Population Genomic Inference Research Articles

Modeling biases from low-pass genome sequencing to enable accurate population genetic inferences.

Low-pass genome sequencing is cost-effective and enables analysis of large cohorts. However, it introduces biases by reducing heterozygous genotypes and low-frequency alleles, impacting subsequent analyses such as demographic history inference. We developed a probabilistic model of low-pass biases from the Genome Analysis Toolkit (GATK) multi-sample calling pipeline, and we implemented it in the population genomic inference software dadi. We evaluated the model using simulated low-pass datasets and found that it alleviated low-pass biases in inferred demographic parameters. We further validated the model by downsampling 1000 Genomes Project data, demonstrating its effectiveness on real data. Our model is widely applicable and substantially improves model-based inferences from low-pass population genomic data.

DORA: an interactive map for the visualization and analysis of ancient human DNA and associated data.

The ability to sequence ancient genomes has revolutionized the way we study evolutionary history by providing access to the most important aspect of evolution-time. Until recently, studying human demography, ecology, biology, and history using population genomic inference relied on contemporary genomic datasets. Over the past decade, the availability of human ancient DNA (aDNA) has increased rapidly, almost doubling every year, opening the way for spatiotemporal studies of ancient human populations. However, the multidimensionality of aDNA, with genotypes having temporal, spatial and genomic coordinates, and integrating multiple sources of data, poses a challenge for developing meta-analyses pipelines. To address this challenge, we developed a publicly-available interactive tool, DORA, which integrates multiple data types, genomic and non-genomic, in a unified interface. This web-based tool enables browsing sample metadata alongside additional layers of information, such as population structure, climatic data, and unpublished samples. Users can perform analyses on genotypes of these samples, or export sample subsets for external analyses. DORA integrates analyses and visualizations in a single intuitive interface, resolving the technical issues of combining datasets from different sources and formats, and allowing researchers to focus on the scientific questions that can be addressed through analysis of aDNA datasets.

Genomic and phenotypic signatures provide insights into the wide adaptation of a global plant invader

Invasive alien species are primary drivers of biodiversity loss and species extinction. Smooth cordgrass (Spartina alterniflora) is one of the most aggressive invasive plants in coastal ecosystems around the world. However, the genomic bases and evolutionary mechanisms underlying its invasion success have remained largely unknown. Here, we assembled a chromosome-level reference genome and performed phenotypic and population genomic analyses between native US and introduced Chinese populations. Our phenotypic comparisons showed that introduced Chinese populations have evolved competitive traits, such as early flowering time and greater plant biomass, during secondary introductions along China’s coast. Population genomic and transcriptomic inferences revealed distinct evolutionary trajectories of low- and high-latitude Chinese populations. In particular, genetic mixture among different source populations, together with independent natural selection acting on distinct target genes, may have resulted in high genome dynamics of the introduced Chinese populations. Our study provides novel phenotypic and genomic evidence showing how smooth cordgrass rapidly adapts to variable environmental conditions in its introduced ranges. Moreover, candidate genes related to flowering time, fast growth, and stress tolerance (i.e., salinity and submergence) provide valuable genetic resources for future improvement of cereal crops.

Population Genomic Evidence for a Repeated Introduction and Rapid Expansion of the Fungal Maize Pathogen Setosphaeria turcica in Europe.

Modern agricultural practices, climate change, and globalization foster the rapid spread of plant pathogens, such as the maize fungal pathogen Setosphaeria turcica, which causes Northern corn leaf blight and expanded into Central Europe during the twentieth century. To investigate the rapid expansion of S. turcica, we sequenced 121 isolates from Europe and Kenya. Population genomic inference revealed a single genetically diverse cluster in Kenya and three clonal lineages with low diversity, as well as one cluster of multiple clonal sublineages in Europe. Phylogenetic dating suggests that all European lineages originated through sexual reproduction outside Europe and were subsequently introgressed multiple times. Unlike isolates from Kenya, European isolates did not show sexual recombination, despite the presence of both MAT1-1 and MAT1-2 mating types. For the clonal lineages, coalescent model selection supported a selectively neutral model with strong exponential population growth, rather than models with pervasive positive selection caused by host defense resistance or environmental adaptation. Within clonal lineages, phenotypic variation in virulence to different monogenic resistances, which defines the pathogen races, suggests that these races may originate from repeated mutations in virulence genes. Association testing based on k-mers did not identify genomic regions linked to pathogen races, but it did uncover strongly differentiated genomic regions between clonal lineages, which harbor genes with putative roles in pathogenicity. In conclusion, the expansion and population growth of S. turcica in Europe are mainly driven by an expansion of the maize cultivation area and not by rapid adaptation.

