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Species persistence can be influenced by the amount, type, and distribution of diversity across the genome, suggesting a potential relationship between historical demography and resilience. In this study, we surveyed genetic variation across single genomes of 240 mammals that compose the Zoonomia alignment to evaluate how historical effective population size (Ne) affects heterozygosity and deleterious genetic load and how these factors may contribute to extinction risk. We find that species with smaller historical Ne carry a proportionally larger burden of deleterious alleles owing to long-term accumulation and fixation of genetic load and have a higher risk of extinction. This suggests that historical demography can inform contemporary resilience. Models that included genomic data were predictive of species' conservation status, suggesting that, in the absence of adequate census or ecological data, genomic information may provide an initial risk assessment.

Recent advances in long-read sequencing technologies have allowed the generation and curation of more complete genome assemblies, enabling the analysis of traditionally neglected chromosomes, such as the human Y chromosome (chrY). Native DNA was sequenced on a MinION Oxford Nanopore Technologies sequencing device to generate genome assemblies for seven major chrY human haplogroups. We analyzed and compared the chrY enrichment of sequencing data obtained using two different selective sequencing approaches: adaptive sampling and flow cytometry chromosome sorting. We show that adaptive sampling can produce data to create assemblies comparable to chromosome sorting while being a less expensive and time-consuming technique. We also assessed haplogroup-specific structural variants, which would be otherwise difficult to study using short-read sequencing data only. Finally, we took advantage of this technology to detect and profile epigenetic modifications among the considered haplogroups. Altogether, we provide a framework to study complex genomic regions with a simple, fast, and affordable methodology that could be applied to larger population genomics datasets.

[INTRODUCTION] Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons., [RATIONALE] Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types., [RESULTS] To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associ, [CONCLUSION] TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution.

[INTRODUCTION] Deciphering the molecular and genetic changes that differentiate humans from our closest primate relatives is critical for understanding our origins. Although earlier studies have prioritized how newly gained genetic sequences or variations have contributed to evolutionary innovation, the role of sequence loss has been less appreciated. Alterations in evolutionary conserved regions that are enriched for biological function could be particularly more likely to have phenotypic effects. We thus sought to identify and characterize sequences that have been conserved across evolution, but are then surprisingly lost in all humans. These human-specific deletions in conserved regions (hCONDELs) may play an important role in uniquely human traits., [RATIONALE] Sequencing advancements have identified millions of genetic changes between chimpanzee and human genomes; however, the functional impacts of the ~1 to 5% difference between our species is largely unknown. hCONDELs are one class of these predominantly noncoding sequence changes. Although large hCONDELs (>1 kb) have been previously identified, the vast majority of all hCONDELs (95.7%) are small (<20 base pairs) and have not yet been functionally assessed. We adapted massively parallel reporter assays (MPRAs) to characterize the effects of thousands of these small hCONDELs and uncovered hundreds with functional effects. By understanding the effects of these hCONDELs, we can gain insight into the mechanistic patterns driving evolution in the human genome., [RESULTS] We identified 10,032 hCONDELs by examining conserved regions across diverse vertebrate genomes and overlapping with confidently annotated, human-specific fixed deletions. We found that these hCONDELs are enriched to delete conserved sequences originating from stem amniotes. Overlap with transcriptional, epigenomic, and phenotypic datasets all implicate neuronal and cognitive functional impacts. We characterized these hCONDELs using MPRA in six different human cell types, including induced pluripotent stem cell–derived neural progenitor cells. We found that 800 hCONDELs displayed species-specific regulatory effect effects. Although many hCONDELs perturb transcription factor–binding sites in active enhancers, we estimate that 30% create or improve binding sites, including activators and repressors. Some hCONDELs exhibit molecular functions that affect core neurodevelopmental genes. One hCONDEL removes a single base in an active enhancer in the neurogenesis gene HDAC5, and another deletes six bases in an alternative promoter of PPP2CA, a gene that regulates neuronal signaling. We deeply characterized an hCONDEL in a putative regulatory element of LOXL2, a gene that controls neuronal differentiation. Using genome engineering to reintroduce the conserved chimpanzee sequence into human cells, we confirmed that the human deletion alters transcriptional output of LOXL2. Single-cell RNA sequencing of these cells uncovered a cascade of myelination and synaptic function–related transcriptional changes induced by the hCONDEL., [CONCLUSION] Our identification of hundreds of hCONDELs with functional impacts reveals new molecular changes that may have shaped our unique biological lineage. These hCONDELs display predicted functions in a variety of biological systems but are especially enriched for function in neuronal tissue. Many hCONDELs induced gains of regulatory activity, a surprising discovery given that deletions of conserved bases are commonly thought to abrogate function. Our work provides a paradigm for the characterization of nucleotide changes shaping species-specific biology across humans or other animals.

