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Geographical map of the populations included in this study. The crude borders of the Khazar Khaganate at three stages of its expansion, along with its capital at Atil, are shown. The Khazar Khaganate (~650-1,000 CE), one of the largest states of medieval Eurasia, extended from the Volga region
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The origin and history of the Ashkenazi Jewish population have long been of great interest, and advances in high-throughput genetic analysis have recently provided a new approach for investigating these topics. We and others have argued on the basis of genome-wide data that the Ashkenazi Jewish population derives its ancestry from a combination of...
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... work has been the rst to assemble extensive genome-wide data from all three regions that have been proposed as ancestral sources for the Ashkenazi Jewish population (Figure 1). The collection of samples from contemporary European, Middle Eastern, and Jewish populations is straightforward, as multiple forms of documentation, including the cultural identities of the populations themselves, link the modern populations to ancestral groups living at the time of the early history of the Ashkenazi Jews. By contrast, obtaining samples representing Khazars, for whom no direct link to extant populations has been established, mandates careful consideration. Recognizing this problem, we proceeded by including as many samples as possible from a region encompassing the geographic range believed to correspond to the Khazar Khaganate. After assembly of the data set, we focused our analysis on the geographic origin of the Ashkenazi Jewish population, employing a variety of analyses of population-genetic structure. Whereas PCA is an unsupervised approach for placing samples in a low- dimensional space, treating all populations as having unknown coordinates a priori, Loco-LD represents a supervised approach in which Jewish populations are placed in a spatial diagram in relation to non-Jewish samples whose geographic locations are treated as known ( Figure 2B). Loco-LD conrms and sharpens the lack of evidence for the Khazar hypothesis observed in PCA, placing the Ashkenazi Jewish sample in close proximity to Italian Jews, North African Jews, Sephardi Jews, and Mediterranean non-Jewish populations such as Cypriots and Italians. Of the three Khazar subsets, the two northern groups are again distant from the Ashkenazi Jews. Among the four South Caucasus populations, the Armenian and Azeri populations in particular lie closer to non-Jewish Middle Eastern populations, including Druze, Iranians, Kurds, and Lebanese, than to Ashkenazi Jews. Strikingly, the Ashkenazi Jewish population shows no overlap even with the South Caucasus groups, and moreover, it is apparent that the South Caucasus Armenian population is genetically closer to Middle Eastern Jewish populations than to Ashkenazi ...
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The current standard model for identifying carriers of high-risk mutations in cancer-susceptibility genes (CSGs) generally involves a process that is not amenable to population-based testing: access to genetic tests is typically regulated by health-care providers on the basis of a labour-intensive assessment of an individual's personal and family h...
Citations
... with training sample size N (ref.68 ), at the UKBB-level sample size (for example, N = 371,018 for our UKBB WB training data), the eigenvalues for the top PCs are usually larger than the ratio of environmental noise variance and genetic variance ...
Polygenic scores (PGSs) have limited portability across different groupings of individuals (for example, by genetic ancestries and/or social determinants of health), preventing their equitable use1–3. PGS portability has typically been assessed using a single aggregate population-level statistic (for example, R²)⁴, ignoring inter-individual variation within the population. Here, using a large and diverse Los Angeles biobank⁵ (ATLAS, n = 36,778) along with the UK Biobank⁶ (UKBB, n = 487,409), we show that PGS accuracy decreases individual-to-individual along the continuum of genetic ancestries⁷ in all considered populations, even within traditionally labelled ‘homogeneous’ genetic ancestries. The decreasing trend is well captured by a continuous measure of genetic distance (GD) from the PGS training data: Pearson correlation of −0.95 between GD and PGS accuracy averaged across 84 traits. When applying PGS models trained on individuals labelled as white British in the UKBB to individuals with European ancestries in ATLAS, individuals in the furthest GD decile have 14% lower accuracy relative to the closest decile; notably, the closest GD decile of individuals with Hispanic Latino American ancestries show similar PGS performance to the furthest GD decile of individuals with European ancestries. GD is significantly correlated with PGS estimates themselves for 82 of 84 traits, further emphasizing the importance of incorporating the continuum of genetic ancestries in PGS interpretation. Our results highlight the need to move away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGSs.
