neolithic farmers of western Anatolia and the Aegean, and most of the remainder from ancient populations related to those of the Caucasus and Iran; differed from one another in deriving additional ancestry from an ultimate source related to the hunter-gatherers of eastern Europe and Siberia; Modern Greeks resemble the Mycenaeans, but with some additional dilution of the Early Neolithic ancestry. The findings support the idea of continuity but not isolation in the history of populations of the Aegean, before and after the time of its earliest civilizations.
Ancient DNA research has traced the principal ancestors of early European farmers to highly similar Neolithic populations of Greece and western Anatolia, beginning in the 7th millennium bc. There is limited genetic evidence suggesting migrations from both the east (the area of Iran and the Caucasus) and the north (eastern Europe and Siberia) contributed to all modern Europeans. The timing and impact of these migrations in the Aegean is, however, unknown.
The Linear A syllabic ideographic and Cretan hieroglyphic scripts used by the culture of the island of Crete have remained undeciphered, obscuring the origins of 'Minoan.'. However, the Linear B script used by the culture of 'Mycenaean' has been deciphered, and its earliest speakers could date back to the 7th millennium down to ~ 1600 bc. Greek is related to other Indo-European languages, which led to diverse theories tracing its earliest speakers from the 7th millennium down to ~ 1600 BC.
At the Bronze Age, geographically separated groups of people inhabited Crete and mainland Greece. These groups were later labeled "Minoan" and "Mycenaean," but it is unclear if these labels correspond to genetically coherent populations. It is also unclear what these groups were related to their neighbors across the Aegean Sea and more distant regions like Anatolia and the Near East. It is possible that they were related to modern Greeks, who inhabit the same area today.
We generated genome-wide data from 19 ancient individuals. These came from 10 Minoans from Crete, four Mycenaeans from mainland Greece and an additional individual from Crete. The data also included a Neolithic sample from Alepotrypa Cave in the southern Peloponnese, a Bronze Age individual from Harmanören Göndürle in Anatolia and three Bronze Age individuals from Anatolia. We processed the ancient remains, extracted DNA, and prepared Illumina libraries in dedicated clean rooms. We used in-solution hybridization 18 to capture 1.2 million single nucleotide polymorphisms on the ancient samples. We assessed contamination by examining the rate at which the ancient samples matched the mitochondrial consensus sequence. We used the dataset of the 19 ancient individuals with 332 other ancient individuals from the literature, 2,614 present-day humans genotyped on the Human Origins array, and 2 present-day Cretans.
We performed PCA, projecting ancient samples onto the first two principal components inferred from present-day West Eurasian populations. The samples of Minoans and Mycenaeans were positioned centrally in the PCA. This component was maximized in the Mesolithic/Neolithic samples from Iran and hunter-gatherers from the Caucasus but was not found in the Neolithic of northwestern Anatolia, Greece, or the Early/Middle Neolithic populations of the rest of Europe. The distribution of this component was only introduced into mainland Europe when it occurred in the Late Neolithic/Bronze Age populations. This happened through migration from the Eurasian steppe.
Although PCA and ADMIXTURE showed different population migrations in ancient Greece and Crete, we found that populations from Iran, the Caucasus, and Eastern Europe shared many alleles with Minoans and Mycenaeans, two ancient Crete populations. Minoan and Mycenaean populations were consistent with being homogeneous. The Minoan plateau of Crete and the coast of Crete were different from mainland and Aegean populations in that they shared fewer alleles with Neolithic people from the Levant, Anatolia, Greece, and Europe. Statistical tests show that both Minoan and Bronze Age Anatolians had more alleles in common with ancient populations in Iran and the Levant, rather than modern populations in Greece and the rest of Europe. This is likely because early populations in the Levant were mixed with both Minoan and Bronze Age Anatolian populations. Ancient populations in the Levant, Anatolia, Greece, and the rest of Europe likely had more alleles in common, which was the case for Mycenaeans.
The Bronze Age populations from the Aegean and Anatolia had an estimated 9-32% 'eastern' ancestry. This type of ancestry was introduced into mainland Europe via Bronze Age pastoralists from the Eurasian steppe. The Bronze Age populations from the Aegean and Anatolia were consistent with deriving most (approximately 62%-86%) of their ancestry from an Anatolian Neolithic-related population. However, they also had a component (approximately 9-32%) of 'eastern' ancestry. During the Bronze Age in central Anatolia, there were genetic changes that were mediated by contact with populations from the east, including Greece and southwestern Anatolia. This ancestry did not have to come from the east of Anatolia, as it was already present during the Neolithic. The Bronze Age population from Greece and southwestern Anatolia was dominated by Y-Chromosome haplogroup J, which was rare or non-existent in earlier populations from Greece and western Anatolia who were dominated by Y-Chromosome haplogroup G2. The spread of Y-chromosome haplogroup J westward accompanied the eastern genomewide influence.
The Minoans could have been a mixture of the Anatolia Neolithic-related substratum with additional 'eastern' ancestry; however, the other two groups had additional ancestry. The Mycenaeans had approximately 4-16% ancestry from a 'northern' ultimate source related to the eastern European hunter-gatherers, while the Bronze Age southwestern Anatolians may have had ~ 6% ancestry related to Neolithic Levantine populations. To identify more proximate sources of the distinctive eastern European/north Eurasian-related ancestry in Mycenaeans, we included later populations as candidate sources. Mycenaeans were a mixture of the Anatolian Neolithic and Chalcolithic-to-Bronze Age populations from Armenia. The populations from Armenia possessed some ancestry related to eastern European hunter-gatherers. This model makes geographical sense, given the proximity of the Chalcolithic-to-Bronze Age populations of Armenia to the Aegean.

