Batch effects in a multiyear sequencing study: False biological trends due to changes in read lengths

Genomic insights into the conservation of wild and domestic animal diversity: A review

Due to environmental change and anthropogenic activities, global biodiversity has suffered an unprecedented loss, and the world is now heading toward the sixth mass extinction event. This urges the need to step up our efforts to promote the sustainable use of animal genetic resources and plan effective strategies for their conservation. Although habitat preservation and restoration are the primary means of conserving biodiversity, genomic technologies offer a variety of novel tools for identifying biodiversity hotspots and thus, support conservation efforts. Conservation genomics is a broad area of science that encompasses the application of genomic data from thousands or tens of thousands of genome-wide markers to address important conservation biology concerns. Genomic approaches have revolutionized the way we understand and manage animal populations, providing tools to identify and preserve unique genetic variants and alleles responsible for adaptive genetic variation, reducing the deleterious consequences of inbreeding, and increasing the adaptive potential of threatened species. The advancement of genomic technologies, particularly comparative genomic approaches, and the increased accessibility of genomic resources in the form of genome-enabled taxa for non-model organisms, provides a distinct advantage in defining conservation units over traditional genetics approaches. The objective of this review is to provide an exhaustive overview of the concept of conservation genomics, discuss the rationale behind the transition from conservation genetics to genomic approaches, and emphasize the potential applications of genomic techniques for conservation purposes. We also highlight interesting case studies in both livestock and wildlife species where genomic techniques have been used to accomplish conservation goals. Finally, we address some challenges and future perspectives in this field.

Using SNP addresses for Salmonella Typhimurium DT104 in routine veterinary outbreak detection

SNP addresses are a pathogen typing method based on whole-genome sequences (WGSs), assigning groups at seven different levels of genetic similarity. Public health surveillance uses it for several gastro-intestinal infections; this work trialled its use in veterinary surveillance for salmonella outbreak detection. Comparisons were made between temporal and spatio-temporal cluster detection models that either defined cases by their SNP address or by phage type, using historical data sets. Clusters of SNP incidents were effectively detected by both methods, but spatio-temporal models consistently detected these clusters earlier than the corresponding temporal models. Unlike phage type, SNP addresses appeared spatially and temporally limited, which facilitated the differentiation of novel, stable, or expanding clusters in spatio-temporal models. Furthermore, these models flagged spatio-temporal clusters containing only two to three cases at first detection, compared with a median of seven cases in phage-type models. The large number of SNP addresses will require automated methods to implement these detection models routinely. Further work is required to explore how temporal changes and different host species may impact the sensitivity and specificity of cluster detection. In conclusion, given validation with more sequencing data, SNP addresses are likely to be a valuable addition to early warning systems in veterinary surveillance.

Evolution of the Mutation Spectrum Across a Mammalian Phylogeny

Although evolutionary biologists have long theorized that variation in DNA repair efficacy might explain some of the diversity of lifespan and cancer incidence across species, we have little data on the variability of normal germline mutagenesis outside of humans. Here, we shed light on the spectrum and etiology of mutagenesis across mammals by quantifying mutational sequence context biases using polymorphism data from thirteen species of mice, apes, bears, wolves, and cetaceans. After normalizing the mutation spectrum for reference genome accessibility and k-mer content, we use the Mantel test to deduce that mutation spectrum divergence is highly correlated with genetic divergence between species, whereas life history traits like reproductive age are weaker predictors of mutation spectrum divergence. Potential bioinformatic confounders are only weakly related to a small set of mutation spectrum features. We find that clock-like mutational signatures previously inferred from human cancers cannot explain the phylogenetic signal exhibited by the mammalian mutation spectrum, despite the ability of these signatures to fit each species' 3-mer spectrum with high cosine similarity. In contrast, parental aging signatures inferred from human de novo mutation data appear to explain much of the 1-mer spectrum's phylogenetic signal in combination with a novel mutational signature. We posit that future models purporting to explain the etiology of mammalian mutagenesis need to capture the fact that more closely related species have more similar mutation spectra; a model that fits each marginal spectrum with high cosine similarity is not guaranteed to capture this hierarchy of mutation spectrum variation among species.

