plant genome editing and the relevance of off-target changes€¦ · 4 ([email protected]),...

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Short title: Relevance of off-target changes in genome editing 1

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Co-Corresponding Authors: Peter L. Morrell ([email protected]); Robert M. Stupar 3

([email protected]), Department of Agronomy and Plant Genetics, 1991 Upper Buford Circle, 4

411 Borlaug Hall, St. Paul, MN 55108-6026 USA 5

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Full title: Plant genome editing and the relevance of off-target changes 7

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Author Names and Affiliations: Nathaniel Grahama,b, Gunvant B. Patilc,1, David M. Bubeckd, 9

Raymond C. Doberte, Kevin C. Glenne, Annie T. Gutsched, Sandeep Kumard, John A. Lindbof, 10

Luis Maasg, Gregory D. Mayd, Miguel E. Vega-Sancheze, Robert M. Stuparc, Peter L. Morrellc 11

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a Department of Genetics, Cell Biology and Development, University of Minnesota, St. Paul, MN 13

55108 USA 14

b Pairwise, Durham, NC 27709 USA 15

c Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108 16

USA 17

d Corteva Agriscience™, Johnston, IA 50131, USA 18

e Bayer Crop Science, Chesterfield, MO 63017 USA 19

f HM Clause, Davis, CA 95618 USA 20

g Enza Zaden Research USA, San Juan Bautista, CA 95045 USA 21

1 Present address: Department of Plant and Soil Science, Texas Tech University, Lubbock, TX 22

79409. 23

Plant Physiology Preview. Published on May 26, 2020, as DOI:10.1104/pp.19.01194

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One Sentence Summary: With well-designed protocols and guide RNAs, off-target changes 24

induced by site-directed nucleases are negligible with fewer genetic differences than from 25

standing variation or induced mutagenesis. 26

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Conflict of interest statement: Some of the authors are employees of Corteva Agriscience, 28

Bayer Crop Science, HM Clause, Enza Zaden, or Pairwise, which are companies involved in 29

targeted genetic modification of plants. R.M.S. is a co-inventor of a patent in the plant gene-30

editing space. All other authors report no conflicts of interest relevant to this article. 31

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List of author contributions: The authors contributed equally in conceiving, drafting, editing, 33

and critical review of the text, tables, and figures of this review. 34

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Abstract 36

Site-directed nucleases (SDNs) used for targeted genome editing are powerful new tools to 37

introduce precise genetic changes into plants. Like traditional approaches, such as conventional 38

crossing and induced mutagenesis, genome editing aims to improve crop yield and nutrition. 39

Next-generation sequencing studies demonstrate that across their genomes, populations of 40

crop species typically carry millions of single nucleotide polymorphisms and many copy number 41

and structural variants. Spontaneous mutations occur at rates of approximately 10-8 to 10-9 per 42

site per generation, while variation induced by chemical treatment or ionizing radiation results in 43

higher mutation rates. In the context of SDNs, an off-target change or edit is an unintended, 44

non-specific mutation occurring at a site with sequence similarity to the targeted edit region. 45

SDN-mediated off-target changes can contribute to a small number of additional genetic 46

variations compared to those that occur naturally in breeding populations or are introduced by 47

induced-mutagenesis methods. Recent studies show that using computational algorithms to 48

design genome editing reagents can mitigate off-target edits in plants. Finally, crops are subject 49



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to strong selection to eliminate off-type plants through well-established multigenerational 50

breeding, selection, and commercial variety development practices. Within this context, off-51

target edits in crops present no new safety concerns compared to other breeding practices. The 52

current generation of genome editing technologies is already proving useful to develop new 53

plant varieties with consumer and farmer benefits. Genome editing will likely undergo improved 54

editing specificity along with new developments in SDN delivery and increasing genomic 55

characterization, further improving reagent design and application. 56

Introduction: Plant Genetic Variability 57

Genetic differences between individuals are the basis of adaptation and evolution. Plant 58

breeding, as a form of directed evolution, has a long history of using genetic diversity for crop 59

improvement. During the process of crop domestication, humans selected individual plants with 60

favorable traits that resulted from novel mutations or standing variation in the ancestral species. 61

The process of selecting plant varieties with favorable characteristics for cultivation and 62

consumption continues to the present day. Modern plant breeding is a more directed process 63

than the crop improvement that occurred through the history and pre-history of most cultivated 64

species, but it continues to involve lengthy development cycles. Progress in our understanding 65

of the genetic basis of traits and refined approaches to select superior progeny have contributed 66

to increased yield for most major crops. Globally, crop yields have increased by greater than 67

one percent each year for much of the past century (Xu et al., 2017). Technological innovations, 68

including those that increased our understanding of nucleic acid sequence and function, have 69

contributed to this progress. Advances have primarily been incremental, with few step changes 70

(Bernardo, 2016). The relatively recent introduction of genome editing, which permits targeted 71

genetic changes, is part of a continuum of development of plant breeding techniques (Lusser et 72

al., 2012). Its primary appeal is that, in contrast to established mutagenesis techniques, 73

changes can be targeted to desired genes. 74



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Over the past several decades, there has been rapid development of genome editing 75

techniques (also referred to as gene editing or genome engineering) (Gaj et al., 2013; 76

Jaganathan et al., 2018; Zhang et al., 2018). These technologies make use of site-directed 77

nucleases (SDNs) and have broad applicability, including applications in many crop plants 78

(Podevin et al., 2013; Voytas, 2013; Weeks et al., 2016). As described in greater detail below, 79

SDNs are enzymes that create DNA double-strand breaks (DSB) at specific, pre-determined 80

nucleotide sequences sites, permitting targeted genetic modifications (Voytas, 2013; Weeks et 81

al., 2016). The most robust and best-characterized SDNs are the Zinc-Finger Nucleases (ZFN), 82

Transcription Activator-Like Effector Nucleases (TALEN), and the type II Clustered Regularly-83

Interspaced Short Palindromic Repeats-CRISPR-associated proteins (e.g., Cas9 or Cas12a) 84

systems, hereafter referred to as “CRISPR” (Gaj et al., 2013; Jaganathan et al., 2018; Zhang et 85

al., 2018). In all cases, the targeted DSBs generated by these precision nucleases are repaired 86

by the cell’s native DNA break repair mechanisms: the non-homologous end-joining (NHEJ) or 87

the homologous recombination (HR) pathways (Danner et al., 2017). 88

One area of consideration for genome editing of plants, animals, and in human therapeutic 89

applications is the potential for “off-target” edits that could lead to unintended mutations at 90

untargeted loci in the genome. An off-target edit is an unintended, non-specific mutation that 91

occurs at a site with sequence similarity to the targeted edit region. Off-target edits are distinct 92

from untargeted mutations that occur at sites without similarity to the target region and that are 93

usually associated with spontaneous or other type of background variation. The fidelity of SDNs 94

to edit only the targeted sequence has been questioned, particularly in mammalian genome 95

editing applications (Carroll, 2011; Pattanayak et al., 2011; Carroll, 2013; Wu et al., 2014; 96

Zhang et al., 2015; Tsai and Joung, 2016; Kosicki et al., 2018). The potential to introduce DNA 97

sequence changes at non-targeted loci in patients undergoing genome editing procedures has 98

become an essential consideration for clinical and disease treatment applications (Araki and 99



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Ishii, 2016). Furthermore, unintended mutations in treated human somatic cells could pose 100

health risks such as unregulated cell proliferation (cancer) (Cox et al., 2015). Off-target edits 101

were detected in the early stages of CRISPR-Cas9 research involving human cancer cell lines 102

(Fu et al., 2013). The off-target edit frequency in human cells ranged from 0 to 150 mutations 103

per genome, and this frequency was closely linked to the single-guide RNA (sgRNA) specificity 104

(Frock et al., 2015). The potential for off-target edits and rate at which they could occur in plants 105

are discussed below. 106

Plants differ from animals in substantive ways that alter the impact of induced changes. First, 107

somatic cell changes in plants differ in consequences from those in mammals. Somatic changes 108

in a portion of the plant are less likely to affect critical (irreplaceable) tissues. Unlike many 109

animals, genetic changes in juvenile plants can be transmitted to reproductive tissues (Schmid-110

Siegert et al., 2017). However, plants frequently develop multiple independent reproductive 111

structures, only a portion of which may be affected by any new mutations. Also, breeding to 112

develop new lines for commercial release involves an intensive process of selection of individual 113

plants with useful phenotypes while eliminating individuals with undesirable mutations or 114

phenotypes (commonly known as “off-types”) (ASTA, 2016; Glenn et al., 2017; Kaiser et al., 115