Drawing a line in the sand: Environmental DNA population genomics.

Environmental DNA (eDNA) sampling uses genetic material in the environment to infer species presence sight-unseen. The method has rapidly become a powerful tool for monitoring biodiversity. However, biological diversity, as per the Convention on Biological Diversity definition of "diversity within species, between species and of ecosystems" is more inclusive than most eDNA studies cover: The vast majority focus only on between-species and ecosystem-level biodiversity. However, a tantalizing prospect, as illustrated by Farrell et al.(2022) in this issue of Molecular Ecology Resources, is that we might also be able to unlock information about individual and population-level diversity via population genomic analysis of these environmental samples. Farrell et al.(2022) found that targeted samples of beach sand contained genetic material not just informative about sea turtle presence, but also indicated the presence of pathogens and genome-wide mitochondrial and nuclear sequences that could accurately infer individual turtle source population. Moving from proof-of-concept to robust, population genomic inference will require a growth of genomic resources for nonmodel organisms and careful study design considerations, some of which have already been pioneered by related fields.

Sequence Capture From Historical Museum Specimens: Maximizing Value for Population and Phylogenomic Studies

The application of high-throughput, short-read sequencing to degraded DNA has greatly increased the feasibility of generating genomic data from historical museum specimens. While many published studies report successful sequencing results from historical specimens; in reality, success and quality of sequence data can be highly variable. To examine predictors of sequencing quality, and methodological approaches to improving data accuracy, we generated and analyzed genomic sequence data from 115 historically collected museum specimens up to 180 years old. Data span both population genomic and phylogenomic scales, including historically collected specimens from 34 specimens of four species of Australian rock-wallabies (genus Petrogale) and 92 samples from 79 specimens of Australo-Papuan murine rodents (subfamily Murinae). For historical rodent specimens, where the focus was sampling for phylogenomics, we found that regardless of specimen age, DNA sequence libraries prepared from toe pad or bone subsamples performed significantly better than those taken from the skin (in terms of proportion of reads on target, number of loci captured, and data accuracy). In total, 93% of DNA libraries from toe pad or bone subsamples resulted in reliable data for phylogenetic inference, compared to 63% of skin subsamples. For skin subsamples, proportion of reads on target weakly correlated with collection year. Then using population genomic data from rock-wallaby skins as a test case, we found substantial improvement in final data quality by mapping to a high-quality “closest sister” de novo assembly from fresh tissues, compared to mapping to a sample-specific historical de novo assembly. Choice of mapping approach also affected final estimates of the number of segregating sites and Watterson's θ, both important parameters for population genomic inference. The incorporation of accurate and reliable sequence data from historical specimens has important outcomes for evolutionary studies at both population and phylogenomic scales. By assessing the outcomes of different approaches to specimen subsampling, library preparation and bioinformatic processing, our results provide a framework for increasing sequencing success for irreplaceable historical specimens.

Revised Species Delimitation in the Giant Water Lily Genus Victoria (Nymphaeaceae) Confirms a New Species and Has Implications for Its Conservation.

