Population genomic history of the endangered Anatolian and Cyprian mouflons in relation to worldwide wild, feral and domestic sheep lineages.

Once widespread in their homelands, the Anatolian mouflon (Ovis gmelini anatolica) and the Cyprian mouflon (Ovis gmelini ophion) were driven to near extinction during the 20th century and are currently listed as endangered populations by the IUCN. While the exact origins of these lineages remain unclear, they have been suggested to be close relatives of domestic sheep or remnants of proto-domestic sheep. Here, we study whole genome sequences of n = 5 Anatolian mouflons and n = 10 Cyprian mouflons in terms of population history and diversity, comparing them to eight other extant sheep lineages. We find reciprocal genetic affinity between Anatolian and Cyprian mouflons and domestic sheep, higher than all other studied wild sheep genomes, including the Iranian mouflon (Ovis gmelini). Studying diversity indices, we detect a considerable load of short runs of homozygosity (ROH) blocks (<2 Mb) in both Anatolian and Cyprian mouflons, reflecting small effective population size (Ne). Meanwhile, Ne as well as mutation load estimates are lower in Cyprian compared to Anatolian mouflons, suggesting the purging of recessive deleterious variants in Cyprian sheep under a small long-term Ne, possibly attributable to founder effects, island isolation, introgression from domestic lineages, or differences in their bottleneck dynamics. Expanding our analyses to worldwide wild and feral Ovis genomes, we observe varying viability metrics among different lineages, and a limited consistency between viability metrics and IUCN conservation status. Factors such as recent inbreeding, introgression, and unique population dynamics may have contributed to the observed disparities.

Genomics of adaptive evolution in the woolly mammoth.

Ancient genomes provide a tool to investigate the genetic basis of adaptations in extinct organisms. However, the identification of species-specific fixed genetic variants requires the analysis of genomes from multiple individuals. Moreover, the long-term scale of adaptive evolution coupled with the short-term nature of traditional time series data has made it difficult to assess when different adaptations evolved. Here, we analyze 23 woolly mammoth genomes, including one of the oldest known specimens at 700,000 years old, to identify fixed derived non-synonymous mutations unique to the species and to obtain estimates of when these mutations evolved. We find that at the time of its origin, the woolly mammoth had already acquired a broad spectrum of positively selected genes, including ones associated with hair and skin development, fat storage and metabolism, and immune system function. Our results also suggest that these phenotypes continued to evolve during the last 700,000 years, but through positive selection on different sets of genes. Finally, we also identify additional genes that underwent comparatively recent positive selection, including multiple genes related to skeletal morphology and body size, as well as one gene that may have contributed to the small ear size in Late Quaternary woolly mammoths.

Evolutionary consequences of genomic deletions and insertions in the woolly mammoth genome.

Woolly mammoths had a set of adaptations that enabled them to thrive in the Arctic environment. Many mammoth-specific single nucleotide polymorphisms (SNPs) responsible for unique mammoth traits have been previously identified from ancient genomes. However, a multitude of other genetic variants likely contributed to woolly mammoth evolution. In this study, we sequenced two woolly mammoth genomes and combined these with previously sequenced mammoth and elephant genomes to conduct a survey of mammoth-specific deletions and indels. We find that deletions are highly enriched in non-coding regions, suggesting selection against structural variants that affect protein sequences. Nonetheless, at least 87 woolly mammoth genes contain deletions or indels that modify the coding sequence, including genes involved in skeletal morphology and hair growth. These results suggest that deletions and indels contributed to the unique phenotypic adaptations of the woolly mammoth, and were potentially critical to surviving in its natural environment.

GenErode: a bioinformatics pipeline to investigate genome erosion in endangered and extinct species.

BACKGROUND: Many wild species have suffered drastic population size declines over the past centuries, which have led to 'genomic erosion' processes characterized by reduced genetic diversity, increased inbreeding, and accumulation of harmful mutations. Yet, genomic erosion estimates of modern-day populations often lack concordance with dwindling population sizes and conservation status of threatened species. One way to directly quantify the genomic consequences of population declines is to compare genome-wide data from pre-decline museum samples and modern samples. However, doing so requires computational data processing and analysis tools specifically adapted to comparative analyses of degraded, ancient or historical, DNA data with modern DNA data as well as personnel trained to perform such analyses. RESULTS: Here, we present a highly flexible, scalable, and modular pipeline to compare patterns of genomic erosion using samples from disparate time periods. The GenErode pipeline uses state-of-the-art bioinformatics tools to simultaneously process whole-genome re-sequencing data from ancient/historical and modern samples, and to produce comparable estimates of several genomic erosion indices. No programming knowledge is required to run the pipeline and all bioinformatic steps are well-documented, making the pipeline accessible to users with different backgrounds. GenErode is written in Snakemake and Python3 and uses Conda and Singularity containers to achieve reproducibility on high-performance compute clusters. The source code is freely available on GitHub ( https://github.com/NBISweden/GenErode ). CONCLUSIONS: GenErode is a user-friendly and reproducible pipeline that enables the standardization of genomic erosion indices from temporally sampled whole genome re-sequencing data.

