Genetic history of Cambridgeshire before and after the Black Death

Abstract

The extent of the devastation of the Black Death pandemic (1346–1353) on European populations is known from documentary sources and its bacterial source illuminated by studies of ancient pathogen DNA. What has remained less understood is the effect of the pandemic on human mobility and genetic diversity at the local scale. Here, we report 275 ancient genomes, including 109 with coverage >0.1×, from later medieval and postmedieval Cambridgeshire of individuals buried before and after the Black Death. Consistent with the function of the institutions, we found a lack of close relatives among the friars and the inmates of the hospital in contrast to their abundance in general urban and rural parish communities. While we detect long-term shifts in local genetic ancestry in Cambridgeshire, we find no evidence of major changes in genetic ancestry nor higher differentiation of immune loci between cohorts living before and after the Black Death.

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

“…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.…”

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

“…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.…”

“…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.…”