2020). For these reasons, off-target edits in crops present fewer safety concerns than those that 116

could arise with therapeutic applications of genome editing. 117

The goal of this review is a quantitative comparison of genetic variation that could arise through 118

genome editing with the variation that exists in crop species or is generated by current breeding 119

practices. The review examines our current understanding of the nature and abundance of 120

standing and induced genetic variation in crops and how they compare to the potential for off-121

target changes during genome editing. 122

Sources and Types of Inherent (Standing) Genomic Variation in Plants 123



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Before the identification of DNA as the molecular basis for the transmission of heritable genetic 124

variation, crop domestication and plant breeding involved visible inspection and selection of 125

desired phenotypes. There was limited knowledge of the types and extent of underlying 126

genomic variation in crop plants and their wild relatives (Zohary, 1999). The development of 127

genomic sequencing methodologies has provided an unprecedented understanding of the 128

molecular basis of diversity in plant species, ranging from single nucleotide variants to macro-129

scale events such as genomic rearrangements or whole genome duplication leading to 130

polyploidy and transposon-directed DNA transposition (Wendel et al., 2016; Gabur et al., 2019). 131

Our understanding of the primary sources of genetic diversity, namely mutations and 132

recombination, and the cellular processes involved in generating this variation, have increased 133

as both DNA sequencing technologies and genome assemblies have improved (Bevan et al., 134

2017). Mutation creates new variants while recombination arranges variants into new 135

combinations (Morrell et al., 2006); both processes rely upon endogenous DNA repair 136

mechanisms (see Box 1 on double-strand DNA break repair). Genetic drift and both positive and 137

purifying selection remove variants from populations, maintaining an equilibrium that permits 138

variation to persist at relatively constant levels in stable populations. Much of the process of 139

plant breeding involves the creation of varieties that combine favorable variants from their 140

parental lines. 141

[INSERT TEXT BOX - The Importance of Double-Strand Break Repairs] 142

Reference genomes and assessment of genomic variation 143

Standing variation in a species can be estimated at the whole genome level by comparing a 144

sample of individuals using Next Generation Sequencing (NGS). High-throughput DNA 145

sequencing, whole-genome reference sequences and assembly, and improved bioinformatic 146

tools have provided an unprecedented understanding of the variation in populations of the 147



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world’s major field and vegetable crop species (Morrell et al., 2011; Huang and Han, 2014; 148

Bevan et al., 2017). NGS and reference-based read mapping have also been used to identify de 149

novo sequence variation in lines accumulating induced and spontaneous mutations over several 150

generations (Ossowski et al., 2010; Belfield et al., 2012; Henry et al., 2014). Many of the field 151

crop and vegetable species that constitute a major part of the global diet now have a high-152

quality reference genome sequence (Michael and VanBuren, 2015). Reference genomes have 153

several limitations, the most apparent being that all genes or gene variants are not present in 154

any single accession (Hirsch et al., 2016; Jiao et al., 2017). Means of accounting for structural 155

variation and gene content are rapidly evolving, but generally involve representing multiple 156

genomes of a single species in a graph-like structure (Church et al., 2015; Paten et al., 2017). 157

A list of the classes of variation detected in cultivated plants is presented in Table 1. Single 158

nucleotide variations (SNVs) (referred to as single nucleotide polymorphisms (SNPs) when 159

segregating in populations), are the most abundant class (Innan et al., 1996). Because of their 160

abundance, SNPs are the most commonly genotyped polymorphism for breeding applications 161

and comparative studies in crops (Morrell et al., 2011). Insertion and deletion (indels) changes 162

are also common forms of variation, but because indel events are superimposed over one 163

another, over evolutionary time it becomes increasingly difficult to distinguish the boundaries of 164

individual insertion or deletion events (Clegg and Morrell, 2004). These naturally occurring 165

changes are similar to those induced by SDNs, with SNVs being equivalent to base edits (see 166

below) and indels resembling the gene knockouts induces by CRISPR edits. 167

[INSERT TABLE 1 HERE] 168

It is important to note that most whole genome sequencing studies to date have used short-read 169

sequencing technologies (Gilad et al., 2009). As a result, the diversity in breeding populations 170

due to structural variation such as variation in transposable element location and abundance, 171



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presence-absence variation (PAV) and gene copy number variants (CNV) has been difficult to 172

measure (Muñoz-Diez et al., 2012). PAV and CNV typically refer to changes that include genes 173

and thus are functionally equivalent to SDN-mediated gene insertion or deletion. Although 174

structural variants are less common than SNPs, they are an important source of variation 175

(Gabur et al., 2019; Schiessl et al., 2019). There are many examples of gene presence/absence 176

or copy number differences impacting agronomic traits including submergence tolerance in rice 177

(Oryza sativa) (Xu et al., 2006), high oleic acid content in sunflower oil (Schuppert et al., 2006), 178

soybean cyst nematode resistance (Cook et al., 2012), and seed coat pigmentation in soybean 179

(Glycine max) (Todd and Vodkin, 1996). Because of the density of naturally occurring variation, 180

intra- and inter-specific crosses of plants that occur during plant breeding can introduce millions 181

of SNPs (see Figure 1), in addition to thousands of PAV or CNV sequence variations into 182

resulting progeny. As more long-read sequencing technologies are deployed, more accurate 183

measurements of the CNV and PAV in breeding populations will be possible. 184

Sources of spontaneous de novo genome variation 185

Table 2 reports current estimates of standing genomic variation in select crop species. 186

Environmental conditions and breeding and genetic practices such as tissue culture propagation 187

(described below) can further impact the rate at which mutations occur. The sources, types, and 188

rates of de novo genomic variation are reviewed below. 189


Spontaneous Nucleotide Variation: 191

De novo mutations arise primarily from errors in the DNA replication process. DNA repair 192

processes (NHEJ and HR) can also result in new mutations, including variants that arise due to 193

environmentally-triggered DNA damage (Jiang et al., 2014; Nisa et al., 2019). Whole-genome 194

sequencing of mutation accumulation lines of Arabidopsis (Arabidopsis thaliana) estimated a 195



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mutation rate of 7 x 10-9 base substitutions per site per sexual generation (Ossowski et al., 196

2010), a rate confirmed in a more recent report (Weng et al., 2019). This rate estimate in 197

Arabidopsis is consistent with earlier estimates of a mutation rate of 5.9 x 10-9 substitutions per 198

site per year based on phylogenetic divergence in monocots (Gaut et al., 1996). Most of the 199

mutations reported by Ossowski et al. (2010) were SNPs, though 1-3 bp indel mutations were 200

also detected at a rate of 4.0 +/-1.6 x 10-10 indels per site per generation. Deletions larger than 3 201

bp occurred at a rate of 0.5 x 10-9 per site per generation, and the average larger deletion size 202

was hundreds of bp per event (Ossowski et al., 2010). In a similar study in rice, the mutation 203

rate was estimated at 5.4 x 10-8 per site per diploid genome (Tang et al., 2018). In maize (Zea 204

mays), the spontaneous mutation rate was reported to be 2.2-3.9 x 10-8 per site per generation 205

(Yang et al., 2017). The general trend for single nucleotide mutation rates in plants is on the 206

order of 10-8 to 10-9 per site per generation, similar to estimates for humans (Nachman and 207

Crowell, 2000; Roach et al., 2010). 208

Transposons as a source of variation: 209

In many plant species, transposable elements (TE) and the remnants of past TE insertion 210

events make up the majority of the genome (Sahebi et al., 2018). For example, TEs make up 211

approximately 85% of the maize genome, 76% of the pepper genome, and between 20-40% of 212

the rice genome, depending upon the rice cultivar (Dubin et al., 2018; Anderson et al., 2019). 213

Transposons are DNA elements that can auto-excise and re-insert in the genome. They have 214

the potential to disrupt genes or create rearrangements. They can also result in complete copies 215

of genetic elements being placed in other areas of the genome. Transposons can have a 216

profound impact on genome structure and gene function (Lisch, 2012). Some types of 217

transposons are known to be activated by environmental conditions (e.g., temperature) and by 218

breeding practices, (e.g., tissue culture, described below) (Vitte et al., 2014; Rey et al., 2016). 219



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Accurate measurements of rates of heritable transposition in a single generation in plants have 220

not been determined. 221

Summary of inherent genomic variation 222

NGS has dramatically improved the estimation of genome-wide standing variation and made it 223

possible to estimate rates of de novo mutations, especially SNVs. Numerous studies have 224

examined the genomic variation between individuals in breeding populations for well-studied 225

crops like maize (Hufford et al., 2012; Lu et al., 2015; Bukowski et al., 2017), soybean (Li et al., 226

