Finding the right coverage: the impact of coverage and sequence quality on single nucleotide polymorphism genotyping error rates

Restriction‐enzyme‐based sequencing methods enable the genotyping of thousands of single nucleotide polymorphism (SNP) loci in nonmodel organisms. However, in contrast to traditional genetic markers, genotyping error rates in SNPs derived from restriction‐enzyme‐based methods remain largely unknown. Here, we estimated genotyping error rates in SNPs genotyped with double digest RAD sequencing from Mendelian incompatibilities in known mother–offspring dyads of Hoffman's two‐toed sloth (Choloepus hoffmanni) across a range of coverage and sequence quality criteria, for both reference‐aligned and de novo‐assembled data sets. Genotyping error rates were more sensitive to coverage than sequence quality and low coverage yielded high error rates, particularly in de novo‐assembled data sets. For example, coverage ≥5 yielded median genotyping error rates of ≥0.03 and ≥0.11 in reference‐aligned and de novo‐assembled data sets, respectively. Genotyping error rates declined to ≤0.01 in reference‐aligned data sets with a coverage ≥30, but remained ≥0.04 in the de novo‐assembled data sets. We observed approximately 10‐ and 13‐fold declines in the number of loci sampled in the reference‐aligned and de novo‐assembled data sets when coverage was increased from ≥5 to ≥30 at quality score ≥30, respectively. Finally, we assessed the effects of genotyping coverage on a common population genetic application, parentage assignments, and showed that the proportion of incorrectly assigned maternities was relatively high at low coverage. Overall, our results suggest that the trade‐off between sample size and genotyping error rates be considered prior to building sequencing libraries, reporting genotyping error rates become standard practice, and that effects of genotyping errors on inference be evaluated in restriction‐enzyme‐based SNP studies.     

Fast and cost‐effective single nucleotide polymorphism (SNP) detection in the absence of a reference genome using semideep next‐generation Random Amplicon Sequencing (RAMseq)

Biodiversity has suffered a dramatic global decline during the past decades, and monitoring tools are urgently needed providing data for the development and evaluation of conservation efforts both on a species and on a genetic level. However, in wild species, the assessment of genetic diversity is often hampered by the lack of suitable genetic markers. In this article, we present Random Amplicon Sequencing (RAMseq), a novel approach for fast and cost‐effective detection of single nucleotide polymorphisms (SNPs) in nonmodel species by semideep sequencing of random amplicons. By applying RAMseq to the Eurasian otter (Lutra lutra), we identified 238 putative SNPs after quality filtering of all candidate loci and were able to validate 32 of 77 loci tested. In a second step, we evaluated the genotyping performance of these SNP loci in noninvasive samples, one of the most challenging genotyping applications, by comparing it with genotyping results of the same faecal samples at microsatellite markers. We compared (i) polymerase chain reaction (PCR) success rate, (ii) genotyping errors and (iii) Mendelian inheritance (population parameters). SNPs produced a significantly higher PCR success rate (75.5% vs. 65.1%) and lower mean allelic error rate (8.8% vs. 13.3%) than microsatellites, but showed a higher allelic dropout rate (29.7% vs. 19.8%). Genotyping results showed no deviations from Mendelian inheritance in any of the SNP loci. Hence, RAMseq appears to be a valuable tool for the detection of genetic markers in nonmodel species, which is a common challenge in conservation genetic studies.     

Double‐digest RAD sequencing using Ion Proton semiconductor platform (ddRADseq‐ion) with nonmodel organisms

Research in evolutionary biology involving nonmodel organisms is rapidly shifting from using traditional molecular markers such as mtDNA and microsatellites to higher throughput SNP genotyping methodologies to address questions in population genetics, phylogenetics and genetic mapping. Restriction site associated DNA sequencing (RAD sequencing or RADseq) has become an established method for SNP genotyping on Illumina sequencing platforms. Here, we developed a protocol and adapters for double‐digest RAD sequencing for Ion Torrent (Life Technologies; Ion Proton, Ion PGM) semiconductor sequencing. We sequenced thirteen genomic libraries of three different nonmodel vertebrate species on Ion Proton with PI chips: Arctic charr Salvelinus alpinus, European whitefish Coregonus lavaretus and common lizard Zootoca vivipara. This resulted in ~962 million single‐end reads overall and a mean of ~74 million reads per library. We filtered the genomic data using Stacks, a bioinformatic tool to process RAD sequencing data. On average, we obtained ~11 000 polymorphic loci per library of 6–30 individuals. We validate our new method by technical and biological replication, by reconstructing phylogenetic relationships, and using a hybrid genetic cross to track genomic variants. Finally, we discuss the differences between using the different sequencing platforms in the context of RAD sequencing, assessing possible advantages and disadvantages. We show that our protocol can be used for Ion semiconductor sequencing platforms for the rapid and cost‐effective generation of variable and reproducible genetic markers.     