[INTRODUCTION] The Anthropocene is marked by an accelerated loss of biodiversity, widespread population declines, and a global conservation crisis. Given limited resources for conservation intervention, an approach is needed to identify threatened species from among the thousands lacking adequate information for status assessments. Such prioritization for intervention could come from genome sequence data, as genomes contain information about demography, diversity, fitness, and adaptive potential. However, the relevance of genomic data for identifying at-risk species is uncertain, in part because genetic variation may reflect past events and life histories better than contemporary conservation status., [RATIONALE] The Zoonomia multispecies alignment presents an opportunity to systematically compare neutral and functional genomic diversity and their relationships to contemporary extinction risk across a large sample of diverse mammalian taxa. We surveyed 240 species spanning from the “Least Concern” to “Critically Endangered” categories, as published in the International Union for Conservation of Nature’s Red List of Threatened Species. Using a single genome for each species, we estimated historical effective population sizes (Ne) and distributions of genome-wide heterozygosity. To estimate genetic load, we identified substitutions relative to reconstructed ancestral sequences, assuming that mutations at evolutionarily conserved sites and in protein-coding sequences, especially in genes essential for viability in mice, are predominantly deleterious. We examined relationships between the conservation status of species and metrics of heterozygosity, demography, and genetic load and used these data to train and test models to distinguish threatened from nonthreatened species., [RESULTS] Species with smaller historical Ne are more likely to be categorized as at risk of extinction, suggesting that demography, even from periods more than 10,000 years in the past, may be informative of contemporary resilience. Species with smaller historical Ne also carry proportionally higher burdens of weakly and moderately deleterious alleles, consistent with theoretical expectations of the long-term accumulation and fixation of genetic load under strong genetic drift. We found weak support for a causative link between fixed drift load and extinction risk; however, other types of genetic load not captured in our data, such as rare, highly deleterious alleles, may also play a role. Although ecological (e.g., physiological, life-history, and behavioral) variables were the best predictors of extinction risk, genomic variables nonrandomly distinguished threatened from nonthreatened species in regression and machine learning models. These results suggest that information encoded within even a single genome can provide a risk assessment in the absence of adequate ecological or population census data., [CONCLUSION] Our analysis highlights the potential for genomic data to rapidly and inexpensively gauge extinction risk by leveraging relationships between contemporary conservation status and genetic variation shaped by the long-term demographic history of species. As more resequencing data and additional reference genomes become available, estimates of genetic load, estimates of recent demographic history, and accuracy of predictive models will improve. We therefore echo calls for including genomic information in assessments of the conservation status of species.