2014; Maldonado dos Santos et al., 2016; Valliyodan et al., 2016), and rice (Zhao et al., 2018). 227

To date, short-read sequencing technology has been the principal tool used for resequencing 228

plant genomes. However, short-read sequence technologies are of more limited utility for 229

detection of large structural variants (such as PAV or CNV) (Gilad et al., 2009; Morrell et al., 230

2011; Fuentes et al., 2019). As long-read sequencing becomes a more standard component of 231

comparative studies and additional reference-quality genomes from individual species are 232

reported, diversity due to large structural changes will become more readily accessible (Gabur 233

et al., 2019; Schiessl et al., 2019). 234

Sources of Induced Genomic Variation in Plants 235

Although naturally-occurring genetic variants have been the primary source of plant genetic 236

diversity used for crop domestication and breeding, geneticists have developed approaches to 237

augment natural plant genomic diversity and accelerate the development of improved cultivars. 238

Genetic variation induced by chemical mutagens and ionizing radiation has been used in plant 239

breeding since early in the 20th century (Friedberg et al., 2006). Somaclonal variation induced 240

by tissue culture has also been a source of variation used in plant breeding. Mutation breeding 241

is considered cost-effective, ubiquitously applicable, non-hazardous, environmentally friendly, 242

and continues to benefit plant breeders and consumers globally (Ahloowalia et al., 2004). Many 243



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types of mutagenic and ionizing agents have been evaluated to maximize genomic variation 244

while minimizing detrimental or lethal effects in plants (Sikora et al., 2011). As described below, 245

each type of mutagenic agent tends to have a predominant type of mutation induced across the 246

genome. 247

The mutations induced by chemical and ionizing radiation occur throughout the genome, but at 248

an increased rate compared to spontaneous mutations (Belfield et al., 2012; Henry et al., 2014). 249

It can be difficult to select individuals with mutations for the desired phenotype, yet lacking 250

mutations that produce undesirable phenotypes (Bolon et al., 2011; Saika et al., 2011). Mutant 251

lines that carry numerous new mutations have been developed for genetic studies and breeding 252

improved varieties in many crops (Tables 3 and 4). Over 3,200 mutant varieties, including 253

cereals (1,584; 49.5%), flowers/ornamentals (700; 21.9%), legumes (480; 15%) and others 254

(435; 13.6%), have been released for commercial use in more than 210 plant species in over 70 255

countries (Figure 2). 256

[INSERT TABLES 3 and 4 HERE] 257

Mutant population screening is commonly used in rice breeding, resulting in 828 registered lines 258

in the Mutant Variety Database (Kharkwal et al., 2004; Shu et al., 2012; Wang and Jia, 2014). 259

Most of the wheat (Triticum aestivum) varieties used for making Italian pasta were derived in 260

part through mutation breeding (Micke et al., 1990; Mba, 2013). For some crops, mutation 261

breeding is more widely utilized for crop improvement within specific geographic regions. For 262

example, nearly 50% of the improved soybean cultivars grown in Vietnam are derived from 263

induced mutations (Vinh et al., 2009). Mutagenesis has also been used to introduce desirable 264

traits in horticultural crops. For example, researchers have used induced mutations to develop 265

variation for tomato (Solanum lycopersicum) fruit size (Just et al., 2013; Park et al., 2014), 266

starch content in potato (Solanum tuberosum) (Muth et al., 2008), and virus resistance in 267



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peppers (Capsicum annuum) (Ibiza et al., 2010). Several grapefruit (Citrus x paradisi Macfad.) 268

varieties, including popular red flesh varieties, were developed using induced mutation (Mba, 269

2013). Although induced mutant plant varieties have enjoyed considerable global commercial 270

success and public acceptance, the molecular and physiological basis for the improved 271

characteristics in these varieties is generally unknown (Shu et al., 2012). 272

The genomic changes induced by mutagenesis or tissue culture derived plants have been 273

evaluated in numerous studies using NGS techniques (Tables 3 and 4). In addition to WGRS 274

methods (Table 3), TILLING studies have been used to estimate induced mutation density by 275

collecting sequencing data on a limited number of genes from a large population of individual 276

mutagenized plants (Table 4). Exome capture and whole exome resequencing studies also rely 277

on targeted sequencing of a portion of the genome, namely the protein coding regions (exons), 278

of mutagenized plant populations. 279

For example, mutations induced via fast neutron irradiation generate a wide variety of mutations 280

that differ in size and copy number, including single nucleotide variants, deletions, insertions, 281

inversions, translocations, and duplications (Bolon et al., 2014; Li et al., 2017; Jo and Kim, 282

2019). Li et al. (2017) generated and deep-sequenced a fast neutron mutant population (1,504 283

lines) in the model rice cultivar, Kitaake. The estimated mutation frequency analyzed was 61 284

mutations/plant (1.6 x 10-7 per bp, as shown in Table 3) with varied types of mutations observed 285

in 58% of all known rice genes. Induced mutations can differ from spontaneous mutations in 286

subtle ways. Transversions (a purine base substitutes for a pyrimidine base, or vice versa) were 287

found to be more frequent in fast neutron populations (Belfield et al., 2012). Transitions (purine 288

to purine or pyrimidine to pyrimidine substitutions) are concentrated at pyrimidine dinucleotide 289

sites, suggesting covalent linkages as a molecular mechanism for these mutations (Belfield et 290

al., 2012). Other mutagens may generate a narrower range of mutation types (Tables 3 and 4; 291

Figure 2). For example, EMS primarily creates transitions at guanine sites (Henry et al., 2014). 292



13

These changes have a slightly elevated probability of being flanked at the 5' neighboring 293

nucleotide by adenine or guanine and the 3' side by cytosine (Henry et al., 2014). There is also 294

evidence that some plant genomes can tolerate elevated mutation rates better than others. The 295

hexaploid wheat and oat polyploid genomes, possessing multiple redundant alleles, can tolerate 296

a larger number of accumulated mutations without any notable impact on survival or agronomic 297

performance (Stadler, 1929; Krasileva et al., 2017; Hussain et al., 2018). 298

Genome variation induced by conventional tissue culture practices 299

Many modern breeding programs use tissue culture or in vitro culture systems to propagate 300

plants, preserve elite genotypes, or for production of pure lines using double haploidy. Similar to 301

chemical and irradiation-induced mutations, in vitro culture methods can also generate de novo 302

genetic variability (referred to as somaclonal variation) (Neelakandan and Wang, 2012). Several 303

studies have examined the rate of de novo sequence variation of endogenous genes in plants 304

regenerated through tissue culture. Jiang et al. (2011) regenerated 28 Arabidopsis plants from 305

root explants (living cells transferred to culture medium) from a single Columbia (Col-0) 306

laboratory strain (Jiang et al., 2011). Five regenerated plants were sequenced and analyzed, 307

and on average, each plant had ~30 de novo sequence changes (totaling 21 indels of 2 bp or 308

fewer, and 131 single nucleotide variants substitutions across the five plants) (Jiang et al., 309

2011). The calculated somaclonal mutation frequencies of spontaneous mutations in this 310

experiment was between 4 x 10-7 and 24 x 10-7 mutations per nucleotide. Tang et al. (2018) 311

reported a mutation rate of 1.86 x 10-7 in rice plants regenerated through tissue culture. These 312

values are nearly two orders of magnitude higher than the rate Ossowski et al. (2010) estimated 313

from greenhouse-grown plants derived from single seed descent (Shaw et al., 2000). 314

Somaclonal variation provides another source of genomic variability in plants (Krishna et al., 315

2016). Nearly two hundred commercial varieties from approximately 55 plant species (e.g., 316

horticultural crops, cereals, legumes, medicinal, and aromatic plants) were derived through 317



14

somaclonal variation (reviewed by (Neelakandan and Wang, 2012; Krishna et al., 2016)). During 318

the in vitro culture process, many regenerated plants develop differences in appearance relative 319

to the parental genotype. Changes induced can include heritable genetic and epigenetic 320

alterations (Bednarek et al., 2007; Machczyńska et al., 2015; Krishna et al., 2016). As an 321

example, in the popular cultivar of banana (Musa acuminata) ‘Cavendish,’ the occurrence of off-322

types (different from the parental clone) from in vitro cultured plantlets ranges from 6 to 38% 323

(Sahijram et al., 2003). The genomic variation induced during in vitro regeneration is influenced 324

by media composition, length of incubation and subculture, explant origin, and genotype. 325

Genome-wide DNA polymorphisms or structural variations resulting from tissue culture have 326

been observed in soybean (Anderson et al., 2016), barley (Hordeum vulgare) (Bednarek et al., 327

