Mutations are the primary source of all genetic variation. Knowledge about their rates is critical for any evolutionary genetic analyses, but for a long time, that knowledge has remained elusive and indirectly inferred. In recent years, parent-offspring comparisons have yielded the first direct mutation rate estimates. The analyses are, however, challenging due to high rate of false positives and no consensus regarding standardized filtering of candidate de novo mutations. Here, we validate the application of a machine learning approach for such a task and estimate the mutation rate for the guppy (Poecilia reticulata), a model species in eco-evolutionary studies. We sequenced 4 parents and 20 offspring, followed by screening their genomes for de novo mutations. The initial large number of candidate de novo mutations was hard-filtered to remove false-positive results. These results were compared with mutation rate estimated with a supervised machine learning approach. Both approaches were followed by molecular validation of all candidate de novo mutations and yielded similar results. The ML method uniquely identified 3 mutations, but overall required more work and had higher rates of false positives and false negatives. We, thus, recommend its application if most of the mutations are expected to be identified or in case of experiment-specific biases. Both methods concordantly showed that guppy mutation rate is among the lowest directly estimated mutation rates in vertebrates. Similarly, low estimates were obtained for two other teleost fishes. We discuss potential explanations for such a pattern, as well as future utility and limitations of machine-learning approaches.
Environmental DNA (eDNA) metabarcoding has gained growing attention as a strategy for monitoring biodiversity in ecology. However, taxa identifications produced through metabarcoding require sophisticated processing of high-throughput sequencing data from taxonomically informative DNA barcodes. Various sets of universal and taxon-specific primers have been developed, extending the usability of metabarcoding across archaea, bacteria, and eukaryotes. Accordingly, a multitude of metabarcoding data analysis tools and pipelines have also been developed. Often, several developed workflows are designed to process the same amplicon sequencing data, making it somewhat puzzling to choose one amongst the plethora of existing pipelines. However, each pipeline has its own specific philosophy, strengths, and limitations, which should be considered depending on the aims of any specific study, as well as the bioinformatics expertise of the user. In this review, we outline the input data requirements, supported operating systems, and particular attributes of thirty-one amplicon processing pipelines with the goal of helping users to select a pipeline for their metabarcoding projects.
Single-nucleotide polymorphism (SNP) analyses are a powerful tool for population genetics, pedigree reconstruction and phenotypic trait mapping. SNPs could also be useful for sexing individuals in species with reduced sexual dimorphism, yet this possibility remains poorly explored. Here, we develop a novel protocol for molecular sexing of birds based on the detection of unique Z- and W-linked SNP markers. Our method is based on the identification of two unique loci, one in each sexual chromosome. Individuals are considered males when they are heterozygotic for the Z-linked SNP and females when they are homozygote for the Z-linked SNP and have the W-linked SNP. We validated the method in the Jackdaw (Corvus monedula), a species whose reduced sexual dimorphism makes it difficult to sex individuals in the wild. We assessed the reliability of the method with 36 individuals of known sex, and found that their sex was correctly assigned in 100% of cases. The sex-linked markers also proved to be widely applicable to discriminate males and females from a sample of 927 genotyped individuals of different maturity stages with an accuracy of 99.5%. Given that SNP markers are increasingly used in quantitative genetic analyses of wild populations, the approach we propose has a great potential to be integrated into broader genetic research programmes without the need of additional sexing techniques.
Inserts of DNA from extranuclear sources, such as organelles and microbes, are common in eukaryote nuclear genomes. However, sequence similarity between the nuclear and extranuclear DNA, and a history of multiple insertions, make the assembly of these regions challenging. Consequently, the number, sequence, and location of these vagrant DNAs cannot be reliably inferred from the genome assemblies of most organisms. We introduce two statistical methods to estimate the abundance of nuclear inserts even in the absence of a nuclear genome assembly. The first (intercept method) only requires low-coverage (<1x) sequencing data, as commonly generated for population studies of organellar and ribosomal DNAs. The second method additionally requires that a subset of the individuals carry extra-nuclear DNA with diverged genotypes. We validated our intercept method using simulations and by re-estimating the frequency of human NUMTs (nuclear mitochondrial inserts). We then applied it to the grasshopper Podisma pedestris, exceptional for both its large genome size and reports of numerous NUMT inserts, estimating that NUMTs make up 0.056% of the nuclear genome, equivalent to >500 times the mitochondrial genome size. We also re-analysed a museomics dataset of the parrot Psephotellus varius, obtaining an estimate of only 0.0043%, in line with reports from other species of bird. Our study demonstrates the utility of low-coverage high-throughput sequencing data for the quantification of nuclear vagrant DNAs. Beyond quantifying organellar inserts, these methods could also be used on endosymbiont-derived sequences. We provide an R implementation of our methods called “vagrantDNA” and code to simulate test datasets.
Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Link-based methods traditionally rely on relating pairwise genetic distance between individuals or demes to their landscape distance (e.g., geographic distance, cost distance). In this study, we present an alternative to conventional statistical approaches to refine cost surfaces by adapting the Gradient Forest (GF) approach to produce a resistance surface. Used in community ecology, GF is an extension of random forest (RF), and has been implemented in genomic studies to model species genetic offset under future climatic scenarios. By design, this adapted method, resGF, has the ability to handle multiple environmental predicators and is not subjected to traditional assumptions of linear models such as independence, normality and linearity. Using genetic simulations, resGF performance was compared to other published methods. In univariate scenarios, resGF was able to distinguish the true surface contributing to genetic diversity among competing surfaces better than the compared methods. In multivariate scenarios, the GF approach performed similarly to the other RF-based approach using least-cost transect analysis (LCTA). Additionally, two worked examples are provided using two previously published datasets. This machine learning algorithm has the potential to improve our understanding of landscape connectivity and can inform long-term biodiversity conservation strategies.
Age is an essential trait for understanding the ecology and management of wildlife. A conventional method of estimating age in wild animals is counting annuli formed in the cementum of teeth. This method has been used in bears despite some disadvantages, such as high invasiveness and the requirement for experienced observers. In this study, we established a novel age estimation method based on DNA methylation levels using blood collected from 49 brown bears of known ages living in both captivity and the wild. We performed bisulfite pyrosequencing and obtained methylation levels at 39 cytosine-phosphate-guanine (CpG) sites adjacent to 12 genes. The methylation levels of CpGs adjacent to four genes showed a significant correlation with age. The best model was based on DNA methylation levels at just four CpG sites adjacent to a single gene, SLC12A5, and it had high accuracy with a mean absolute error of 1.3 years and median absolute error of 1.0 year after leave-one-out cross-validation. This model represents the first epigenetic method of age estimation in brown bears, which provides benefits over tooth-based methods, including high accuracy, less invasiveness, and a simple procedure. Our model has the potential for application to other bear species, which will greatly improve ecological research, conservation, and management.
A large part of the soil protist diversity is missed in metabarcoding studies based on 0.25 g of soil environmental DNA (eDNA) and universal primers due to ca. 80 % co-amplification of non-target plants, animals and fungi. To overcome this problem, enrichment of the substrate used for eDNA extraction is an easyly implemented option but its effect has not yet been tested. In this study, we evaluated the effect of a 150 µm mesh size filtration and sedimentation method to improve the recovery of protist eDNA, while reducing the co-extraction of plant, animal and fungal eDNA, using a set of contrasted forest and alpine soils from La Réunion, Japan, Spain and Switzerland. Biodiversity of the whole eukaryotic community was estimated with V4 18S rRNA metabarcoding and classical amplicon sequence variant calling. A 2-3-fold enrichment in shelled protists (Euglyphida, Arcellinida and Chrysophyceae) was observed at the sample level with the proposed method, with, at the same time, a 2-fold depletion of Fungi and a 3-fold depletion of Embryophyceae. Protist alpha diversity was slightly lower in filtered samples due to reduced coverage in Variosea and Sarcomonadea, but significant differences were observed in only one region. Beta diversity was mostly impacted by region and habitat, and explained the same variance in bulk soil and filtered samples. The increase resolution in the soil protist diversity provided by the filtration-sedimentation method is a strong argument to include it in the standard preparation of any future soil for protist eDNA metabarcoding studies.