[INTRODUCTION] Human accelerated regions (HARs) are evolutionarily conserved sequences that acquired an unexpectedly high number of nucleotide substitutions in the human genome since divergence from our common ancestor with chimpanzees. Prior work has established that many HARs are gene regulatory enhancers that function during embryonic development, particularly in neurodevelopment, and that most HARs show signatures of positive selection. However, the events that caused the sudden change in selective pressures on HARs remain a mystery., [RATIONALE] Because HARs acquired many substitutions in our ancestors after millions of years of extreme constraint across diverse mammals, we reasoned that their conserved roles in regulating development of the brain and other organs must have changed during human evolution. One mechanism that could drive such a functional shift is enhancer hijacking, whereby the target gene repertoire of a noncoding sequence is changed through alterations in three-dimensional genome folding. The regulatory information encoded in a hijacked enhancer would likely need to change to avoid deleterious expression of the altered target gene while also possibly supporting modified expression patterns. Structural variants—large genomic insertions, deletions, and rearrangements—are the greatest sources of sequence differences between the human and chimpanzee genomes, and they have the potential to affect how a region of the genome folds and localizes in the nucleus. We therefore hypothesized that some HARs were generated through enhancer hijacking triggered by nearby human-specific structural variants (hsSVs)., [RESULTS] We leveraged an alignment of hundreds of mammalian genomes plus a Nextflow pipeline that we wrote for automating the detection of lineage-specific accelerated regions to identify 312 high-confidence HARs (zooHARs). Through massively parallel reporter assays and machine learning integration of hundreds of epigenomic datasets, we showed that many zooHARs function as neurodevelopmental enhancers and that their human substitutions alter transcription factor binding sites, consistent with previous studies. We further mapped zooHARs to specific cell types and tissues using single-cell open chromatin and gene expression data, and we found that they represent a more diverse set of neurodevelopmental processes than a parallel set of chimpanzee accelerated regions. To test the enhancer hijacking hypothesis, we first examined the three-dimensional neighborhoods of zooHARs using publicly available chromatin capture (Hi-C) data, finding a significant enrichment of zooHARs in domains with hsSVs. This motivated us to use deep learning to predict how hsSVs changed genome folding in the human versus the chimpanzee genomes. We found that 30% of zooHARs occur within 500 kb of an hsSV that substantially alters local chromatin interactions, and we confirmed this association in Hi-C data that we generated in human and chimpanzee neural progenitor cells. Finally, we showed that chromatin domains containing zooHARs and hsSVs are enriched for genes differentially expressed in human versus chimpanzee neurodevelopment., [CONCLUSION] The origin of many HARs may be explained by human-specific structural variants that altered three-dimensional genome folding, causing evolutionarily conserved enhancers to adapt to different target genes and regulatory domains.

[INTRODUCTION] Comparative genomics provides valuable insights into gene function, phylogeny, molecular evolution, and associations between phenotypic and genomic differences. Such analyses require knowledge about which genes originated from a speciation event (orthologs) or from a duplication event (paralogs). Existing methods to detect orthologs in turn require knowledge of the location of genes in the genome (gene annotation), which is itself a challenging problem, resulting in a growing gap between sequenced and annotated genomes., [RATIONALE] We developed TOGA (Tool to infer Orthologs from Genome Alignments), a genomics method that integrates orthology inference and gene annotation. TOGA takes as input a gene annotation of a reference species (e.g., human, mouse, or chicken) and a whole-genome alignment between the reference and a query genome (e.g., other mammals or birds). It infers orthologous gene loci in the query genome, annotates and classifies orthologous genes, detects gene losses and duplications, and generates protein and codon alignments. Orthology detection relies on the principle that orthologous sequences are generally more similar to each other than to paralogous sequences. Whereas existing methods work with annotated protein-coding sequences, TOGA extends this similarity principle to non-exonic regions (introns and intergenic regions) and uses machine learning to detect orthologous gene loci based on alignments of intronic and intergenic regions., [RESULTS] We demonstrate that TOGA’s machine learning classifier detects orthologous gene loci with a very high accuracy, and also works for orthologous genes that underwent translocations or inversions. TOGA improves ortholog detection and comprehensively annotates conserved genes, even if transcriptomics data are available. Although homology-based methods such as TOGA cannot annotate orthologs of genes that are not present in the reference, we show that reference bias can be effectively counteracted by integrating annotations generated with multiple reference species. TOGA can also be applied to highly fragmented genome assemblies, where genes are often split across scaffolds. By accurately identifying and joining orthologous gene fragments, TOGA annotates entire genes and thus increases the utility of fragmented genomes for comparative analyses. TOGA’s gene classification explicitly distinguishes between genes with missing sequences (indicative of assembly incompleteness) and genes with inactivating mutations (potentially indicative of base errors). We show that this classification provides a superior benchmark for assembly completeness and quality. As genomes are generated at an increasing rate, annotation and orthology inference methods that can handle hundreds or thousands of genomes are needed. TOGA’s reference species methodology scales linearly with the number of query species. By applying TOGA with human and mouse as references to 488 placental mammal assemblies and using chicken as a reference for 501 bird assemblies, we created large comparative resources for mammals and birds that comprise gene annotations, ortholog sets, lists of inactivated genes, and multiple codon alignments., [CONCLUSION] TOGA provides a general strategy to cope with the annotation and orthology inference bottleneck. We envision three major uses. First, TOGA enables phylogenomic analyses of orthologous genes and screens for gene changes (e.g., selection, loss, and duplication) that are associated with phenotypic differences. Second, TOGA provides annotations of genes that are conserved in newly sequenced genomes, which can be supplemented with transcriptomics data to detect lineage-specific genes or exons. Finally, TOGA’s gene classification provides a powerful genome assembly quality benchmark., [ABSTRACT] Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era.