Reliably documenting plant diversity is necessary to protect and sustainably benefit from it. At the heart of this documentation lie species concepts and the practical methods used to delimit taxa. Here, we apply a total-evidence, iterative methodology to delimit and document species in the South American genus Victoria (Nymphaeaceae). The systematics of Victoria has thus far been poorly characterized due to difficulty in attributing species identities to biological collections. This research gap stems from an absence of type material and biological collections, also the confused diagnosis of V. cruziana. With the goal of improving systematic knowledge of the genus, we compiled information from historical records, horticulture and geography and assembled a morphological dataset using citizen science and specimens from herbaria and living collections. Finally, we generated genomic data from a subset of these specimens. Morphological and geographical observations suggest four putative species, three of which are supported by nuclear population genomic and plastid phylogenomic inferences. We propose these three confirmed entities as robust species, where two correspond to the currently recognized V. amazonica and V. cruziana, the third being new to science, which we describe, diagnose and name here as V. boliviana Magdalena and L. T. Sm. Importantly, we identify new morphological and molecular characters which serve to distinguish the species and underpin their delimitations. Our study demonstrates how combining different types of character data into a heuristic, total-evidence approach can enhance the reliability with which biological diversity of morphologically challenging groups can be identified, documented and further studied.

Emmer Wheat Eco-Geographic and Genomic Congruence Shapes Phenotypic Performance under Mediterranean Climate.

Emmer wheat (Triticum turgidum ssp. dicoccum) is one of the world’s oldest domesticated crops, and it harbors a potentially rich reservoir of agronomic and nutritional quality trait variations. The growing global demand for plant-based health-food niche markets has promoted new commercial interest in ancient grains, including Emmer wheat. Although T. dicoccum can also perform well under harsh environments, its cultivation along the Mediterranean agro-ecosystems is sparse. Here, we analyze a unique tetraploid wheat collection (n = 121) representing a wide geographic range of Emmer accessions, using 9897 DArTseq markers and on-field phenotypic characterization to quantify the extent of diversity among populations and the interactions between eco-geographic, genetic, and phenotypic attributes. Population genomic inferences based on the DArTseq data indicated that the collection could be split into four distinguished clusters in accordance with their eco-geographic origin although significant phenotypic variation was observed within clusters. Superior early vegetative vigor, shorter plant height, and early phenology were observed among emmer wheat accessions from Ethiopia compared to accessions from northern regions. This adaptive advantage highlights the potential of emmer wheat as an exotic germplasm for wheat improvement through breeding. The direct integration of such germplasm into conventional or organic farming agro-systems under the Mediterranean basin climate is also discussed.

Long-distance migration is a major factor driving local adaptation at continental scale in Coho salmon.

Inferring the genomic basis of local adaptation is a long-standing goal of evolutionary biology. Beyond its fundamental evolutionary implications, such knowledge can guide conservation decisions for populations of conservation and management concern. Here, we investigated the genomic basis of local adaptation in the Coho salmon (Oncorhynchus kisutch) across its entire North American range. We hypothesized that extensive spatial variation in environmental conditions and the species' homing behaviour may promote the establishment of local adaptation. We genotyped 7829 individuals representing 217sampling locations at more than 100,000high-quality RADseq loci to investigate how recombination might affect the detection of loci putatively under selection and took advantage of the precise description of the demographic history of the species from our previous work to draw accurate population genomic inferences about local adaptation. The results indicated that genetic differentiation scans and genetic-environment association analyses were both significantly affected by variation in recombination rate as low recombination regions displayed an increased number of outliers. By taking these confounding factors into consideration, we revealed that migration distance was the primary selective factor driving local adaptation and partial parallel divergence among distant populations. Moreover, we identified several candidate single nucleotide polymorphisms associated with long-distance migration and altitude including a gene known to be involved in adaptation to altitude in other species. The evolutionary implications of our findings are discussed along with conservation applications.