... [5] However, evidence shows that the AJ population are genetically distinct and have been linked to Irano-Turko-Slavic origins with the strongest geo-localisation linking Ashkenazi people to Turkey and into the Middle East. [6] It is less clear whether Ashkenazi also originate from the Caucasus region. [6][7][8][9] Using analysis of mitochondrial DNA, Behar et al were able to identify four major founder clusters, representing 40% of Ashkenazi mtDNA variation, that had ancestry rooted in prehistoric Europe, [10] with the remaining 60% of Ashkenazi lineages unattributable to European origins. ...
... [6] It is less clear whether Ashkenazi also originate from the Caucasus region. [6][7][8][9] Using analysis of mitochondrial DNA, Behar et al were able to identify four major founder clusters, representing 40% of Ashkenazi mtDNA variation, that had ancestry rooted in prehistoric Europe, [10] with the remaining 60% of Ashkenazi lineages unattributable to European origins. The majority influence, however, is from the Middle East and Turkey. ...
Purpose:
Polygenic risk scores (PRS) are a major component of accurate breast cancer (BC) risk prediction but require ethnicity-specific calibration. Ashkenazi-Jews-(AJ) are assumed to be of White-European-(WE) origin in some commercially-available PRS despite differing effect-allele-frequencies-(EAFs). We conducted a case-control study of WE and AJ women from the PROCAS-(Predicting-Risk-of-Cancer-at-Screening) study. The BCINIS-(Breast-Cancer-in-Northern-Israel-study) provided a separate AJ population-based case-control validation series.
Methods:
All women underwent Illumina Oncoarray SNP-analysis. Two PRS were assessed, SNP142&SNP78. 221/2243 WE (Discovery:cases=111;controls=110;Validation:cases=651;controls=1772) and 221 AJ (cases=121;controls=110) women were included from the UK study; the Israeli series consisted of 2045 AJ women (cases=1331;controls=714). EAFs were obtained from gnomAD.
Results:
In the UK study the mean SNP142PRS demonstrated good calibration and discrimination in WEs: mean PRS in cases=1.33-(95%CI=1.18-1.48) and controls=1.01-(95%CI=0.89-1.13). In AJs from Manchester, the mean PRS in cases=1.54-(1.38-1.70) and controls=1.20-(1.08-1.32) demonstrated good discrimination but overestimation of BC relative-risk. After adjusting for AJ EAFs, mean risk was corrected (mean SNP142-PRS cases=1.30-(95%CI=1.16-1.44) and controls=1.02-(95%CI=0.92-1.12)). This was recapitulated in the larger Israeli dataset with good discrimination (AUC=0.632-(95%CI=0.607-0.657) for SNP142).
Conclusion:
AJ women should not be given BC relative-risk predictions based on PRS calibrated to EAFs from WEs. PRS need to be recalibrated using AJ-derived-EAFs. A simple recalibration using the mean PRS adjustment ratio likely performs well.
... We used the 1000 Genomes Project (1KGP) and Human Genome Diversity Project (HGDP) datasets which are openly available from the respected sources [9,[16][17][18]. We also considered population data from datasets of the open genome-wide marker data repository which can be found on the server of the Estonian Biocentre [19,20]. We used also the Allen Ancient DNA Resource (AADR) dataset which is openly available from the David Reich lab on the Harvard University [21]. ...
Background
German-derived ethnicities are one of the largest ethnic groups in Hungary, dating back to the formation of the Kingdom of Hungary, which took place at the beginning of the 11th century. Germans arrived in Hungary in many waves. The most significant immigration wave took place following the collapse of the Ottoman Empire in East-Central Europe which closed the 150 year long Ottoman occupation. To date, there are no comprehensive genome-wide studies investigating the genetic makeup of the Danube Swabians. Here we analyzed 47 Danube Swabian samples collected from elderly Swabian individuals living in the Dunaszekcső-Bár area, in Danube side villages of Southwest Hungary. These Swabians, according to self-declaration, did not admix with other ethnic groups for 3–6 succeeding generations. Using Illumina Infinium 720 K Beadchip genotype data, we applied allele frequency-based and haplotype-based genome-wide marker data analyses to investigate the ancestry and genetic composition of the collected Danube Swabian samples.