2007), rice (Miyao et al., 2012; Zhang et al., 2014), potato (Fossi et al., 2019), papaya (Carica 328

papaya) (Kaity et al., 2009), banana (Ray et al., 2006), and several other plant species. 329

Bednarek et al. (2007) assessed somaclonal variation in barley and identified 6% gene 330

expression variation (1.7% of this variation was due to nucleotide changes and the balance was 331

due to altered methylation). Recently, Li et al. (2019) performed whole-genome sequencing of 332

cotton plants derived from in vitro culture and CRISPR-Cas9 edited plants. They determined 333

that most of the mutations were induced either by somaclonal variation or were attributable to 334

heterogeneity present in parental plants (Li et al., 2019). Tissue culture and somaclonal 335

variation have been successfully used to generate valuable heritable genomic changes in crops, 336

and have been widely accepted by breeders for germplasm development and release of new 337

varieties. 338

In mutation breeding (i.e., irradiation, chemical, or somaclonal), efforts are focused on 339

identifying beneficial and novel phenotypes. However, in addition to the beneficial mutation(s), 340

other “coincident” mutations or rearrangements can be generated elsewhere in the genome 341

(Figure 3). While most of these mutations will have negligible or no phenotypic effect, it can be 342



15

necessary to remove undesirable mutations with observable deleterious effects (off-types) 343

through backcrossing and selection. Each generation of backcrossing is expected to reduce the 344

presence of coincident mutations by 50%. However, given their number and genomic 345

distribution, some mutations may persist through this process. This process of backcrossing and 346

selection is one reason why mutation breeding has been successfully used for decades with a 347

recognized history of safe use (Food and Drug Administration, 1992). 348

Genome Editing as a Source of Variation 349

The advantage of classical mutation breeding is that it allows for the rapid generation of 350

genomic variation that can be used as a source of traits not currently identified in a crop’s 351

germplasm. The drawback of these methods is that variation is induced randomly both at 352

desired locations and across the genome. Because of these factors, screening, isolating, and 353

introgressing desired mutations into elite germplasm requires large populations and lengthy 354

breeding development timelines. Thus, classical mutation breeding as a tool to generate 355

variation is of great value but is relatively inefficient. 356

Genome editing is a targeted and more exact form of mutation breeding. These tools, based on 357

the use of SDNs, provide researchers the ability to modify sequences at specific locations within 358

the genome. SDNs generate targeted DSBs that, regardless of the SDN used, are repaired by 359

the same native DNA break repair mechanisms, the NHEJ or HR pathways described 360

previously. As described below and depicted in Figure 1, these methods generally result in 361

many fewer mutations elsewhere in the genome relative to earlier mutagenic techniques. SDNs 362

continue to be improved to further reduce the likelihood of non-targeted mutations (Zhao and 363

Wolt, 2017; Hahn and Nekrasov, 2018). 364

The genome editing toolbox and its utility 365



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Though still a relatively new technology, genome editing using SDNs has demonstrated great 366

promise as a tool for crop improvement. Recent reviews catalog the increasing number of 367

phenotypes and characteristics in crop plants that have been generated using SDNs 368

(Jaganathan et al., 2018; Zhang et al., 2018; Chen et al., 2019). Some of these traits were 369

derived from loss-of-function mutations within coding regions or gene regulatory regions that 370

resulted from NHEJ-mediated repair leading to mutations (insertions/deletions) at the break site. 371

Other traits were derived from specific nucleotide insertion or substitution, and gene/allele 372

replacement edits, which can be generated by repairing SDN-generated DSBs with a DNA 373

donor template (Townsend et al., 2009; Li et al., 2015; Petolino et al., 2016; Filler Hayut et al., 374

2017; Shi et al., 2017; Yu et al., 2017). Several types of SDNs that have been used in plants 375

rely on either protein/DNA or RNA/DNA binding to provide editing specificity. As shown in Figure 376

4 (Panel A-B), ZFN and TALEN are chimeric proteins consisting of DNA recognition/binding 377

domains fused to the catalytic domain of the FokI nuclease (Christian et al., 2010; Carroll, 2011; 378

Weeks et al., 2016). TALENs are derived from transcription activator-like (TAL) effectors found 379

in the plant pathogen Xanthomonas. TAL effectors bind to specific DNA sequences by using 380

tandem amino acid repeats. Variations in these repeats, knowns as the repeat variable di-381

residues (RVDs), allow for specificity to nucleotide sequences. For both ZFNs and TALENs, 382

specificity for a chosen target sequence is improved by expanding the length of DNA targets 383

(e.g., 18 to 32 bp for the paired nuclease) and the spacer (i.e., the distance between paired 384

nucleases). The designed proteins interact as heteromeric pairs, with DSBs occurring only when 385

both protein halves are bound at the same genomic location, further increasing the specificity of 386

these reagents for creating DSBs only at the desired site. 387

RNA-Guided Endonucleases (RGENs) function as a protein complex that is directed to a 388

genomic target site through a short non-coding guide RNA (gRNA). The nuclease protein and 389

the gRNA are transcribed separately and then form a complex in the cell nucleus. A portion of 390



17

the gRNA contains a sequence that is directly complementary to a genomic target site that 391

guides the induction of DSB formation at that location. In the case of the CRISPR system, Cas9 392

or another Cas or Cpf nuclease recognizes the gRNA-DNA pair, thus targeting this site for a 393

DSB (Figure 4, Panel C) (Jinek et al., 2012; Cong et al., 2013; Weeks et al., 2016). The gRNA 394

of the most widely used Cas9, Streptococcus pyogenes Cas9 (SpCas9), binds to a 20 bp 395

genomic site, termed the protospacer, that is adjacent to a triplet DNA sequence known as the 396

protospacer adjacent motif (PAM). Unlike complex protein engineering needed for DNA binding 397

using ZFNs and TALENs, Cas9 can be programmed to new target sites by changing the spacer 398

sequence of the guide RNA, which simplifies vector design and construction while substantially 399

reducing the cost. Due to this ease of design and greater efficiency, CRISPR has become the 400

SDN of choice for genome editing (Globus and Qimron, 2018). 401

Recently developed CRISPR editing systems do not rely on DSB formation to induce targeted 402

changes, but instead involve chimeric protein units consisting of a disabled nuclease and an 403

additional protein domain. In these systems, the disabled nuclease is used to target the 404

additional protein units to specific genomic locations for different applications. Applications 405

include targeted base editing with deaminase domains (Komor et al., 2016; Gaudelli et al., 406

2017; Adli, 2018), transcriptional knock down using repressors (Qi et al., 2013; Thakore et al., 407

2015), targeted DNA methylation and histone modification (Kearns et al., 2015; Stepper et al., 408

2016), gene activation using strong transcriptional activators (Cheng et al., 2013), activation of 409

developmentally silent endogenous loci through chromatin looping (Deng et al., 2014; Morgan et 410

al., 2017), DNA transposition (Strecker et al., 2019), site-specific recombination (Standage-411

Beier et al., 2019), and prime editing (Anzalone et al., 2019). The targeting components of the 412

nucleases are still intact, allowing for nucleotide changes in a site-directed manner. For 413

example, cytosine and adenine base editors (converts C to T and A to G, respectively) fuse a 414

nickase-type Cas9 with a deaminase domain and thus do not induce DSBs. Both are useful 415



18

genome editing tools, however the cytosine base editor has been reported to induce off-target 416

editing both in animal cells (Zuo et al., 2019) and in plants ((Jin et al., 2019); discussed in more 417

detail below). 418

Designing genome editing protocols and methods to optimize editing specificity and 419

reduce the potential for off-target edits 420

Optimizing the specificity of genome editing methods can be achieved in multiple ways, often 421

through altering SDNs delivery into plants. At present, the most common practice involves 422

stably integrating a transgene into the plant genome that expresses the SDN “reagents” (protein 423

or protein/guide RNA complex that is encoded by the transgene) and template DNA molecule 424

for HR edits. The transgene(s) encoding the SDN components are typically integrated at a locus 425

that is unlinked to the gene(s) being edited. A subsequent outcross or selfing generation results 426

in the independent assortment of unlinked genes to segregate the SDN-encoding transgene 427

away from the edited target sequence. Resulting progeny plants can be screened to select non-428

transgenic plants carrying the targeted edit and lacking the SDN-encoding insert (Gao et al., 429