Intra‐Alpine Islands: Population genomic inference reveals high degree of isolation between freshwater spring habitats

AbstractAimAlpine spring ecosystems have long been considered as highly isolated, island‐like habitats. This presumption, however, is insufficiently supported empirically and conclusions about spring isolation have been based on indirect evidence. Therefore, we investigated the population genomic structure of Partnunia steinmanni Walter, 1906, a strictly spring‐dwelling water mite (Hydrachnidia) species, to shed light on the degree of interconnection among freshwater spring habitats.LocationProtected areas across the Alps, Central Europe.MethodsPartnunia steinmanni populations were sampled by hand‐net from 12 springs. Population genomic structure was inferred with 2263 polymorphic restriction site‐associated DNA (RADseq) loci of 256 individuals. We assessed genomic admixture, the phylogenetic relationship, isolation by distance, contemporary migration, effective population sizes, and genetic diversity among individuals from different springs.ResultsWe observed strong genetic differentiation between individuals from different springs. Water mites from each spring qualified as well‐delimited distinct populations with only little intra‐spring migration, even when these were located in close geographic proximity. Furthermore, we found subtle shared genetic structure among springs within the same area, and a southwestern genotype associated with the Rhône catchment that extended into eastern populations. Effective population size estimates and standing genetic variation within springs were generally low.Main conclusionsOur findings indicate strong insularity of freshwater springs and headwater areas, likely caused by intra‐alpine Pleistocene isolation and limited dispersal abilities of strictly spring‐bound species like P. steinmanni. Our results support the concept of spring habitat isolation and highlight the importance of alpine protected areas to conserve springs as substantial components of freshwater biodiversity.

A beginner's guide to low-coverage whole genome sequencing for population genomics.

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and nonmodel species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analysed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency, genetic diversity, and linkage disequilibrium estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in nonmodel species, and discuss current limitations and future perspectives for lcWGS-based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption.

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.

Linked‐read sequencing enables haplotype‐resolved resequencing at population scale

The feasibility to sequence entire genomes of virtually any organism provides unprecedented insights into the evolutionary history of populations and species. Nevertheless, many population genomic inferences - including the quantification and dating of admixture, introgression and demographic events, and inference of selective sweeps - are still limited by the lack of high-quality haplotype information. The newest generation of sequencing technology now promises significant progress. To establish the feasibility of haplotype-resolved genome resequencing at population scale, we investigated properties of linked-read sequencing data of songbirds of the genus Oenanthe across a range of sequencing depths. Our results based on the comparison of downsampled (25×, 20×, 15×, 10×, 7×, and 5×) with high-coverage data (46-68×) of seven bird genomes mapped to a reference suggest that phasing contiguities and accuracies adequate for most population genomic analyses can be reached already with moderate sequencing effort. At 15× coverage, phased haplotypes span about 90% of the genome assembly, with 50% and 90% of phased sequences located in phase blocks longer than 1.25-4.6Mb (N50) and 0.27-0.72Mb (N90). Phasing accuracy reaches beyond 99% starting from 15× coverage. Higher coverages yielded higher contiguities (up to about 7Mb/1Mb [N50/N90] at 25× coverage), but only marginally improved phasing accuracy. Phase block contiguity improved with input DNA molecule length; thus, higher-quality DNA may help keeping sequencing costs at bay. In conclusion, even for organisms with gigabase-sized genomes like birds, linked-read sequencing at moderate depth opens an affordable avenue towards haplotype-resolved genome resequencing at population scale.

Asymmetrical gene flow in five co-distributed syngnathids explained by ocean currents and rafting propensity.

Ocean circulation driving macro-algal rafting is believed to serve as an important mode of dispersal for many marine organisms, leading to predictions on population-level genetic connectivity and the directionality of effective dispersal. Here, we use genome-wide single nucleotide polymorphism data to investigate whether gene flow directionality in two seahorses (Hippocampus) and three pipefishes (Syngnathus) follows the predominant ocean circulation patterns in the Gulf of Mexico and northwestern Atlantic. In addition, we explore whether gene flow magnitudes are predicted by traits related to active dispersal ability and habitat preference. We inferred demographic histories of these co-distributed syngnathid species, and coalescent model-based estimates indicate that gene flow directionality is in agreement with ocean circulation data that predicts eastward and northward macro-algal transport. However, the magnitude to which ocean currents influence this pattern appears strongly dependent on the species-specific traits related to rafting propensity and habitat preferences. Higher levels of gene flow and stronger directionality are observed in Hippocampus erectus, Syngnathus floridae and Syngnathus louisianae, which closely associated with the pelagic macro-algae Sargassum spp., compared to Hippocampus zosterae and the Syngnathus scovelli/Syngnathus fuscus sister-species pair, which prefer near shore habitats and are weakly associated with pelagic Sargassum. This study highlights how the combination of population genomic inference together with ocean circulation data can help explain patterns of population structure and diversity in marine ecosystems.