Genotyping‐in‐Thousands by sequencing (GT‐seq) panel development and application to minimally invasive DNA samples to support studies in molecular ecology

Minimally invasive sampling (MIS) is widespread in wildlife studies; however, its utility for massively parallel DNA sequencing (MPS) is limited. Poor sample quality and contamination by exogenous DNA can make MIS challenging to use with modern genotyping‐by‐sequencing approaches, which have been traditionally developed for high‐quality DNA sources. Given that MIS is often more appropriate in many contexts, there is a need to make such samples practical for harnessing MPS. Here, we test the ability for Genotyping‐in‐Thousands by sequencing (GT‐seq), a multiplex amplicon sequencing approach, to effectively genotype minimally invasive cloacal DNA samples collected from the Western Rattlesnake (Crotalus oreganus), a threatened species in British Columbia, Canada. As there was no previous genetic information for this species, an optimized panel of 362 SNPs was selected for use with GT‐seq from a de novo restriction site‐associated DNA sequencing (RADseq) assembly. Comparisons of genotypes generated within and among RADseq and GT‐seq for the same individuals found low rates of genotyping error (GT‐seq: 0.50%; RADseq: 0.80%) and discordance (2.57%), the latter likely due to the different genotype calling models employed. GT‐seq mean genotype discordance between blood and cloacal swab samples collected from the same individuals was also minimal (1.37%). Estimates of population diversity parameters were similar across GT‐seq and RADseq data sets, as were inferred patterns of population structure. Overall, GT‐seq can be effectively applied to low‐quality DNA samples, minimizing the inefficiencies presented by exogenous DNA typically found in minimally invasive samples and continuing the expansion of molecular ecology and conservation genetics in the genomics era.     

Cross‐platform compatibility of de novo‐aligned SNPs in a nonmodel butterfly genus

High‐throughput sequencing methods for genotyping genome‐wide markers are being rapidly adopted for phylogenetics of nonmodel organisms in conservation and biodiversity studies. However, the reproducibility of SNP genotyping and degree of marker overlap or compatibility between datasets from different methodologies have not been tested in nonmodel systems. Using double‐digest restriction site‐associated DNA sequencing, we sequenced a common set of 22 specimens from the butterfly genus Speyeria on two different Illumina platforms, using two variations of library preparation. We then used a de novo approach to bioinformatic locus assembly and SNP discovery for subsequent phylogenetic analyses. We found a high rate of locus recovery despite differences in library preparation and sequencing platforms, as well as overall high levels of data compatibility after data processing and filtering. These results provide the first application of NGS methods for phylogenetic reconstruction in Speyeria and support the use and long‐term viability of SNP genotyping applications in nonmodel systems.     

Dealing with paralogy in RADseq data: in silico detection and single nucleotide polymorphism validation in Robinia pseudoacacia L.

The RADseq technology allows researchers to efficiently develop thousands of polymorphic loci across multiple individuals with little or no prior information on the genome. However, many questions remain about the biases inherent to this technology. Notably, sequence misalignments arising from paralogy may affect the development of single nucleotide polymorphism (SNP) markers and the estimation of genetic diversity. We evaluated the impact of putative paralog loci on genetic diversity estimation during the development of SNPs from a RADseq dataset for the nonmodel tree species Robinia pseudoacacia L. We sequenced nine genotypes and analyzed the frequency of putative paralogous RAD loci as a function of both the depth of coverage and the mismatch threshold allowed between loci. Putative paralogy was detected in a very variable number of loci, from 1% to more than 20%, with the depth of coverage having a major influence on the result. Putative paralogy artificially increased the observed degree of polymorphism and resulting estimates of diversity. The choice of the depth of coverage also affected diversity estimation and SNP validation: A low threshold decreased the chances of detecting minor alleles while a high threshold increased allelic dropout. SNP validation was better for the low threshold (4×) than for the high threshold (18×) we tested. Using the strategy developed here, we were able to validate more than 80% of the SNPs tested by means of individual genotyping, resulting in a readily usable set of 330 SNPs, suitable for use in population genetics applications.     