**Jovialis** · 30-03-23, 20:32

Ancient Rome: A genetic crossroads of Europe and the Mediterranean

In the 8th century before the common era, Rome was one of many city-states on the Italian Peninsula. In less than 1000 years, it grew into the largest urban center of the ancient world. Rome controlled territory on three continents, spanning the entirety of the Mediterranean Sea, or Mare Nostrum, "our sea," as the Romans called it. As part of the Italian Peninsula, Rome occupies a distinctive geographic location. It is partially insulated by the Alps to the north, which formed a natural barrier to movement of languages, material cultures, and people. Rome is also highly connected to regions around the Mediterranean Sea, particularly after Bronze Age advances in seafaring.
To characterize the genetic composition of Rome's population throughout the trajectory of the empire, we assembled a time series of genetic data from 127 ancient individuals who lived from Roman prehistory to the fall of the Roman empire.

Results

We generated data for 127 ancient individuals from 29 archaeological sites in Rome and central Italy. Data was obtained by direct radiocarbon dating (n = 33 individuals) and inference from archaeological context (n = 94) through the processing of cochlear petrous bones. We built partially uracil-DNA glycosylase-treated libraries (6) for screening for endogenous DNA concentration, DNA damage patterns, and contamination. We performed whole-genome sequencing to a median depth of 1.05× genome-wide coverage (range 0.4 to 4.0×). Data was analyzed using principal component analysis (PCA), ADMIXTURE (8), f-statistics (9), and qpAdm admixture modeling (10) on pseudo-haploid genotypes; and ChromoPainter (11) on imputed diploid genotypes.
There are three major genetic clusters described by the chronology of individuals and their genetic profile according to PCA and ADMIXTURE. These groups are: (1) Mesolithic hunter-gatherers, (2) early farmers of the Neolithic and Copper Age, and (3) the broad historic cluster from the Iron Age to the present. The historic individuals approximate modern Mediterranean and European populations in PCA space, but these groups are highly variable ancestries among the historic individuals, both within and across time periods.

The Mesolithic

The oldest genomes they've seen are from three Mesolithic hunter-gatherers who lived around 10,000 to 7,000 BCE. They project close to Western hunter-gatherers from elsewhere in Europe, like those from the Villabruna cave in northern Italy and from Grotta dOriente in Sicily.
Since humans began farming, the genetic diversity of these groups has gone up and down over time. For example, genetic diversity was low in central Italy in the Neolithic. It increased during the Middle Ages and reached modern levels by around 2000 years before present.

