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  • Review Article
  • Published:

Harnessing the power of RADseq for ecological and evolutionary genomics

Nature Reviews Genetics volume 17, pages 81–92 (2016)Cite this article

Subjects

Key Points

  • RADseq has fuelled studies in ecological, evolutionary and conservation genomics by using next-generation sequencing to uncover hundreds or thousands of polymorphic loci across the genome in a single, simple and cost-effective experiment. RADseq does not require any prior genomic information for the taxa being studied, and is therefore particularly advantageous for studies of non-model organisms.

  • Numerous technical variations on RADseq have been developed, which promise to increase the flexibility and decrease the cost and effort of genomics studies. Differences among the methods lead to important considerations for all steps of genomics studies, from the types of scientific questions that can be addressed and the costs of library preparation and sequencing to the types of bias and error that are inherent in the resulting data.

  • Allele dropout, PCR duplicates and variance in depth of coverage among loci are important sources of error and bias in RADseq studies, and the prevalence of these phenomena will vary across RADseq methods.

  • Other important considerations when designing a RADseq study include the number, length and coverage of loci needed to address the research question; the availability of prior genomic resources; the budget; and the consistency of data across sequencing runs and laboratories.

  • There is no single best or most flexible RADseq method. Researchers must consider the trade-offs of the different methods, and choose the approach that is best suited to their study goals.

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.

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Figure 1: Step-by-step illustration of five RADseq library preparation protocols.
Figure 2: Sources of error and bias in RADseq data.

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References

  1. [No authors listed]. Breakthrough of the year. Scorecard. Science 330, 1608–1609 (2010).

  2. Davey, J. W. et al. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat. Rev. Genet. 12, 499–510 (2011). Reviews methods for genomic marker discovery and genotyping using next-generation sequencing methods.

    CAS  PubMed  Google Scholar 

  3. Luikart, G., England, P. R., Tallmon, D., Jordan, S. & Taberlet, P. The power and promise of population genomics: from genotyping to genome typing. Nat. Rev. Genet. 4, 981–994 (2003).

    CAS  PubMed  Google Scholar 

  4. Baird, N. A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE 3, e3376 (2008). Introduces one of the most widely used RADseq methods, which we describe as original RAD throughout.

    PubMed  PubMed Central  Google Scholar 

  5. Narum, S. R., Buerkle, C. A., Davey, J. W., Miller, M. R. & Hohenlohe, P. A. Genotyping-by-sequencing in ecological and conservation genomics. Mol. Ecol. 22, 2841–2847 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Elshire, R. J. et al. A robust, simple Genotyping-by-Sequencing (GBS) approach for high diversity species. PLoS ONE 6, e19379 (2011). Introduces GBS, one of the most widely used RADseq methods.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Avise, J. C., Lansman, R. A. & Shade, R. O. Use of restriction endonucleases to measure mitochondrial DNA sequence relatedness in natural populations. I. Population structure and evolution in the genus Peromyscus. Genetics 92, 279–295 (1979).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Brown, W. M. Polymorphism in mitochondrial DNA of humans as revealed by restricion endonuclease analysis. Proc. Natl Acad. Sci. USA 77, 3605–3609 (1980).

    CAS  PubMed  Google Scholar 

  9. Altshuler, D. et al. An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature 407, 513–516 (2000).

    CAS  PubMed  Google Scholar 

  10. Van Tassell, C. P. et al. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat. Methods 5, 247–252 (2008).

    CAS  PubMed  Google Scholar 

  11. Wiedmann, R. T., Smith, T. P. L. & Nonneman, D. J. SNP discovery in swine by reduced representation and high throughput pyrosequencing. BMC Genet. 9, 81 (2008).

    PubMed  PubMed Central  Google Scholar 

  12. Cariou, M., Duret, L. & Charlat, S. Is RAD-seq suitable for phylogenetic inference? An in silico assessment and optimization. Ecol. Evol. 3, 846–852 (2013).

    PubMed  PubMed Central  Google Scholar 

  13. Poland, J. A. & Rife, T. W. Genotyping-by-sequencing for plant breeding and genetics. Plant Genome 5, 92–102 (2012).

    CAS  Google Scholar 

  14. Graham, C. et al. Impacts of degraded DNA on restriction enzyme associated DNA sequencing (RADSeq). Mol. Ecol. Resour. 15, 1304–1315 (2015).

