Evidence of Coevolution Between Cronartium harknessii Lineages and Their Corresponding Hosts, Lodgepole Pine and Jack Pine.

Variation in rate of infection and susceptibility of Pinus spp. to the fungus Cronartium harknessii (syn. Endocronartium harknessii), the causative agent of western gall rust, has been well documented. To test the hypothesis that there is a coevolutionary relationship between C. harknessii and its hosts, we examined genetic structure and virulence of C. harknessii associated with lodgepole pine (P. contorta var. latifolia), jack pine (P. banksiana), and their hybrids. A secondary objective was to improve assessment and diagnosis of infection in hosts. Using 18 microsatellites, we assessed genetic structure of C. harknessii from 90 sites within the ranges of lodgepole pine and jack pine. We identified two lineages (East and West, FST = 0.677) associated with host genetic structure (r = 0.81, P = 0.001), with East comprising three sublineages. In parallel, we conducted a factorial experiment in which lodgepole pine, jack pine, and hybrid seedlings were inoculated with spores from the two primary genetic lineages. With this experiment, we refined the phenotypic categories associated with infection and demonstrated that stem width can be used as a quantitative measure of host response to infection. Overall, each host responded differentially to the fungal lineages, with jack pine exhibiting more resiliency to infection than lodgepole pine and hybrids exhibiting intermediate resiliency. Taken together, the shared genetic structure between fungus and host species, and the differential interaction of the fungal species with the hosts, supports a coevolutionary relationship between host and pathogen.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Ancient hybridization patterns between bighorn and thinhorn sheep.

Whole-genome sequencing has advanced the study of species evolution, including the detection of genealogical discordant events such as ancient hybridization and incomplete lineage sorting (ILS). The evolutionary history of bighorn (Ovis canadensis) and thinhorn (Ovis dalli) sheep present an ideal system to investigate evolutionary discordance due to their recent and rapid radiation and putative secondary contact between bighorn and thinhorn sheep subspecies, specifically the dark pelage Stone sheep (O. dalli stonei) and predominately white Dall sheep (O. dalli dalli), during the last ice age. Here, we used multiple genomes of bighorn and thinhorn sheep, together with snow (O. nivicola) and the domestic sheep (O. aries) as outgroups, to assess their phylogenomic history, potential introgression patterns and their adaptive consequences. Among the Pachyceriforms (snow, bighorn and thinhorn sheep) a consistent monophyletic species tree was retrieved; however, many genealogical discordance patterns were observed. Alternative phylogenies frequently placed Stone and bighorn as sister clades. This relationship occurred more often and was less divergent than that between Dall and bighorn. We also observed many blocks containing introgression signal between Stone and bighorn genomes in which coat colour genes were present. Introgression signals observed between Dall and bighorn were more random and less frequent, and therefore probably due to ILS or intermediary secondary contact. These results strongly suggest that Stone sheep originated from a complex series of events, characterized by multiple, ancient periods of secondary contact with bighorn sheep.

Confidently identifying the correct K value using the ΔK method: When does K = 2?

Populations delineated based on genetic data are commonly used for wildlife conservation and management. Many studies use the program structure combined with the ΔK method to identify the most probable number of populations (K). We recently found K = 2 was identified more often when studies used ΔK compared to studies that did not. We suggested two reasons for this: hierarchical population structure leads to underestimation, or the ΔK method does not evaluate K = 1 causing an overestimation. The present contribution aims to develop a better understanding of the limits of the method using one, two and three population simulations across migration scenarios. From these simulations we identified the "best K" using model likelihood and ΔK. Our findings show that mean probability plots and ΔK are unable to resolve the correct number of populations once migration rate exceeds 0.005. We also found a strong bias towards selecting K = 2 using the ΔK method. We used these data to identify the range of values where the ΔK statistic identifies a value of K that is not well supported. Finally, using the simulations and a review of empirical data, we found that the magnitude of ΔK corresponds to the level of divergence between populations. Based on our findings, we suggest researchers should use the ΔK method cautiously; they need to report all relevant data, including the magnitude of ΔK, and an estimate of connectivity for the research community to assess whether meaningful genetic structure exists within the context of management and conservation.

The influence of a priori grouping on inference of genetic clusters: simulation study and literature review of the DAPC method.