Results
Haplotype-based analyses like identity by descent segment analysis show that the investigated Danube Swabians possess significant German and other West European ancestry, but their Hungarian ancestry is also prominent. Our results suggest that their main source of ancestry can be traced back to Western Europe, presumably to the region of Germany.
Conclusion
This is the first analysis of Danube Swabian population samples based on genome-wide autosomal data. Our results establish the basis for conducting further comprehensive research on Danube Swabians and on other German ethnicities of the Carpathian basin, which can help reconstruct their origin, and identify their major archaic genomic patterns.
... Genetic evidence supports a mixed Middle Eastern (ME) and European (EU) ancestry in AJ. This is based on uniparental markers with origins in either region (Behar et al., 2006(Behar et al., , 2017Costa et al., 2013;Hammer et al., 2000Hammer et al., , 2009Nebel et al., 2001), as well as autosomal studies showing that AJ have ancestry intermediate between ME and EU populations (Atzmon et al., 2010;Behar et al., 2010Behar et al., , 2013Bray et al., 2010;Carmi et al., 2014a;Granot-Hershkovitz et al., 2018;Guha et al., 2012;Kopelman et al., 2020). These and other autosomal studies also showed that individuals with AJ ancestry are genetically distinguishable from those of other ancestries. ...
... However, there are subtle average differences in ancestry between AJ with origins in Eastern vs. Western Europe (Behar et al., 2013;Gladstein and Hammer, 2019;Granot-Hershkovitz et al., 2018) (Data S1, section 1). ...
We report genome-wide data from 33 Ashkenazi Jews (AJ), dated to the 14th century, obtained following a salvage excavation at the medieval Jewish cemetery of Erfurt, Germany. The Erfurt individuals are genetically similar to modern AJ, but they show more variability in Eastern European-related ancestry than modern AJ. A third of the Erfurt individuals carried a mitochondrial lineage common in modern AJ and eight carried pathogenic variants known to affect AJ today. These observations, together with high levels of runs of homozygosity, suggest that the Erfurt community had already experienced the major reduction in size that affected modern AJ. The Erfurt bottleneck was more severe, implying substructure in medieval AJ. Overall, our results suggest that the AJ founder event and the acquisition of the main sources of ancestry pre-dated the 14th century and highlight late medieval genetic heterogeneity no longer present in modern AJ.
... 4 It is therefore ironic that when other scholars have tried to counter his claims by citing the results of DNA analyses that show Ashkenazi Jews as possessing a significant degree of Near Eastern genetic ancestry (cf. Behar et al. 2010;Behar et al. 2013), Sand has bluntly rejected the validity of these data by remarking that "Hitler would certainly have been very pleased" to see descendants of Holocaust survivors searching for a biological Jewish identity (Sand 2010: 319). Yet, it is Sand himself who has made ethnicity contingent on an unbroken blood line to ancestors of the remote past, a claim that can indeed be tested by DNA analysis, albeit such tests cannot tell us anything about the ethnicity of the peoples of the past to whom the person in question is genetically related. ...
... We assembled a dataset comprehensive of 2,662 present-day individuals from the Mediterranean basin, Europe and Africa genotyped with different Illumina SNP arrays [39,48,61,[64][65][66][67][68][69][70][71][72] (Table S4). ...
