Article series: Study designs

Harnessing the power of RADseq for ecological and evolutionary genomics

Journal name:
Nature Reviews Genetics
Volume:
17,
Pages:
81–92
Year published:
DOI:
doi:10.1038/nrg.2015.28
Published online

Abstract

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.

At a glance

Figures

  1. Step-by-step illustration of five RADseq library preparation protocols.
    Figure 1: Step-by-step illustration of five RADseq library preparation protocols.

    All protocols begin by digesting high-molecular-weight genomic DNA with one or more restriction enzymes. For most protocols, the sequencing adaptors (oligonucleotides) are added in two stages, with one set of oligonucleotides added during a ligation step early in the protocol, and a second set of oligonucleotides incorporated during a final PCR step. The second set of oligonucleotides extends the length of the total fragment to produce the entire Illumina adaptor sequences. By contrast, the original RADseq adds adaptors in three stages. For Illumina sequencing, the adaptors on either end of each DNA fragment must differ, and therefore some protocols (for example, original RADseq, double digest RAD (ddRAD) and ezRAD) use Y-adaptors that are structured to ensure that only fragments with different adaptors at either end are PCR-amplified (illustrated here as Y-shaped adaptors). Other protocols (for example, genotyping by sequencing (GBS)) simply rely on the fact that fragments without the correct adaptors will not be sequenced. To generate fragments of an ideal length for sequencing, most methods use common-cutter enzymes (for example, 4–6 bp cutters) to generate a wide range of fragment sizes, followed by a direct size selection (gel-cutting or magnetic beads, for example, ezRAD and ddRAD) or an indirect size selection (as a consequence of PCR amplification or sequencing efficiency, for example, GBS).

  2. Sources of error and bias in RADseq data.
    Figure 2: Sources of error and bias in RADseq data.

    a | An example of allele dropout for a restriction site-associated DNA sequencing (RADseq) protocol that uses size selection to reduce the number of loci to be sequenced. Grey lines represent chromosomes within one individual, red squares represent restriction cut sites, coloured squares represent heterozygous SNPs, and square brackets represent genomic regions that are sequenced. Mutation in cut site B for haplotype 1 makes the post-digestion fragment containing the SNP too long to be retained during size selection for haplotype 1, eliminating the possibility of sequencing of any loci on that fragment, and causing the individual to appear homozygous at the heterozygous SNP. b | An example of fragments produced after PCR for one heterozygous locus for different RADseq protocols, and the reads retained after bioinformatic analyses. PCR duplicates are shown with the same symbol (circle, square, asterisk or triangle) as the parent fragment from the original template DNA. By chance, some alleles will amplify more than others during PCR. For all protocols, PCR duplicates will be identical in sequence composition and length to the original template molecule. For the original RADseq, this feature (that is, identical length) can be used to identify and remove PCR duplicates bioinformatically, because original template molecules for a given locus will not be identical in length. For alternative RADseq methods, this feature cannot be used to identify PCR duplicates, because all original template molecules for a given locus are identical in length. High frequencies of PCR duplicates can cause heterozygotes to appear as homozygotes or can cause PCR errors to appear as true diversity. Part b is adapted with permission from Ref. 37, Wiley.

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Affiliations

  1. Department of Fish and Wildlife Sciences, University of Idaho, 875 Perimeter Drive MS 1136, Moscow, Idaho 83844-1136, USA.

    • Kimberly R. Andrews
  2. University of Montana, Division of Biological Sciences, 32 Campus Dr. HS104, Missoula, Montana 59812, USA.

    • Jeffrey M. Good
  3. Department of Animal Science, University of California, One Shields Avenue, Davis, California 95616, USA.

    • Michael R. Miller
  4. Flathead Lake Biological Station, Fish and Wildlife Genomics Group, Division of Biological Sciences, University of Montana, Polson, Montana 59860, USA.

    • Gordon Luikart
  5. Institute for Bioinformatics and Evolutionary Studies, Department of Biological Sciences, University of Idaho, Moscow, Idaho 83843, USA.

    • Paul A. Hohenlohe

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The authors declare no competing interests.

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Author details

  • Kimberly R. Andrews

    Kimberly R. Andrews is a postdoctoral research fellow in Lisette Waits' group (Department of Fish & Wildlife Sciences) collaborating with Paul Hohenlohe's group (Department of Biological Sciences) at the University of Idaho, USA. Her work focuses on investigating the ecological and evolutionary factors that drive dispersal, gene flow, adaptation and speciation, and on applying this knowledge to conservation and management issues. She received her PhD from the University of Hawai'i at Mānoa, USA, and has conducted postdoctoral research at the University of Hawai'i and as a Marie Curie Research Fellow at Durham University.

  • Jeffrey M. Good

    Jeffrey M. Good is an assistant professor in the Division of Biological Sciences at the University of Montana, Missoula, USA. His laboratory combines population, comparative and functional genomics to understand the genetic basis of speciation and adaptation in model and non-model systems.

  • Michael R. Miller

    Michael R. Miller is an assistant professor of population and quantitative genetics in the Department of Animal Science at University of California, Davis, USA. His research group is broadly interested in developing and applying genetics concepts and methods to help solve environmental issues. Much of their work focuses on using genetics to characterize demography and adaptation in Pacific salmon and trout with the goal of improving conservation, management and restoration programmes. Michael received his B.S. and Ph.D. degrees from the University of Oregon, USA.

  • Gordon Luikart

    Gordon Luikart is a professor at the Flathead Lake Biological Station and the Montana Conservation Genomic Laboratory, University of Montana, USA. His research develops and applies molecular and computational approaches to bridge the gaps between theory, basic science and practical applications in conservation biology and evolutionary ecology. He received his Ph.D. at the University of Montana, and was a research scientist at the Centre National de la Recherche Scientifique (CNRS), Grenoble, France, and at the Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO), Vairão, Portugal.

  • Paul A. Hohenlohe

    Paul A. Hohenlohe is an assistant professor in the Institute for Bioinformatics and Evolutionary Studies, and the Departments of Biological Sciences and Statistical Science at the University of Idaho (UI), USA. His research group focuses on evolutionary and conservation genomics in a wide variety of organisms, using RADseq and other tools. He received his Ph.D. from the University of Washington, USA, worked as a conservation biologist and conducted postdoctoral research at Oregon State University and the University of Oregon before joining UI in 2011. Paul A. Hohenlohe's homepage.

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  1. Supplementary information S1 (figure) (273 KB)

    Numbers of articles citing the original papers describing each type of RADseq protocol over time.

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