Inference of genetic clusters is a key aim of population genetics, sparking development of numerous analytical methods. Within these, there is a conceptual divide between finding de novo structure versus assessment of a priori groups. Recently developed, Discriminant Analysis of Principal Components (DAPC), combines discriminant analysis (DA) with principal component (PC) analysis. When applying DAPC, the groups used in the DA (specified a priori or described de novo) need to be carefully assessed. While DAPC has rapidly become a core technique, the sensitivity of the method to misspecification of groups and how it is being empirically applied, are unknown. To address this, we conducted a simulation study examining the influence of a priori versus de novo group designations, and a literature review of how DAPC is being applied. We found that with a priori groupings, distance between genetic clusters reflected underlying FST. However, when migration rates were high and groups were described de novo there was considerable inaccuracy, both in terms of the number of genetic clusters suggested and placement of individuals into those clusters. Nearly all (90.1%) of 224 studies surveyed used DAPC to find de novo clusters, and for the majority (62.5%) the stated goal matched the results. However, most studies (52.3%) omit key run parameters, preventing repeatability and transparency. Therefore, we present recommendations for standard reporting of parameters used in DAPC analyses. The influence of groupings in genetic clustering is not unique to DAPC, and researchers need to consider their goal and which methods will be most appropriate.

Evolution and expression of the duck TRIM gene repertoire.

Tripartite motif (TRIM) proteins are involved in development, innate immunity, and viral restriction. TRIM gene repertoires vary between species, likely due to diversification caused by selective pressures from pathogens; however, this has not been explored in birds. We mined a de novo assembled transcriptome for the TRIM gene repertoire of the domestic mallard duck (Anas platyrhynchos), a reservoir host of influenza A viruses. We found 57 TRIM genes in the duck, which represent all 12 subfamilies based on their C-terminal domains. Members of the C-IV subfamily with C-terminal PRY-SPRY domains are known to augment immune responses in mammals. We compared C-IV TRIM proteins between reptiles, birds, and mammals and show that many C-IV subfamily members have arisen independently in these lineages. A comparison of the MHC-linked C-IV TRIM genes reveals expansions in birds and reptiles. The TRIM25 locus with related innate receptor modifiers is adjacent to the MHC in reptile and marsupial genomes, suggesting the ancestral organization. Within the avian lineage, both the MHC and TRIM25 loci have undergone significant TRIM gene reorganizations and divergence, both hallmarks of pathogen-driven selection. To assess the expression of TRIM genes, we aligned RNA-seq reads from duck tissues. C-IV TRIMs had high relative expression in immune relevant sites such as the lung, spleen, kidney, and intestine, and low expression in immune privileged sites such as in the brain or gonads. Gene loss and gain in the evolution of the TRIM repertoire in birds suggests candidate immune genes and potential targets of viral subversion.

A roadmap to robust discriminant analysis of principal components.

Identification of population structure is a common goal for a variety of applications, including conservation, wildlife management, and medical genetics. The outcome of these analyses can have far reaching implications; therefore, it is important to ensure an understanding of the strengths and weaknesses of the methodologies used. Increasing in popularity, the discriminant analysis of principal components (DAPC) method incorporates combinations of genetic variables (alleles) into a model that differentiates individuals into genetic clusters. However, users may not have a full understanding of how to best parameterize the model. In this issue of Thia (Molecular Ecology Resources, 2022) looks under the hood of the DAPC. Using simulated data, he demonstrates the importance of careful parameter selection in developing a DAPC model, what the implications are for over-fitting the model, and finally, how best to evaluate the results of DAPC models. This work highlights the issues that can arise when over-parameterizing the DAPC model when gene flow is high among clusters and provides important guidelines to ensure researchers are making conclusions that are biologically relevant.

Predicting the spread-risk potential of chronic wasting disease to sympatric ungulate species.

Wildlife disease incidence is increasing, resulting in negative impacts on the economy, biodiversity, and potentially human health. Chronic wasting disease (CWD) is a fatal, transmissible spongiform encephalopathy of cervids (wild and captive) which continues to spread geographically resulting in exposure to potential new host species. The disease agent (PrPCWD) is a misfolded conformer of the cellular prion protein (PrPC). In Canada, the disease is endemic in Alberta and Saskatchewan, affecting mule and white-tail deer, with lesser impact on elk and moose. As the disease continues to expand, additional wild ungulate species including bison, bighorn sheep, mountain goat, and pronghorn antelope may be exposed. To better understand the species-barrier, we reviewed the current literature on taxa naturally or experimentally exposed to CWD to identify susceptible and resistant species. We created a phylogeny of these taxa using cytochrome B and found that CWD susceptibility followed the species phylogeny. Using this phylogeny we estimated the probability of CWD susceptibility for wild ungulate species. We then compared PrPC amino acid polymorphisms among these species to identify which sites segregated between susceptible and resistant species. We identified sites that were significantly associated with susceptibility, but they were not fully discriminating. Finally, we sequenced Prnp from 578 wild ungulates to further evaluate their potential susceptibility. Together, these data suggest the host-range for CWD will potentially include pronghorn, mountain goat and bighorn sheep, but bison are likely to be more resistant. These findings highlight the need for monitoring potentially susceptible species as CWD continues to expand.

