Although cancer rarely acts as an infectious disease, a recently emerged transmissible cancer in Tasmanian devils (Sarcophilus harrisii) is virtually 100% fatal. Devil facial tumour disease (DFTD) has swept across nearly the entire species' range, resulting in localized declines exceeding 90% and an overall species decline of more than 80% in less than 20 years. Despite epidemiological models that predict extinction, populations in long-diseased sites persist. Here we report rare genomic evidence of a rapid, parallel evolutionary response to strong selection imposed by a wildlife disease. We identify two genomic regions that contain genes related to immune function or cancer risk in humans that exhibit concordant signatures of selection across three populations. DFTD spreads between hosts by suppressing and evading the immune system, and our results suggest that hosts are evolving immune-modulated resistance that could aid in species persistence in the face of this devastating disease.

The golden jackal of Africa (Canis aureus) has long been considered a conspecific of jackals distributed throughout Eurasia, with the nearest source populations in the Middle East. However, two recent reports found that mitochondrial haplotypes of some African golden jackals aligned more closely to gray wolves (Canis lupus), which is surprising given the absence of gray wolves in Africa and the phenotypic divergence between the two species. Moreover, these results imply the existence of a previously unrecognized phylogenetically distinct species despite a long history of taxonomic work on African canids. To test the distinct-species hypothesis and understand the evolutionary history that would account for this puzzling result, we analyzed extensive genomic data including mitochondrial genome sequences, sequences from 20 autosomal loci (17 introns and 3 exon segments), microsatellite loci, X- and Y-linked zinc-finger protein gene (ZFX and ZFY) sequences, and whole-genome nuclear sequences in African and Eurasian golden jackals and gray wolves. Our results provide consistent and robust evidence that populations of golden jackals from Africa and Eurasia represent distinct monophyletic lineages separated for more than one million years, sufficient to merit formal recognition as different species: C. anthus (African golden wolf) and C. aureus (Eurasian golden jackal). Using morphologic data, we demonstrate a striking morphologic similarity between East African and Eurasian golden jackals, suggesting parallelism, which may have misled taxonomists and likely reflects uniquely intense interspecific competition in the East African carnivore guild. Our study shows how ecology can confound taxonomy if interspecific competition constrains size diversification.

Reconstructing species’ demographic histories is a central focus of molecular ecology and evolution. Recently, an expanding suite of methods leveraging either the sequentially Markovian coalescent (SMC) or the site-frequency spectrum has been developed to reconstruct population size histories from genomic sequence data. However, few studies have investigated the robustness of these methods to genome assemblies of varying quality. In this study, we first present an improved genome assembly for the Tasmanian devil using the Chicago library method. Compared with the original reference genome, our new assembly reduces the number of scaffolds (from 35,975 to 10,010) and increases the scaffold N90 (from 0.101 to 2.164 Mb). Second, we assess the performance of four contemporary genomic methods for inferring population size history (PSMC, MSMC, SMC++, Stairway Plot), using the two devil genome assemblies as well as simulated, artificially fragmented genomes that approximate the hypothesized demographic history of Tasmanian devils. We demonstrate that each method is robust to assembly quality, producing similar estimates of Ne when simulated genomes were fragmented into up to 5,000 scaffolds. Overall, methods reliant on the SMC are most reliable between ∼300 generations before present (gbp) and 100 kgbp, whereas methods exclusively reliant on the site-frequency spectrum are most reliable between the present and 30 gbp. Our results suggest that when used in concert, genomic methods for reconstructing species’ effective population size histories 1) can be applied to nonmodel organisms without highly contiguous reference genomes, and 2) are capable of detecting independently documented effects of historical geological events.

Identifying the genetic architecture of complex phenotypes is a central goal of modern biology, particularly for disease-related traits. Genome-wide association methods are a classical approach for identifying the genomic basis of variation in disease phenotypes, but such analyses are particularly challenging in natural populations due to sample size difficulties. Extensive mark-recapture data, strong linkage disequilibrium and a lethal transmissible cancer make the Tasmanian devil (Sarcophilus harrisii) an ideal model for such an association study. We used a RAD-capture approach to genotype 624 devils at ~16,000 loci and then used association analyses to assess the heritability of three cancer-related phenotypes: infection case-control (where cases were infected devils and controls were devils that were never infected), age of first infection and survival following infection. The SNP array explained much of the phenotypic variance for female survival (>80%) and female case-control (>61%). We found that a few large-effect SNPs explained much of the variance for female survival (~5 SNPs explained >61% of the total variance), whereas more SNPs (~56) of smaller effect explained less of the variance for female case-control (~23% of the total variance). By contrast, these same SNPs did not account for a significant proportion of phenotypic variance in males, suggesting that the genetic bases of these traits and/or selection differ across sexes. Loci involved with cell adhesion and cell-cycle regulation underlay trait variation, suggesting that the devil immune system is rapidly evolving to recognize and potentially suppress cancer growth through these pathways. Overall, our study provided necessary data for genomics-based conservation and management in Tasmanian devils.

New computational methods and next‐generation sequencing (NGS) approaches have enabled the use of thousands or hundreds of thousands of genetic markers to address previously intractable questions. The methods and massive marker sets present both new data analysis challenges and opportunities to visualize, understand, and apply population and conservation genomic data in novel ways. The large scale and complexity of NGS data also increases the expertise and effort required to thoroughly and thoughtfully analyze and interpret data. To aid in this endeavor, a recent workshop entitled “Population Genomic Data Analysis,” also known as “ConGen 2017,” was held at the University of Montana. The ConGen workshop brought 15 instructors together with knowledge in a wide range of topics including NGS data filtering, genome assembly, genomic monitoring of effective population size, migration modeling, detecting adaptive genomic variation, genomewide association analysis, inbreeding depression, and landscape genomics. Here, we summarize the major themes of the workshop and the important take‐home points that were offered to students throughout. We emphasize increasing participation by women in population and conservation genomics as a vital step for the advancement of science. Some important themes that emerged during the workshop included the need for data visualization and its importance in finding problematic data, the effects of data filtering choices on downstream population genomic analyses, the increasing availability of whole‐genome sequencing, and the new challenges it presents. Our goal here is to help motivate and educate a worldwide audience to improve population genomic data analysis and interpretation, and thereby advance the contribution of genomics to molecular ecology, evolutionary biology, and especially to the conservation of biodiversity.