Southern Italy was characterised by a complex prehistory that started with different Palaeolithic cultures, later followed by the Neolithization and the demic dispersal from the Pontic-Caspian Steppe during the Bronze Age. Archaeological and historical evidences point to a link between Southern Italians and the Balkans still present in modern times. To shed light on these dynamics, we analysed around 700 South Mediterranean genomes combined with informative ancient DNAs. Our findings revealed high affinities of South-Eastern Italians with modern Eastern Peloponnesians, and a closer affinity of ancient Greek genomes with those from specific regions of South Italy than modern Greek genomes. The higher similarity could be associated with a Bronze Age component ultimately originating from the Caucasus with high Iranian and Anatolian Neolithic ancestries. Furthermore, extremely differentiated allele frequencies among Northern and Southern Italy revealed putatively adapted SNPs in genes involved in alcohol metabolism, nevi features and immunological traits.
... The first Jewish community of Erfurt (pre-1349) was the oldest in Thuringia, and its cemetery also served nearby towns [39,40]. During the 1349 pogrom, most Jews of Erfurt and nearby communities were murdered or expelled [37,41,42]. ...
... We used whole-genome sequencing data for = 637 individuals [19,59]. The inferred bottleneck parameters were = 1563 (diploid individuals; 95% confidence interval (CI): [1364-1751]), = 41 (generations; 95% CI: [39][40][41][42][43]), and = 20 (generations; 95% CI: [15][16][17][18][19][20][21][22][23][24]; see Figure 3A and Table S10, model (A)). [See Table S10, model (B) for the inferred parameters when fixing = 1, as in previous studies [19,23].] ...
... For the analyses of MAJ of Eastern European vs Western European origin, we merged the EAJ genomes with those from Behar et al. (2013) [40]. The merged dataset included 245,792 autosomal SNPs. ...
We report genome-wide data for 33 Ashkenazi Jews (AJ), dated to the 14 th century, following a salvage excavation at the medieval Jewish cemetery of Erfurt, Germany. The Erfurt individuals are genetically similar to modern AJ and have substantial Southern European ancestry, but they show more variability in Eastern European-related ancestry than modern AJ. A third of the Erfurt individuals carried the same nearly-AJ-specific mitochondrial haplogroup and eight carried pathogenic variants known to affect AJ today. These observations, together with high levels of runs of homozygosity, suggest that the Erfurt community had already experienced the major reduction in size that affected modern AJ. However, the Erfurt bottleneck was more severe, implying substructure in medieval AJ. Together, our results suggest that the AJ founder event and the acquisition of the main sources of ancestry pre-dated the 14 th century and highlight late medieval genetic heterogeneity no longer present in modern AJ.
... To conduct the PC and ADMIXTURE analyses, we combined the newly generated genotypes with the data from previous studies (Li et al. 2008;Behar et al. 2010Behar et al. , 2013Rasmussen et al. 2010;Metspalu et al. 2011;Yunusbayev et al. 2012;Fedorova et al. 2013;Raghavan et al. 2013). Individuals with missing genotypes greater than 1.5% were excluded from the combined dataset. ...
... To confirm the ethnic identity of the patients, we compared their genetic components with different populations of the world using a PC analysis (Patterson et al. 2006) and performed a global ancestry inference with ADMIXTURE (Alexander et al. 2009). For the PC and ADMIXTURE analyses, we combined the genotypes of the patients with the data from previous studies (Li et al. 2008;Behar et al. 2010Behar et al. , 2013Rasmussen et al. 2010;Metspalu et al. 2011;Yunusbayev et al. 2012;Fedorova et al. 2013;Raghavan et al. 2014). Figure 1 shows that all individuals homozygous for c.-23 + 1G > A were clustered according to their ethnic identity and geographic origin (Fig. 1b). ...
Mutations in the GJB2 gene are known to be a major cause of autosomal recessive deafness 1A (OMIM 220290). The most common pathogenic variants of the GJB2 gene have a high ethno-geographic specificity in their distribution, being attributed to a founder effect related to the Neolithic migration routes of Homo sapiens. The c.-23 + 1G > A splice site variant is frequently found among deaf patients of both Caucasian and Asian origins. It is currently unknown whether the spread of this mutation across Eurasia is a result of the founder effect or if it could have multiple local centers of origin. To determine the origin of c.-23 + 1G > A, we reconstructed haplotypes by genotyping SNPs on an Illumina OmniExpress 730 K platform of 23 deaf individuals homozygous for this variant from different populations of Eurasia. The analyses revealed the presence of common regions of homozygosity in different individual genomes in the sample. These data support the hypothesis of the common founder effect in the distribution of the c.-23 + 1G > A variant of the GJB2 gene. Based on the published data on the c.-23 + 1G > A prevalence among 16,177 deaf people and the calculation of the TMRCA of the modified f2-haplotypes carrying this variant, we reconstructed the potential migration routes of the carriers of this mutation around the world. This analysis indicates that the c.-23 + 1G > A variant in the GJB2 gene may have originated approximately 6000 years ago in the territory of the Caucasus or the Middle East then spread throughout Europe, South and Central Asia and other regions of the world.