Breaking RAD: an evaluation of the utility of restriction site‐associated DNA sequencing for genome scans of adaptation

Understanding how and why populations evolve is of fundamental importance to molecular ecology. Restriction site‐associated DNA sequencing (RADseq), a popular reduced representation method, has ushered in a new era of genome‐scale research for assessing population structure, hybridization, demographic history, phylogeography and migration. RADseq has also been widely used to conduct genome scans to detect loci involved in adaptive divergence among natural populations. Here, we examine the capacity of those RADseq‐based genome scan studies to detect loci involved in local adaptation. To understand what proportion of the genome is missed by RADseq studies, we developed a simple model using different numbers of RAD‐tags, genome sizes and extents of linkage disequilibrium (length of haplotype blocks). Under the best‐case modelling scenario, we found that RADseq using six‐ or eight‐base pair cutting restriction enzymes would fail to sample many regions of the genome, especially for species with short linkage disequilibrium. We then surveyed recent studies that have used RADseq for genome scans and found that the median density of markers across these studies was 4.08 RAD‐tag markers per megabase (one marker per 245 kb). The length of linkage disequilibrium for many species is one to three orders of magnitude less than density of the typical recent RADseq study. Thus, we conclude that genome scans based on RADseq data alone, while useful for studies of neutral genetic variation and genetic population structure, will likely miss many loci under selection in studies of local adaptation.     

Estimation of genotyping error rate from repeat genotyping,unintentional recaptures and known parent–offspring comparisons in 16 microsatellite loci for brown rockfish (Sebastes auriculatus)

Genotyping errors are present in almost all genetic data and can affect biological conclusions of a study, particularly for studies based on individual identification and parentage. Many statistical approaches can incorporate genotyping errors, but usually need accurate estimates of error rates. Here, we used a new microsatellite data set developed for brown rockfish (Sebastes auriculatus) to estimate genotyping error using three approaches: (i) repeat genotyping 5% of samples, (ii) comparing unintentionally recaptured individuals and (iii) Mendelian inheritance error checking for known parent–offspring pairs. In each data set, we quantified genotyping error rate per allele due to allele drop‐out and false alleles. Genotyping error rate per locus revealed an average overall genotyping error rate by direct count of 0.3%, 1.5% and 1.7% (0.002, 0.007 and 0.008 per allele error rate) from replicate genotypes, known parent–offspring pairs and unintentionally recaptured individuals, respectively. By direct‐count error estimates, the recapture and known parent–offspring data sets revealed an error rate four times greater than estimated using repeat genotypes. There was no evidence of correlation between error rates and locus variability for all three data sets, and errors appeared to occur randomly over loci in the repeat genotypes, but not in recaptures and parent–offspring comparisons. Furthermore, there was no correlation in locus‐specific error rates between any two of the three data sets. Our data suggest that repeat genotyping may underestimate true error rates and may not estimate locus‐specific error rates accurately. We therefore suggest using methods for error estimation that correspond to the overall aim of the study (e.g. known parent–offspring comparisons in parentage studies).     

Minimum sample sizes for population genomics: an empirical study from an Amazonian plant species

High‐throughput DNA sequencing facilitates the analysis of large portions of the genome in nonmodel organisms, ensuring high accuracy of population genetic parameters. However, empirical studies evaluating the appropriate sample size for these kinds of studies are still scarce. In this study, we use double‐digest restriction‐associated DNA sequencing (ddRADseq) to recover thousands of single nucleotide polymorphisms (SNPs) for two physically isolated populations of Amphirrhox longifolia (Violaceae), a nonmodel plant species for which no reference genome is available. We used resampling techniques to construct simulated populations with a random subset of individuals and SNPs to determine how many individuals and biallelic markers should be sampled for accurate estimates of intra‐ and interpopulation genetic diversity. We identified 3646 and 4900 polymorphic SNPs for the two populations of A. longifolia, respectively. Our simulations show that, overall, a sample size greater than eight individuals has little impact on estimates of genetic diversity within A. longifolia populations, when 1000 SNPs or higher are used. Our results also show that even at a very small sample size (i.e. two individuals), accurate estimates of F_ST can be obtained with a large number of SNPs (≥1500). These results highlight the potential of high‐throughput genomic sequencing approaches to address questions related to evolutionary biology in nonmodel organisms. Furthermore, our findings also provide insights into the optimization of sampling strategies in the era of population genomics.     