2016; Char et al., 2017). The resulting plant variety is often molecularly indistinguishable from 430

varieties with mutations that either occur naturally or are produced by induced mutagenic 431

techniques. It should be noted that chromosomal re-arrangements due to T-DNA insertions 432

have been observed (Clark and Krysan, 2010; Hu et al., 2017), but these can generally be 433

detected by next-generation sequencing-based methods and removed by segregation and 434

selection. Transient delivery methods, which seek to limit the overall exposure of the genome to 435

the SDN and avoid the stable integration of a transgene, have also been developed in plants 436

(Woo et al., 2015; Kelliher et al., 2019). The most common transient delivery method uses a 437

ribonucleoprotein (RNP) complex that consists of a purified RGEN protein bound to a guide 438

RNA (Kim et al., 2017; Andersson et al., 2018). The complex is delivered directly to plant cells, 439



19

through bombardment or other means (Ran et al., 2017), so that the active protein can cause a 440

DSB, but the SDN-encoding DNA is not integrated into the genome (Chen et al., 2018). 441

Off-target edits, and the means to reduce such edits, have received a great deal of attention in 442

the human therapeutic and medical fields (Cho et al., 2014; Shen et al., 2014; Lee et al., 2016; 443

Akcakaya et al., 2018; Lee et al., 2018; Moon et al., 2019) and are also of interest in the plant 444

science community (Feng et al., 2014; Wolt et al., 2016; Tang et al., 2018; Jin et al., 2019; Li et 445

al., 2019; Young et al., 2019). As described in the Introduction, changes observed following 446

genome editing protocols can be split into those that occur at sites without similarity to the 447

targeted region, referred to as untargeted mutations, and “off-target edits” that occur at sites 448

with sequences similar to the targeted region. Numerous studies discussed below have found 449

that the potential for and level of off-target activity in plants can be predicted by comparing the 450

sequence of the target site to the rest of the genome. SDNs bind to specific nucleotide 451

sequences, with the most likely location of potential off-target editing to occur at sites with 452

nucleotide-sequence level similarity to the intended target (Jiang and Doudna, 2017). 453

Bioinformatics prediction tools have been designed to predict sites that may result in an off-454

target edit. Programs have been developed for all of the major SDNs [PROGNOS (Fine et al., 455

2014); TALE-NT (Doyle et al., 2012); CAS-OFF Finder (Bae et al., 2014)], and are routinely 456

included in the design of SDN-based editing protocols (Liu et al., 2017; Listgarten et al., 2018; 457

Minkenberg et al., 2019). Prediction algorithms are useful for identifying potential off-target sites 458

in well-sequenced genomes. Plant species with limited reference genome sequence data may 459

be poorly assessed by these tools. For plant species without a comprehensive genome 460

sequence, it may be difficult to predict potential off-target SDN activity. However, the amount of 461

variation introduced would likely be less than traditional mutagenesis techniques classically 462

utilized in plant breeding strategies (Figure 1). As described in more detail below, it can also be 463



20

difficult to assess and discriminate between bona fide off-target SDN activity versus variation 464

attributable to transformation or standing variation. 465

Off-target Edit Evaluation and Frequency 466

The approaches reported for the identification of off-target edits vary widely and can impact the 467

level of off-target activity observed. As noted above, many studies rely on algorithms to predict 468

the optimal on-target activity and potential off-target edit sites based on similarity with the gRNA 469

sequence. The most common method used to assess whether the off-target activity has 470

occurred at these site(s) is PCR amplification, followed by amplicon sequencing. PCR 471

approaches provide a rapid method for determining if SDN-mediated off-target edits have 472

occurred at a predicted or suspected site. However, it is reliant on accurate predictions of 473

potential off-target sites, which in turn relies on a well-sequenced source or reference genome 474

for thorough analysis. Several alternative, “non-biased” methods to assess and detect DSB and 475

edits have been developed for off-target edit analysis (reviewed in (Zischewski et al., 2017)), 476

although few have been evaluated in plants. 477

Recently, high-throughput whole genome resequencing (WGRS) has been used to evaluate off-478

target edits and untargeted mutations in plants. While WGRS is the most comprehensive 479

strategy currently available, it requires that researchers carefully control for differences between 480

the reference genome and the experimental line, heterogeneity in experimental lines, and 481

sequencing errors (Michno and Stupar, 2018). WGRS cannot always distinguish between 482

standing variants and de novo untargeted mutations generated by tissue culture or SDN 483

treatments or attributable to sequencing miscalls. Several recent studies have used WGRS to 484

examine genome-wide mutational profiles in genome-edited rice (Tang et al., 2018), maize (Lee 485

et al., 2019), and cotton (Li et al., 2019). A large-scale study examined the amount of variation 486

that occurs due to various genome editing techniques (i.e., RGENs, CRISPR-Cas9 and Cpf1) 487

(Tang et al., 2018). The study reported WGRS of 69 individual plants that had been regenerated 488



21

via tissue culture (with and without Agrobacterium treatment), edited with various SDNs 489

reagents, or received no treatment (field grown). WGRS analysis revealed the presence of 490

variation (e.g., SNPs, indels) throughout the genome in all plants, though more so in plants that 491

had gone through the tissue culture process. Relative to plants that went through tissue culture, 492

there was no increase in the variation observed from the plants that had been exposed to SDN 493

treatment. The exception was in plants edited with a gRNA purposefully designed to be more 494

likely to elicit off-target edits (Tang et al., 2018). The detected off-target edits in these plants 495

were predicted by the program Cas-OFFinder (Bae et al., 2014), confirming that intentionally 496

inadequate guide design led to the observed off-target activity. A recent study in maize 497

determined that regardless of the SDN delivery method (Agrobacterium, particle bombardment, 498

or RNP), proper design of gRNAs leads to undetectable levels of off-target edits (Young et al., 499

2019). Studies examining rice (Tang et al., 2018), cotton (Li et al., 2019), and maize (Lee et al., 500

2019; Young et al., 2019) report that nearly all of the observed variation from RGEN 501

experiments is attributable to tissue culture or natural variation, and not the delivered nucleases. 502

Tang et al. (2018) found that plants regenerated via tissue culture contained ~100-150 SNVs 503

and ~40-80 indels. The number of de novo variants attributable to tissue-culture and SDN 504

delivery is small relative to standing variation that differentiates individual lines (Table 2). 505

A recent study has applied the WGRS approach to evaluate the off-target edit frequency of base 506

editors in rice (Jin et al., 2019). For potential off-target edits, the authors found that SNVs were 507

not significantly different between control plants (exposed to tissue culture with no editing 508

machinery) and adenine base edited plants. The rate of off-target edits was significantly higher 509

only in plants generated with cytosine (C to T) base editors, irrespective of whether a gRNA was 510

supplied. The percentage of changes that were C to T transitions was higher in plants 511

containing cytosine base editing constructs, suggesting these reagents were inducing 512

background variation. A larger number of off-target mutations suggests that the source of off-513



22

target variation was not the SDN, but the enzymatic domain of the cytosine base editing 514

complex. Previous studies had found that the cytosine deaminase domain, and the additional 515

uracil glycosylase inhibitor domain used in cytosine base editors, generate higher background 516

levels of C to T changes (Radany et al., 2000; Harris et al., 2002). The studies suggest that 517

increased optimization of the cytosine base editor would be necessary to minimize off-target 518

activity. However, it is worth noting that when compared to the frequencies of mutagenesis 519

reported in rice for somaclonal variation (Miyao et al., 2011, Tang et al, 2018) or other forms of 520

induced mutagenesis (see Tables 3 and 4), mutation rates with the cytosine base editor are 521

comparable to methods used in conventional and mutation breeding. This study suggests that, 522

as new protein motifs and editing strategies are developed, the intended activity and potential 523

for off-target activity should be evaluated. 524

WGRS studies (Tang et al., 2018; Jin et al., 2019; Li et al., 2019) show that the amount of 525

induced background variation as a result of introducing editing enzymes into plants is generally 526

low, especially for editing applications with SDNs. The identification of high background 527

mutation rates for C-to-T base editor enzymes (Jin et al., 2019) suggests the need for careful 528

screening of the effects of new approaches. Taken together, these results suggest that WGRS 529

is best applied to new approaches and may not be necessary for routine application of SDNs, 530

as unintended activity will generally be predictable by computational algorithms that allow for the 531

optimal design of gRNAs (Tang et al., 2018; Lee et al., 2019; Young et al., 2019). Indeed, 532

conventional mutagenesis methods have been found to be safe using existing agronomic 533

evaluations, generally in the absence of WGRS evaluation. 534

Conclusions 535

Whole-genome examinations of variability in plants and improvements in understanding of gene 536

function are occurring concurrently with advances in genome editing technology. Targeted 537

genome editing utilizing SDN techniques are the most recent option for the introduction of 538



23

precise genetic variation for crop improvement (Figure 5). SDNs are designed to introduce 539

mutations with a high degree of sequence specificity, resulting in fewer unintended genomic 540

changes than earlier mutagenesis techniques. As the understanding of nuclease activity has 541

increased, SDN protocols and associated prediction algorithms to more specifically target 542

intended sequences have improved and been aided by the increasing number of well-annotated 543

and sequenced genomes. NGS has facilitated a more thorough understanding of plant genomes 544

and the genetic changes that accompany the development of new plant varieties. These 545

methods allow more accurate estimations of de novo genomic variation. Each plant generation 546

has new genetic variants that are the result of a range of naturally-occurring processes. The 547

potential for, and possible effects of, SDN-mediated off-target changes must be put in the 548

context of naturally occurring standing variation in crops and mutations induced or introduced 549

during plant breeding practices (Table 5). 550


The history of safe consumption of foods from plants has been built on the fact that many 552

mutations, regardless of origin, have no phenotypic effect such that breeders cannot remove 553

these “neutral” genetic changes from plant populations. By comparison, plants displaying off-554

type phenotypes due to unintended mutations are eliminated or ameliorated through well-555

established, multigenerational breeding, selection, and commercial variety development 556

practices (Figure 5). Varieties developed through genome editing will be subjected to the same 557

screening and selection practices as is used for improved varieties developed using other 558

sources of genetic variation (Figure 5). While off-target edits are deemed to be unacceptable for 559

therapeutic applications of genome editing, off-target edits in the context of crops are smaller in 560

magnitude than that of current crop improvement methods, such as conventional or mutation 561

breeding (see Outstanding Questions). The current generation of genome editing technologies 562

has facilitated the efficient generation of desirable genomic variation and new plant varieties that 563



24

would have been more challenging to achieve through other breeding or molecular approaches. 564

Genome editing technology continues to be refined, with improved editing specificity, 565

developments in the delivery of editing enzymes, and ever-increasing genomic characterization. 566

Acknowledgments 567

The authors wish to acknowledge Wayne A. Parrott for comments on an earlier version of the 568

manuscript and Emily E. Vonderharr for copy editing of the text. We also thank Allison Devitre 569

and Emily Turnbough for their help with figure art. Funding provided by USDA / National 570

Institute of Food and Agriculture to Robert M. Stupar and Peter L. Morrell (2015-33522-24096 571

and 2019-33522-30200). 572

573

Box 1: The importance of double-strand break repairs 574

Box 1 cited articles: Pacher and Puchta, 2017; Puchta, 2005; Schuermann et al., 2005; 575

Waterworth et al., 2010; Waterworth et al., 2011; Waterworth et al., 2016; Friedberg et al., 2006; 576

Voytas, 2013; Pacher et al., 2017; Brunet and Jasin, 2018; Čermák et al., 2017; Knoll et al., 577

2014; Gaut et al., 2007; Hajjar and Hodgkin, 2007 578

579

580

581

582

583

584

585



25

586

TABLES 587

Table 1. Common types of genomic variation in the breeding populations of field crops 588

and vegetables. 589



26

Variant type Variation Description/comment

Nucleotide

polymorphisms

SNP

Single Nucleotide

Polymorphism

A variant at a single position where one base

replaces another base. SNPs arise from errors

during DNA replication or repair. Also referred to

as single nucleotide variant (SNV) or single base

substitution (SBS).

MNP

Multi-nucleotide

polymorphisms

Changes at 2 or more adjacent nucleotides MNPs

are relatively rare.

Structural

variants

Indel

Insertion/deletions

Sites where DNA is lost (deletion) or gained

(insertion) typically describing relatively small (1-

20 bp but up to 1000 bp) changes. Usually

classified as sequence differences smaller than

PAV.

SSR

Simple sequence repeats

SSRs are repeats of 2 to 6 nucleotides that vary

in the number of repeat motifs present. Also

referred to as short tandem repeats (STRs) or

microsatellites.

Can be considered a type of CNV

CNV

Copy number variation

Sequences with variable copy number between

individuals. The term CNV generally applies genic

regions. Can be due to duplications, deletions or

insertions. Simple sequence repeats (SSR) can

be considered as very small CNVs. Typically,



27

CNVs refer to larger sequence repeats (>1kb for

example) and may encompass one or more

genes or parts of genes.

PAV

Presence/Absence

Variation

Large sequence block (>1 kb) present in one

line/individual but completely missing in another

line. Like CNV, PAV typically refers to a region

containing genes and can be thought of as the

extreme example of CNV.

TE

Transposable elements

Mobile genetic elements. The number and

location of each element frequently differ from the

reference genome.

Large Structural Variation

(SV) and ploidy (whole

and partial genome

duplication)

Includes DNA rearrangements such as

chromosomal inversions, translocations, partial

genome duplications, duplications, etc.

590

591

592



28

Table 2: Estimated genomic diversity in the breeding populations of select field and 593

vegetable crops. Not all forms of genomic variation were assessed for all species listed. This is 594

denoted by ND (Not Determined). In addition, because of the short-read sequencing technologies used, 595

the number of PAV/CNV structural variants, representing large deletions, inversions and duplications, is 596

likely underestimated. 597

598



29

Plant

Species

Evaluated

sample size

and description

Estimate

d

Genome

Sizea

(Mbp)

Estimate

d SNPs

present

(million)

Average

Pairwise

Diversityb

Estimate

d Indels

(million)

Structur

al

Variants

(CNV &

PAV)

References

Bean

(Phaseolus

vulgaris)

37 521 45.0 ~5.0 x 10-

3

Mesoame

rican

~1.7 x 10-

3 Andean

2.1 18.5 k (Schmutz et

al., 2014;

Lobaton et al.,

2018)

Wild

Cabbage

(Brassica

oleracea)

10 (1 wild

species)

489 4.8 ND ND 11.5 k

(Golicz et al.,

2016)

Chickpea

(Cicer

arietinum)

35 738 2.0 ND 0.3

9.9 k (Thudi et al.,

2016)

Cucumber

(Cucumis

sativus)

115 194 3.3 ND 0.03 0.8 k (Zhang et al.,

2015)

(Qi et al.,

2013)



30

Maize

(Zea mays)

2815 accessions

13 lines

2,400 0.7

2.91

~6.0 x 10-

3

ND

0.4

1.1 M (Gore et al.,

2009; Romay

et al., 2013;

Wang et al.,

2017)

Rice (Oryza

sativa ssp.

Japonica)

3010 375 29.0 ~1.0 x 10-

3

2.4 94 k (Liu et al.,

2017; Wang et

al., 2018)

Soybean

(Glycine

max)

106 (Landraces,

US cultivars and

wild)

978 10.4 1.8 x 10-3

0.7 7.9 k (Valliyodan et

al., 2016)

Tomato

(Solanum

lycopersicu

m)

69 (incl 12 wild

species)

950 17.0

~2 x 10-4

3.6 1.7 k (Causse et al.,

2013; Sauvage

et al., 2017)

Pepper

(Capsicum

annuum)

20 (3 wild

species)

3,500 9.8 ~2.6 x 10-

3

0.24 ND (Aguilar-

Meléndez et

al., 2009; Qin

et al., 2014)

599

a Estimated genome sizes from references or https://plants.ensemble.org April 2019. 600

b Pairwise diversity (also referred to as nucleotide diversity) is the average number of nucleotide 601

differences per site between any two DNA sequences in all possible pairs in the sample population 602


https://plants.ensemble.org/


31

(Tajima, 1983). It is a measure that allows for diversity/variation to be compared across organisms that 603

vary widely in genome size. 604

605



32

Table 3: Examples of induced mutation types and frequency in food crops and plant model species evaluated via whole 606

genome resequencing (WGRS) analyses 607

Plant

[Assembled

Genome Size] a

Mutagen

No. Plants

analyzed

Mean mutations

per plant b (per

diploid genome)

Total mutation

frequency c

(mutations per

nucleotide site per

experiment)

Reference

Rice

(Oryza sativa)

[375 Mb]

FN

1,504

29 SNPs

26 Indels

3 translocations

3 inversion

/duplications

61 total

[1.6 x 10-7

]

(Li et al., 2017)

GR 6

2,419 SNPs

452 Indels

69 SV

383 CNV

3,323 total

[8.86 x 10-6

] (Li et al., 2016)

Tissue culture 2

114 SNPs

36 Indels

150 total

1.86 x 10-7

(Tang et al., 2018)



33

Sorghum

(Sorghum

bicolor)

[708 Mb]

EMS 256 7,660 SNPs [1.1 x 10-5

] (Jiao et al., 2016)

Soybean

(Glycine max)