The Neolithic transition

The transition from hunter-gatherer to farmer was first major ancestry transition in the time series that occurred between 7000 and 6000 BCE. Both Neolithic individuals from central Italy and early Neolithic populations from other parts of Europe carry some ancestry from a lineage which is found at high levels in Neolithic Iranian farmers and Caucasus hunter-gatherers. Despite this, Neolithic Italian farmers are more likely to have ancestry from a source population which is mostly found in central Anatolia or northern Greece, rather than from the Anatolian region.
Small, gradual rebound of WHG ancestry in Neolithic and Copper Age. Might reflect admixture with communities that had high levels of WHG ancestry persisting into the Neolithic. Local or neighboring regions.

The Iron Age and the origins of Rome

The second major ancestry shift happened between 2900 and 900 BCE and was most likely caused by the development of chariots and wagons and the expansion of Greek, Phoenician, and Punic colonies.
We collected DNA from 11 Iron Age people dating from 900 to 200 BCE and saw a clear ancestry shift from the Copper Age, as well as an increased Neolithic Iranian component. Using qpAdm, we model this shift with ancestry from Bronze and Iron Age nomadic populations from the Pontic-Caspian Steppe. The presence of Steppe-related ancestry in Iron Age Italy could have happened through genetic exchange with intermediary populations. Multiple source populations could have contributed to the ancestry transition before Iron Age; by 900 BCE at the latest, the inhabitants of central Italy had begun to approximate the genetics of modern Mediterranean populations.
The beginnings of Rome are not well understood, but there is evidence of contact with Greek and Phoenician-Punic colonies. Material from these colonies is incorporated with art and culture from Rome.
The Iron Age individuals are composed of individuals from Italy and Central Asia. Although the individuals could have been mixed with a population from Asia, the differences between the groups are minimal. Two individuals from Latin sites (R437 and R850) are a mixture of local people and an ancient Near Eastern population. African ancestry was identified in one Etruscan individual (R475) and could be modeled with ~53% ancestry from Late Neolithic Morocco. The Iron Age individuals suggest substantial genetic heterogeneity within the Etruscans and Latins. There is no significant genetic differentiation between the two groups despite the small sample size.
The Iron Age individuals share many of the same physical characteristics as modern Europeans and Mediterraneans. They display diverse ancestries as central Italy becomes increasingly connected to distant communities through new networks of trade, colonization, and conflict.