Linking genotype to phenotype to identify genetic variation relating to host susceptibility in the mountain pine beetle system.

Identifying genetic variants responsible for phenotypic variation under selective pressure has the potential to enable productive gains in natural resource conservation and management. Despite this potential, identifying adaptive candidate loci is not trivial, and linking genotype to phenotype is a major challenge in contemporary genetics. Many of the population genetic approaches commonly used to identify adaptive candidates will simultaneously detect false positives, particularly in nonmodel species, where experimental evidence is seldom provided for putative roles of the adaptive candidates identified by outlier approaches. In this study, we use outcomes from population genetics, phenotype association, and gene expression analyses as multiple lines of evidence to validate candidate genes. Using lodgepole and jack pine as our nonmodel study species, we analyzed 17 adaptive candidate loci together with 78 putatively neutral loci at 58 locations across Canada (N > 800) to determine whether relationships could be established between these candidate loci and phenotype related to mountain pine beetle susceptibility. We identified two candidate loci that were significant across all population genetic tests, and demonstrated significant changes in transcript abundance in trees subjected to wounding or inoculation with the mountain pine beetle fungal associate Grosmannia clavigera. Both candidates are involved in central physiological processes that are likely to be invoked in a trees response to stress. One of these two candidate loci showed a significant association with mountain pine beetle attack status in lodgepole pine. The spatial distribution of the attack-associated allele further coincides with other indicators of susceptibility in lodgepole pine. These analyses, in which population genetics was combined with laboratory and field experimental validation approaches, represent first steps toward linking genetic variation to the phenotype of mountain pine beetle susceptibility in lodgepole and jack pine, and provide a roadmap for more comprehensive analyses.

Fully automated sequence alignment methods are comparable to, and much faster than, traditional methods in large data sets: an example with hepatitis B virus.

Aligning sequences for phylogenetic analysis (multiple sequence alignment; MSA) is an important, but increasingly computationally expensive step with the recent surge in DNA sequence data. Much of this sequence data is publicly available, but can be extremely fragmentary (i.e., a combination of full genomes and genomic fragments), which can compound the computational issues related to MSA. Traditionally, alignments are produced with automated algorithms and then checked and/or corrected "by eye" prior to phylogenetic inference. However, this manual curation is inefficient at the data scales required of modern phylogenetics and results in alignments that are not reproducible. Recently, methods have been developed for fully automating alignments of large data sets, but it is unclear if these methods produce alignments that result in compatible phylogenies when compared to more traditional alignment approaches that combined automated and manual methods. Here we use approximately 33,000 publicly available sequences from the hepatitis B virus (HBV), a globally distributed and rapidly evolving virus, to compare different alignment approaches. Using one data set comprised exclusively of whole genomes and a second that also included sequence fragments, we compared three MSA methods: (1) a purely automated approach using traditional software, (2) an automated approach including by eye manual editing, and (3) more recent fully automated approaches. To understand how these methods affect phylogenetic results, we compared resulting tree topologies based on these different alignment methods using multiple metrics. We further determined if the monophyly of existing HBV genotypes was supported in phylogenies estimated from each alignment type and under different statistical support thresholds. Traditional and fully automated alignments produced similar HBV phylogenies. Although there was variability between branch support thresholds, allowing lower support thresholds tended to result in more differences among trees. Therefore, differences between the trees could be best explained by phylogenetic uncertainty unrelated to the MSA method used. Nevertheless, automated alignment approaches did not require human intervention and were therefore considerably less time-intensive than traditional approaches. Because of this, we conclude that fully automated algorithms for MSA are fully compatible with older methods even in extremely difficult to align data sets. Additionally, we found that most HBV diagnostic genotypes did not correspond to evolutionarily-sound groups, regardless of alignment type and support threshold. This suggests there may be errors in genotype classification in the database or that HBV genotypes may need a revision.