[978 Mb]

EMS 12 12,796 SNPs

12,854 SNPs (Md) [1.3 x 10

-5] (Tsuda et al., 2015)

Tomato

(Solanum

lycopersicum)

[950 Mb]d

EMS 4

1348 SNPs

17 Indels

1365 total

[1.4 x 10-6

]

(Shirasawa et al., 2016)

GR 3 137 SNPs

40 Indels

177 total

[1.9 x 10-7

]

Lotus japonica

[472 Mb] d

EMS 2 2,201 SNPs [4.7 x 10-6

] (Mohd-Yusoff et al.,

2015)

Arabidopsis

thaliana

[135 Mb]

GR 6

11 SNPs (8.5 Md)

7 Indels (6.5 Md)

18 total

3.6×10 -7

(Belfield et al., 2012)

Tissue culture 5

26 SNP (18 Md)

4 Indels (5 Md)

30 total

4.2×10−7

-24.2×10−7

(Jiang et al., 2011)

608



34

Abbreviations: EMS: Ethyl Methanesulphonate; FN: Fast Neutron; NaN3-MNU: sodium azide-methyl-nitro urea; GR: Gamma Ray; Indel: 609

Insertion/deletion; SNP: single nucleotide polymorphism; SV: structural variant; CNV: copy number variation; WGRS: whole genome 610

resequencing; kb: kilobase; Mb: Megabase. 611

a Assembled genome size based on EnsemblPlants database (http://plants.ensembl.org) unless otherwise noted. 612

b Mean number of mutations per analyzed plant are either values as published or calculated based on total mutations observed across all 613

plants divided by the number of plants analyzed. All plants listed are diploids and studies confirmed full (>90%) genomic coverage unless 614

otherwise noted in footnote c. For some studies, median values (Md) for number of mutations per plant could also be calculated. 615

c Total mutation frequency represents total mutations per nucleotide site per experiment as published or numbers in brackets were calculated by 616

dividing total mutations per plant by the assembled genome size. As noted, total mutation frequency was not limited to a single type of mutation. 617

d Average genome sequence coverage was <90% for tomato (86% coverage; 815 Mb) and Lotus japonica (67% coverage; 315 Mb); partial 618

genomic coverage values were used in normalizing number of mutations per diploid genome and in calculating total mutation frequency. 619

620

621

622

623

624

625


http://plants.ensembl.org/

35

Table 4: Examples of induced mutation types and frequency in food crops using TILLING or exome resequencing genomic 626

analyses 627

Plant

[Assembled

Genome Size] a

Mutagen

No. Plants

analyzed

Reported mutation

density

[calculated mutation

frequency] b

Number of

genes/genomic

region analyzed c

[total Mb

analyzed]

Reference

Rice

(Oryza sativa)

[375 Mb]

NaN3-MNU 768 1 SNP/265 kb

[3.8×10-6

]

10 genes

[7.9 Mb total] (Till et al., 2007)

Maize

(Zea mays)

[2,400 Mb]

EMS 1,086 1 SNP/774 kb

[1.3 x 10-6

]

Whole exome

resequence

[139 Mb/plant]

(Lu et al., 2018)

Wheat

(Triticum

aestivum)

[14,540 Mb]

EMS 1,535 tetraploid

1,200 hexaploid

23 SNPs/Mb (4n)

[2.3 x 10-5

]

33 SNPs/Mb (6n)

[3.3 x 10-5

]

Whole exome

resequence

[119 Mb/plant (4n)

162 Mb/plant (6n)]

(Krasileva et al.,

2017)

Barley

(Hordeum NaN3 3,148

1 SNP/374 kb

[2.7 x 10-6

]

4 genes

[8.2 Mb total]

(Talamè et al.,

2008)



36

vulgare)

[8,050 Mb]

Cotton

(Gossypium

hirsutum)

[2,100 Mb]

EMS 8,000 M2 lines

(2 varieties)

1 SNP/153 kb

[6.5 x 10-6

]

1 SNP/326 kb

[3.1 x 10-6

]

8 genes

[58.1 Mb/variety]

(Aslam et al.,

2016)

Tomato

(Solanum

lycopersicum)

[950 Mb]

EMS 1,352 M3 lines 1 SNP/322 kb

[3.1 x 10-6

]

7 genes

[10.9 Mb total]

(Minoia et al.,

2010)

628

629

Abbreviations: EMS: Ethyl Methanesulphonate; NaN3-MNU: sodium azide-methyl-nitro urea; TILLING: Targeting Induced Local Lesions In 630

Genomes; SNP: single nucleotide polymorphism; kb: kilobase; Mb: Megabase. 631

a Assembled genome size based on EnsemblPlants database (http://plants.ensembl.org). 632

b Mutation density (mutations/kb or Mb) is as reported and was determined by dividing the total genomic region evaluated (kb) by the number of 633

nucleotide changes observed. Calculated mutation frequency is the arithmetic calculation of the mutations per nucleotide site per experiment. 634

c Number of gene or genomic regions analyzed in mutagenized plants are as reported. The total DNA length analyzed across all plants and 635

genes (for TILLING) or per plant (for exome sequencing) is also included. 636


http://plants.ensembl.org/


37

637

Table 5. Examples of genetic variation introduced by different breeding methods, as 638

reported in recent studies in rice. 639

a Wang et al., 2018 640

b Tang et al., 2018 641

c Li et al., 2017 642

643

Variation Type Breeding Method

Re-mixing standing

variants

de novo variants

Cross pollinationa Self pollinationb

Classical

mutagenesis

(Fast

neutron)c

Tissue

cultureb

Genome

editingb

Number of single

nucleotide variants

introduced

2 × 106 23 43 114 0

Number of

insertions/deletions

introduced

> 5 × 103 18 48 36 1

Total number of

mutations

introduced

> 2 × 106 41 91 150 1



38

Figure Legends 644

Figure 1: Comparison of the average number of SNPs and indels per genome (individual) 645

introduced into tomato by different breeding strategies. The data represents approximate 646

number of SNPs between the genome sequence of S. lycopersicum (Hz 1706 reference 647

genome) and other cultivars or wild relatives which have been used in breeding of modern 648

tomato cultivars. After selfing (X Self), each individual plant in the next generation will have (on 649

average) approximately 6 random single nucleotide polymorphisms (SNPs) (assuming the de 650

novo rate of spontaneous heritable SNP formation in tomato is similar to that of lab-grown 651

Arabidopsis (Ossowski et al., 2010). Most modern elite tomato lines commercially grown 652

typically have four or more disease resistance genes that have been introgressed by crossing 653

with wild tomato species such as (Solanum pennellii or S. pimpinellifolium) (Foolad, 654

2007). These initial elite x wild species hybridization events introduced millions to tens of 655

millions of SNPs in addition to indels, copy number (CNV) and presence/absence variation 656

(PAV) (Aflitos et al., 2014). Crosses with more closely related domestic tomato lines or 657

landraces (i.e., cultivar [cv.] San Marzano) will introduce (on average) hundreds of thousands of 658

SNPs/indels (Ercolano et al., 2014). Creating random variation by treating seeds with the 659

chemical mutagen ethyl methanesulphonate (EMS) typically introduces thousands of SNPs per 660

individual (Minoia et al., 2010). Treating cells with a well-designed gene editing reagent 661

(including a guide RNA homologous to target sequence and adjacent protospacer adjacent 662

motif [PAM]) can create a single SNP or indel at a precise, pre-determined location. Crossing 663

with wild or closely related species can also introduce additional indels, CNV and PAV into the 664

genome, which is not considered in this figure. 665

Figure 2. Number of officially released mutant varieties (top 20 countries) showing direct 666

release of improved varieties (orange bars) and mutants used as breeding material (blue 667



39

bars). Note: European Union countries denoted by asterisks. Data Source: Mutant Variety 668

Database, https://mvd.iaea.org. 669

Figure 3: Overview of sources genomic variation in crop plants. The simplified gene 670

models depict observed variation among individual crop species. The comparison of 671

inherent/standing (genotype X and Y) and induced variation (chemical and irradiation) with the 672

reference genome shows a range of intended and unintended mutations, including single 673

nucleotide polymorphisms (SNPs), small indels, transposon insertions/movement, or a large 674

segmental deletion. However, the intended edit induced via a site-directed nuclease (SDN) 675

occurs at the target site. Note: not drawn to the scale; intergenic variation not shown. 676

Figure 4. Most widely used SDNs. A. TALENs are composed of a DNA binding domain and 677

the nuclease Fok1; the DNA binding domain contains an array of nearly identical protein 678

subdomains each varying at two specific amino acids, known as the repeat variable di-residues 679