    CAS  PubMed  Google Scholar 

  15. Etter, P. D., Preston, J. L., Bassham, S., Cresko, W. A. & Johnson, E. A. Local de novo assembly of RAD paired-end contigs using short sequencing reads. PLoS ONE 6, e18561 (2011). Introduces a method for generating long contigs from paired-end RADseq data.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Toonen, R. J. et al. ezRAD: a simplified method for genomic genotyping in non-model organisms. PeerJ 1, e203 (2013).

    PubMed  PubMed Central  Google Scholar 

  17. Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012). Introduces ddRAD, one of the most widely used RADseq methods.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Hohenlohe, P. A. et al. Genomic patterns of introgression in rainbow and westslope cutthroat trout illuminated by overlapping paired-end RAD sequencing. Mol. Ecol. 22, 3002–3013 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Willing, E.-M., Hoffmann, M., Klein, J. D., Weigel, D. & Dreyer, C. Paired-end RAD-seq for de novo assembly and marker design without available reference. Bioinformatics 27, 2187–2193 (2011).

    CAS  PubMed  Google Scholar 

  20. Waples, R. K., Seeb, L. W. & Seeb, J. E. Linkage mapping with paralogs exposes regions of residual tetrasomic inheritance in chum salmon (Oncorhynchus keta). Mol. Ecol. Resour. http://dx.doi.org/10.1111/1755-0998.12394 (2015).

  21. Amish, S. J. et al. RAD sequencing yields a high success rate for westslope cutthroat and rainbow trout species-diagnostic SNP assays. Mol. Ecol. Resources 12, 653–660 (2012).

    CAS  Google Scholar 

  22. Ali, O. A. et al. RAD capture (Rapture): flexible and efficient sequence-based genotyping. BioRxiv http://dx.doi.org/10.1101/028837 (2015). Extends RADseq with the addition of a sequence-capture step to target a subset of RAD loci, and also presents a substantially revised new version of the original RADseq protocol.

  23. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Hohenlohe, P. A. et al. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet. 6, e1000862 (2010). An early application of RADseq for population genomics, identifies loci under selection in multiple, independently derived freshwater stickleback populations.

    PubMed  PubMed Central  Google Scholar 

  25. Nielsen, R., Korneliussen, T., Albrechtsen, A., Li, Y. & Wang, J. SNP calling, genotype calling, and sample allele frequency estimation from new-generation sequencing data. PLoS ONE 7, e37558 (2012). Introduces Bayesian methods for SNP-calling using the sample allele frequency spectra estimated from next-generation sequencing data.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Fumagalli, M. et al. Quantifying population genetic differentiation from next-generation sequencing data. Genetics 195, 979–992 (2013).

    PubMed  PubMed Central  Google Scholar 

  27. Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013). Introduces Stacks, a widely used software package for locus discovery, genotyping and population genomic analysis using RADseq data.

    PubMed  PubMed Central  Google Scholar 

  28. Eaton, D. A. R. PyRAD: assembly of de novo RADseq loci for phylogenetic analyses. Bioinformatics 30, 1844–1849 (2014).

    CAS  PubMed  Google Scholar 

  29. Lu, F. et al. Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genet. 9, e1003215 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Bradbury, P. J. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635 (2007).

    CAS  PubMed  Google Scholar 

  31. Ilut, D. C., Nydam, M. L. & Hare, M. P. Defining loci in restriction-based reduced representation genomic data from nonmodel species: sources of bias and diagnostics for optimal clustering. Biomed. Res. Int. 2014, 675158 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. Mastretta-Yanes, A. et al. Gene duplication, population genomics, and species-level differentiation within a tropical mountain shrub. Genome Biol. Evol. 6, 2611–2624 (2014).

    PubMed  PubMed Central  Google Scholar 

  33. Leaché, A. D. et al. Phylogenomics of phrynosomatid lizards: conflicting signals from sequence capture versus restriction site associated dna sequencing. Genome Biol. Evol. 7, 706–719 (2015).

    PubMed  PubMed Central  Google Scholar 

  34. Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008).