Imperial Rome and the expanding empire

During the Republican and Imperial periods, Rome expanded from a city-state on the Tiber river into an empire that spanned the entire Mediterranean and extended onto all three surrounding continents. Rome's overseas expansion began during the Punic Wars with Carthage in present-day Tunisia (264 to 146 BCE). This growth continued for much of the next 300 years. Rome itself had a population of over 1 million people and it is estimated that the empire had a population of between 50 and 90 million. The empire facilitated the movement of people through trade networks, new road infrastructure, military campaigns and slavery. Beyond the boundaries of the empire, Rome engaged in trade with northern Europe, sub-Saharan Africa, the Indian subcontinent, and across Asia. Little is known about the genetic impacts of these contacts.
Imperial Romans have an ancestry shift towards the eastern Mediterranean with very few individuals of mainly western European ancestry. This shift is accompanied by an increase in the Neolithic Iranian component in ADMIXTURE and is supported by f-statistics. This shift is accompanied by significant introgression signals in admixture f 3.
The Imperial population was a mixture of the Iron Age population and another population. The results of the analysis suggest that the Imperial population was not just a mix of the Iron Age and Roman. The data was difficult to fit into any simple two-way combination of population types.
Although the shift in the ancestry of Imperial individuals is towards eastern populations, they also share ancestry with other populations. To further characterize this, they assess haplotype sharing using ChromoPainter. Specifically, they measure the genetic affinity between ancient Italian individuals and modern populations by the total length of the shared haplotype segments. They subsequently cluster ancient individuals by their relative haplotype sharing with modern populations, which gets plotted in PCA.
ChromoPainter analysis revealed five distinct clusters of Imperial-era individuals. The European cluster is the most minor group, with only 2 out of 48 Imperial Romans falling in it. Two-thirds of the sampled Imperial Romans belong to two major clusters that overlap with central and eastern Mediterranean populations. In addition, one-quarter of the sampled Imperial Romans form a cluster with high amounts of haplotype sharing with Levantine and Near Eastern populations. The remaining Imperial Romans are in a large cluster (C6) with high percent of sharing with central and eastern Mediterranean populations, such as populations from southern and central Italy, Greece, Cyprus, and Malta. The two individuals that were identified in this cluster are similar to North Africans and can be modeled with 30 to 50% North African ancestry.
Our data show how people and cultures in the eastern Mediterranean were more heavily influenced than anywhere else in the Empire.
There is evidence of the settlement of immigrants from the east in Rome. Other languages, such as Aramaic and Hebrew, were also used. Additionally, birthplaces recorded in burial inscriptions indicate that immigrants were commonly from the east. Temples and shrines to Greek, Phrygian, Syrian, and Egyptian gods were also common. The first known synagogue in Europe was established in the port town of Ostia.
There is evidence of connections between Rome and the west. For example, slaves were brought back to Rome from these regions following imperial expansions, such as Scipio Africanus's victory over Carthage and Julius Caesar's conquest of Gaul. Rome also received large amounts of trade goods from the western Mediterranean, such as wine, garum, and olive oil from Gaul and Iberia. Unexpectedly, few Latin individuals have strong genetic affinities to western Mediterranean populations. One possible explanation for the predominance of gene flow from the east into Rome is the higher population density in the east than the west. Historians have suggested that the large population size and the presence of megacities, such as Athens, Antioch, and Alexandria, may have driven a net flow of people from east to west during antiquity. In addition to direct immigration, eastern ancestry may have arrive in Rome indirectly from Greek, Phoenician, and Punic diasporas that were established through colonies across the Mediterranean prior to Roman Imperial expansion.
People with diverse ancestries have been living in Rome for a long time, with many of them going to the port city of Portus Romae. The people of Portus Romae are buried in the necropolis of Isola Sacra and the individuals there fall in both the Near Eastern and European clusters. They have δ 18 O isotope ratios compatible with the area where they grew up.

Late Antiquity and the fall of Rome

The Western Roman Empire collapsed and split into Eastern and Western halves as a result of deep demographic changes and political reorganisations.
The Late Antique individuals - 24 in total - are descended from the Near East and can be modeled as two-way mixtures of the Imperial Roman individuals and 38 to 41% ancestry from a late Imperial period individual from Bavaria or modern Basque. The data also reflects this shift in ancestry in ChromoPainter, with a strong shift away from the Near East (C4) and the expansion of the European cluster (C7).
The eastern Mediterranean was no longer part of the trade, grain supply, and governance network that flowed to and from Rome, resulting in a weakened genetic affinity to that region. The relocation of the Roman capital and the split of the Roman Empire also disrupted many of the networks of trade, grain supply, and governance that had previously flowed through Rome to the east. Additionally, large-scale movements of people from central Europe into Italy began during the 5th and 6th centuries CE and the long-term settlement of the Lombards in the region beginning in the 6th and 7th centuries CE. Furthermore, the decline in Rome's population meant that even moderate amounts of immigration could have driven a significant change in the average ancestry of the people in the region.
The big genetic diversity that was once seen in Imperial Rome is still present in Late Antiquity. Three outliers are genetically distinct from the rest of the population. Those individuals include R104, who is genetically like Sardinians, and R106 and R31, who overlap with modern Europeans in PCA. They have a number of sources for this diversity, including trade, migration, slavery, and conquest, as well as trade networks in the western Mediterranean and the movement of Visigoths, Vandals, and Lombards into Italy.
One of the sites, Crypta Balbi, had individuals classified into the European cluster (C7). They were differentiated from the preceding Roman Imperial population and individuals from the Lombard-associated cemeteries in Collegno and Hungary. This site may have been an extension of Lombard settlement into Rome.