"Evolution of the mutation spectrum across a mammalian phylogeny"

Little is known about how the spectrum and etiology of germline mutagenesis might vary among mammalian species. To shed light on this mystery, we quantify variation in mutational sequence context biases using polymorphism data from thirteen species of mice, apes, bears, wolves, and cetaceans. After normalizing the mutation spectrum for reference genome accessibility and k -mer content, we use the Mantel test to deduce that mutation spectrum divergence is highly correlated with genetic divergence between species, whereas life history traits like reproductive age are weaker predictors of mutation spectrum divergence. Potential bioinformatic confounders are only weakly related to a small set of mutation spectrum features. We find that clocklike mutational signatures previously inferred from human cancers cannot explain the phylogenetic signal exhibited by the mammalian mutation spectrum, despite the ability of these clocklike signatures to fit each species' 3-mer spectrum with high cosine similarity. In contrast, parental aging signatures inferred from human de novo mutation data appear to explain much of the mutation spectrum's phylogenetic signal when fit to non-context-dependent mutation spectrum data in combination with a novel mutational signature. We posit that future models purporting to explain the etiology of mammalian mutagenesis need to capture the fact that more closely related species have more similar mutation spectra; a model that fits each marginal spectrum with high cosine similarity is not guaranteed to capture this hierarchy of mutation spectrum variation among species.

The systematics and evolution of the Sri Lankan rainforest land snail Corilla: New insights from RADseq-based phylogenetics

The stylommatophoran land-snail genus Corilla is endemic to Sri Lanka and India's Western Ghats. On the basis of habitat distribution and shell morphology, the 10 extant Sri Lankan species fall into two distinct groups, lowland and montane. Here, we use phylogenetic analyses of restriction-site-associated DNA sequencing (RADseq) data and ancestral-state reconstructions of habitat association and shell morphology to clarify the systematics and evolution of Sri Lankan Corilla. Our dataset consists of 9 species of Corilla. Phylogenetic analyses were based on 88 assemblies (9,604-4,132,850 bp) generated by the RADseq assembler ipyrad, using four parameter combinations and different levels of missing data. Trees were inferred using a maximum likelihood (ML) approach. Ancestral states were reconstructed using maximum parsimony (MP) and ML approaches, with 1 binary state character analysed for habitat association (lowland vs montane) and 6 binary state characters analysed for shell morphology (shape, colour, lip width, length of upper palatal folds, orientation of upper palatal folds and collabral sculpture). Over a wide range of missing data (40-87 % missing individuals per locus) and assembly sizes (62,279-4,132,850 bp), nearly all trees conformed to one of two topologies (A and B), most relationships were strongly supported and total branch support approached the maximal value. Apart from the position of Corilla odontophora 'south', topologies A and B showed similar, well-resolved relationships at and above the species level. Our study agrees with the shell-based taxonomy of C. adamsi, C. beddomeae, C. carabinata, C. colletti and C. humberti (all maximally supported as monophyletic species). It shows that C. erronea and C. fryae constitute a single relatively widespread species (for which the valid name is C. erronea) and that the names C. gudei and C. odontophora each apply to at least two distinct, yet conchologically-cryptic species. The MP and ML ancestral-state reconstructions yielded broadly similar results and provide firm evidence that diversification in Sri Lankan Corilla has involved evolutionary convergence in the shell morphology of lowland lineages, with a pale shell and wide lip having evolved on at least two separate occasions (in C. carabinata and C. colletti) from montane ancestors having a dark, narrow-lipped shell.

Genomic reaction norms inform predictions of plastic and adaptive responses to climate change

Genomic reaction norms represent the range of gene expression phenotypes (usually mRNA transcript levels) expressed by a genotype along an environmental gradient. Reaction norms derived from common‐garden experiments are powerful approaches for disentangling plastic and adaptive responses to environmental change in natural populations. By treating gene expression as a phenotype in itself, genomic reaction norms represent invaluable tools for exploring causal mechanisms underlying organismal responses to climate change across multiple levels of biodiversity.

Our goal is to provide the context, framework and motivation for applying genomic reaction norms to study the responses of natural populations to climate change.