Probabilistic Species Tree Distances: Implementing the Multispecies Coalescent to Compare Species Trees Within the Same Model-Based Framework Used to Estimate Them.

Despite the ubiquitous use of statistical models for phylogenomic and population genomic inferences, this model-based rigor is rarely applied to post hoc comparison of trees. In a recent study, Garba et al. derived new methods for measuring the distance between two gene trees computed as the difference in their site pattern probability distributions. Unlike traditional metrics that compare trees solely in terms of geometry, these measures consider gene trees and associated parameters as probabilistic models that can be compared using standard information theoretic approaches. Consequently, probabilistic measures of phylogenetic tree distance can be far more informative than simply comparisons of topology and/or branch lengths alone. However, in their current form, these distance measures are not suitable for the comparison of species tree models in the presence of gene tree heterogeneity. Here, we demonstrate an approach for how the theory of Garba et al. (2018), which is based on gene tree distances, can be extended naturally to the comparison of species tree models. Multispecies coalescent (MSC) models parameterize the discrete probability distribution of gene trees conditioned upon a species tree with a particular topology and set of divergence times (in coalescent units), and thus provide a framework for measuring distances between species tree models in terms of their corresponding gene tree topology probabilities. We describe the computation of probabilistic species tree distances in the context of standard MSC models, which assume complete genetic isolation postspeciation, as well as recent theoretical extensions to the MSC in the form of network-based MSC models that relax this assumption and permit hybridization among taxa. We demonstrate these metrics using simulations and empirical species tree estimates and discuss both the benefits and limitations of these approaches. We make our species tree distance approach available as an R package called pSTDistanceR, for open use by the community.

SNP genotyping and parameter estimation in polyploids using low-coverage sequencing data.

Genotyping and parameter estimation using high throughput sequencing data are everyday tasks for population geneticists, but methods developed for diploids are typically not applicable to polyploid taxa. This is due to their duplicated chromosomes, as well as the complex patterns of allelic exchange that often accompany whole genome duplication (WGD) events. For WGDs within a single lineage (autopolyploids), inbreeding can result from mixed mating and/or double reduction. For WGDs that involve hybridization (allopolyploids), alleles are typically inherited through independently segregating subgenomes. We present two new models for estimating genotypes and population genetic parameters from genotype likelihoods for auto- and allopolyploids. We then use simulations to compare these models to existing approaches at varying depths of sequencing coverage and ploidy levels. These simulations show that our models typically have lower levels of estimation error for genotype and parameter estimates, especially when sequencing coverage is low. Finally, we also apply these models to two empirical datasets from the literature. Overall, we show that the use of genotype likelihoods to model non-standard inheritance patterns is a promising approach for conducting population genomic inferences in polyploids. A C ++ program, EBG, is provided to perform inference using the models we describe. It is available under the GNU GPLv3 on GitHub: https://github.com/pblischak/polyploid-genotyping. blischak.4@osu.edu. Supplementary data are available at Bioinformatics online.

Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection.

