William A. Marsh

Archaeological evidence suggests that dogs diverged from wolves during the Palaeolithic, more than 15,000 years ago ^1–7 . The earliest unequivocal genetic evidence, however, is associated with dog remains from Mesolithic archaeological contexts approximately 10,900 years ago ^8,9 . Here we generate both nuclear and mitochondrial genomes from canid remains at Pınarbaşı in Türkiye (15,800 years ago) ¹⁰ and Gough’s Cave in the UK (14,300 years ago) ¹¹ , as well as from dogs excavated from two Mesolithic sites in Serbia (Padina between 11,500–7,900 years ago and Vlasac 8,900 years ago) ^12,13 . Our analyses indicate that a genetically homogeneous dog population was already widely distributed across Europe and Anatolia during the Late Upper Palaeolithic (by at least 14,300 years ago). This finding suggests that dogs were exchanged among genetically and culturally distinct western Eurasian Late Palaeolithic human populations, namely the Magdalenian, Epigravettian and Anatolian hunter-gatherers ^10,14–16 . Last, we identify a major influx of eastern Eurasian dog ancestry during the Mesolithic, concomitant with the movement of eastern hunter-gatherer populations into Europe ¹⁴ , which led to the establishment of the primary ancestry characteristics that define European dog populations today.

While DNA barcoding methods are an increasingly important tool in biological conservation, the resource requirements of constructing reference libraries frequently reduce their efficacy. One efficient way of sourcing taxonomically validated DNA for reference libraries is to use museum collections. However, DNA degradation intrinsic to historical museum specimens can, if not addressed in the wet lab, lead to low quality data generation and severely limit scientific output. Several DNA extraction and library build methods that are designed to work with degraded DNA have been developed, although the ability to implement these methods at scale and at low cost has yet to be formally addressed. Here, the performance of widely used DNA extraction and library build methods are compared using museum specimens. We find that while our selected DNA extraction methods do not significantly differ in DNA yield, the Santa Cruz Reaction (SCR) library build method is not only the most effective at retrieving degraded DNA from museum specimens but also easily implemented at high throughput for low cost. Results highlight the importance of lab protocol on data yield. An optimised “sample to sequencing” high-throughput protocol which incorporates SCR is included to allow for easy uptake by the wider scientific community.

Societal Impact Statement: Diverse gene pools are fundamental to crop improvement, biodiversity maintenance and environmental management. The UKCropDiversity‐HPC high‐performance computing resource enables seven UK institutes to perform plant and conservation research with increased efficiency, cost‐effectiveness and environmental sustainability. It supports research across numerous areas, including bioinformatics, genetics, phenomics and conservation ‐ including Artificial Intelligence approaches. Its utilisation supports many United Nations Sustainable Development Goals, including Goals‐2 (Zero Hunger), −13 (Climate Action), −15 (Life on Land), −9 (Industry, Innovation and Infrastructure) and −4 (Quality Education). Accordingly, UKCropDiversity‐HPC helps maximise the societal impact of research undertaken at our seven institutes, driving positive change for future generations.

The blue antelope or bluebuck (Hippotragus leucophaeus) is an extinct species of antelope that lived in South Africa until ca. 1799–1800. Disappearing only 34 years after it was described, it was the first large African mammal species to have become extinct in recent times. Therefore, current scientific knowledge of the blue antelope is limited to museum specimens. However, these are extremely rare and further complicated by uncertainties surrounding their taxonomic status due to morphological similarity with the closely related extant roan antelope (Hippotragus equinus) and sable antelope (Hippotragus niger). A previous study found that only four (out of ten) genetically analysed historical museum specimens could be confirmed as Hippotragus leucophaeus. Here, we analyse a further five potential blue antelope specimens, two of which had been morphologically identified as blue antelope (one from the Natural History Museum (NHM) in London and one from National Museums Scotland in Edinburgh), plus an additional three specimens identified here as candidate blue antelope specimens (from the NHM). We find that only one of the five specimens can be genetically validated as Hippotragus leucophaeus, whereas the other four specimens were identified as H. equinus. Furthermore, using hybridisation capture, we generated two complete mitochondrial genomes for blue antelope specimens that were formerly known only from partial mitochondrial genomes. The diversity analyses including all newly generated genomes further confirm that the mitochondrial nucleotide diversity across this species was low.

Background: The inference of biological relations between individuals is fundamental to understanding past human societies. Caregiving, resource sharing and sexual behaviours are often mediated by biological kinship and yet the identification and interpretation of kin relationships in prehistoric human groups is difficult. In recent years, the advent of archaeogenetic techniques have offered a fresh approach, and when combined with more traditional osteological and interpretive archaeological methods, allows for improved interpretation of the burial practices, cultural behaviours, and societal stratification in ancient societies. Although archaeogenetic techniques are developing at pace, questions remain as to their accuracy, particularly when applied to the low coverage datasets that results from the sequencing of DNA derived from highly degraded ancient material. Results: The performance of six of the most commonly used kinship identifcation software methods was explored at a range of low and ultra low genome coverages. An asymmetrical response was observed across packages, with decreased genome coverage resulting in differences in both direction and degree of change of calculated kinship scores and thus pairwise relatedness estimates are dependant on both package used and genome coverage. Methods reliant upon genotype likelihoods methods (lcMLkin, NGSrelate and NGSremix) show a decreased level of prediction at coverage below 1x, although were consistent in the particular relationships identified at these coverages when compared to the pseudohaploid reliant methods tested (READ, the Kennett 2017 method and TKGWV2.0). The three pseudohaploid methods show predictive potential at coverages as low as 0.05x, although the accuracy of the relationships identified is questionable given the increase in the number of relationships identifIed at the low coverage (type I errors). Conclusion: Two pseudohaploid methods (READ and Kennett 2017) show relatively consistent inference of kin relationships at low coverage (0.5x), with READ only showing a significant performance drop off at ultralow coverages (< 0.2x). More generally, our results reveal asymmetrical kinship classifications in some software packages even at high coverages, highlighting the importance of applying multiple methods to authenticate kin relationships in ancient material, along with the continuing need to develop laboratory methods that maximise data output for downstream analyses.