The Medieval period and increasing ties to Europe

The Medieval and early modern population of Europe is organized into two clusters in the PCA analysis and a third cluster in the ChromoPainter analysis. The Medieval population is roughly centered on central Italians. It can be modeled as a combination of the late Antique population of Rome and a European population. This shift is consistent with the growing political ties between Medieval Rome and mainland Europe. The Normans expanded into a number of regions, including Sicily and the southern portion of the Italian Peninsula, where they established the Kingdom of Sicily. PCA (a to f) identifies samples as outliers and compares them to the current population. The map (right) illustrates the territorial expanse of the political body encompassing Rome at the date specified at the bottom. No model provides an adequate fit for the Imperial Roman population (C). The PCA identifies individuals as outliers and labels them with their sample IDs (table S27). The PCA identifies present-day populations and labels them with gray points.

**Jovialis** · 31-03-23, 12:38

" The genetic origins of the British people of the late bronze age"

The genetic origins of the British people of the late bronze age have been revealed by a study of DNA from 793 individuals. For present-day variation, PC1 and PC2 broadly reflect geography, forming a V-shaped pattern from Scandinavians via individuals from northern Germany and the Netherlands towards those from Britain and Ireland. However, most of the early medieval samples from England plot together with the ancient individuals from the continental North Sea area along with the present-day CNEs. The divergence between prehistoric and early medieval individuals from England is also seen in the distribution of genetic distances (F ST ) as well as shared alleles (F 4 ) on both the population (Extended Data Fig. 1) and the individual scale (Supplementary Fig. 3.3). This demonstrates that there was a large scale migration into Britain during this time period, which was unanticipated in earlier debates about Beaker origins.
References

Patterson et al. 2021
Section 1, paragraphs [1], [2]
2021_Armit_Reich_Beaker_Antiquity.pdf
Section 2, paragraph [1]
Section 3, paragraph [1]
Section 5, paragraph [7]
The Anglo-Saxon migration and formation of the early English gene pool
Section 1, paragraph [5]
Section 3, paragraphs [1], [3]
Section 12, paragraph [2]
Fowler et al. 2021
Section 7, paragraph [1]

^^I had asked it to write a summary paragraph based on the title I had written. It also provided the referenced based on the studies I put in the dedicated folder I made for ancient British DNA.

**Jovialis** · 31-03-23, 14:45

" The genetic origins of the British people of the neolithic"

The genetic origins of the British people of the neolithic can be traced back to Iberian and Central European Neolithic-related populations. Most of the ancestry in British Neolithic individuals can be attributed to ANFs, indicating a substantial shift in ancestry with the transition to farming. This genetic similarity among British and European Mesolithic individuals spans a period in Britain (circa 8500-4000 bc) that includes the cultural transition to the Late Mesolithic and the separation of Britain from continental Europe. Our analyses indicate that the appearance of Neolithic practices and domesticates in Britain circa 4000 bc was mediated overwhelmingly by immigration of farmers from continental Europe, and strongly reject the hypothesized adoption of farming by indigenous hunter-gatherers as the main process.
References

Brace et al. 2019
Section 2, paragraphs [2], [3], [4], [5]
Section 3, paragraphs [1], [2], [3]
2021_Armit_Reich_Beaker_Antiquity.pdf
Section 3, paragraph [1]
Fowler et al. 2021
Section 7, paragraph [1]
Patterson et al. 2021
Section 16, paragraph [2]

**Jovialis** · 31-03-23, 15:00

If you ask it to summarize the title of a study, it will summarize the whole paper:

Summary: "An integrative skeletal and paleogenomic analysis of stature variation suggests relatively reduced health for early European farmers"

The study found that the average Neolithic European was relatively short, even after accounting for their individual genetic contributions to adult stature. This may reflect reduced nutrition and/or increased infectious disease burden. The study also preliminarily developed a framework for further consideration of these results in the context of particular paleopathological indicators of childhood stress.