    CAS  PubMed  Google Scholar 

  35. Gautier, M. et al. The effect of RAD allele dropout on the estimation of genetic variation within and between populations. Mol. Ecol. 22, 3165–3178 (2013). Uses computer simulations to investigate the influence of allele dropout on population genomic statistics for RADseq data.

    CAS  PubMed  Google Scholar 

  36. Arnold, B., Corbett-Detig, R. B., Hartl, D. & Bomblies, K. RADseq underestimates diversity and introduces genealogical biases due to nonrandom haplotype sampling. Mol. Ecol. 22, 3179–3190 (2013).

    CAS  PubMed  Google Scholar 

  37. Andrews, K. R. et al. Trade-offs and utility of alternative RADseq methods: reply to Puritz et al. 2014. Mol. Ecol. 23, 5943–5946 (2014).

    CAS  PubMed  Google Scholar 

  38. Schweyen, H., Rozenberg, A. & Leese, F. Detection and removal of PCR duplicates in population genomic ddRAD studies by addition of a degenerate base region (dbr) in sequencing adapters. Biol. Bull. 227, 146–160 (2014).

    CAS  PubMed  Google Scholar 

  39. Casbon, J. A., Osborne, R. J., Brenner, S. & Lichtenstein, C. P. A method for counting PCR template molecules with application to next-generation sequencing. Nucleic Acids Res. 39, e81 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Tin, M. M. Y., Rheindt, F. E., Cros, E. & Mikheyev, A. S. Degenerate adaptor sequences for detecting PCR duplicates in reduced representation sequencing data improve genotype calling accuracy. Mol. Ecol. Resour. 15, 329–336 (2015).

    CAS  PubMed  Google Scholar 

  41. Davey, J. W. et al. Special features of RAD Sequencing data: implications for genotyping. Mol. Ecol. 22, 3151–3164 (2013).

    CAS  PubMed  Google Scholar 

  42. DaCosta, J. M. & Sorenson, M. D. Amplification biases and consistent recovery of loci in a double-digest RAD-seq protocol. PLoS ONE 9, e106713 (2014).

    PubMed  PubMed Central  Google Scholar 

  43. Benjamini, Y. & Speed, T. P. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Lepais, O. & Weir, J. T. SimRAD: an R package for simulation-based prediction of the number of loci expected in RADseq and similar genotyping by sequencing approaches. Mol. Ecol. Resour. 14, 1314–1321 (2014).

    CAS  PubMed  Google Scholar 

  45. Cruaud, A. et al. Empirical assessment of RAD sequencing for interspecific phylogeny. Mol. Biol. Evol. 31, 1272–1274 (2014).

    CAS  PubMed  Google Scholar 

  46. Kardos, M., Luikart, G. & Allendorf, F. W. Measuring individual inbreeding in the age of genomics: marker-based measures are better than pedigrees. Heredity 115, 63–72 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Nadeau, N. J. et al. Population genomics of parallel hybrid zones in the mimetic butterflies, H. melpomene and H. erato. Genome Res. 24, 1316–1333 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Ruegg, K., Anderson, E. C., Boone, J., Pouls, J. & Smith, T. B. A role for migration-linked genes and genomic islands in divergence of a songbird. Mol. Ecol. 23, 4757–4769 (2014).

    PubMed  Google Scholar 

  49. Kirin, M. et al. Genomic runs of homozygosity record population history and consanguinity. PLoS ONE 5, e13996 (2010).

    PubMed  PubMed Central  Google Scholar 

  50. Hoffman, J. I. et al. High-throughput sequencing reveals inbreeding depression in a natural population. Proc. Natl Acad. Sci. USA 111, 3775–3780 (2014).

    CAS  PubMed  Google Scholar 

  51. Allendorf, F. & Thorgaard, G. in Evolutionary Genetics of Fishes Monographs in Evolutionary Biology Ch. 1 (ed. Turner, B. J.) 1–53 (Springer, 1984).

    Google Scholar 

  52. Adams, K. L. & Wendel, J. F. Polyploidy and genome evolution in plants. Curr. Opin. Plant Biol. 8, 135–141 (2005).

    CAS  PubMed  Google Scholar 

  53. Charles, M. et al. Dynamics and differential proliferation of transposable elements during the evolution of the B and A genomes of wheat. Genetics 180, 1071–1086 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Palmieri, N. & Schloetterer, C. Mapping accuracy of short reads from massively parallel sequencing and the implications for quantitative expression profiling. PLoS ONE 4, e6323 (2009).

