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Agriculture and Food Sciences Research 
Vol. 11, No. 2, 195-202, 2024 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/aesr.v11i2.6252 

© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Genomic selection strategies in crop science for sustainable agriculture in Korea 

 
Sang Hoon Han1   

Yu-Na Kim2   

Soo-Kwon Park3   

Jung Jae Lee4   

 

 
( Corresponding Author) 

 
1,4JJ Company 54, Hyeonchon 1-ro, Pyeongtaek-Si, Gyeonggi-do, Republic of Korea. 
1Email: aslksang@gmail.com  
4Email: jungjae.ansc@gmail.com 
2,3Crop Foundation Research Division, National Institute of Crop Science, Rural Development Administration, 
Wanju-Gun, Republic of Korea. 
2Email: kyn7622@korea.kr  
3Email: sookwonpark@korea.kr  

 
Abstract 

Genomic selection (GS) is an effective approach to address the growing need for increasing and 
improving food production and quality, respectively, under challenging environmental conditions. 
In Korea, GS significantly advanced major crops such as rice and wheat by improving critical 
agronomic traits. However, minor crops such as mung bean and sesame still require development 
because of limited resources and breeding infrastructure. These crops, though less prioritized, are 
vital for their nutritional and functional properties and contribute significantly to agricultural 
diversity and rural livelihoods. Addressing the research gap in minor crops is essential to prevent 
the loss of genetic diversity and to enhance their resilience to environmental changes. Unlike 
traditional selection methods, GS enhances the accuracy of complex trait selection, accelerates 
breeding cycles, and boosts genetic gain. It leverages genome-wide markers to predict genomic 
estimated breeding values (GEBVs) with high precision, enabling faster and more effective 
breeding strategies, especially for traits controlled by multiple genes. Genotyping techniques, 
such as Genotyping by Sequencing and Whole Genome Resequencing, are integral to GS, each 
offering specific benefits and limitations. Multispecies single nucleotide polymorphism arrays 
present a cost-effective solution for improving GS in minor crops by capturing the genetic 
diversity across species and enabling cross-species data integration. Expanding the GS in minor 
crops is essential for preserving genetic diversity, improving adaptability, and supporting 
sustainable agriculture and food security in South Korea. By addressing these challenges, GS can 
transform breeding programs, ensuring a sustainable and resilient agricultural system for the 
future. 

 
Keywords: Breeding, Crops, Genomic selection, Genotyping by Sequencing, Single nucleotide polymorphism platform, Sustainable 
agriculture. 

 
Citation | Han, S. H., Kim, Y.-N., Park, S.-K., & Lee, J. J. (2024). 
Genomic selection strategies in crop science for sustainable 
agriculture in Korea. Agriculture and Food Sciences Research, 11(2), 
195–202. 10.20448/aesr.v11i2.6252 
History:  
Received: 15 November 2024 
Revised: 18 December 2024 
Accepted: 20 December 2024 
Published: 26 December 2024 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding:  This research is supported by the Cooperative Research Program 
for Agriculture Science and Technology Development, Rural Development 
Administration, Republic of Korea (Grant number: RS-2023-00232329). 
Institutional Review Board Statement: Not applicable. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study; that no vital features of the 
study have been omitted; and that any discrepancies from the study as planned 
have been explained. This study followed all ethical practices during writing. 
Competing Interests: The authors declare that they have no competing 
interests. 
Authors’ Contributions: All authors contributed equally to the conception 
and design of the study. All authors have read and agreed to the published 
version of the manuscript. 

 

Contents 
1. Introduction .................................................................................................................................................................................... 196 
2. Advantages of GS ........................................................................................................................................................................... 196 
3. Genotyping Techniques for GS in Crop Science ..................................................................................................................... 197 
4. Limitation of Application in Korea ............................................................................................................................................ 198 
5. Future Challenges .......................................................................................................................................................................... 199 
6. Conclusions ..................................................................................................................................................................................... 200 
References ............................................................................................................................................................................................ 200 
 

mailto:aslksang@gmail.com
mailto:jungjae.ansc@gmail.com
mailto:kyn7622@korea.kr
mailto:sookwonpark@korea.kr
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/aesr.v11i2.6252
https://orcid.org/0009-0008-5219-0754
https://orcid.org/0009-0003-5323-6738
https://orcid.org/0000-0002-4941-3848
https://orcid.org/0000-0002-6145-8862


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Contribution of this paper to the literature 
This study uniquely focuses on applying genomic selection (GS) to under-researched minor 
crops in Korea, including mung bean and sesame. By proposing multispecies single nucleotide 
polymorphism (SNP) arrays as a cost-effective tool, it bridges the gap between major and minor 
crop research, emphasizing genetic diversity preservation and adaptability. 

