Genetic history of Cambridgeshire before and after the Black Death

Hui, Ruoyun; Scheib, Christiana L.; D’Atanasio, Eugenia; Inskip, Sarah A.; Cessford, Craig; Biagini, Simone A.; Wohns, Anthony W.; Ali, Muhammad Q.A.; Griffith, Samuel J.; Solnik, Anu; Niinemäe, Helja; Ge, Xiangyu Jack; Rose, Alice K.; Beneker, Owyn; O’Connell, Tamsin C.; Robb, John E.; Kivisild, Toomas

doi:10.1126/sciadv.adi5903

Cited by 11 publications

(3 citation statements)

References 150 publications

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“…Increasing sample sizes in ancient DNA studies have motivated a number of studies aiming to detect selection in genome-wide scans or to investigate phenotypes in ancient populations (e.g. Mathieson et al, 2015; Cox et al, 2022; Klunk et al, 2022; Gopalakrishnan et al, 2022; Mathieson and Terhorst, 2022; Davy et al, 2023; Barton et al, 2023; Hui et al, 2024). Such investigations are potentially very sensitive to biases and uncertainties in genotype calls or allele frequencies at individual sites while certain effects will average out for genome-wide estimates such as ancestry proportions.…”

Section: Discussionmentioning

confidence: 99%

Estimating allele frequencies, ancestry proportions and genotype likelihoods in the presence of mapping bias

Günther,

Schraiber

2024

Preprint

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Population genomic analyses rely on an accurate and unbiased characterization of the genetic setup of the studied population. For short-read, high-throughput sequencing data, mapping sequencing reads to a linear reference genome can bias population genetic inference due to mismatches in reads carrying non-reference alleles. In this study, we investigate the impact of mapping bias on allele frequency estimates from pseudohaploid data, commonly used in ultra-low coverage ancient DNA sequencing. To mitigate mapping bias, we propose an empirical adjustment to genotype likelihoods. Simulating ancient DNA data with realistic post-mortem damage, we compare widely used methods for estimating ancestry proportions under different scenarios, including reference genome selection, population divergence, and sequencing depth. Our findings reveal that mapping bias can lead to differences in estimated admixture proportion of up to 4% depending on the reference population. However, the choice of method has a much stronger impact, with some methods showing differences of 10%. qpAdm appears to perform best at estimating simulated ancestry proportions, but it is sensitive to mapping bias and its applicability may vary across species due to its requirement for additional populations beyond the sources and target population. Our adjusted genotype likelihood approach largely mitigates the effect of mapping bias on genome-wide ancestry estimates from genotype likelihood-based tools. However, it cannot account for the bias introduced by the method itself or the noise in individual site allele frequency estimates due to low sequencing depth. Overall, our study provides valuable insights for obtaining precise estimates of allele frequencies and ancestry proportions in empirical studies.

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Section: Discussionmentioning

confidence: 99%

Estimating allele frequencies, ancestry proportions and genotype likelihoods in the presence of mapping bias

Günther,

Schraiber

2024

Preprint

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“…Furthermore, the 1000 Genomes Project [40] dataset that was used for testing only included a very limited number of third-degree relatives. Nevertheless, other researchers have modified READv1 to classify up to third-degree relatives [25,41], suggesting that the READ approach might be able to perform such classifications in certain situations. For Figure 2, we also tested the ability of READv2 to classify third-degree relatives.…”

Section: Figurementioning

confidence: 99%

“…The analysis of biological relatedness has become an established part of the archaeogenomic toolkit [1,2]. It has provided us with important insights into the social structures of prehistoric groups [3–25], including Neandertals [26]. Furthermore, it serves as a quality control (QC) step in many bioinformatic pipelines to identify sample duplicates or exclude close relatives from population genomic analyses.…”

Section: Introductionmentioning

confidence: 99%

READv2: Advanced and user-friendly detection of biological relatedness in archaeogenomics

Alaçamlı,

Naidoo,

Aktürk

et al. 2024

Preprint

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The possibility to obtain genome-wide ancient DNA data from multiple individuals has facilitated an unprecedented perspective into prehistoric societies. Studying biological relatedness in these groups requires tailored approaches for analyzing ancient DNA due to its low coverage, post-mortem damage, and potential ascertainment bias. Here we present READv2 (Relatedness Estimation from Ancient DNA version 2), an improved Python 3 re-implementation of the most widely used tool for this purpose. While providing increased portability and making the software future-proof, we are also able to show that READv2 (a) is orders of magnitude faster than its predecessor; (b) has increased power to detect pairs of relatives using optimized default parameters; and, when the number of overlapping SNPs is sufficient, (c) can differentiate between full-siblings and parent-offspring, and (d) can classify pairs of third-degree relatedness. We further use READv2 to analyze a large empirical dataset that has previously needed two separate tools to reconstruct complex pedigrees. We show that READv2 yields results and precision similar to the combined approach but is faster and simpler to run. READv2 will become a valuable part of the archaeogenomic toolkit in providing an efficient and user-friendly classification of biological relatedness from pseudohaploid ancient DNA data.

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Health inequality in medieval Cambridge, 1200–1500 CE

Dittmar,

Inskip,

Rose

et al. 2024

American Journal of Biological Anthropology

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Health inequality is not only a major problem today; it left its mark upon past societies too. For much of the past, health inequality has been poorly studied, mostly because bioarchaeologists have concentrated upon single sites rather than a broader social landscape. This article compares 476 adults in multiple locations of medieval Cambridge (UK). Samples include ordinary townspeople (All Saints), people living in a charitable institution (the Hospital of St. John), and members of a religious order (the Augustinian Friary). These groups shared many conditions of life, such as a similar range of diseases, risk of injury, and vertebral disk degeneration. However, people living on charity had more indicators of poor childhood health and diet, lower adult stature, and a younger age at death, reflecting the health effects of poverty. In contrast, the Augustinian friars were members of a prosperous, well‐endowed religious house. Compared with other groups, they were taller (perhaps a result of a richer diet during their adolescent growth period); their adult carbon and nitrogen isotope values are higher, suggesting a diet higher in terrestrial and/or marine animal protein; and they had the highest prevalence of foot problems related to fashionable late medieval footwear. As this illustrates, health inequality will take particular forms depending upon the specificities of a social landscape; except in unusual circumstances where a site and its skeletal samples represent a real cross‐section of society, inequality is best investigated by comparison across sites.

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Genetic history of Cambridgeshire before and after the Black Death

Cited by 11 publications

References 150 publications

Estimating allele frequencies, ancestry proportions and genotype likelihoods in the presence of mapping bias

Estimating allele frequencies, ancestry proportions and genotype likelihoods in the presence of mapping bias

READv2: Advanced and user-friendly detection of biological relatedness in archaeogenomics

Health inequality in medieval Cambridge, 1200–1500 CE

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