**Jovialis** · 31-03-23, 15:29

write a summary paragraph titled "Neolithic Greece"

The Neolithic period in Greece saw the arrival of new farming techniques and a change in culture, which may have been influenced by people from the east. The genetic makeup of the people of Greece today is largely a result of this period of change.
References

Lazaridis et al. 2017
Section 1, paragraphs [1], [6], [7]
Section 2, paragraphs [1], [2]
Lazaridis et al. 2022
Section 2, paragraph [1]
Section 3, paragraph [15]
Clemente et al. 2021.pdf
Section 2, paragraph [5]
Section 6, paragraph [4]
Section 8, paragraph [3]

**Jovialis** · 31-03-23, 15:38

Wow!

I didn't even know that. I thought it may have been an error, but I checked and it was accurate!

**Salento** · 03-04-23, 06:28

After the produced summaries, I'd ask AI to join some of them and to formulate his own hypothesis, if it has any :)

**Jovialis** · 07-04-23, 22:36

Originally Posted by Salento

After the produced summaries, I'd ask AI to join some of them and to formulate his own hypothesis, if it has any :)

I've actually decided to ditch this software because I've discovered that Chat GPT plus now has plug-ins that will allow for infinitely more possibilities.

Also I found that the genei software wasn't offering much to justify the price tag.

I was also very put off that I wasn't even able t o do basic summarization without credit, nor any other feature! Despite the fact the software explicitly says it only applies to advanced summarization.

Honestly I cannot see any redeeming factor as to why I should use this software, instead of chat gpt plus.

If this was 2021, I'd say it's worth it. But now Open AI has ruthlessly undercut these lesser platforms.

**Jovialis** · 07-04-23, 22:40

Originally Posted by Jovialis

I've actually decided to ditch this software because I've discovered that Chat GPT plus now has plug-ins that will allow for infinitely more possibilities.
Also I found that the genei software wasn't offering much to justify the price tag.
I was also very put off that I wasn't even able t o do basic summarization without credit, nor any other feature! Despite the fact the software explicitly says it only applies to advanced summarization.
Honestly I cannot see any redeeming factor as to why I should use this software, instead of chat gpt plus.
If this was 2021, I'd say it's worth it. But now Open AI has ruthlessly undercut these lesser platforms.

Just as an aside after spending sometime with AI software, some of the harsh criticism I've seen, even from bloggers I like, like Razib Khan, seems overstated.

It sort of reminds me of how people who don't understand how genetic testing works, get disappointed when they learn different companies give different results.

**Jovialis** · 07-04-23, 23:38

The plugin I have my eyes on specifically is Wolfram.

https://www.wolfram.com/wolfram-u/co...chatgpt-wsg39/

This will let Chatgpt do what Genei does, plus a ton more, like data visualization. So I could tell it to produce graphics based on data as well.

**Jovialis** · 17-04-23, 15:50

Originally Posted by Jovialis

The best way I could describe using this software is comparable to a PCA, in that using too much information breaks it (Creates errors, and inaccuracies). However, if you are looking at specific studies that are all on a similar topic, you are likely to get extremely informative and accurate results.

For example, if you dedicate a folder to have just studies on a particular topic like Ancient Greeks. It will be a very useful tool

But if you just search all studies at once, you are bound to get some issues in terms of accuracy.

Just like the way you can't expect to project every single DNA sample on a single PCA; you can't expect to get a highly accurate AI for using all aDNA studies. The topic, although niche within and of itself, is far too broad. Thus, AI at least at this point, needs some assistance from people familiar in the topic. Thus if I could arrange studies that match each other, I could start asking it very particular questions about aDNA.

I spoke with a friend of mine who I haven't seen in a long time, who has a career working in AI. He said that the PCA analogy is accurate for how AI works.

