REVIEW
RADseq approaches and applications for forest tree genetics
Thomas L. Parchman
1
&
Joshua P. Jahner
1
&
Kathryn A. Uckele
1
&
Lanie M. Galland
1
&
Andrew J. Eckert
2
Received: 30 August 2017 /Revised: 30 April 2018 /Accepted: 2 May 2018 /Published online: 21 May 2018
#
Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
As tree species vary extensively in genome size, complexity, and resource development, reduced representation methods have
been increasingly employed for the generation of population genomic data. By allowing rapid marker discovery and genotyping
for thousands of genomic regions in many individuals without requiring genomic resources, restriction site-associated DNA
sequencing (RADseq) methods have dramatically improved our ability to bring population genomic perspectives to non-model
trees. The rapid recent increase in studies of trees utilizing RADseq suggests that it is likely to become among the most common
approaches for generating genome-wide data for a variety of applications. Here we provide a practical review of RADseq and its
application to research areas of tree genetics. We briefly review RADseq laboratory methods and consider analytical approaches
for assembly, variant calling, and bioinformatic processing. To guide considerations for study design, we use in silico analyses of
eight available tree genomes to illustrate how expected marker number and density vary across laboratory approaches and
genome sizes, and to consider the ability of RADseq designs to query coding regions. We review the empirical use of
RADseq for different research objectives, considering its strengths and limitations. Many studies have used RADseq data to
perform genome scans for selection, although limited marker density and linkage disequilibrium will often compromise its utility
for such analyses. Regardless of this limitation, RADseq offers a powerful and inexpensive technique for generating genome-
wide SNP data that can greatly contribute to research spanning phylogenetic and population genetic inference, linkage mapping,
and quantitative genetic parameter estimation for tree genetics.
Keywords Genome scan
.
Genotyping by sequencing
.
Linkage mapping
.
Phylogenetics
.
Population genomics
.
RADseq
Understanding how genetic variation is shaped across land-
scapes and genomes is critical for inferring the action of evo-
lutionary processes on forest tree populations, and for
predicting and managing their response to environmental
change (González-Martínez et al. 2006;Aitkenetal.2008;
Alberto et al. 2013;Sorketal.2013). Until recently, population
genetic analyses in tree species were often limited to traditional
molecular markers (e.g., SSRs), Sanger sequencing of modest
numbers of genes, or to relatively expensive genotyping ap-
proaches that were dependent on genomic or transcriptomic
resources for development (e.g., Eckert et al. 2013; Geraldes
et al. 2013;Pavyetal.2013). The emergence of DNA sequenc-
ing technologies that allow inexpensive and massively parallel
sequencing of short DNA reads has changed this rapidly
(Mardis 2013) and is driving a steady increase in genomic
resources. Transcriptomes have been sequenced, assembled,
and annotated for many tree species (e.g., Pinosio et al. 2014;
Yeaman et al. 2014). Whole genome reference sequences have
been published for a growing number of angiosperms (e.g.,
Eucalyptus grandis,Myburgetal.2014; Malus domestica,
Velas co et al. 2010; Populus trichocarpa,Tuskanetal.2006;
Prunus persica, Verde et al. 2013) and even for a number of the
massive-genomed conifers (e.g., Picea abies, Nystedt et al.
2013; Picea glauca, Birol et al. 2013; Pinus taeda, Neale
et al. 2014; Pinus lambertiana, Stevens et al. 2016).
By increasing the ability to genotype far larger numbers of
loci and individuals, high-throughput sequencing technolo-
Communicated by S. C. González-Martínez
* Thomas L. Parchman
tparchman@unr.edu
1
Department of Biology and Program in Ecology, Evolution, and
Conservation Biology, University of Nevada Reno,
Reno, NV 89557, USA
2
Department of Biology, Virginia Commonwealth University,
Richmond, VA 23284, USA
Tree Genetics & Genomes (2018) 14: 39
https://doi.org/10.1007/s11295-018-1251-3
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