Here, we describe the utility of integrating genomics with common‐garden‐gradient experiments under a reaction norm analytical framework to answer fundamental questions about phenotypic plasticity, local adaptation, their interaction (i.e. genetic variation in plasticity) and future adaptive potential.

An experimental and analytical framework for constructing and analysing genomic reaction norms is presented within the context of polygenic climate change responses of structured populations with gene flow. Intended for a broad eco‐evo readership, we first briefly review adaptation with gene flow and the importance of understanding the genomic basis and spatial scale of adaptation for conservation and management of structured populations under anthropogenic change. Then, within a high‐dimensional reaction norm framework, we illustrate how to distinguish plastic, differentially expressed (difference in reaction norm intercepts) and differentially plastic (difference in reaction norm slopes) genes, highlighting the areas of opportunity for applying these concepts.

We conclude by discussing how genomic reaction norms can be incorporated into a holistic framework to understand the eco‐evolutionary dynamics of climate change responses from molecules to ecosystems. We aim to inspire researchers to integrate gene expression measurements into common‐garden experimental designs to investigate the genomics of climate change responses as sequencing costs become increasingly accessible.

Comparative Genomics of Disease and Carriage Serotype 1 Pneumococci

The isolation of Streptococcus pneumoniae serotypes in systemic tissues of patients with invasive disease versus the nasopharynx of healthy individuals with asymptomatic carriage varies widely. Some serotypes are hyper-invasive, particularly serotype 1, but the underlying genetics remain poorly understood due to the rarity of carriage isolates, reducing the power of comparison with invasive isolates. Here, we use a well-controlled genome-wide association study to search for genetic variation associated with invasiveness of serotype 1 pneumococci from a serotype 1 endemic setting in Africa. We found no consensus evidence that certain genomic variation is overrepresented among isolates from patients with invasive disease than asymptomatic carriage. Overall, the genomic variation explained negligible phenotypic variability, suggesting a minimal effect on the disease status. Furthermore, changes in lineage distribution were seen with lineages replacing each other over time, highlighting the importance of continued pathogen surveillance. Our findings suggest that the hyper-invasiveness is an intrinsic property of the serotype 1 strains, not specific for a "disease-associated" subpopulation disproportionately harboring unique genomic variation.

Weaving place‐based knowledge for culturally significant species in the age of genomics: Looking to the past to navigate the future

Relationships with place provide critical context for characterizing biocultural diversity. Yet, genetic and genomic studies are rarely informed by Indigenous or local knowledge, processes, and practices, including the movement of culturally significant species. Here, we show how place‐based knowledge can better reveal the biocultural complexities of genetic or genomic data derived from culturally significant species. As a case study, we focus on culturally significant southern freshwater kōura (crayfish) in Aotearoa me Te Waipounamu (New Zealand, herein Aotearoa NZ). Our results, based on genotyping‐by‐sequencing markers, reveal strong population genetic structure along with signatures of population admixture in 19 genetically depauperate populations across the east coast of Te Waipounamu. Environment association and differentiation analyses for local adaptation also indicate a role for hydroclimatic variables—including temperature, precipitation, and water flow regimes—in shaping local adaptation in kōura. Through trusted partnerships between community and researchers, weaving genomic markers with place‐based knowledge has both provided invaluable context for the interpretation of data and created opportunities to reconnect people and place. We envisage such trusted partnerships guiding future genomic research for culturally significant species in Aotearoa NZ and beyond.

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

Long-read sequencing reveals the evolutionary drivers of intra-host diversity across natural RNA mycovirus infections

Intra-host dynamics are a core component of virus evolution but most intra-host data come from a narrow range of hosts or experimental infections. Gaining broader information on the intra-host diversity and dynamics of naturally occurring virus infections is essential to our understanding of evolution across the virosphere. Here we used PacBio long-read HiFi sequencing to characterize the intra-host populations of natural infections of the RNA mycovirus Cryphonectria hypovirus 1 (CHV1). CHV1 is a biocontrol agent for the chestnut blight fungus (Cryphonectria parasitica), which co-invaded Europe alongside the fungus. We characterized the mutational and haplotypic intra-host virus diversity of thirty-eight natural CHV1 infections spread across four locations in Croatia and Switzerland. Intra-host CHV1 diversity values were shaped by purifying selection and accumulation of mutations over time as well as epistatic interactions within the host genome at defense loci. Geographical landscape features impacted CHV1 inter-host relationships through restricting dispersal and causing founder effects. Interestingly, a small number of intra-host viral haplotypes showed high sequence similarity across large geographical distances unlikely to be linked by dispersal.