    PubMed  PubMed Central  Google Scholar 

  55. Hand, B. K. et al. Genomics and introgression: discovery and mapping of thousands of species-diagnostic SNPs using RAD sequencing. Curr. Zool. 61, 146–154 (2015).

    Google Scholar 

  56. Andolfatto, P. et al. Multiplexed shotgun genotyping for rapid and efficient genetic mapping. Genome Res. 21, 610–617 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Swarts, K. et al. Novel methods to optimize genotypic imputation for low-coverage, next-generation sequence data in crop plants. Plant Genome http://dx.doi.org/10.3835/plantgenome2014.05.0023 (2014).

  58. Heffelfinger, C. et al. Flexible and scalable genotyping-by-sequencing strategies for population studies. BMC Genomics 15, 979 (2014).

    PubMed  PubMed Central  Google Scholar 

  59. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Jones, M. & Good, J. Targeted capture in evolutionary and ecological genomics. Mol. Ecol. http://dx.doi.org/10.1111/mec.13304 (2015).

  61. Ellegren, H. et al. The genomic landscape of species divergence in Ficedula flycatchers. Nature 491, 756–760 (2012).

    CAS  PubMed  Google Scholar 

  62. Kardos, M. et al. Whole genome resequencing uncovers molecular signatures of natural and sexual selection in wild bighorn sheep. Mol. Ecol. 24, 5616–5632 (2015).

    CAS  PubMed  Google Scholar 

  63. Schlötterer, C., Tobler, R., Kofler, R. & Nolte, V. Sequencing pools of individuals-mining genome-wide polymorphism data without big funding. Nat. Rev. Genet. 15, 749–763 (2014).

    PubMed  Google Scholar 

  64. Huddleston, J. et al. Reconstructing complex regions of genomes using long-read sequencing technology. Genome Res. 24, 688–696 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Putnam, N. et al. Chromosome-scale shotgun assembly using an in vitro method for long-range linkage. arXiv http://arxiv.org/abs/1502.05331 (2015).

  66. Miller, M. R., Dunham, J. P., Amores, A., Cresko, W. A. & Johnson, E. A. Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res. 17, 240–248 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Wang, S., Meyer, E., McKay, J. K. & Matz, M. V. 2b-RAD: a simple and flexible method for genome-wide genotyping. Nat. Methods 9, 808–812 (2012).

    CAS  PubMed  Google Scholar 

  68. Guo, Y. et al. An improved 2b-RAD approach (I2b-RAD) offering genotyping tested by a rice (Oryza sativa L.) F2 population. BMC Genomics 15, 956 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. Truong, H. T. et al. Sequence-based genotyping for marker discovery and co-dominant scoring in germplasm and populations. PLoS ONE 7, e37565 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. van Orsouw, N. J. et al. Complexity reduction of polymorphic sequences (CRoPS (TM)): a novel approach for large-scale polymorphism discovery in complex genomes. PLoS ONE 2, e1172 (2007).

    PubMed  PubMed Central  Google Scholar 

  71. Greminger, M. P. et al. Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms. BMC Genomics 15, 16 (2014).

    PubMed  PubMed Central  Google Scholar 

  72. Schield, D. R. et al. EpiRADseq: scalable analysis of genomewide patterns of methylation using next-generation sequencing. Methods Ecol. Evol. http://dx.doi.org/10.1111/2041-210X.12435 (2015).

  73. Stolle, E. & Moritz, R. F. A. RESTseq — efficient benchtop population genomics with RESTriction fragment SEQuencing. PLoS ONE 8, e63960 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Pukk, L. et al. Less is more: extreme genome complexity reduction with ddRAD using ion torrent semiconductor technology. Mol. Ecol. Resour. 15, 1145–1152 (2015).

    CAS  PubMed  Google Scholar 

  75. Recknagel, H., Jacobs, A., Herzyk, P. & Elmer, K. R. Double-digest RAD sequencing using Ion Proton semiconductor platform (ddRADseq-ion) with nonmodel organisms. Mol. Ecol. Resour. 15, 1316–1329 (2015).