[INTRODUCTION] Thousands of genetic variants have been associated with human diseases and traits through genome-wide association studies (GWASs). Translating these discoveries into improved therapeutics requires discerning which variants among hundreds of candidates are causally related to disease risk. To date, only a handful of causal variants have been confirmed. Here, we leverage 100 million years of mammalian evolution to address this major challenge., [RATIONALE] We compared genomes from hundreds of mammals and identified bases with unusually few variants (evolutionarily constrained). Constraint is a measure of functional importance that is agnostic to cell type or developmental stage. It can be applied to investigate any heritable disease or trait and is complementary to resources using cell type– and time point–specific functional assays like Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTEx)., [RESULTS] Using constraint calculated across placental mammals, 3.3% of bases in the human genome are significantly constrained, including 57.6% of coding bases. Most constrained bases (80.7%) are noncoding. Common variants (allele frequency ≥ 5%) and low-frequency variants (0.5% ≤ allele frequency < 5%) are depleted for constrained bases (1.85 versus 3.26% expected by chance, P < 2.2 × 10−308). Pathogenic ClinVar variants are more constrained than benign variants (P < 2.2 × 10−16). The most constrained common variants are more enriched for disease single-nucleotide polymorphism (SNP)–heritability in 63 independent GWASs. The enrichment of SNP-heritability in constrained regions is greater (7.8-fold) than previously reported in mammals and is even higher in primates (11.1-fold). It exceeds the enrichment of SNP-heritability in nonsynonymous coding variants (7.2-fold) and fine-mapped expression quantitative trait loci (eQTL)–SNPs (4.8-fold). The enrichment peaks near constrained bases, with a log-linear decrease of SNP-heritability enrichment as a function of the distance to a constrained base. Zoonomia constraint scores improve functionally informed fine-mapping. Variants at sites constrained in mammals and primates have greater posterior inclusion probabilities and higher per-SNP contributions. In addition, using both constraint and functional annotations improves polygenic risk score accuracy across a range of traits. Finally, incorporating constraint information into the analysis of noncoding somatic variants in medulloblastomas identifies new candidate driver genes., [CONCLUSION] Genome-wide measures of evolutionary constraint can help discern which variants are functionally important. This information may accelerate the translation of genomic discoveries into the biological, clinical, and therapeutic knowledge that is required to understand and treat human disease.

[INTRODUCTION] An estimated 160 million years have passed since the first placental mammals evolved. These eutherians are categorized into 19 orders consisting of nearly 4000 extant species, with ~70% being bats or rodents. Broad, in-depth, and comparative genomic studies across Eutheria have previously been unachievable because of the lack of genomic resources. The collaboration of the Zoonomia Consortium made available hundreds of high-quality genome assemblies for comparative analysis. Our focus within the consortium was to investigate the evolution of transposable elements (TEs) among placental mammals. Using these data, we identified previously known TEs, described previously unknown TEs, and analyzed the TE distribution among multiple taxonomic levels., [RATIONALE] The emergence of accurate and affordable sequencing technology has propelled efforts to sequence increasingly more nonmodel mammalian genomes in the past decade. Most of these efforts have traditionally focused on genic regions searching for patterns of selection or variation in gene regulation. The common trend of ignoring or trivializing TE annotation with newly published genomes has resulted in severe lag of TE analyses, leading to extensive undiscovered TE variation. This oversight has neglected an important source of evolution because the accumulation of TEs is attributable to drastic alterations in genome architecture, including insertions, deletions, duplications, translocations, and inversions. Our approach to the Zoonomia dataset was to provide future inquirers accurate and meticulous TE curations and to describe taxonomic variation among eutherians., [RESULTS] We annotated the TE content of 248 mammalian genome assemblies, which yielded a library of 25,676 consensus TE sequences, 8263 of which were previously unidentified TE sequences (available at https://dfam.org). We affirmed that the largest component of a typical mammalian genome is comprised of TEs (average 45.6%). Of the 248 assemblies, the lowest genomic percentage of TEs was found in the star-nosed mole (27.6%), and the largest percentage was seen in the aardvark (74.5%), whose increase in TE accumulation drove a corresponding increase in genome size—a correlation we observed across Eutheria. The overall genomic proportions of recently accumulated TEs were roughly similar across most mammals in the dataset, with a few notable exceptions (see the figure). Diversity of recently accumulated TEs is highest among multiple families of bats, mostly driven by substantial DNA transposon activity. Our data also exhibit an increase of recently accumulated DNA transposons among carnivore lineages over their herbivorous counterparts, which suggests that diet may play a role in determining the genomic content of TEs., [CONCLUSION] The copious TE data provided in this work emanated from the largest comprehensive TE curation effort to date. Considering the wide-ranging effects that TEs impose on genomic architecture, these data are an important resource for future inquiries into mammalian genomics and evolution and suggest avenues for continued study of these important yet understudied genomic denizens.