Batch effects in population genomic studies with low-coverage whole genome sequencing data: Causes, detection and mitigation

Over the past few decades, there has been an explosion in the amount of publicly available sequencing data. This opens new opportunities for combining data sets to achieve unprecedented sample sizes, spatial coverage or temporal replication in population genomic studies. However, a common concern is that nonbiological differences between data sets may generate patterns of variation in the data that can confound real biological patterns, a problem known as batch effects. In this paper, we compare two batches of low-coverage whole genome sequencing (lcWGS) data generated from the same populations of Atlantic cod (Gadus morhua). First, we show that with a "batch-effect-naive" bioinformatic pipeline, batch effects systematically biased our genetic diversity estimates, population structure inference and selection scans. We then demonstrate that these batch effects resulted from multiple technical differences between our data sets, including the sequencing chemistry (four-channel vs. two-channel), sequencing run, read type (single-end vs. paired-end), read length (125 vs. 150 bp), DNA degradation level (degraded vs. well preserved) and sequencing depth (0.8× vs. 0.3× on average). Lastly, we illustrate that a set of simple bioinformatic strategies (such as different read trimming and single nucleotide polymorphism filtering) can be used to detect batch effects in our data and substantially mitigate their impact. We conclude that combining data sets remains a powerful approach as long as batch effects are explicitly accounted for. We focus on lcWGS data in this paper, which may be particularly vulnerable to certain causes of batch effects, but many of our conclusions also apply to other sequencing strategies.

Opportunities and challenges of macrogenetic studies

The rapidly emerging field of macrogenetics focuses on analysing publicly accessible genetic datasets from thousands of species to explore large-scale patterns and predictors of intraspecific genetic variation. Facilitated by advances in evolutionary biology, technology, data infrastructure, statistics and open science, macrogenetics addresses core evolutionary hypotheses (such as disentangling environmental and life-history effects on genetic variation) with a global focus. Yet, there are important, often overlooked, limitations to this approach and best practices need to be considered and adopted if macrogenetics is to continue its exciting trajectory and reach its full potential in fields such as biodiversity monitoring and conservation. Here, we review the history of this rapidly growing field, highlight knowledge gaps and future directions, and provide guidelines for further research.

Uneven Missing Data Skew Phylogenomic Relationships within the Lories and Lorikeets

The resolution of the Tree of Life has accelerated with advances in DNA sequencing technology. To achieve dense taxon sampling, it is often necessary to obtain DNA from historical museum specimens to supplement modern genetic samples. However, DNA from historical material is generally degraded, which presents various challenges. In this study, we evaluated how the coverage at variant sites and missing data among historical and modern samples impacts phylogenomic inference. We explored these patterns in the brush-tongued parrots (lories and lorikeets) of Australasia by sampling ultraconserved elements in 105 taxa. Trees estimated with low coverage characters had several clades where relationships appeared to be influenced by whether the sample came from historical or modern specimens, which were not observed when more stringent filtering was applied. To assess if the topologies were affected by missing data, we performed an outlier analysis of sites and loci, and a data reduction approach where we excluded sites based on data completeness. Depending on the outlier test, 0.15% of total sites or 38% of loci were driving the topological differences among trees, and at these sites, historical samples had 10.9× more missing data than modern ones. In contrast, 70% data completeness was necessary to avoid spurious relationships. Predictive modeling found that outlier analysis scores were correlated with parsimony informative sites in the clades whose topologies changed the most by filtering. After accounting for biased loci and understanding the stability of relationships, we inferred a more robust phylogenetic hypothesis for lories and lorikeets.