Estimating and accounting for genotyping errors in RAD‐seq experiments

In non‐model organisms, evolutionary questions are frequently addressed using reduced representation sequencing techniques due to their low cost, ease of use, and because they do not require genomic resources such as a reference genome. However, evidence is accumulating that such techniques may be affected by specific biases, questioning the accuracy of obtained genotypes, and as a consequence, their usefulness in evolutionary studies. Here, we introduce three strategies to estimate genotyping error rates from such data: through the comparison to high quality genotypes obtained with a different technique, from individual replicates, or from a population sample when assuming Hardy‐Weinberg equilibrium. Applying these strategies to data obtained with Restriction site Associated DNA sequencing (RAD‐seq), arguably the most popular reduced representation sequencing technique, revealed per‐allele genotyping error rates that were much higher than sequencing error rates, particularly at heterozygous sites that were wrongly inferred as homozygous. As we exemplify through the inference of genome‐wide and local ancestry of well characterized hybrids of two Eurasian poplar (Populus) species, such high error rates may lead to wrong biological conclusions. By properly accounting for these error rates in downstream analyses, either by incorporating genotyping errors directly or by recalibrating genotype likelihoods, we were nevertheless able to use the RAD‐seq data to support biologically meaningful and robust inferences of ancestry among Populus hybrids. Based on these findings, we strongly recommend carefully assessing genotyping error rates in reduced representation sequencing experiments, and to properly account for these in downstream analyses, for instance using the tools presented here.     

Comparison of 454 pyrosequencing methods for characterizing the major histocompatibility complex of nonmodel species and the advantages of ultra deep coverage

Characterization and population genetic analysis of multilocus genes, such as those found in the major histocompatibility complex (MHC) is challenging in nonmodel vertebrates. The traditional method of extensive cloning and Sanger sequencing is costly and time‐intensive and indirect methods of assessment often underestimate total variation. Here, we explored the suitability of 454 pyrosequencing for characterizing multilocus genes for use in population genetic studies. We compared two sample tagging protocols and two bioinformatic procedures for 454 sequencing through characterization of a 185‐bp fragment of MHC DRB exon 2 in wolverines (Gulo gulo) and further compared the results with those from cloning and Sanger sequencing. We found 10 putative DRB alleles in the 88 individuals screened with between two and four alleles per individual, suggesting amplification of a duplicated DRB gene. In addition to the putative alleles, all individuals possessed an easily identifiable pseudogene. In our system, sequence variants with a frequency below 6% in an individual sample were usually artefacts. However, we found that sample preparation and data processing procedures can greatly affect variant frequencies in addition to the complexity of the multilocus system. Therefore, we recommend determining a per‐amplicon‐variant frequency threshold for each unique system. The extremely deep coverage obtained in our study (approximately 5000×) coupled with the semi‐quantitative nature of pyrosequencing enabled us to assign all putative alleles to the two DRB loci, which is generally not possible using traditional methods. Our method of obtaining locus‐specific MHC genotypes will enhance population genetic analyses and studies on disease susceptibility in nonmodel wildlife species.     

A new method for quantifying genotyping errors for noninvasive genetic studies

More and more noninvasive genetic data are being produced but a general methodology to quantify genotyping error rates from non-pilot data remains lacking. Here we propose a mathematical approach to estimate genotyping error rates by exploring the relationship between errors and PCR replicates. This method can be used to quantify the error rates for either the multi-tubes approach designed by Taberlet et al. (Nucleic Acids Res 24: 3189–3194, 1996) or the pilot method by Prugh et al. (Mol Ecol 14: 1585–1596, 2005).     

SNP development from RNA‐seq data in a nonmodel fish: how many individuals are needed for accurate allele frequency prediction?