**Hawk** · 17-04-23, 20:51

As Steve Jobs said for computers, they are like bicycle of the mind, i can say that to me specifically GPT LLM Artificial Intelligence tools look like they are bicycle of the mind. Man, they are like sorcery. Crazy stuff. I have been using for debugging, creating, testing code, it's just unbelievable how it knows the context.

No doubt, revolutionary tool. A lot are scared, mainly due to being afraid they will lose their jobs, and they will become redundant, but let's see, i guess if old jobs die new jobs shall arise, if there is no jobs there is no one spending money around, that means no business.

**Jovialis** · 17-04-23, 21:20

I definitely want to embrace it as possible. I'm as thinking about learning python.

**Hawk** · 17-04-23, 21:58

It's a good bet. Personally i never liked Python, mostly because of syntax, but it could just be me. I want to give it a shot. My first preference was Rust but i'll think Python is a good bet.

**Jovialis** · 18-04-23, 12:33

Originally Posted by Hawk

It's a good bet. Personally i never liked Python, mostly because of syntax, but it could just be me. I want to give it a shot. My first preference was Rust but i'll think Python is a good bet.

I've been told by people in the software field that python is particularly good for dealing with big amounts of data.

**Hawk** · 18-04-23, 13:20

Originally Posted by Jovialis

I've been told by people in the software field that python is particularly good for dealing with big amounts of data.

Perhaps i am not the right person to judge, because i have never used Python, but i really doubt Python is better at dealing with big amounts of data in comparison with lower-level languages like C++, Rust, Golang (on terms of time and space performance). I am 100% convinced on this. The difference is that Python is a higher-level language and has a lot of available ready made modules/scripts which help achieve the goals. And when put into a tradeoff of how much computing/memory power it consumes, i guess it's more reasonable to go with Python due to sheer availability of modules and scripts and not so much difference on terms of computing power due to recent spike off of having more and more powerful computers and having more memory available which one need not to worry about and hence why using a dynamic and scripting language like Python which achieves the same goal with less code is more convenient than using a compiled and more strictly typed languages like C++ or Java which development might be slower than a dynamic typing language like Python.

I will definitely delve into it. I am usually into JavaScript, but expanding into Python as well might be a very good bet. At the end of the day all programming languages share the same paradigms and very similar syntax, they differ on their approach. Hence mastering the core computing skills like algorithms, data structures, problem-solving skills, some skills in discrete math will be crucial. AI and Machine learning has Statistics and Probability as crucial part of their curriculum.

**Jovialis** · 18-04-23, 15:43

Originally Posted by Hawk

Perhaps i am not the right person to judge, because i have never used Python, but i really doubt Python is better at dealing with big amounts of data in comparison with lower-level languages like C++, Rust, Golang (on terms of time and space performance). I am 100% convinced on this. The difference is that Python is a higher-level language and has a lot of available ready made modules/scripts which help achieve the goals. And when put into a tradeoff of how much computing/memory power it consumes, i guess it's more reasonable to go with Python due to sheer availability of modules and scripts and not so much difference on terms of computing power due to recent spike off of having more and more powerful computers and having more memory available which one need not to worry about and hence why using a dynamic and scripting language like Python which achieves the same goal with less code is more convenient than using a compiled and more strictly typed languages like C++ or Java which development might be slower than a dynamic typing language like Python.

I will definitely delve into it. I am usually into JavaScript, but expanding into Python as well might be a very good bet. At the end of the day all programming languages share the same paradigms and very similar syntax, they differ on their approach. Hence mastering the core computing skills like algorithms, data structures, problem-solving skills, some skills in discrete math will be crucial. AI and Machine learning has Statistics and Probability as crucial part of their curriculum.

Thanks for the insights, I figure if I tinker with it, and look at some tutorials on YouTube; I'll eventually get the hang of it.

Nevertheless, soon, there will be AI plugins that will help people write code more perfectly. But understanding how it works from a coding language perspective would allow for better understanding of how to prompt the AI to do what you want.

My friend that works in tech, downloaded Visual Studio Code on my PC for me to play around with so I could learn.

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