In the face of global biodiversity declines, surveys of beneficial and antagonistic arthropod diversity as well as the ecological services that they provide are increasingly important in both natural and agro-ecosystems. Conventional survey methods used to monitor these communities often require extensive taxonomic expertise and are time-intensive, potentially limiting their application in industries such as agriculture, where arthropods often play a critical role in productivity (e.g. pollinators, pests and predators). Environmental DNA (eDNA) metabarcoding of a novel substrate, crop flowers, may offer an accurate and high throughput alternative to aid in the detection managed and unmanaged arthropod taxa (e.g. flower-visiting insects and potential pollinators). Here, we compared the arthropod communities detected with eDNA metabarcoding of flowers, from an agricultural species (Persea americana - ‘Hass’ avocado), with two conventional survey techniques; Digital Video Recording (DVR) devices and pan traps. In total, 80 eDNA flower samples, 96 hours of DVRs and 48 pan trap samples were collected. Across the three methods, 49 arthropod families were identified, of which 12 were unique to the eDNA dataset. Alpha diversity levels did not differ across the three survey methods although taxonomic composition varied significantly, with only 12% of arthropod families found to be common across all three methods. This study demonstrates that eDNA metabarcoding of flowers to detect visiting arthropods, although in a developmental stage, can complement traditional survey methods and increase the diversity of taxa detected with implications for both natural and agro-ecosystems.
Genotype environment association (GEA) studies have the potential to identify the genetic basis of local adaptation in natural populations. Specifically, GEA approaches look for a correlation between allele frequencies and putatively selective features of the environment. Genetic markers with extreme evidence of correlation with the environment are presumed to be tagging the location of alleles that contribute to local adaptation. In this study, we propose a new method for GEA studies called the weighted-Z analysis (WZA) that combines information from closely linked sites into analysis windows in a way that was inspired by methods for calculating FST. We analyze simulations modelling local adaptation to heterogeneous environments to compare the WZA with existing methods. In the majority of cases we tested, the WZA either outperformed single-SNP based approaches or performed similarly. In particular, the WZA outperformed individual SNP approaches when a small number of individuals or demes was sampled. We apply the WZA to previously published data from lodgepole pine and identified candidate loci that were not found in the original study.
There is growing interest in the role of structural variants (SVs) as drivers of local adaptation and speciation. From a biodiversity genomics perspective, the characterisation of genome-wide SVs provides an exciting opportunity to complement single nucleotide polymorphisms (SNPs). However, little is known about the impacts of SV discovery and genotyping strategies on the characterisation of genome-wide SV diversity within and among populations. Here, we explore a near whole-species resequence dataset, and long-read sequence data for a subset of highly represented individuals in the critically endangered kākāpō (Strigops habroptilus). We demonstrate that even when using a highly contiguous reference genome, different discovery and genotyping strategies can significantly impact the type, size and location of SVs characterised genome-wide. Further, we found that the mean number of SVs in each of two kākāpō lineages differed both within and across generations. These combined results suggest that genome-wide characterisation of SVs remains challenging at the population-scale. We are optimistic that increased accessibility to long-read sequencing and advancements in bioinformatic approaches including multi-reference approaches like genome graphs will alleviate at least some of the challenges associated with resolving SV characteristics below the species level. In the meantime, we address caveats, highlight considerations, and provide recommendations for the characterization of genome-wide SVs in biodiversity genomic research.
The Harbour porpoise (Phocoena phocoena) is a highly mobile cetacean species which primarily occurs in coastal and shelf waters across the Northern hemisphere. It inhabits heterogeneous seascapes that vary broadly in salinity and temperature. Here we produced 74 whole genomes at intermediate coverage to study Harbour porpoise’s evolutionary history and investigate the role of local adaptation in the diversification into subspecies and populations. We identified ~6 million high quality SNPs sampled at 8 localities across the North Atlantic and adjacent waters, which we used for population structure, demographic, and genotype-environment association analyses. Our results support a genetic differentiation between three subspecies, and three distinct populations within the subspecies P.p. phocoena: Atlantic, Belt Sea and Proper Baltic Sea. Effective population size and Tajima’s D levels suggest a population contraction in both Black Sea and Iberian porpoises while a population expansion in the P.p. phocoena populations. Phylogenetic trees indicate a post-glacial colonization of Harbour porpoises from a southern refugium. Genotype-environment association analysis identified salinity as a major driver in genomic variation and we identified candidate genes putatively underlying adaptation to different salinity levels. Our study highlights the value of whole genome resequencing to unravel subtle population structure in highly mobile species and shows how strong environmental gradients and local adaptation may lead to population differentiation. The results have great conservation implications as we found major levels of inbreeding and low genetic diversity in the endangered Black Sea subspecies and identified the critically endangered Proper Baltic Sea porpoises as a separate population.
