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Post by Admin on Jan 25, 2024 18:06:37 GMT
We also searched for runs of homozygosity (ROH) tracks in the imputed genomes using hapROH (5) and found that most individuals have no or very few ROH tracks that are longer than 4 cM (table S8), while three individuals (PSN357 from the Hospital, PSN870 from the Baptist Chapel, and PSN412 from Holy Trinity) have up to 150 to 175 cM in ROH, which is compatible with the parents being fourth- to fifth-degree relatives, including second- to third-cousin marriage (Fig. 3C).
Before/after the Black Death
Because of the broad error range of the radiocarbon dates as well as the limited accuracy of the assignments to the pre/post–Black Death groups based on associated finds and position in the sequence of dated stratigraphic context, some individuals in the post–Black Death group might have been born before 1348, although two obvious cases from Bene’t Street have been excluded from analysis. This might limit our power to detect changes after the Black Death. None of the individuals in the pre– or post–Black Death groups are among those tested positive for Yersinia pestis previously (10, 35, 36). Our analyses of genetic ancestry (Figs. 1 and 2, figs. S1 to S11, and table S2) were unable to detect changes in rates of long-distance migration associated with the Black Death comparable to those recently shown in case of Trondheim population (15). However, besides the potential effect on broader regional ancestry, the Black Death pandemic could have left other detectable signatures on genetic diversity of the population at the genome scale, or, it could have affected specific genes and variants associated with infectious disease vulnerability. To examine its effect on the genetic diversity of the Cambridge medieval population further, we estimated heterozygosity and nucleotide diversity genome-wide and in the human leukocyte antigen (HLA) locus in imputed genomes.
Genome-wide heterozygosity and nucleotide diversity are sensitive to demographic events such as bottlenecks, founder events, or admixture. High mortality during the pandemic could be detectable in extreme cases in a small isolated population as a reduction of diversity across all loci. Changes in the HLA region might capture possible signals of selection specifically at immunity loci. If any one or a few variants in this locus responded to selection, they would have been expected to affect the whole region because of linkage. Consistent with the long-term effect of balancing selection on HLA locus, we find this region has higher density of heterozygous positions at common variants and nucleotide diversity in our pool of imputed genomes (Fig. 4A and fig. S13). However, the “before” and “after” the Black Death cohorts do not show higher than average allele frequency differentiation within the HLA region (Fig. 4B) nor notable differences in the heterozygote density (Fig. 4C). Within a subset of 50 imputed genomes assigned to either before or after the Black Death and coverage >0.1×, we observed no significant differences in genome-wide (two-tailed t test, P = 0.205) nor HLA locus (P = 0.700) heterozygosities (fig. S12). Similarly, we did not detect changes in nucleotide diversity in the HLA region or genome-wide after the Black Death (fig. S13).
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Post by Admin on Jan 29, 2024 16:09:38 GMT
 Fig. 4. Heterozygote density and allele frequency differentiation in the HLA locus. (A) Distribution of average heterozygote density in 1-Mbp windows of chromosome 6. Gray line shows the density of heterozygous sites at common variant positions with minor allele frequency higher than 0.05 in the HRC imputation panel in chromosome 6 for 50 imputed (>0.1× coverage) pre– and post–Black Death genomes from Cambridge. The orange line highlights windows containing genes in the HLA locus. (B) Scatter plot of Max(FST) - maximum FST between before and after the Black Death cohorts - and Het density values by 1-Mbp windows of chromosome 6. (C) Het density in the before (n = 31) and after (n = 19) the Black Death cohorts. Our analyses of 70 pre– and post–Black Death imputed genomes for changes in allele frequency in 25 variants previously identified as potential targets of selection in humans against viral and bacterial pathogens as well as four variants recently highlighted as selection targets specifically against Y. pestis (16) revealed (tables S4 and S6) one significant (P = 0.003) difference at individual test level at the rs42490 SNP in the RIPK2 gene. The RIPK2 allele previously shown to be protective against leprosy (37) showed increased allele frequency after the Black Death. This result would not remain, however, significant after applying multiple test corrections and considering the limited sample size of our cohort it requires further validation in an independent dataset. None of the four immunity variants identified by Klunk et al. (16) with significant allele frequency changes both in their London and Danish cohorts were replicated between Cambridge before and after Black Death cohorts (table S9). Using simulations, we demonstrated that this is unlikely to be caused by a lack of power due to our sample size (fig. S14). We did observe a similar enrichment (1.4-fold, P = 0.0001) of variants related to immunity among highly differentiated variants (FST > 95th percentile) when using the same list of immunity-related and neutrally evolving variants as the authors (tables S9 and S10). Of the 245 highly differentiated immunity-related variants identified in their London cohort, 22 were replicated, significantly more than expected by chance (P = 0.0001); However, 10 of the 22 overlapping variants that are above the 95th threshold in the Cambridge cohort and 2 of the 3 variants above the 99th threshold show opposite directionality of allele frequency change in time in London and Cambridge cohorts (table S9). While the minor allele frequencies of the immunity variants appear to be highly correlated between our studies (r = 0.90, P < 1 × 10−15), the FST between the pre- and postpandemic cohorts are not (r = 0.019, P = 0.26). The significant enrichment of immunity genes cannot be reproduced with our data when using the full list of 37,574 neutral regions defined in (38) instead of the relatively small number of variants ascertained by Klunk et al. (16) in its subset of 250 regions (table S10). We observe a reduction (1.14-fold, P = 0.29) of high FST values among the Klunk et al. (16) immunity variants when we define the neutral 95th threshold using 55,965 variants from the full range of the 37,574 neutral regions, which becomes significant (1.73-fold, P < 1 × 10−10) when also using an expanded set of immunity variants from InnateDB (table S10). Notably, within the pool of highly differentiated immune locus variants identified by Klunk et al. (16), we observe significant excess of “gwas” variants, i.e., positions that had previously been confirmed to be polymorphic (table S10) over immunity variants ascertained in the exonic regions, suggesting that ascertainment of previously unidentified variants from low-coverage data (as by Klunk et al. (16) in their “exon” and “neutral” categories) is one possible cause for the disappearance of the signal when we used the full set of neutral regions defined in (38) that overlap with positions confirmed to be polymorphic in the Haplotype Reference Consortium (HRC) panel (39) to define the threshold. DISCUSSION |
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Post by Admin on Jan 30, 2024 20:52:39 GMT
We extracted aDNA and generated whole-genome shotgun sequence data with a mean coverage of 0.228 from a total of 250 later medieval and 25 postmedieval skeletons, retrieving for further analyses 190 genomes at coverage >0.01× (Table 1 and table S1). They form the most extensive bioarchaeological sampling within a focused temporal and geographical range to date. The examined medieval sites represent burials of individuals from different social and cause of death backgrounds, including urban cemeteries of the charitable poor from the Hospital of St. John, All Saints parish cemetery, Augustinian Friary, Bene’t Street plague burial, and rural cemeteries of Cherry Hinton and Clopton (Table 1, Fig. 1A, and the Supplementary Materials). The analyses of the medieval genomes were performed in context of postmedieval genomes from four sites in Cambridgeshire (Table 1) as well as published genomic data of the Late Iron Age/Roman (c. 100 BCE to 400 CE) and Early Saxon periods (c. 400–700 CE) from Cambridgeshire and elsewhere from England (21, 22). Average endogenous human DNA content was 13% and average contamination rate 1.06%, with 209 individuals under 5%. Average damage in the first 5 base pairs (bp) was 8.02% (table S1). A subset of 143 genomes sequenced to >0.05× coverage were imputed to study the changes in phenotypes related to health and lifestyle. The imputed genomes include 109 individuals with coverage >0.1×, which were subsequently used to resolve genetic ancestry, kinship, recent inbreeding, and heterozygosity.
Genetic ancestry
The frequencies of mitochondrial DNA (mtDNA) haplogroups in England have remained relatively stable since the Neolithic (table S2). Similarly, principal components analysis (PCA) reveals that all 109 individuals with >0.1× coverage from later medieval Cambridgeshire (Fig. 1, A and B) share their autosomal ancestry with modern northern and western European populations without evidence of migration from more distant regions (Fig. 1C and fig. S1); the same conclusion is supported when projecting pseudohaploid genomes without imputation onto PC space established by modern genomes (figs. S2 to S11). In contrast to genomes from the Roman or Early Saxon periods (21, 22), most later medieval genomes cluster with those from the modern English genomes from the UK Biobank data (Fig. 1C). Individual outliers who, similarly to most Early Saxon period individuals, are placed among modern Dutch and Danish populations, include a few from Cherry Hinton and the Hospital of St John. Two of them (PSN332 from the Hospital and PSN930 from Cherry Hinton) are also outliers in terms of dental enamel 87Sr/86Sr values (PSN332 = 0.7122, PSN930 = 0.7108) (23). These values, particularly for PSN332, are higher than the estimated biosphere 87Sr/86Sr values for the East of England (24), indicating that they did not spend their childhoods in the area local to where they were buried.
To study the genetic affinity changes across time at finer geographic resolution, we defined interindividual connections by identifying long [>5 centimorgan (cM)] shared allele intervals (LSAIs) with IBIS (25) and explored the modularity of individual connectedness (PiC) (26) among the historical and modern genomes. Similarly to PCA results, we find that the majority of historical genomes from Cambridgeshire cluster by their connectedness with modern UK Biobank genomes from East England (Fig. 1D and table S3) whereas a small fraction of later medieval and Roman period genomes, which display low LSAI sharing with any population (Fig. 1E), cluster with the UK Biobank donors born in France who also display low levels of LSAI sharing. The Early Saxon period genomes show higher connectedness with Scandinavian genomes, which is also reflected in individual PCA outliers from Cherry Hinton. Overall, we observe regional shifts in individual connectedness over time (Fig. 1F). We observe increasing Danish connectedness in the transition from Roman to Early Saxon period; later, during and after the later medieval period, there is an increase of LSAI sharing with both modern Dutch genomes [mirroring documentary evidence showing the Dutch as the most common late medieval immigrants locally (27, 28)] and genomes from a broader zone of England. Last, we identify a major shift in modern East England toward higher LSAI sharing with Wales and Scotland, clearly reflecting the political and economic integration of recent Britain.
Our analyses of individual connectedness in the People of the British Isles (29) data suggest that all later medieval genomes from Cambridgeshire likely draw most of their genetic ancestry broadly from the same sources as present-day central/eastern England population (Fig. 2). Although we are able to distinguish certain regional differences in the modern data with our approach, such as between Cornwall and Devon or between North and South Yorkshire, we observe less resolution in a broad area between Lincolnshire and Surrey where our ancient genomes come from (Fig. 2). This means that even if some of the individuals had come from Kent or Lincolnshire, for example, we would not be able to detect such fine-scale migration patterns among regions within that area.
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