Significance: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of COVID-19, a major pandemic that threatens millions of human lives and the global economy. We identified a large number of mammals that can potentially be infected by SARS-CoV-2 via their ACE2 proteins. This can assist the identification of intermediate hosts for SARS-CoV-2 and hence reduce the opportunity for a future outbreak of COVID-19. Among the species we found with the highest risk for SARS-CoV-2 infection are wildlife and endangered species. These species represent an opportunity for spillover of SARS-CoV-2 from humans to other susceptible animals. Given the limited infectivity data for the species studied, we urge caution not to overinterpret the predictions of the present study.

Patterns of genetic and genomic variance are informative in inferring population history for human, model species and endangered populations. Here the genome sequence of wild-born African cheetahs reveals extreme genomic depletion in SNV incidence, SNV density, SNVs of coding genes, MHC class I and II genes, and mitochondrial DNA SNVs. Cheetah genomes are on average 95 % homozygous compared to the genomes of the outbred domestic cat (24.08 % homozygous), Virunga Mountain Gorilla (78.12 %), inbred Abyssinian cat (62.63 %), Tasmanian devil, domestic dog and other mammalian species. Demographic estimators impute two ancestral population bottlenecks: one >100,000 years ago coincident with cheetah migrations out of the Americas and into Eurasia and Africa, and a second 11,084–12,589 years ago in Africa coincident with late Pleistocene large mammal extinctions. MHC class I gene loss and dramatic reduction in functional diversity of MHC genes would explain why cheetahs ablate skin graft rejection among unrelated individuals. Significant excess of non-synonymous mutations in AKAP4 (p80 %) pleiomorphic sperm. The study provides an unprecedented genomic perspective for the rare cheetah, with potential relevance to the species’ natural history, physiological adaptations and unique reproductive disposition.

Species is the fundamental taxonomic unit in biology and its delimitation has implications for conservation. In giraffe (Giraffa spp.), multiple taxonomic classifications have been proposed since the early 1900s.1 However, one species with nine subspecies has been generally accepted,2 likely due to limited in-depth assessments, subspecies hybridizing in captivity,3,4 and anecdotal reports of hybrids in the wild.5 Giraffe taxonomy received new attention after population genetic studies using traditional genetic markers suggested at least four species.6,7 This view has been met with controversy,8 setting the stage for debate.9,10 Genomics is significantly enhancing our understanding of biodiversity and speciation relative to traditional genetic approaches and thus has important implications for species delineation and conservation.11 We present a high-quality de novo genome assembly of the critically endangered Kordofan giraffe (G. camelopardalis antiquorum)12 and a comprehensive whole-genome analysis of 50 giraffe representing all traditionally recognized subspecies. Population structure and phylogenomic analyses support four separately evolving giraffe lineages, which diverged 230-370 ka ago. These lineages underwent distinct demographic histories and show different levels of heterozygosity and inbreeding. Our results strengthen previous findings of limited gene flow and admixture among putative giraffe species6,7,9 and establish a genomic foundation for recognizing four species and seven subspecies, the latter of which should be considered as evolutionary significant units. Achieving a consensus over the number of species and subspecies in giraffe is essential for adequately assessing their threat level and will improve conservation efforts for these iconic taxa.

Background: The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum (AFS), the distribution of allele frequencies in populations. The joint AFS is commonly used to reconstruct the demographic history of multiple populations, and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. Results: Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA). Conclusions: We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history.