[INTRODUCTION] Mammals, including humans, achieve high levels of organismal complexity largely due to how their proteins are regulated; characterizing the regulatory landscape of the human genome is a longstanding goal of modern biology. Contemporary approaches measure genome-wide biochemical signals, including chromatin accessibility, histone modifications, DNA methylation, and binding of ~1600 transcription factors (TFs) by the human genome. Using these methods, the ENCODE consortium defined almost one million candidate cis-regulatory elements (cCREs). Another approach uses evolutionary conservation to identify potential regulatory regions. We combine these approaches, examining how different functional classes of regulatory elements respond to evolutionary pressures., [RATIONALE] cCREs tend to be conserved and cCRE classes exhibit varying levels of conservation, suggesting interesting evolutionary dynamics. We examine these dynamics in placental mammals using tools developed by the Zoonomia project: the evolutionary constraint in placental mammals and the reference-free 241-genome alignment. We identify the human cCREs and transcription factor binding sites (TFBSs) conserved in the mammalian lineage, characterize the evolutionary histories of cCREs and TFBSs and identify the driving forces behind their gains and losses and—using biochemical and epigenomic data—assess the likelihood that conserved cCREs and TFBSs are functional in humans and other mammals., [RESULTS] We explored the ENCODE cCREs derived from epigenomic data and the binding sites of 367 TFs from chromatin immunoprecipitation data. We found a spectrum of mammalian conservation for regulatory elements: on one end lies the highly conserved cCREs and constrained TFBSs, and on the other are primate-specific cCREs and TFBSs overlapping transposable elements (TEs). Conserved elements predominate near genes that function in fundamental cellular processes (metabolism, development) and tend to be functional in other mammalian genomes whereas unconstrained elements lie near genes involved in interaction with the environment. We identified ~439 thousand deeply conserved cCREs (47.5% of cCREs and 4% of the human genome) and 2 million TFBSs (0.8% of the human genome) under mammalian constraint. Using a panel of 69 genome-wide association studies, we found that conserved cCREs and constrained TFBSs achieved high heritability enrichment, demonstrating their utility for functional interpretation of human genetic variants. Meanwhile, more than 85% of primate-specific TFBSs—representing more than 20% of all TFBSs—are derived from TEs. Phylogenetic analysis revealed a staggering number of TFBS clusters sharing patterns of presence and absence across primate genomes and enrichment in specific TE families, suggesting that multiple waves of TE insertion spread these TFBSs during primate evolution., [CONCLUSION] We charted the evolutionary landscapes of cCREs and TFBSs among placental mammals, identifying a subset of elements under purifying selection in the mammalian lineage. These elements are highly enriched in the human genetic variants associated with a panel of diverse, complex traits, with heritability enrichment contributed by both nucleotides under mammalian and nucleotides under primate constraint.