 
1. Introduction 

Although more food is required for the rapidly growing human population, food quality needs to be improved, 
particularly with increased nutrient content [1]. The ability to increase or even sustain crop yield and quality in 
the face of dynamic environmental and biotic threats is particularly challenging in the face of rapid global 
environmental changes [2]. In response to challenges such as climate change and mechanization, the gradual 
shrinkage of cultivation areas led to an increased reliance on imports. Concurrently, a demand exists for high-
quality varieties, including those rich in functional compounds, adaptable to climate change, resistant to pests and 
diseases, and suitable for mechanization and processing. 

Modern crop breeding must prioritize the development of crop varieties with agronomic traits to address the 
dual demands of increased food production and improved nutritional quality. Particularly in Korea, both major and 
minor crops are essential not only for food security but also for nutritional quality and economic stability. Major 
crops, such as rice, wheat, and maize, have seen considerable advancements in breeding, focusing on yield, disease 
resistance, and environmental adaptability [3]. Contrastingly, minor crops, which are valuable for their nutritional 
and functional properties, have lagged in breeding improvements and require significant genetic advancements to 
meet their full potential. 

Genomic Selection (GS) is a transformative tool for genetic improvement that enables breeders to predict the 
genetic value of crops based on genome-wide markers [4]. Unlike traditional phenotypic selection, which depends 
on observable traits that may not be consistently expressed under various environmental conditions, GS allows for 
the accumulation of minor genetic effects across the genome essential for complex traits, such as yield, disease 
resistance, and drought tolerance [5, 6]. Many countries extensively adopted GS for major crops, benefiting from 
faster breeding cycles and greater selection accuracy, particularly for traits controlled by multiple genes [4]. In 
Korea, GS have begun to influence major crops, including rice and wheat, showing promise in improving 
agronomic traits [7]. However, for minor crops, the adoption of GS faced limitations due to economic challenges, 
lack of accessible high-throughput genotyping resources, and less-established breeding infrastructure compared to 
those of staple crops. Consequently, minor crops receive limited breeding resources despite their cultural and 
nutritional significance, raising concerns regarding the possible loss of valuable genetic diversity. Applying GS to 
minor crops can safeguard their unique genetic traits and enhance yield stability and environmental adaptability, 
making them better suited to the climate challenges and agricultural shifts anticipated in Korea. 

In this study, we examined the potential of GS in addressing breeding challenges for both major and minor 
crops in Korea. While GS demonstrated substantial gains in yield, resilience, and disease resistance in major crops 
such as rice and wheat, minor crops remain underserved due to limited resources and infrastructure. We discuss 
the critical need to expand GS applications to minor crops to ensure the preservation of genetic diversity and 
enhance their adaptability to climate and agricultural demands. Furthermore, we highlight that the methodologies 
explored in this study can be applied to regions with agricultural challenges similar to Korea, thereby providing 
solutions to enhance food security and economic stability in these contexts. 

 

2. Advantages of GS 
Conventional plant breeding methods historically relied on phenotypic selection where observable traits such 

as plant height, disease resistance, and grain yield are used to select individuals for breeding programs. Although 
phenotypic selection proved successful for simple traits, it struggles with complex traits due to their polygenic 
nature and environmental sensitivity. One of the major challenges is the GxE interactions (genotype-by-
environment), in which the same genotype may be expressed differently under varying environmental conditions 
[8]. Hence, phenotypic selection is less effective for complex traits controlled by multiple genes and significantly 
influenced by environmental factors [9]. 

To overcome these challenges, marker-assisted selection (MAS), which utilizes Deoxyribo Nucleic Acid (DNA) 
markers linked to specific traits to guide breeding decisions, was developed. MAS showed particular success for 
simple Mendelian traits such as disease resistance or other qualitative traits involving one or a few major effect loci 
[10]. However, complex traits like grain yield or drought resistance influenced by numerous small-effect genes 
scattered across the genome, MAS proved to be less effective due to its dependence on a limited number of markers 
[11]. 