Human immunodeficiency virus (HIV) is a rapidly evolving pathogen that causes chronic infections, so genetic diversity within a single infection can be very high. High-throughput "deep" sequencing can now measure this diversity in unprecedented detail, particularly since it can be performed at different time points during an infection, and this offers a potentially powerful way to infer the evolutionary dynamics of the intrahost viral population. However, population genomic inference from HIV sequence data is challenging because of high rates of mutation and recombination, rapid demographic changes, and ongoing selective pressures. In this article we develop a new method for inference using HIV deep sequencing data, using an approach based on importance sampling of ancestral recombination graphs under a multilocus coalescent model. The approach further extends recent progress in the approximation of so-called conditional sampling distributions, a quantity of key interest when approximating coalescent likelihoods. The chief novelties of our method are that it is able to infer rates of recombination and mutation, as well as the effective population size, while handling sampling over different time points and missing data without extra computational difficulty. We apply our method to a data set of HIV-1, in which several hundred sequences were obtained from an infected individual at seven time points over 2 years. We find mutation rate and effective population size estimates to be comparable to those produced by the software BEAST. Additionally, our method is able to produce local recombination rate estimates. The software underlying our method, Coalescenator, is freely available.

Antiviral drug resistance as an adaptive process.

Antiviral drug resistance is a matter of great clinical importance that, historically, has been investigated mostly from a virological perspective. Although the proximate mechanisms of resistance can be readily uncovered using these methods, larger evolutionary trends often remain elusive. Recent interest by population geneticists in studies of antiviral resistance has spurred new metrics for evaluating mutation and recombination rates, demographic histories of transmission and compartmentalization, and selective forces incurred during viral adaptation to antiviral drug treatment. We present up-to-date summaries on antiviral resistance for a range of drugs and viral types, and review recent advances for studying their evolutionary histories. We conclude that information imparted by demographic and selective histories, as revealed through population genomic inference, is integral to assessing the evolution of antiviral resistance as it pertains to human health.

The aggregate site frequency spectrum for comparative population genomic inference.

Understanding how assemblages of species responded to past climate change is a central goal of comparative phylogeography and comparative population genomics, an endeavour that has increasing potential to integrate with community ecology. New sequencing technology now provides the potential to perform complex demographic inference at unprecedented resolution across assemblages of nonmodel species. To this end, we introduce the aggregate site frequency spectrum (aSFS), an expansion of the site frequency spectrum to use single nucleotide polymorphism (SNP) data sets collected from multiple, co-distributed species for assemblage-level demographic inference. We describe how the aSFS is constructed over an arbitrary number of independent population samples and then demonstrate how the aSFS can differentiate various multispecies demographic histories under a wide range of sampling configurations while allowing effective population sizes and expansion magnitudes to vary independently. We subsequently couple the aSFS with a hierarchical approximate Bayesian computation (hABC) framework to estimate degree of temporal synchronicity in expansion times across taxa, including an empirical demonstration with a data set consisting of five populations of the threespine stickleback (Gasterosteus aculeatus). Corroborating what is generally understood about the recent postglacial origins of these populations, the joint aSFS/hABC analysis strongly suggests that the stickleback data are most consistent with synchronous expansion after the Last Glacial Maximum (posterior probability = 0.99). The aSFS will have general application for multilevel statistical frameworks to test models involving assemblages and/or communities, and as large-scale SNP data from nonmodel species become routine, the aSFS expands the potential for powerful next-generation comparative population genomic inference.

SNP genotyping and population genomics from expressed sequences - current advances and future possibilities.

With the rapid increase in production of genetic data from new sequencing technologies, a myriad of new ways to study genomic patterns in nonmodel organisms are currently possible. Because genome assembly still remains a complicated procedure, and because the functional role of much of the genome is unclear, focusing on SNP genotyping from expressed sequences provides a cost-effective way to reduce complexity while still retaining functionally relevant information. This review summarizes current methods, identifies ways that using expressed sequence data benefits population genomic inference and explores how current practitioners evaluate and overcome challenges that are commonly encountered. We focus particularly on the additional power of functional analysis provided by expressed sequence data and how these analyses push beyond allele pattern data available from nonfunction genomic approaches. The massive data sets generated by these approaches create opportunities and problems as well - especially false positives. We discuss methods available to validate results from expressed SNP genotyping assays, new approaches that sidestep use of mRNA and review follow-up experiments that can focus on evolutionary mechanisms acting across the genome.