Understanding the evolutionary consequences of anthropogenic change is imperative for estimating long-term species resilience. While contemporary genomic data can provide us with important insights into recent demographic histories, investigating past change using present genomic data alone has limitations. In comparison, temporal genomics studies, defined herein as those that incorporate time series genomic data, leverage museum collections and repeated field sampling to directly examine evolutionary change. As temporal genomics is applied to more systems, species, and questions, best practices can be helpful guides to make the most efficient use of limited resources. Here, we conduct a systematic literature review to synthesize the effects of temporal genomics methodology on our ability to detect evolutionary changes. We focus on studies investigating recent change within the past 200 years, highlighting evolutionary processes that have occurred during the past two centuries of accelerated anthropogenic pressure. We first identify the most frequently studied taxa, systems, questions, and drivers, before highlighting overlooked areas where further temporal genomic studies may be particularly enlightening. Then, we provide guidelines for future study and sample designs while identifying key considerations that may influence statistical and analytical power. Our aim is to provide recommendations to a broad array of researchers interested in using temporal genomics in their work.
The ability to gather genetic information from organisms obtained directly from environmental samples is crucial to determine biodiversity baselines and understanding population dynamics in the marine realm. While DNA metabarcoding is effective in evaluating biodiversity at community level, genetic patterns within species are often concealed in metabarcoding studies and overlooked for marine invertebrates. In the present study, we implement recently developed bioinformatics tools to investigate intraspecific genetic variability for invertebrate taxa in the Mediterranean Sea. Using metabarcoding samples from Autonomous Reef Monitoring Structures (ARMS) deployed in three locations, we present haplotypes and diversity estimates for 145 unique species. While overall genetic diversity was low, we identified several species with high diversity records and potential cryptic lineages. Further, we emphasize the spatial scale of genetic variability, which was observed from locations to individual sampling units (ARMS). We carried out a population genetic analysis of several important yet understudied species, which highlights the current knowledge gap concerning intraspecific genetic patterns for the target taxa in the Mediterranean basin. Our approach considerably enhances biodiversity monitoring of charismatic and understudied Mediterranean species, which can be incorporated into ARMS surveys.
Identifying sex-linked markers in genomic datasets is important, because their analyses can reveal sex-specific biology, and their presence in supposedly neutral autosomal datasets can result in incorrect estimates of genetic diversity, population structure and parentage. But detecting sex-linked loci can be challenging, and available scripts neglect some categories of sex-linked variation. Here, we present new R functions to (1) identify and separate sex-linked loci in ZW and XY sex determination systems and (2) infer the genetic sex of individuals based on these loci. Two additional functions are presented, to (3) remove loci with artefactually high heterozygosity, and (4) produce input files for parentage analysis. We test these functions on genomic data for two sexually-monomorphic bird species, including one with a neo-sex chromosome system, by comparing biological inferences made before and after removing sex-linked loci using our function. We found that standard filters, such as low read depth and call rate, failed to remove up to 28.7% of sex-linked loci. This led to (i) overestimation of population FIS by ≤ 9%, and the number of private alleles by ≤ 8%; (ii) wrongly inferring significant sex-differences in heterozygosity, (iii) obscuring genetic population structure, and (iv) inferring ~11% fewer correct parentages. We discuss how failure to remove sex-linked markers can lead to incorrect biological inferences (e.g., sex-biased dispersal and cryptic population structure) and misleading management recommendations. For reduced-representation datasets with at least 15 known-sex individuals of each sex, our functions offer convenient, easy-to-use resources to avoid this, and to sex the remaining individuals.
Although plastid genome (plastome) structure is highly conserved across most seed plants, investigations during the past two decades have revealed several disparately related lineages that have experienced substantial rearrangements. Most plastomes have two inverted repeat regions and two single-copy regions with few dispersed repeats. However, the plastomes of some taxa do harbor long repeat sequences (>300 bp). These long repeats make it difficult to assemble complete plastomes using short read data, leading to misassemblies and consensus sequences that have spurious rearrangements. Long read sequencing can potentially overcome these challenges. However, there is no consensus as to the most effective method for accurately assembling plastomes using long read data. Here, we generated a pipeline, plastid Genome Assembly Using Long-read data (ptGAUL) to address the problem of assembling of plastomes using long read data from Oxford Nanopore Technologies (ONT) or Pacific Biosciences (Pacbio) platforms. We demonstrated the efficacy of the ptGAUL pipeline using 16 published long read datasets. We showed that ptGAUL produces accurate and unbiased assemblies. Additionally, we applied ptGAUL to assemble four Juncus (Juncaceae) plastomes using ONT long reads. Our results revealed many long repeats and rearrangements in Juncus plastomes compared with basal lineages of Poales.
