Linking genomics and population genetics with R

The Invasion of Megachile policaris (Hymenoptera: Megachilidae) to Hawai'i

Islands are insular environments that are negatively impacted by invasive species. In Hawai'i, at least 21 non-native bees have been documented to date, joining the diversity of >9,000 non-native and invasive species to the archipelago. The goal of this study is to describe the persistence, genetic diversity, and natural history of the most recently established bee to Hawai'i, Megachile policaris Say, 1831 (Hymenoptera: Megachilidae). Contemporary surveys identify that M. policaris is present on at least O'ahu, Maui, and Hawai'i Island, with the earliest detection of the species in 2017. Furthermore, repeated surveys and observations by community members support the hypothesis that M. policaris has been established on Hawai'i Island from 2017 to 2020. DNA sequenced fragments of the cytochrome oxidase I locus identify two distinct haplotypes on Hawai'i Island, suggesting that at least two founders have colonized the island. In their native range, M. policaris is documented to forage on at least 21 different plant families, which are represented in Hawai'i. Finally, ensemble species distribution models (SDMs) constructed with four bioclimatic variables and occurrence data from the native range of M. policaris predicts high habitat suitability on the leeward side of islands throughout the archipelago and at high elevation habitats. While many of the observations presented in our study fall within the predicted habitat suitability on Hawai'i, we also detected the M. policaris on the windward side of Hawai'i Island suggesting that the SDMs we constructed likely do not capture the bioclimatic niche flexibility of the species.

Asexuality Associated with Marked Genomic Expansion of Tandemly Repeated rRNA and Histone Genes

How does asexual reproduction influence genome evolution? Although is it clear that genomic structural variation is common and important in natural populations, we know very little about how one of the most fundamental of eukaryotic traits-mode of genomic inheritance-influences genome structure. We address this question with the New Zealand freshwater snail Potamopyrgus antipodarum, which features multiple separately derived obligately asexual lineages that coexist and compete with otherwise similar sexual lineages. We used whole-genome sequencing reads from a diverse set of sexual and asexual individuals to analyze genomic abundance of a critically important gene family, rDNA (the genes encoding rRNAs), that is notable for dynamic and variable copy number. Our genomic survey of rDNA in P. antipodarum revealed two striking results. First, the core histone and 5S rRNA genes occur between tandem copies of the 18S-5.8S-28S gene cluster, a unique architecture for these crucial gene families. Second, asexual P. antipodarum harbor dramatically more rDNA-histone copies than sexuals, which we validated through molecular and cytogenetic analysis. The repeated expansion of this genomic region in asexual P. antipodarum lineages following distinct transitions to asexuality represents a dramatic genome structural change associated with asexual reproduction-with potential functional consequences related to the loss of sexual reproduction.

Population Genomics Training for the Next Generation of Conservation Geneticists: ConGen 2018 Workshop

The increasing availability and complexity of next-generation sequencing (NGS) data sets make ongoing training an essential component of conservation and population genetics research. A workshop entitled “ConGen 2018” was recently held to train researchers in conceptual and practical aspects of NGS data production and analysis for conservation and ecological applications. Sixteen instructors provided helpful lectures, discussions, and hands-on exercises regarding how to plan, produce, and analyze data for many important research questions. Lecture topics ranged from understanding probabilistic (e.g., Bayesian) genotype calling to the detection of local adaptation signatures from genomic, transcriptomic, and epigenomic data. We report on progress in addressing central questions of conservation genomics, advances in NGS data analysis, the potential for genomic tools to assess adaptive capacity, and strategies for training the next generation of conservation genomicists.

Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis

The 16S rRNA gene has been a mainstay of sequence-based bacterial analysis for decades. However, high-throughput sequencing of the full gene has only recently become a realistic prospect. Here, we use in silico and sequence-based experiments to critically re-evaluate the potential of the 16S gene to provide taxonomic resolution at species and strain level. We demonstrate that targeting of 16S variable regions with short-read sequencing platforms cannot achieve the taxonomic resolution afforded by sequencing the entire (~1500 bp) gene. We further demonstrate that full-length sequencing platforms are sufficiently accurate to resolve subtle nucleotide substitutions (but not insertions/deletions) that exist between intragenomic copies of the 16S gene. In consequence, we argue that modern analysis approaches must necessarily account for intragenomic variation between 16S gene copies. In particular, we demonstrate that appropriate treatment of full-length 16S intragenomic copy variants has the potential to provide taxonomic resolution of bacterial communities at species and strain level.