[INTRODUCTION] It has been almost 100 years since the sled dog Balto helped save the community of Nome, Alaska, from a diphtheria outbreak. Today, Balto symbolizes the indomitable spirit of the sled dog. He is immortalized in statue and film, and is physically preserved and on display at the Cleveland Museum of Natural History. Balto represents a dog population that was reputed to tolerate harsh conditions at a time when northern communities were reliant on sled dogs. Investigating Balto’s genome sequence using technologies for sequencing degraded DNA offers a new perspective on this historic population., [RATIONALE] Analyzing high-coverage (40.4-fold) DNA sequencing data from Balto through comparison with large genomic data resources offers an opportunity to investigate genetic diversity and genome function. We leveraged the genome sequence data from 682 dogs, including both working sled dogs and dog breeds, as well as evolutionary constraint scores from the Zoonomia alignment of 240 mammals, to reconstruct Balto’s phenotype and investigate his ancestry and what might distinguish him from modern dogs., [RESULTS] Balto shares just part of his diverse ancestry with the eponymous Siberian husky breed and was more genetically diverse than both modern breeds and working sled dogs. Both Balto and working sled dogs had a lower burden of rare, potentially damaging variation than modern breeds and fewer potentially damaging variants, suggesting that they represent genetically healthier populations. We inferred Balto’s appearance on the basis of genomic variants known to shape physical characteristics in dogs today. We found that Balto had a combination of coat features atypical for modern sled dog breeds and a slightly smaller stature, inferences that are confirmed by comparison to historical photographs. Balto’s ability to digest starch was enhanced compared to wolves and Greenland sled dogs but reduced compared to modern breeds. He carried a compendium of derived homozygous coding variants at constrained positions in genes connected to bone and skin development, which may have conferred a functional advantage., [CONCLUSION] Balto belonged to a population of small, fast, and fit sled dogs imported from Siberia. By sequencing his genome from his taxidermied remains and analyzing these data in the context of large comparative and canine datasets, we show that Balto and his working sled dog contemporaries were more genetically diverse than modern breeds and may have carried variants that helped them survive the harsh conditions of 1920s Alaska. Although the era of Balto and his contemporaries has passed, comparative genomics, supported by a growing collection of modern and past genomes, can provide insights into the selective pressures that shaped them.

Captive breeding programmes represent the most intensive type of ex situ population management for threatened species. One example is the Cuvier's gazelle programme that started in 1975 with only four founding individuals, and after more than four decades of management in captivity, a reintroduction effort was undertaken in Tunisia in 2016, to establish a population in an area historically included within its range. Here, we aim to determine the genetic consequences of this reintroduction event by assessing the genetic diversity of the founder stock as well as of their descendants. We present the first whole-genome sequencing dataset of 30 Cuvier's gazelles including captive-bred animals, animals born in Tunisia after a reintroduction and individuals from a genetically unrelated Moroccan population. Our analyses revealed no difference between the founder and the offspring cohorts in genome-wide heterozygosity and inbreeding levels, and in the amount and length of runs of homozygosity. The captive but unmanaged Moroccan gazelles have the lowest genetic diversity of all genomes analysed. Our findings demonstrate that the Cuvier's gazelle captive breeding programme can serve as source populations for future reintroductions of this species. We believe that this study can serve as a starting point for global applications of genomics to the conservation plan of this species.

Captive breeding programmes represent the most intensive type of ex situ population management for threatened species. One example is the Cuvier’s gazelle programme that started in 1975 with only four founding individuals, and after more than four decades of management in captivity, a reintroduction effort was undertaken in Tunisia in 2016, to establish a population in an area historically included within its range. Here, we aim to determine the genetic consequences of this reintroduction event by assessing the genetic diversity of the founder stock as well as of their descendants. We present the first whole-genome sequencing dataset of 30 Cuvier’s gazelles including captive-bred animals, animals born in Tunisia after a reintroduction and individuals from a genetically unrelated Moroccan population. Our analyses revealed no difference between the founder and the offspring cohorts in genome-wide heterozygosity and inbreeding levels, and in the amount and length of runs of homozygosity. The captive but unmanaged Moroccan gazelles have the lowest genetic diversity of all genomes analysed. Our findings demonstrate that the Cuvier’s gazelle captive breeding programme can serve as source populations for future reintroductions of this species. We believe that this study can serve as a starting point for global applications of genomics to the conservation plan of this species.