Harnessing the power of RADseq for ecological and evolutionary genomics

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.


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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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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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


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.


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.


(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.


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.


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


Sequences originating through duplication within the genome.


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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