Development and Optimization of a Silica Column-Based Extraction Protocol for Ancient DNA.

Rapid and cost-effective retrieval of endogenous DNA from ancient specimens remains a limiting factor in palaeogenomic research. Many methods have been developed to increase ancient DNA yield, but modifications to existing protocols are often based on personal experience rather than systematic testing. Here, we present a new silica column-based extraction protocol, where optimizations were tested in controlled experiments. Using relatively well-preserved permafrost samples, we tested the efficiency of pretreatment of bone and tooth powder with a bleach wash and a predigestion step. We also tested the recovery efficiency of MinElute and QIAquick columns, as well as Vivaspin columns with two molecular weight cut-off values. Finally, we tested the effect of uracil-treatment with two different USER enzyme concentrations. We find that neither bleach wash combined with a predigestion step, nor predigestion by itself, significantly increased sequencing efficiency. Initial results, however, suggest that MinElute columns are more efficient for ancient DNA extractions than QIAquick columns, whereas different molecular weight cut-off values in centrifugal concentrator columns did not have an effect. Uracil treatments are effective at removing DNA damage even at concentrations of 0.15 U/µL (as compared to 0.3 U/µL) of ancient DNA extracts.

Integrating multi-taxon palaeogenomes and sedimentary ancient DNA to study past ecosystem dynamics.

Ancient DNA (aDNA) has played a major role in our understanding of the past. Important advances in the sequencing and analysis of aDNA from a range of organisms have enabled a detailed understanding of processes such as past demography, introgression, domestication, adaptation and speciation. However, to date and with the notable exception of microbiomes and sediments, most aDNA studies have focused on single taxa or taxonomic groups, making the study of changes at the community level challenging. This is rather surprising because current sequencing and analytical approaches allow us to obtain and analyse aDNA from multiple source materials. When combined, these data can enable the simultaneous study of multiple taxa through space and time, and could thus provide a more comprehensive understanding of ecosystem-wide changes. It is therefore timely to develop an integrative approach to aDNA studies by combining data from multiple taxa and substrates. In this review, we discuss the various applications, associated challenges and future prospects of such an approach.

Million-year-old DNA sheds light on the genomic history of mammoths.

Temporal genomic data hold great potential for studying evolutionary processes such as speciation. However, sampling across speciation events would, in many cases, require genomic time series that stretch well back into the Early Pleistocene subepoch. Although theoretical models suggest that DNA should survive on this timescale1, the oldest genomic data recovered so far are from a horse specimen dated to 780-560 thousand years ago2. Here we report the recovery of genome-wide data from three mammoth specimens dating to the Early and Middle Pleistocene subepochs, two of which are more than one million years old. We find that two distinct mammoth lineages were present in eastern Siberia during the Early Pleistocene. One of these lineages gave rise to the woolly mammoth and the other represents a previously unrecognized lineage that was ancestral to the first mammoths to colonize North America. Our analyses reveal that the Columbian mammoth of North America traces its ancestry to a Middle Pleistocene hybridization between these two lineages, with roughly equal admixture proportions. Finally, we show that the majority of protein-coding changes associated with cold adaptation in woolly mammoths were already present one million years ago. These findings highlight the potential of deep-time palaeogenomics to expand our understanding of speciation and long-term adaptive evolution.

Competitive mapping allows for the identification and exclusion of human DNA contamination in ancient faunal genomic datasets.

BACKGROUND: After over a decade of developments in field collection, laboratory methods and advances in high-throughput sequencing, contamination remains a key issue in ancient DNA research. Currently, human and microbial contaminant DNA still impose challenges on cost-effective sequencing and accurate interpretation of ancient DNA data. RESULTS: Here we investigate whether human contaminating DNA can be found in ancient faunal sequencing datasets. We identify variable levels of human contamination, which persists even after the sequence reads have been mapped to the faunal reference genomes. This contamination has the potential to affect a range of downstream analyses. CONCLUSIONS: We propose a fast and simple method, based on competitive mapping, which allows identifying and removing human contamination from ancient faunal DNA datasets with limited losses of true ancient data. This method could represent an important tool for the ancient DNA field.

Inference of natural selection from ancient DNA.

Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists. In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods.