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 International Union for Conservation of Nature. 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 with 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 (O. gmelini). Studying diversity indices, we detect a considerable load of short runs of homozygosity blocks (<2 Mb) in both Anatolian and Cyprian mouflons, reflecting small effective population size (Ne). Meanwhile, Ne and mutation load estimates are lower in Cyprian compared with 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 International Union for Conservation of Nature 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.

•

Two new high-quality woolly mammoth genomes have been generated

•

A new method was used to identify deletions and insertions in woolly mammoths

•

At least 87 genes have been affected by deletions or indels in the mammoth lineage

•

Genes involved in skeletal morphology and hair growth are affected by deletions

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04757-0.

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.

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.