A robust sequencing assay of a thousand amplicons for the high-throughput population monitoring of Alpine ibex immunogenetics

Polymorphism for immune functions can explain significant variation in health and reproductive success within species. Drastic loss in genetic diversity at such loci constitutes an extinction risk and should be monitored in species of conservation concern. However, effective implementations of genome‐wide immune polymorphism sets into high‐throughput genotyping assays are scarce. Here, we report the design and validation of a microfluidics‐based amplicon sequencing assay to comprehensively capture genetic variation in Alpine ibex (Capra ibex). This species represents one of the most successful large mammal restorations recovering from a severely depressed census size and a massive loss in diversity at the major histocompatibility complex (MHC). We analysed 65 whole‐genome sequencing sets of the Alpine ibex and related species to select the most representative markers and to prevent primer binding failures. In total, we designed ~1,000 amplicons densely covering the MHC, further immunity‐related genes as well as randomly selected genome‐wide markers for the assessment of neutral population structure. Our analysis of 158 individuals shows that the genome‐wide markers perform equally well at resolving population structure as RAD‐sequencing or low‐coverage genome sequencing data sets. Immunity‐related loci show unexpectedly high degrees of genetic differentiation within the species. Such information can now be used to define highly targeted individual translocations. Our design strategy can be realistically implemented into genetic surveys of a large range of species. In conclusion, leveraging whole‐genome sequencing data sets to design targeted amplicon assays allows the simultaneous monitoring of multiple genetic risk factors and can be translated into species conservation recommendations.

Disentangling adaptation from drift in bottlenecked and reintroduced populations of Alpine ibex

Identifying local adaptation in bottlenecked species is essential for conservation management. Selection detection methods have an important role in species management plans, assessments of adaptive capacity, and looking for responses to climate change. Yet, the allele frequency changes exploited in selection detection methods are similar to those caused by the strong neutral genetic drift expected during a bottleneck. Consequently, it is often unclear what accuracy selection detection methods have across bottlenecked populations. In this study, simulations were used to explore if signals of selection could be confidently distinguished from genetic drift across 23 bottlenecked and reintroduced populations of Alpine ibex (Capra ibex). The meticulously recorded demographic history of the Alpine ibex was used to generate comprehensive simulated SNP data. The simulated SNPs were then used to benchmark the confidence we could place in outliers identified in empirical Alpine ibex RADseq derived SNP data. Within the simulated data set, the false positive rates were high for all selection detection methods (F_ST outlier scans and Genetic‐Environment Association analyses) but fell substantially when two or more methods were combined. True positive rates were consistently low and became negligible with increased stringency. Despite finding many outlier loci in the empirical Alpine ibex SNPs, none could be distinguished from genetic drift‐driven false positives. Unfortunately, the low true positive rate also prevents the exclusion of recent local adaptation within the Alpine ibex. The baselines and stringent approach outlined here should be applied to other bottlenecked species to ensure the risk of false positive, or negative, signals of selection are accounted for in conservation management plans.

Sequencing platform shifts provide opportunities but pose challenges for combining genomic data sets

Technological advances in DNA sequencing over the last decade now permit the production and curation of large genomic data sets in an increasing number of nonmodel species. Additionally, these new data provide the opportunity for combining data sets, resulting in larger studies with a broader taxonomic range. Whilst the development of new sequencing platforms has been beneficial, resulting in a higher throughput of data at a lower per-base cost, shifts in sequencing technology can also pose challenges for those wishing to combine new sequencing data with data sequenced on older platforms. Here, we outline the types of studies where the use of curated data might be beneficial, and highlight potential biases that might be introduced by combining data from different sequencing platforms. As an example of the challenges associated with combining data across sequencing platforms, we focus on the impact of the shift in Illumina's base calling technology from a four-channel system to a two-channel system. We caution that when data are combined from these two systems, erroneous guanine base calls that result from the two-channel chemistry can make their way through a bioinformatic pipeline, eventually leading to inaccurate and potentially misleading conclusions. We also suggest solutions for dealing with such potential artefacts, which make samples sequenced on different sequencing platforms appear more differentiated from one another than they really are. Finally, we stress the importance of archiving tissue samples and the associated sequences for the continued reproducibility and reusability of sequencing data in the face of ever-changing sequencing platform technology.