Single nucleotide polymorphisms (SNPs) are rapidly becoming the marker of choice in population genetics due to a variety of advantages relative to other markers, including higher genomic density, data quality, reproducibility and genotyping efficiency, as well as ease of portability between laboratories. Advances in sequencing technology and methodologies to reduce genomic representation have made the isolation of SNPs feasible for nonmodel organisms. RNA‐seq is one such technique for the discovery of SNPs and development of markers for large‐scale genotyping. Here, we report the development of 192 validated SNP markers for parentage analysis in Tripterygion delaisi (the black‐faced blenny), a small rocky‐shore fish from the Mediterranean Sea. RNA‐seq data for 15 individual samples were used for SNP discovery by applying a series of selection criteria. Genotypes were then collected from 1599 individuals from the same population with the resulting loci. Differences in heterozygosity and allele frequencies were found between the two data sets. Heterozygosity was lower, on average, in the population sample, and the mean difference between the frequencies of particular alleles in the two data sets was 0.135 ± 0.100. We used bootstrap resampling of the sequence data to predict appropriate sample sizes for SNP discovery. As cDNA library production is time‐consuming and expensive, we suggest that using seven individuals for RNA sequencing reduces the probability of discarding highly informative SNP loci, due to lack of observed polymorphism, whereas use of more than 12 samples does not considerably improve prediction of true allele frequencies.     

wisepair: a computer program for individual matching in genetic tracking studies

Individual‐based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A ‘permanent’ genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python‐based computer‐modelling program, wisepair . We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without‐reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.     

Museum specimens provide reliable SNP data for population genomic analysis of a widely distributed but threatened cockatoo species

Natural history museums harbour a plethora of biological specimens which are of potential use in population and conservation genetic studies. Although technical advancements in museum genomics have enabled genome‐wide markers to be generated from aged museum specimens, the suitability of these data for robust biological inference is not well characterized. The aim of this study was to test the utility of museum specimens in population and conservation genomics by assessing the biological and technical validity of single nucleotide polymorphism (SNP) data derived from such samples. To achieve this, we generated thousands of SNPs from 47 red‐tailed black cockatoo (Calyptorhychus banksii) traditional museum samples (i.e. samples that were not collected with the primary intent of DNA analysis) and 113 fresh tissue samples (cryopreserved liver/muscle) using a restriction site‐associated DNA marker approach (DArTseq^?). Thousands of SNPs were successfully generated from most of the traditional museum samples (with a mean age of 44 years, ranging from 5 to 123 years), although 38% did not provide useful data. These SNPs exhibited higher error rates and contained significantly more missing data compared with SNPs from fresh tissue samples, likely due to considerable DNA fragmentation. However, based on simulation results, the level of genotyping error had a negligible effect on inference of population structure in this species. We did identify a bias towards low diversity SNPs in older samples that appears to compromise temporal inferences of genetic diversity. This study demonstrates the utility of a RADseq‐based method to produce reliable genome‐wide SNP data from traditional museum specimens.     

A 34K SNP genotyping array for Populus trichocarpa: Design,application to the study of natural populations and transferability to other Populus species

Genetic mapping of quantitative traits requires genotypic data for large numbers of markers in many individuals. For such studies, the use of large single nucleotide polymorphism (SNP) genotyping arrays still offers the most cost‐effective solution. Herein we report on the design and performance of a SNP genotyping array for Populus trichocarpa (black cottonwood). This genotyping array was designed with SNPs pre‐ascertained in 34 wild accessions covering most of the species latitudinal range. We adopted a candidate gene approach to the array design that resulted in the selection of 34 131 SNPs, the majority of which are located in, or within 2 kb of, 3543 candidate genes. A subset of the SNPs on the array (539) was selected based on patterns of variation among the SNP discovery accessions. We show that more than 95% of the loci produce high quality genotypes and that the genotyping error rate for these is likely below 2%. We demonstrate that even among small numbers of samples (n = 10) from local populations over 84% of loci are polymorphic. We also tested the applicability of the array to other species in the genus and found that the number of polymorphic loci decreases rapidly with genetic distance, with the largest numbers detected in other species in section Tacamahaca. Finally, we provide evidence for the utility of the array to address evolutionary questions such as intraspecific studies of genetic differentiation, species assignment and the detection of natural hybrids.     