Population Genomic Approaches for Weed Science

Genomic approaches are opening avenues for understanding all aspects of biological life, especially as they begin to be applied to multiple individuals and populations. However, these approaches typically depend on the availability of a sequenced genome for the species of interest. While the number of genomes being sequenced is exploding, one group that has lagged behind are weeds. Although the power of genomic approaches for weed science has been recognized, what is needed to implement these approaches is unfamiliar to many weed scientists. In this review we attempt to address this problem by providing a primer on genome sequencing and provide examples of how genomics can help answer key questions in weed science such as: (1) Where do agricultural weeds come from; (2) what genes underlie herbicide resistance; and, more speculatively, (3) can we alter weed populations to make them easier to control? This review is intended as an introduction to orient weed scientists who are thinking about initiating genome sequencing projects to better understand weed populations, to highlight recent publications that illustrate the potential for these methods, and to provide direction to key tools and literature that will facilitate the development and execution of weed genomic projects.

Emerging and Enduring Issues in Primate Conservation Genetics

Conservation genetics is a branch of conservation biology that uses molecular data to assist in the conservation and management of imperiled populations, subspecies, and species. In this review, I examine conservation action plans (CAPs)—instrumental documents designed to influence conservation policy—for selected primate species. I use the information contained in CAPs as a means to guide this review. The primary genetics-based topics that are mentioned in CAPs are genetic connectivity, inbreeding, and subspecies/species delimitation. I discuss these topics as well as historical demographic inference and hybridization using examples from wild primate species to illustrate the myriad ways in which genetics can assist in conservation efforts. I also discuss some recent technological advances such as genomic capture techniques and the potential to do molecular work in remote locations.

Inferring Variation in Copy Number Using High Throughput Sequencing Data in R

Inference of copy number variation presents a technical challenge because variant callers typically require the copy number of a genome or genomic region to be known a priori. Here we present a method to infer copy number that uses variant call format (VCF) data as input and is implemented in the R package vcfR. This method is based on the relative frequency of each allele (in both genic and non-genic regions) sequenced at heterozygous positions throughout a genome. These heterozygous positions are summarized by using arbitrarily sized windows of heterozygous positions, binning the allele frequencies, and selecting the bin with the greatest abundance of positions. This provides a non-parametric summary of the frequency that alleles were sequenced at. The method is applicable to organisms that have reference genomes that consist of full chromosomes or sub-chromosomal contigs. In contrast to other software designed to detect copy number variation, our method does not rely on an assumption of base ploidy, but instead infers it. We validated these approaches with the model system of Saccharomyces cerevisiae and applied it to the oomycete Phytophthora infestans, both known to vary in copy number. This functionality has been incorporated into the current release of the R package vcfR to provide modular and flexible methods to investigate copy number variation in genomic projects.

Best Practices for Population Genetic Analyses

Population genetic analysis is a powerful tool to understand how pathogens emerge and adapt. However, determining the genetic structure of populations requires complex knowledge on a range of subtle skills that are often not explicitly stated in book chapters or review articles on population genetics. What is a good sampling strategy? How many isolates should I sample? How do I include positive and negative controls in my molecular assays? What marker system should I use? This review will attempt to address many of these practical questions that are often not readily answered from reading books or reviews on the topic, but emerge from discussions with colleagues and from practical experience. A further complication for microbial or pathogen populations is the frequent observation of clonality or partial clonality. Clonality invariably makes analyses of population data difficult because many assumptions underlying the theory from which analysis methods were derived are often violated. This review provides practical guidance on how to navigate through the complex web of data analyses of pathogens that may violate typical population genetics assumptions. We also provide resources and examples for analysis in the R programming environment.