High-throughput techniques based on restriction site-associated DNA sequencing (RADseq) are enabling the low-cost discovery and genotyping of thousands of genetic markers for any species, including non-model organisms, which is revolutionizing ecological, evolutionary and conservation genetics. Technical differences among these methods lead to important considerations for all steps of genomics studies, from the specific scientific questions that can be addressed, and the costs of library preparation and sequencing, to the types of bias and error inherent in the resulting data. In this Review, we provide a comprehensive discussion of RADseq methods to aid researchers in choosing among the many different approaches and avoiding erroneous scientific conclusions from RADseq data, a problem that has plagued other genetic marker types in the past.

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Harnessing the power of RADseq for ecological and evolutionary genomics

UNLABELLED New applications of next-generation sequencing technologies use pools of DNA from multiple individuals to estimate population genetic parameters. However, no publicly available tools exist to analyse single-nucleotide polymorphism (SNP) calling results directly for evolutionary parameters important in detecting natural selection, including nucleotide diversity and gene diversity. We have developed SNPGenie to fill this gap. The user submits a FASTA reference sequence(s), a Gene Transfer Format (.GTF) file with CDS information and a SNP report(s) in an increasing selection of formats. The program estimates nucleotide diversity, distance from the reference and gene diversity. Sites are flagged for multiple overlapping reading frames, and are categorized by polymorphism type: nonsynonymous, synonymous, or ambiguous. The results allow single nucleotide, single codon, sliding window, whole gene and whole genome/population analyses that aid in the detection of positive and purifying natural selection in the source population. AVAILABILITY AND IMPLEMENTATION SNPGenie version 1.2 is a Perl program with no additional dependencies. It is free, open-source, and available for download at https://github.com/hugheslab/snpgenie. CONTACT nelsoncw@email.sc.edu or austin@biol.sc.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

SNPGenie: estimating evolutionary parameters to detect natural selection using pooled next-generation sequencing data

The boom of massive parallel sequencing (MPS) technology and its applications in conservation of natural and managed populations brings new opportunities and challenges to meet the scientific questions that can be addressed. Genomic conservation offers a wide range of approaches and analytical techniques, with their respective strengths and weaknesses that rely on several implicit assumptions. However, finding the most suitable approaches and analysis regarding our scientific question are often difficult and time-consuming. To address this gap, a recent workshop entitled ‘ConGen 2015’ was held at Montana University in order to bring together the knowledge accumulated in this field and to provide training in conceptual and practical aspects of data analysis applied to the field of conservation and evolutionary genomics. Here, we summarize the expertise yield by each instructor that has led us to consider the importance of keeping in mind the scientific question from sampling to management practices along with the selection of appropriate genomics tools and bioinformatics challenges.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mec.13647

Conservation genomics of natural and managed populations: building a conceptual and practical framework.

The advent of high throughput sequencing and genotyping technologies enables the comparison of patterns of polymorphisms at a very large number of markers. While the characterization of genetic structure from individual sequencing data remains expensive for many nonmodel species, it has been shown that sequencing pools of individual DNAs (Pool-seq) represents an attractive and cost-effective alternative. However, analyzing sequence read counts from a DNA pool instead of individual genotypes raises statistical challenges in deriving correct estimates of genetic differentiation. In this article, we provide a method-of-moments estimator of F-ST for Pool-seq data, based on an analysis-of-variance framework. We show, by means of simulations, that this new estimator is unbiased and outperforms previously proposed estimators. We evaluate the robustness of our estimator to model misspecification, such as sequencing errors and uneven contributions of individual DNAs to the pools. Finally, by reanalyzing published Pool-seq data of different ecotypes of the prickly sculpin Cottus asper, we show how the use of an unbiased F-ST estimator may question the interpretation of population structure inferred from previous analyses.

https://www.genetics.org/content/genetics/210/1/315.full.pdf

Measuring Genetic Differentiation from Pool-seq Data.

The degree of genetic differentiation among populations experiencing high levels of gene flow is expected to be low for neutral genomic sites, but substantial divergence can occur in sites subject to directional selection. Studies of highly mobile marine fish populations provide an opportunity to investigate this kind of heterogeneous genomic differentiation, but most studies to this effect have focused on a relatively low number of genetic markers and/or few populations. Hence, the patterns and extent of genomic divergence in high-gene-flow marine fish populations remain poorly understood. We here investigated genome-wide patterns of genetic variability and differentiation in ten marine populations of three-spined stickleback (Gasterosteus aculeatus) distributed across a steep salinity and temperature gradient in the Baltic Sea, by utilizing >30,000 single nucleotide polymorphisms obtained with a pooled RAD-seq approach. We found that genetic diversity and differentiation varied widely across the genome, and identified numerous fairly narrow genomic regions exhibiting signatures of both divergent and balancing selection. Evidence was uncovered for substantial genetic differentiation associated with both salinity and temperature gradients, and many candidate genes associated with local adaptation in the Baltic Sea were identified. The patterns of genetic diversity and differentiation, as well as candidate genes associated with adaptation, in Baltic Sea sticklebacks were similar to those observed in earlier comparisons between marine and freshwater populations, suggesting that similar processes may be driving adaptation to brackish and freshwater environments. Taken together, our results provide strong evidence for heterogenic genomic divergence driven by local adaptation in the face of gene flow along an environmental gradient in the post-glacially formed Baltic Sea.

/pdf/population-genomic-evidence-for-adaptive-differentiation-in-54x5syawdx.pdf

Population genomic evidence for adaptive differentiation in Baltic Sea three-spined sticklebacks

Although pooled-population sequencing has become a widely used approach for estimating allele frequencies, most work has proceeded in the absence of a proper statistical framework. We introduce a self-sufficient, closed-form, maximum-likelihood estimator for allele frequencies that accounts for errors associated with sequencing, and a likelihood-ratio test statistic that provides a simple means for evaluating the null hypothesis of monomorphism. Unbiased estimates of allele frequencies (where N is the number of individuals sampled) appear to be unachievable, and near-certain identification of a polymorphism requires a minor-allele frequency . A framework is provided for testing for significant differences in allele frequencies between populations, taking into account sampling at the levels of individuals within populations and sequences within pooled samples. Analyses that fail to account for the two tiers of sampling suffer from very large false-positive rates and can become increasingly misleading with increasing depths of sequence coverage. The power to detect significant allele-frequency differences between two populations is very limited unless both the number of sampled individuals and depth of sequencing coverage exceed 100.

/pdf/population-genetic-inference-from-pooled-sequencing-data-1jxdruwhht.pdf

Sade Wilson

Papers

Population-genetic inference from pooled-sequencing data.