Rapture-ready darters: Choice of reference genome and genotyping method (whole-genome or sequence capture) influence population genomic inference in Etheostoma

Researchers studying nonmodel organisms have an increasing number of methods available for generating genomic data. However, the applicability of different methods across species, as well as the effect of reference genome choice on population genomic inference, remain difficult to predict in many cases. We evaluated the impact of data type (whole-genome vs. reduced representation) and reference genome choice on data quality and on population genomic and phylogenomic inference across several species of darters (subfamily Etheostomatinae), a highly diverse radiation of freshwater fish. We generated a high-quality reference genome and developed a hybrid RADseq/sequence capture (Rapture) protocol for the Arkansas darter (Etheostoma cragini). Rapture data from 1,900 individuals spanning four darter species showed recovery of most loci across darter species at high depth and consistent estimates of heterozygosity regardless of reference genome choice. Loci with baits spanning both sides of the restriction enzyme cut site performed especially well across species. For low-coverage whole-genome data, choice of reference genome affected read depth and inferred heterozygosity. For similar amounts of sequence data, Rapture performed better at identifying fine-scale genetic structure compared to whole-genome sequencing. Rapture loci also recovered an accurate phylogeny for the study species and demonstrated high phylogenetic informativeness across the evolutionary history of the genus Etheostoma. Low cost and high cross-species effectiveness regardless of reference genome suggest that Rapture and similar sequence capture methods may be worthwhile choices for studies of diverse species radiations.

Range reduction of Oblong Rocksnail, Leptoxis compacta, shapes riverscape genetic patterns

Many freshwater gastropod species face extinction, including 79% of species in the family Pleuroceridae. The Oblong Rocksnail, Leptoxis compacta, is a narrow range endemic pleurocerid from the Cahaba River basin in central Alabama that has seen rapid range contraction in the last 100 years. Such a decline is expected to negatively affect genetic diversity in the species. However, precise patterns of genetic variation and gene flow across the restricted range of L. compacta are unknown. This lack of information limits our understanding of human impacts on the Cahaba River system and Pleuroceridae. Here, we show that L. compacta has likely seen a species-wide decline in genetic diversity, but remaining populations have relatively high genetic diversity. We also report a contemporary range extension compared to the last published survey. Our findings indicate that historical range contraction has resulted in the absence of common genetic patterns seen in many riverine taxa like isolation by distance as the small distribution of L. compacta allows for relatively unrestricted gene flow across its remaining range despite limited dispersal abilities. Two collection sites had higher genetic diversity than others, and broodstock sites for future captive propagation and reintroduction efforts should utilize sites identified here as having the highest genetic diversity. Broadly, our results support the hypothesis that range contraction will result in the reduction of species-wide genetic diversity, and common riverscape genetic patterns cannot be assumed to be present in species facing extinction risk.

Footprints of local adaptation span hundreds of linked genes in the Atlantic silverside genome

The study of local adaptation in the presence of ongoing gene flow is the study of natural selection in action, revealing the functional genetic diversity most relevant to contemporary pressures. In addition to individual genes, genome‐wide architecture can itself evolve to enable adaptation. Distributed across a steep thermal gradient along the east coast of North America, Atlantic silversides (Menidia menidia) exhibit an extraordinary degree of local adaptation in a suite of traits, and the capacity for rapid adaptation from standing genetic variation, but we know little about the patterns of genomic variation across the species range that enable this remarkable adaptability. Here, we use low‐coverage, whole‐transcriptome sequencing of Atlantic silversides sampled along an environmental cline to show marked signatures of divergent selection across a gradient of neutral differentiation. Atlantic silversides sampled across 1371 km of the southern section of its distribution have very low genome‐wide differentiation (median F_ST = 0.006 across 1.9 million variants), consistent with historical connectivity and observations of recent migrants. Yet almost 14,000 single nucleotide polymorphisms (SNPs) are nearly fixed (F_ST > 0.95) for alternate alleles. Highly differentiated SNPs cluster into four tight linkage disequilibrium (LD) blocks that span hundreds of genes and several megabases. Variants in these LD blocks are disproportionately nonsynonymous and concentrated in genes enriched for multiple functions related to known adaptations in silversides, including variation in lipid storage, metabolic rate, and spawning behavior. Elevated levels of absolute divergence and demographic modeling suggest selection maintaining divergence across these blocks under gene flow. These findings represent an extreme case of heterogeneity in levels of differentiation across the genome, and highlight how gene flow shapes genomic architecture in continuous populations. Locally adapted alleles may be common features of populations distributed along environmental gradients, and will likely be key to conserving variation to enable future responses to environmental change.