(RVDs); specific RVDs recognize unique bases on the target DNA molecule. B. ZFNs are also 680

composed of DNA binding domains and the Fok1 nuclease; each ZFN is composed of three 681

zinc-finger domains that are custom designed to recognize a triplet of DNA bases on the target 682

sequence. For TALENs and ZFNs, two subunits are needed per target region, each binding 683

closely spaced DNA sequences. C. The Cas9 nuclease binds to the target sequence via an 684

RNA molecule, known as the sgRNA; the 5’ region of the sgRNA, the proto-spacer, is typically 685

20 nucleotides long and is complementary to the target DNA; a PAM sequence is also required 686

for recognition (shown here in bold and underlined). 687

Figure 5: An overview of conventional and modern mutation breeding approaches for 688

crop improvement. The figure illustrates the systematic approach for introducing intended and 689

unintended genomic variation in crops for trait discovery, germplasm development and 690

commercial release of new crop varieties. After the introduction of genomic variation, the 691


https://mvd.iaea.org/


40

mutant populations go through a series of selection and testing to remove undesirable 692

mutations and phenotypic variations. The best performing line is selected for subsequent 693

commercial release. This breeding process is common to all methods used for crop product 694

development (Kaiser et al., 2020). 695

696

697

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1248



ADVANCES

• The importance and potential impact of “off-target” genetic changes during genome editing are subjects of ongoing debate.

• Recent studies have demonstrated that with appropriate design of genome editing reagents, off-target edits in plants are nearly undetectable.

• Off-target edits with base editing proteins can be minimized through optimization of the base-editing enzyme domain.



OUTSTANDING QUESTIONS

• What additional technical and non-technical roadblocks will need to be overcome to facilitate further research and commercial use of plant genome editing tools?

• As new genome editing tools and more facile reagent delivery methods are developed, what is an appropriate level of evaluation for potential off-target activity in plants?

• How can the plant science community effectively convey to society at large the relevance of potential off-target genomic changes when placed in the context of highly variable and dynamic plant genomes?



BOX 1. The Importance of Double-Strand Break Repairs Double-strand breaks (DSB) in DNA occur as a part of routine biological functions in all organisms (Pacher and Puchta, 2017). DNA DSB repair is crucial to many ordinary cellular functions such as DNA replication, homologous recombination, and in response to transposon replication and movement (Puchta, 2005; Schuermann et al., 2005; Waterworth et al., 2011). DSBs also occur during the seed dormancy process and subsequent seed germination is dependent on the effective repair of DSBs (Waterworth et al., 2010; Waterworth et al., 2011; Waterworth et al., 2016). Mutagenesis (including radiation-based techniques such as fast-neutron mutagenesis) and genome editing with SDNs generate DSBs (Friedberg et al., 2006; Voytas, 2013) which are repaired by endogenous repair mechanisms. Regardless of their origin, DSB in a somatic plant cell typically initiate break repair by one of two primary mechanisms; NHEJ or homology-dependent repair (HDR) (Pacher and Puchta, 2017). NHEJ is the predominant repair mechanism in plant somatic cells, accounting

for nearly all repair events (Puchta, 2005). Repair typically results in restoration of the original sequence, but small (typically 1-20 base pair (bp)) insertions or deletions (indels) also occur (Waterworth et al., 2011; Brunet and Jasin, 2018). It has been recently shown in plants that only 15% of DSBs generated by CRISPR-Cas9 are repaired imperfectly, leading to mutations (Čermák et al., 2017). HDR in plant somatic cells is rare (Knoll et al., 2014) but is of critical importance during meiosis leading to recombination between intact homologous regions. Sequences used as templates in HDR can be allelic (from homologous chromosomes), ectopic (from non-homologous chromosomes), or intrachromosomal (from the same chromosome) (Puchta, 2005). Homologous and non-homologous recombination following DSB repair contributes to genomic rearrangements and has allowed the introgression of many beneficial traits from crop wild relatives into modern cultivars by conventional cross-breeding (Gaut et al., 2007; Hajjar and Hodgkin, 2007).



7,000,000

4,000,000

170,000 ~3,000~6

X Self X S. pennelli X S. pimpin-ellifolium

X cv. SanMarzano

EMS Genome Editing

1

Target Sequence

Genomic DNA Cas9

Guide RNA

PAM

H3C

O

S OO

CH3

I I

I I

Appr

oxim

ate N

umbe

r of

SNPs

Crossing Plants Treat in Lab

Random, Unknown and Uncharacterized Genetic Changes Precise Change www.plantphysiol.orgon August 23, 2020 - Published by Downloaded from

Copyright © 2020 American Society of Plant Biologists. All rights reserved.


0

100

200

300

400

500

600

700

800No

. of m

utan

t var

ieties

Direct use of an induced mutant

Breeding material



Reference genome

Inherent Variation (genotype X)

Inherent Variation (genotype Y)

Mutation breeding (chemical)

Mutation breeding (irradiation)

Genome Editing (SDN)

GOI

Gene SNP Small InDel Space between genes Large deletion Transposons GOI: Gene of Interest www.plantphysiol.orgon August 23, 2020 - Published by Downloaded from Copyright © 2020 American Society of Plant Biologists. All rights reserved.


Fokl

Fokl

5 ` -ACGTAGCGTCATCGCCACTAGCGTATGATCGTACTGTGCAGTTGTGGTTTGTCTACCGTA3 ` -TGCATCGCAGTAGCGGTGATCGCATACTAGCATCAGACGTCAACACCAAACAGATGGCAT

Fokl

HD NI NG HD NN HD HD NI HD NG NI NN HD NN NGRepeat Variable Di-residues

Target DNA

DNA-binding domain nuclease

C A T C G C C A C T A G C G T

A. TAL effector nucleases (TALENs)

Fokl

5 ` -CGCCACTAGCGTATGATCGTACTGTGCAGTTGTGGTTTG3 ` -CGCGTGATCGCATACTAGCATCAGACGTCAACACCAAAC

DNA-binding domainnuclease

B. Zinc-finger nucleases (ZFN)Fokl

C. Clustered Regularly-Interspaced Short Palindromic Repeats (CRISPR) Associated Protein 9 (Cas9)

5 ` -ACGTAGCGT

5 `-CATCGCCACTAGCGTATGAT

CATCGCCACTAGCGTATGAT

GTAGCGGTGATCGCATACTA3 ` -TGCATCGCA

CGGACTG- 3´GCCTCAG- 5´

-3´

Cas9 nuclease

Proto-spacer Adjacent Motif (PAM)

sgRNA proto-spacersequence

sgRNA



Introduction of genetic variation for crop improvement

Conventional breeding Genome editingChemical/radiation mutagenesis

Nucleotidepolymorphisms

Gene content polymorphisms

Chromosomalchanges

SNPs Indels CNV/PAV Deletions Duplications InversionsTranslocations

Selection & testing• Removal of off-types• Phenotypic characteristics• Performance attributes• Processing features• End-user attributes

Genotyping & Inbreeding Small-scale field trials

Large-scale field trials

Crosses & multi-year, multi-location field trials

Commercial release



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Anderson SN, Stitzer MC, Brohammer AB, Zhou P, Noshay JM, O'Connor CH, Hirsch CD, Ross-Ibarra J, Hirsch CN, Springer NM (2019)Transposable elements contribute to dynamic genome content in maize. The Plant Journal 100: 1052-1065


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https://www.ncbi.nlm.nih.gov/pubmed?term=Li Z%2C Liu ZB%2C Xing A%2C Moon BP%2C Koellhoffer JP%2C Huang L%2C Ward RT%2C Clifton E%2C Falco SC%2C Cigan AM %282015%29 Cas9%2DGuide RNA Directed Genome Editing in Soybean%2E Plant Physiology&dopt=abstract


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https://www.ncbi.nlm.nih.gov/pubmed?term=Listgarten J%2C Weinstein M%2C Kleinstiver BP%2C Sousa AA%2C Joung JK%2C Crawford J%2C Gao K%2C Hoang L%2C Elibol M%2C Doench JG%2C Fusi N %282018%29 Prediction of off%2Dtarget activities for the end%2Dto%2Dend design of CRISPR guide RNAs%2E Nature Biomedical Engineering&dopt=abstract

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https://www.ncbi.nlm.nih.gov/pubmed?term=Michno JM%2C Stupar RM %282018%29 The importance of genotype identity%2C genetic heterogeneity%2C and bioinformatic handling for properly assessing genomic variation in transgenic plants%2E BMC Biotechnol&dopt=abstract

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plant genome editing and the relevance of off-target changes€¦ · 4 ([email protected]),...

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