Dispersal is a crucial mechanism to living beings, allowing them to reach new resources such that populations and species can explore new environments. However, directly observing the dispersal mechanisms of widespread species can be costly or even impracticable, which is the case for mangrove trees. The influence of ocean currents on the mangroves’ propagules’ movement has been increasingly evident; however, few studies mechanistically relate the patterns of population distribution with the dispersal by oceanic currents under an integrated framework. Here, we evaluate the role of oceanic currents on dispersal and connectivity of Rhizophora mangle along the Southwest Atlantic. We inferred population genetic structure and migration rates based on single nucleotide polymorphisms, simulated the displacement of propagules along the region and tested our hypotheses with Mantel tests and redundancy analysis. We observed a two populations structure, north and south, which is corroborated by other studies with Rhizophora and other coastal plants. The inferred recent migration rates do not indicate gene flow between the sampled sites. Conversely, long-term migration rates were low across groups and contrasting dispersal patterns within each one, which is consistent with long-distance dispersal events. Our hypothesis tests suggests that both isolation by distance and isolation by oceanography (derived from the oceanic currents) can explain the neutral genetic variation of R. mangle in the region. Our findings expand current knowledge of mangrove connectivity and highlight how the association of molecular methods with oceanographic simulations improve the interpretation power of the dispersal process, which has ecological and evolutionary implications.
Genomics can play important roles in biodiversity conservation, especially for Extinct-in-the-Wild species where genetic factors can influence total extinction risk and probability of successful reintroductions. The Christmas Island blue-tailed skink (Cryptoblepharus egeriae) and Lister’s gecko (Lepidodactylus listeri) are two endemic reptile species that went extinct in the wild shortly after the introduction of a predatory snake. After a decade of management, captive populations have expanded from 66 skinks and 43 geckos to several thousand individuals; however, little is known about patterns of genetic variation in these species. Here, we use PacBio HiFi long-read and Hi-C sequencing to generate contiguous reference genomes for both species, including the XY chromosome pair in the skink. We then analyze patterns of genetic diversity to infer ancient demography and more recent histories of inbreeding. We observe high genome-wide heterozygosity in the blue-tailed skink (0.007) and Lister’s gecko (0.005), consistent with large historical population sizes. However, nearly 10% of the skink reference genome falls within long runs of homozygosity (ROH), resulting in homozygosity at all major histocompatibility complex (MHC) loci, whereas we detect only a single ROH in the gecko. We infer from the ROH lengths that related skinks may have established the captive populations. Despite a shared recent extinction in the wild, our results suggest important differences in species’ histories and implications for management. We show how reference genomes can provide evolutionary and conservation insights in the absence of resequencing data, and we provide a resource for future population-level and comparative genomic studies in reptiles.
Lifespan is a key attribute of a species’ life cycle and varies extensively among major lineages of animals. In fish, lifespan varies by several orders of magnitude, with reported values ranging from less than one year to approximately 400 years. Lifespan information is particularly useful for species management, as it can be used to estimate invasion potential, extinction risk and sustainable harvest rates. Despite its utility, lifespan is unknown for most fish species. This is due to the difficulties associated with accurately identifying the oldest individual(s) of a given species, and/or deriving lifespan estimates that are representative for an entire species. Recently it has been shown that CpG density in gene promoter regions can be used to predict lifespan in mammals and other vertebrates, with variable accuracy across taxa. To improve accuracy of lifespan prediction in a non-mammalian vertebrate, here we develop a fish-specific genomic lifespan predictor. Addressing previous issues of low sample size and sequence dissimilarity, we incorporate more than eight times the number of fish species used previously (n = 442) and use fish-specific gene promoters as reference sequences. Our model predicts fish lifespan from genomic CpG density alone (measured as CpG observed/expected ratio), explaining 64 % of the variance between known and predicted lifespans. The results demonstrate the value of promoter CpG density as a universal predictor of fish lifespan that can applied where empirical data are unavailable, or impracticable to obtain.