The presence and impact of reference bias on population genomic studies of prehistoric human populations

Haploid high quality reference genomes are an important resource in genomic research projects. A consequence is that DNA fragments carrying the reference allele will be more likely to map successfully, or receive higher quality scores. This reference bias can have effects on downstream population genomic analysis when heterozygous sites are falsely considered homozygous for the reference allele. In palaeogenomic studies of human populations, mapping against the human reference genome is used to identify endogenous human sequences. Ancient DNA studies usually operate with low sequencing coverages and fragmentation of DNA molecules causes a large proportion of the sequenced fragments to be shorter than 50 bp-reducing the amount of accepted mismatches, and increasing the probability of multiple matching sites in the genome. These ancient DNA specific properties are potentially exacerbating the impact of reference bias on downstream analyses, especially since most studies of ancient human populations use pseudo-haploid data, i.e. they randomly sample only one sequencing read per site. We show that reference bias is pervasive in published ancient DNA sequence data of prehistoric humans with some differences between individual genomic regions. We illustrate that the strength of reference bias is negatively correlated with fragment length. Most genomic regions we investigated show little to no mapping bias but even a small proportion of sites with bias can impact analyses of those particular loci or slightly skew genome-wide estimates. Therefore, reference bias has the potential to cause minor but significant differences in the results of downstream analyses such as population allele sharing, heterozygosity estimates and estimates of archaic ancestry. These spurious results highlight how important it is to be aware of these technical artifacts and that we need strategies to mitigate the effect. Therefore, we suggest some post-mapping filtering strategies to resolve reference bias which help to reduce its impact substantially.

Reference Genomes from Distantly Related Species Can Be Used for Discovery of Single Nucleotide Polymorphisms to Inform Conservation Management

Threatened species recovery programmes benefit from incorporating genomic data into conservation management strategies to enhance species recovery. However, a lack of readily available genomic resources, including conspecific reference genomes, often limits the inclusion of genomic data. Here, we investigate the utility of closely related high-quality reference genomes for single nucleotide polymorphism (SNP) discovery using the critically endangered kakī/black stilt (Himantopus novaezelandiae) and four Charadriiform reference genomes as proof of concept. We compare diversity estimates (i.e., nucleotide diversity, individual heterozygosity, and relatedness) based on kakī SNPs discovered from genotyping-by-sequencing and whole genome resequencing reads mapped to conordinal (killdeer, Charadrius vociferus), confamilial (pied avocet, Recurvirostra avosetta), congeneric (pied stilt, Himantopus himantopus) and conspecific reference genomes. Results indicate that diversity estimates calculated from SNPs discovered using closely related reference genomes correlate significantly with estimates calculated from SNPs discovered using a conspecific genome. Congeneric and confamilial references provide higher correlations and more similar measures of nucleotide diversity, individual heterozygosity, and relatedness. While conspecific genomes may be necessary to address other questions in conservation, SNP discovery using high-quality reference genomes of closely related species is a cost-effective approach for estimating diversity measures in threatened species.

Wild GWAS-association mapping in natural populations

The increasing affordability of sequencing and genotyping technologies has transformed the field of molecular ecology in recent decades. By correlating marker variants with trait variation using association analysis, large-scale genotyping and phenotyping of individuals from wild populations has enabled the identification of genomic regions that contribute to phenotypic differences among individuals. Such "gene mapping" studies are enabling us to better predict evolutionary potential and the ability of populations to adapt to challenges, such as changing environment. These studies are also allowing us to gain insight into the evolutionary processes maintaining variation in natural populations, to better understand genotype-by-environment and epistatic interactions and to track the dynamics of allele frequency change at loci contributing to traits under selection. Gene mapping in the wild using genomewide association scans (GWAS) do, however, come with a number of methodological challenges, not least the population structure in space and time inherent to natural populations. We here provide an overview of these challenges, summarize the exciting methodological advances and applications of association mapping in natural populations reported in this special issue and provide some guidelines for future "wild GWAS" research.