    CAS  PubMed  Google Scholar 

  76. Chen, Q. et al. Genotyping by genome reducing and sequencing for outbred animals. PLoS ONE 8, e67500 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Chutimanitsakun, Y. et al. Construction and application for QTL analysis of a restriction site associated DNA (RAD) linkage map in barley. BMC Genomics 12, 4 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Evans, B. J., Zeng, K., Esselstyn, J. A., Charlesworth, B. & Melnick, D. J. Reduced representation genome sequencing suggests low diversity on the sex chromosomes of Tonkean macaque monkeys. Mol. Biol. Evol. 31, 2425–2440 (2014).

    CAS  PubMed  Google Scholar 

  79. Larson, W. A., Seeb, J. E., Pascal, C. E., Templin, W. D. & Seeb, L. W. Single-nucleotide polymorphisms (SNPs) identified through genotyping-by-sequencing improve genetic stock identification of Chinook salmon (Oncorhynchus tshawytscha) from western Alaska. Can. J. Fisheries Aquat. Sci. 71, 698–708 (2014).

    CAS  Google Scholar 

  80. Candy, J. R. et al. Population differentiation determined from putative neutral and divergent adaptive genetic markers in Eulachon (Thaleichthys pacificus, Osmeridae), an anadromous Pacific smelt. Mol. Ecol. Resourc. 15, 1421–1434 (2015).

    Google Scholar 

  81. Dann, T. H., Habicht, C., Baker, T. T. & Seeb, J. E. Exploiting genetic diversity to balance conservation and harvest of migratory salmon. Can. J. Fisheries Aquat. Sci. 70, 785–793 (2013).

    Google Scholar 

  82. Emerson, K. J. et al. Resolving postglacial phylogeography using high-throughput sequencing. Proc. Natl Acad. Sci. USA 107, 16196–16200 (2010).

    CAS  PubMed  Google Scholar 

  83. Combosch, D. J. & Vollmer, S. V. Trans-Pacific RAD-Seq population genomics confirms introgressive hybridization in Eastern Pacific Pocillopora corals. Mol. Phylogenet. Evol. 88, 154–162 (2015).

    PubMed  Google Scholar 

  84. Gaither, M. R. et al. Genomic signatures of geographic isolation and natural selection in coral reef fishes. Mol. Ecol. 24, 1543–1557 (2015).

    CAS  PubMed  Google Scholar 

  85. Eaton, D. A. R. & Ree, R. H. Inferring phylogeny and introgression using RADseq data: an example from flowering plants (Pedicularis: Orobanchaceae). Syst. Biol. 62, 689–706 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Ford, A. G. P. et al. High levels of interspecific gene flow in an endemic cichlid fish adaptive radiation from an extreme lake environment. Mol. Ecol. 24, 3421–3440 (2015).

    PubMed  PubMed Central  Google Scholar 

  87. Wagner, C. E. et al. Genome-wide RAD sequence data provide unprecedented resolution of species boundaries and relationships in the Lake Victoria cichlid adaptive radiation. Mol. Ecol. 22, 787–798 (2013).

    CAS  PubMed  Google Scholar 

  88. Futschik, A. & Schlöetterer, C. The next generation of molecular markers from massively parallel sequencing of pooled DNA samples. Genetics 186, 207–218 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Gautier, M. et al. Estimation of population allele frequencies from next-generation sequencing data: pool-versus individual-based genotyping. Mol. Ecol. 22, 3766–3779 (2013).

    CAS  PubMed  Google Scholar 

  90. Anderson, E. C., Skaug, H. J. & Barshis, D. J. Next-generation sequencing for molecular ecology: a caveat regarding pooled samples. Mol. Ecol. 23, 502–512 (2014).

    CAS  PubMed  Google Scholar 

  91. Zhu, Y., Bergland, A. O., Gonzalez, J. & Petrov, D. A. Empirical validation of pooled whole genome population re-sequencing in Drosophila melanogaster. PLoS ONE 7, e41901 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Lynch, M., Bost, D., Wilson, S., Maruki, T. & Harrison, S. Population-genetic inference from pooled-sequencing data. Genome Biol. Evol. 6, 1210–1218 (2014).