Characterizing restriction enzyme‐associated loci in historic ragweed (Ambrosia artemisiifolia) voucher specimens using custom‐designed RNA probes

Population genetic studies of nonmodel organisms frequently employ reduced representation library (RRL) methodologies, many of which rely on protocols in which genomic DNA is digested by one or more restriction enzymes. However, because high molecular weight DNA is recommended for these protocols, samples with degraded DNA are generally unsuitable for RRL methods. Given that ancient and historic specimens can provide key temporal perspectives to evolutionary questions, we explored how custom‐designed RNA probes could enrich for RRL loci (Restriction Enzyme‐Associated Loci baits, or REALbaits). Starting with genotyping‐by‐sequencing (GBS) data generated on modern common ragweed (Ambrosia artemisiifolia L.) specimens, we designed 20 000 RNA probes to target well‐characterized genomic loci in herbarium voucher specimens dating from 1835 to 1913. Compared to shotgun sequencing, we observed enrichment of the targeted loci at 19‐ to 151‐fold. Using our GBS capture pipeline on a data set of 38 herbarium samples, we discovered 22 813 SNPs, providing sufficient genomic resolution to distinguish geographic populations. For these samples, we found that dilution of REALbaits to 10% of their original concentration still yielded sufficient data for downstream analyses and that a sequencing depth of ~7m reads was sufficient to characterize most loci without wasting sequencing capacity. In addition, we observed that targeted loci had highly variable rates of success, which we primarily attribute to similarity between loci, a trait that ultimately interferes with unambiguous read mapping. Our findings can help researchers design capture experiments for RRL loci, thereby providing an efficient means to integrate samples with degraded DNA into existing RRL data sets.     

Ecological genomics of local adaptation in Cornus florida L. by genotyping by sequencing

Discovering local adaptation, its genetic underpinnings, and environmental drivers is important for conserving forest species. Ecological genomic approaches coupled with next‐generation sequencing are useful means to detect local adaptation and uncover its underlying genetic basis in nonmodel species. We report results from a study on flowering dogwood trees (Cornus florida L.) using genotyping by sequencing (GBS). This species is ecologically important to eastern US forests but is severely threatened by fungal diseases. We analyzed subpopulations in divergent ecological habitats within North Carolina to uncover loci under local selection and associated with environmental–functional traits or disease infection. At this scale, we tested the effect of incorporating additional sequencing before scaling for a broader examination of the entire range. To test for biases of GBS, we sequenced two similarly sampled libraries independently from six populations of three ecological habitats. We obtained environmental–functional traits for each subpopulation to identify associations with genotypes via latent factor mixed modeling (LFMM) and gradient forests analysis. To test whether heterogeneity of abiotic pressures resulted in genetic differentiation indicative of local adaptation, we evaluated F_st per locus while accounting for genetic differentiation between coastal subpopulations and Piedmont‐Mountain subpopulations. Of the 54 candidate loci with sufficient evidence of being under selection among both libraries, 28–39 were Arlequin–BayeScan F_st outliers. For LFMM, 45 candidates were associated with climate (of 54), 30 were associated with soil properties, and four were associated with plant health. Reanalysis of combined libraries showed that 42 candidate loci still showed evidence of being under selection. We conclude environment‐driven selection on specific loci has resulted in local adaptation in response to potassium deficiencies, temperature, precipitation, and (to a marginal extent) disease. High allele turnover along ecological gradients further supports the adaptive significance of loci speculated to be under selection.     

RADseq as a valuable tool for plants with large genomes—A case study in cycads

Full genome sequencing of organisms with large and complex genomes is intractable and cost ineffective under most research budgets. Cycads (Cycadales) represent one of the oldest lineages of the extant seed plants and, partly due to their age, have incredibly large genomes up to ~60 Gbp. Restriction site‐associated DNA sequencing (RADseq) offers an approach to find genome‐wide informative markers and has proven to be effective with both model and nonmodel organisms. We tested the application of RADseq using ezRAD across all 10 genera of the Cycadales including an example data set of Cycas calcicola representing 72 samples from natural populations. Using previously available plastid and mitochondrial genomes as references, reads were mapped recovering plastid and mitochondrial genome regions and nuclear markers for all of the genera. De novo assembly generated up to 138,407 high‐depth clusters and up to 1,705 phylogenetically informative loci for the genera, and 4,421 loci for the example assembly of C. calcicola. The number of loci recovered by de novo assembly was lower than previous RADseq studies, yet still sufficient for downstream analysis. However, the number of markers could be increased by relaxing our assembly parameters, especially for the C. calcicola data set. Our results demonstrate the successful application of RADseq across the Cycadales to generate a large number of markers for all genomic compartments, despite the large number of plastids present in a typical plant cell. Our modified protocol was adapted to be applied to cycads and other organisms with large genomes to yield many informative genome‐wide markers.