Population structure in a continuously distributed coastal marine species, the harbor porpoise, based on microhaplotypes derived from poor-quality samples

Are rabid raccoons (Procyon lotor) ready for the rapture? Determining the geographic origin of rabies virus-infected raccoons using RADcapture and microhaplotypes

North America is recognized for the exceptional richness of rabies virus (RV) wildlife reservoir species. Management of RV is accomplished through vaccination targeting mesocarnivore reservoir populations, such as the raccoon (Procyon lotor) in Eastern North America. Raccoons are a common generalist species, and populations may reach high densities in developed areas, which can result in contact with humans and pets with potential exposures to the raccoon variant of RV throughout the eastern United States. Understanding the spatial movement of RV by raccoon populations is important for monitoring and refining strategies supporting the landscape-level control and local elimination of this lethal zoonosis. We developed a high-throughput genotyping panel for raccoons based on hundreds of microhaplotypes to identify population structure and genetic diversity relevant to rabies management programs. Throughout the eastern United States, we identified hierarchical population genetic structure with clusters that were connected through isolation-by-distance. We also illustrate that this genotyping approach can be used to support real-time management priorities by identifying the geographic origin of a rabid raccoon that was collected in an area of the United States that had been raccoon RV-free for 8 years. The results from this study and the utility of the microhaplotype panel and genotyping method will provide managers with information on raccoon ecology that can be incorporated into future management decisions.

Estimating microhaplotype allele frequencies from low-coverage or pooled sequencing data

BACKGROUND

Microhaplotypes have the potential to be more cost-effective than SNPs for applications that require genetic panels of highly variable loci. However, development of microhaplotype panels is hindered by a lack of methods for estimating microhaplotype allele frequency from low-coverage whole genome sequencing or pooled sequencing (pool-seq) data.

RESULTS

We developed new methods for estimating microhaplotype allele frequency from low-coverage whole genome sequence and pool-seq data. We validated these methods using datasets from three non-model organisms. These methods allowed estimation of allele frequency and expected heterozygosity at depths routinely achieved from pooled sequencing.

CONCLUSIONS

These new methods will allow microhaplotype panels to be designed using low-coverage WGS and pool-seq data to discover and evaluate candidate loci. The python script implementing the two methods and documentation are available at https://www.github.com/delomast/mhFromLowDepSeq .

SNP4OrphanSpecies: A bioinformatics pipeline to isolate molecular markers for studying genetic diversity of orphan species

Background

For several decades, an increase in disease or pest emergences due to anthropogenic introduction or environmental changes has been recorded. This increase leads to serious threats to the genetic and species diversity of numerous ecosystems. Many of these events involve species with poor or no genomic resources (called here "orphan species"). This lack of resources is a serious limitation to our understanding of the origin of emergent populations, their ability to adapt to new environments and to predict future consequences to biodiversity. Analyses of genetic diversity are an efficient method to obtain this information rapidly, but require available polymorphic genetic markers.

New information

We developed a generic bioinformatics pipeline to rapidly isolate such markers with the goal for the pipeline to be applied in studies of invasive taxa from different taxonomic groups, with a special focus on forest fungal pathogens and insect pests. This pipeline is based on: 1) an automated de novo genome assembly obtained from shotgun whole genome sequencing using paired-end Illumina technology; 2) the isolation of single-copy genes conserved in species related to the studied emergent organisms; 3) primer development for multiplexed short sequences obtained from these conserved genes. Previous studies have shown that intronic regions of these conserved genes generally contain several single nucleotide polymorphisms within species. The pipeline's functionality was evaluated with sequenced genomes of five invasive or expanding pathogen and pest species in Europe (Armillariaostoyae (Romagn.) Herink 1973, Bursaphelenchusxylophilus Steiner & Buhrer 1934, Sphaeropsissapinea (fr.) Dicko & B. Sutton 1980, Erysiphealphitoides (Griffon & Maubl.) U. Braun & S. Takam. 2000, Thaumetopoeapityocampa Denis & Schiffermüller, 1775). We successfully isolated several pools of one hundred short gene regions for each assembled genome, which can be amplified in multiplex. The bioinformatics pipeline is user-friendly and requires little computational resources. This easy-to-set-up and run method for genetic marker identification will be useful for numerous laboratories studying biological invasions, but with limited resources and expertise in bioinformatics.

The population genetics characteristics of a 90 locus panel of microhaplotypes

Single-nucleotide polymorphisms (SNPs) and small genomic regions with multiple SNPs (microhaplotypes, MHs) are rapidly emerging as novel forensic investigative tools to assist in individual identification, kinship analyses, ancestry inference, and deconvolution of DNA mixtures. Here, we analyzed information for 90 microhaplotype loci in 4009 individuals from 79 world populations in 6 major biogeographic regions. The study included multiplex microhaplotype sequencing (mMHseq) data analyzed for 524 individuals from 16 populations and genotype data for 3485 individuals from 63 populations curated from public repositories. Analyses of the 79 populations revealed excellent characteristics for this 90-plex MH panel for various forensic applications achieving an overall average effective number of allele values (A_e) of 4.55 (range 1.04–19.27) for individualization and mixture deconvolution. Population-specific random match probabilities ranged from a low of 10^–115 to a maximum of 10^–66. Mean informativeness (I_n) for ancestry inference was 0.355 (range 0.117–0.883). 65 novel SNPs were detected in 39 of the MHs using mMHseq. Of the 3018 different microhaplotype alleles identified, 1337 occurred at frequencies > 5% in at least one of the populations studied. The 90-plex MH panel enables effective differentiation of population groupings for major biogeographic regions as well as delineation of distinct subgroupings within regions. Open-source, web-based software is available to support validation of this technology for forensic case work analysis and to tailor MH analysis for specific geographical regions.

Big Data in Conservation Genomics: Boosting Skills, Hedging Bets, and Staying Current in the Field

A current challenge in the fields of evolutionary, ecological, and conservation genomics is balancing production of large-scale datasets with additional training often required to handle such datasets. Thus, there is an increasing need for conservation geneticists to continually learn and train to stay up-to-date through avenues such as symposia, meetings, and workshops. The ConGen meeting is a near-annual workshop that strives to guide participants in understanding population genetics principles, study design, data processing, analysis, interpretation, and applications to real-world conservation issues. Each year of ConGen gathers a diverse set of instructors, students, and resulting lectures, hands-on sessions, and discussions. Here, we summarize key lessons learned from the 2019 meeting and more recent updates to the field with a focus on big data in conservation genomics. First, we highlight classical and contemporary issues in study design that are especially relevant to working with big datasets, including the intricacies of data filtering. We next emphasize the importance of building analytical skills and simulating data, and how these skills have applications within and outside of conservation genetics careers. We also highlight recent technological advances and novel applications to conservation of wild populations. Finally, we provide data and recommendations to support ongoing efforts by ConGen organizers and instructors-and beyond-to increase participation of underrepresented minorities in conservation and eco-evolutionary sciences. The future success of conservation genetics requires both continual training in handling big data and a diverse group of people and approaches to tackle key issues, including the global biodiversity-loss crisis.