    PubMed  PubMed Central  Google Scholar 

  93. Ferretti, L., Ramos-Onsins, S. E. & Perez-Enciso, M. Population genomics from pool sequencing. Mol. Ecol. 22, 5561–5576 (2013).

    PubMed  Google Scholar 

  94. Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Kayser, M., Brauer, S. & Stoneking, M. A genome scan to detect candidate regions influenced by local natural selection in human populations. Mol. Biol. Evol. 20, 893–900 (2003).

    CAS  PubMed  Google Scholar 

  96. Nielsen, R. et al. Genomic scans for selective sweeps using SNP data. Genome Res. 15, 1566–1575 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Ekblom, R. & Galindo, J. Applications of next generation sequencing in molecular ecology of non-model organisms. Heredity 107, 1–15 (2011).

    CAS  PubMed  Google Scholar 

  98. Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).

    CAS  PubMed  Google Scholar 

  99. Montgomery, S. B. et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464, 773–777 (2010).

    CAS  PubMed  Google Scholar 

  100. Piskol, R., Ramaswami, G. & Li, J. B. Reliable identification of genomic variants from RNA-seq data. Am. J. Hum. Genet. 93, 641–651 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Briggs, A. W. et al. Targeted retrieval and analysis of five Neandertal mtDNA genomes. Science 325, 318–321 (2009).

    CAS  PubMed  Google Scholar 

  102. Hodges, E. et al. Genome-wide in situ exon capture for selective resequencing. Nat. Genet. 39, 1522–1527 (2007).

    CAS  PubMed  Google Scholar 

  103. Gnirke, A. et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat. Biotechnol. 27, 182–189 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Mamanova, L. et al. Target-enrichment strategies for next-generation sequencing. Nat. Methods 7, 111–118 (2010).

    CAS  PubMed  Google Scholar 

  105. Henning, F., Lee, H. J., Franchini, P. & Meyer, A. Genetic mapping of horizontal stripes in Lake Victoria cichlid fishes: benefits and pitfalls of using RAD markers for dense linkage mapping. Mol. Ecol. 23, 5224–5240 (2014).

    CAS  PubMed  Google Scholar 

  106. Good, J. M. et al. Comparative population genomics of the ejaculate in humans and the Great Apes. Mol. Biol. Evol. 30, 964–976 (2013).

    CAS  PubMed  Google Scholar 

  107. Hedtke, S. M., Morgan, M. J., Cannatella, D. C. & Hillis, D. M. Targeted enrichment: maximizing orthologous gene comparisons across deep evolutionary time. PLoS ONE 8, e67908 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Bi, K. et al. Transcriptome-based exon capture enables highly cost-effective comparative genomic data collection at moderate evolutionary scales. BMC Genomics 13, 403 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Faircloth, B. C. et al. Ultraconserved elements anchor thousands of genetic markers spanning multiple evolutionary timescales. Syst. Biol. 61, 717–726 (2012).

    PubMed  Google Scholar 

  110. McCormack, J. E. et al. Ultraconserved elements are novel phylogenomic markers that resolve placental mammal phylogeny when combined with species-tree analysis. Genome Res. 22, 746–754 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Burbano, H. A. et al. Targeted investigation of the Neandertal genome by array-based sequence capture. Science 328, 723–725 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Bos, K. I. et al. A draft genome of Yersinia pestis from victims of the Black Death. Nature 478, 506–510 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Avila-Arcos, M. C. et al. Application and comparison of large-scale solution-based DNA capture-enrichment methods on ancient DNA. Sci. Rep. 1, 74 (2011).

    PubMed  PubMed Central  Google Scholar 

  114. Bos, K. I. et al. Pre-Columbian mycobacterial genomes reveal seals as a source of New World human tuberculosis. Nature 514, 494–497 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Carpenter, M. L. et al. Pulling out the 1%: whole-genome capture for the targeted enrichment of ancient DNA sequencing libraries. Am. J. Hum. Genet. 93, 852–864 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Castellano, S. et al. Patterns of coding variation in the complete exomes of three Neandertals. Proc. Natl Acad. Sci. USA 111, 6666–6671 (2014).

    CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank M. Gaither, E. Carroll, A. Moura, R. Bracewell and M. Jones for helpful discussions. K.R.A. was supported by the University of Idaho College of Natural Resources, USA. P.A.H. received support from US National Institutes of Health (NIH) grant P30 GM103324 and NSF grant 1316549. J.M.G. is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD73439) and the National Institute of General Medical Sciences (R01GM098536) of the National Institutes of Health. G.L. was supported by grants from US National Science Foundation (DEB-0742181 and DEB-1067613) and NASA-(NNX14AB84G).

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Authors and Affiliations

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

    Kimberly R. Andrews

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

    Jeffrey M. Good

  3. Department of Animal Science, University of California, One Shields Avenue, Davis, 95616, California, USA

    Michael R. Miller

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

    Gordon Luikart

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

    Paul A. Hohenlohe

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  1. Kimberly R. Andrews
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  2. Jeffrey M. Good
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  3. Michael R. Miller
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  4. Gordon Luikart
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  5. Paul A. Hohenlohe
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Corresponding author

Correspondence to Kimberly R. Andrews.

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Supplementary information

Supplementary information S1 (figure)

Numbers of articles citing the original papers describing each type of RADseq protocol over time. (PDF 273 kb)

PowerPoint slides

Glossary

Restriction site-associated DNA sequencing

(RADseq). A method for sequencing thousands of genetic loci adjacent to restriction cut sites across the genome using massively parallel (next-generation) sequencing. Also known as genotyping by sequencing.

Next-generation sequencing

(Also known as massively parallel sequencing). Technology that first emerged around 2005 that sequences millions of DNA molecules simultaneously.

Depth of coverage

The number of sequence reads for a given locus or nucleotide site.

Adaptors

Double-stranded oligonucleotides that must be ligated to DNA fragments before next-generation sequencing. Illumina adaptors contain regions that anneal to the flow cell, an 'index' sequence that act as a barcode to identify individual samples, and primer binding sites for bridge amplification and sequencing of the DNA fragment and indexes.

Barcodes

(Also known as in-line barcodes). Short unique sequences (typically 6–12 bp) used to identify individual samples. Occur at the end of the adaptor that is immediately adjacent to the genomic DNA fragment after adaptor ligation. The barcode is sequenced immediately before sequencing of the DNA fragment, and thus the barcode sequence will appear at the beginning of the sequence reads.

Sequencing library

DNA prepared for next-generation sequencing. The DNA must be an appropriate length for sequencing and must have sequencing adaptors ligated.

Sticky end

(Also known as DNA overhang). The string of single-stranded DNA that remains on the end of a DNA fragment that has been digested with a restriction enzyme. Some restriction enzymes produce blunt ends (double-stranded ends) rather than sticky ends.

IIB restriction enzymes

Restriction enzymes that cut DNA on both sides of the recognition site.

Pooling

Combining multiple individual samples into a DNA library with only one unique identifier (for example, one barcode or one index).

Combinatorial barcoding

Using two different barcoding methods, usually a standard Illumina index and an inline barcode. This method can reduce the number of adaptors that must be purchased, thus reducing library preparation cost.

Illumina index

A unique 6 bp or 8 bp sequence incorporated into Illumina adaptors that functions as a barcode to identify individual samples.

Single-end sequencing

Illumina sequencing of only one end of each DNA fragment.

Paired-end sequencing

Illumina sequencing of both ends of each DNA fragment.

Contigs

A group of overlapping sequence reads assembled to form a longer sequence.

Paralogues

Sequences originating through duplication within the genome.

Filtering

Removing unwanted sequence reads from a data set owing to low sequence quality, low depth of coverage, evidence for paralogy and other reasons.

Allele dropout

Failure of an allele present in a sample to be detected by sequencing.

Null alleles

Alleles present in a sample that fail to be identified by genotyping. The presence of a null allele leads to allele dropout.

Linkage disequilibrium

Nonrandom association of alleles at different loci.

Sliding window analyses

Analyses in which summary statistics are calculated within a chromosomal segment (window), as the window is incrementally advanced along each chromosome.

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Andrews, K., Good, J., Miller, M. et al. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet 17, 81–92 (2016). https://doi.org/10.1038/nrg.2015.28

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