... Even though using Behar's datasets, Elhaik (2013) concludes that his findings support the Khazar hypothesis. This led to a further research study by Behar et al. (2013), who published a rejection of Elhaik's conclusions. As representatives of the Khazars, Elhaik (2013) uses the samples of the contemporary populations from the central and southern Caucasus. ...
... As representatives of the Khazars, Elhaik (2013) uses the samples of the contemporary populations from the central and southern Caucasus. Behar et al. (2013) added samples from the north of the Caucasus to their data. This is based on historical and archaeological findings by Khazar expert Peter Golden and others that support a Khazar presence in the northern Caucasus (Behar et al. 2013;Golden 2007). ...
... Behar et al. (2013) added samples from the north of the Caucasus to their data. This is based on historical and archaeological findings by Khazar expert Peter Golden and others that support a Khazar presence in the northern Caucasus (Behar et al. 2013;Golden 2007). ...
Scientific studies on the genetic proximity of Jews undertake to shed light on "who or what Jews really are". However, various scientists and scholars have warned that such studies reify racial thinking. This essay delineates and contextualizes the debate held between various geneticists and social scientists on the danger of reification within the Jewish context. This is mainly a debate about the impact of (traditional, religious, and Zionist) narratives on scientific research as well as on the ethical responsibility of scientists. The paper claims that such genetic studies test Jewish religious narratives against genetic research results and do not necessarily enforce old notions of distinctiveness.
... Genome-wide autosomal marker data from other open genotype databases was also considered and applied in the study. We used the Human Genome Diversity Project (HGDP) dataset openly available from the server of Stanford University and also applied datasets from the open genomewide marker data repository of the Estonian Biocentre [7][8][9][10]. Mainly populations from the European, Caucasus and South Asian regions were applied from the HGDP dataset, however, for outgroup purposes we also considered the Uyghurs, Han Chinese, and Yoruba. ...
Background: German-derived ethnicities are one of the largest ethnic groups in Hungary, dating back to the formation of the Kingdom of Hungary, which took place at the beginning of the 11th century. Germans came to Hungary in many waves. The most significant immigration wave took place following the collapse of the Ottoman Empire in East-Central Europe which closed the 150-year long Ottoman occupation. To date, there are no comprehensive genome-wide studies investigating the genetic makeup of the Danube Swabians. Here we analyzed 47 Danube Swabian samples collected from elderly Danube Swabian individuals living in the Dunaszekcső-Bár area, Danube side villages in Southwest Hungary. Based on self-declaration they did not admix with other ethnic groups for 3-6 succeeding generations. Using Illumina Infinium 720K Beadchip genotype data, we applied allele frequency-based and haplotype-based genome-wide marker data analyses to investigate the ancestry and genetic composition of the collected Danube Swabian samples.
Results: Haplotype-based analyses like identity by descent and homozygosity by descent analyses show that the investigated Danube Swabians remained isolated from other ethnic groups which was supported by the D-statistics results. According to our results, the investigated Danube Swabian individuals do not possess detectable recent admixture, and our results suggest that their main source of ancestry can be traced back to the region of Germany.
Conclusions: This is the first analysis of Danube Swabian population samples based on genome-wide autosomal data. Our results establish the basis for conducting further comprehensive research on Danube Swabians and on other German ethnicities of the Carpathian basin, which can help reconstruct their origin, and identify their major archaic genomic patterns.