GS represents a major leap forward by enabling the prediction of breeding values based on genome-wide 
markers without the need to identify individual trait-specific markers [12]. GS uses statistical models estimating 
the contribution of each single nucleotide polymorphism (SNP) across the genome to the overall genetic merit of 
an individual, thereby capturing major and minor allele effects. This allows for the prediction of genomic estimated 
breeding value (GEBV) an individual with high accuracy, even in the absence of phenotypic data. A key advantage 
of GS over MAS is its ability to capture the cumulative effects of numerous small effect genes across the genome 
often missed by MAS [13]. This comprehensive genomic information enables more accurate selection and 
improves genetic gains over time. 

One of the main benefits of GS is that it reduces breeding cycle time. Traditional breeding methods rely on the 
phenotypic evaluations of multiple generations, whereas GS allows for selection based on genetic information alone 
[4]. Additionally, GS is particularly advantageous for crops with long generation intervals or those grown in 
environments where phenotyping is difficult or expensive, such as minor crops [14]. In these crops, GS can 
potentially accelerate genetic improvement, even in the absence of extensive phenotypic or genomic resources [15, 
16]. 



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In contrast to traditional breeding methods, GS allows breeders to maximize genetic gain per unit of time by 
focusing on genomic predictions rather than on phenotypic performance alone [15, 17]. As GS evolves with 
advances in sequencing technologies and reduced genotyping costs, it is set to revolutionize breeding programs for 
both the animal and crop industries by improving the selection of traits difficult to measure or have low heritability 
[18, 19]. Table 1 illustrates the characteristics of breeding methods from phenotypic selection to genomic selection. 

 
Table 1. Advancements in selection strategies in crop breeding. 

Method Features Advantages Limitations 

Phenotypic 

selection 

Relies on observable traits and 
phenotype measurements. 

Simple Time-consuming 

Low-cost Long breeding cycles 

Widely applicable Inefficient for complex traits 

Marker-

assisted 

selection 

Uses specific DNA markers 

linked to traits of interest. 

High accuracy for simple traits Limited for small-effect loci 

Specific DNA markers Ineffective for complex traits 

Effective for major genes Requires known marker-trait association 

Genomic 

selection 

Utilizes genome-wide markers 

to predict breeding values. 

Genome-wide coverage High costs with high density platforms 

Accurate for complex traits Requires computational resources 

Shortens breeding cycles Data-intensive process 
 

 

3. Genotyping Techniques for GS in Crop Science 
3.1. Genotyping by Sequencing (GBS) 

The GBS has been widely adopted in modern crop breeding, particularly for species with complex genomes or 
limited genomic resources. This innovative technique, which was developed as a cost-effective, high-throughput 
alternative to traditional genotyping methods, allows for a more efficient exploration of genetic diversity [20, 21]. 
GBS operates by digesting genomic DNA with restriction enzymes to simplify the genome and simultaneously 
sequence multiple DNA fragments, effectively capturing a wide range of genetic variations across the genome [22].  

Another key feature of GBS is its flexibility in handling diverse species and populations, including biparental, 
multi-parental, and natural populations. This adaptability allows researchers to conduct genome-wide association 
studies (GWAS), quantitative trait locus (QTL) mapping, and GS in a wide range of species [22-25]. Furthermore, 
GBS can generate dense marker sets crucial for capturing recombination events and improving the accuracy of 
genomic predictions in breeding programs [20]. 

However, despite its cost effectiveness and utility, GBS has some limitations. It focuses only on specific parts of 
the genome, and certain regions may not be represented or completely missed. Additionally, imputing missing 
genotypes can be challenging, particularly in species with high heterozygosity or complex structural variation 
[20]. To mitigate these issues, GBS data require stringent quality control and thorough bioinformatic processing 
to ensure reliable downstream analyses [26]. 

As sequencing technologies improve, GBS has advanced with better bioinformatics pipelines, such as Trait 
Analysis by association, Evolution and Linkage (TASSEL)-GBS, which increase the efficiency of SNP discovery 
and genotype calling [27]. These innovations, combined with a reduction in sequencing costs, make GBS an 
indispensable tool for accelerating genetic gain in crop breeding programs, particularly for minor crops such as 
mung bean and adzukibean, which are critical for global food security and agricultural sustainability. 

 

3.2. Whole Genome Resequencing (WGR) 
The WGR emerged as one of the most powerful tools for unraveling genetic diversity and identifying genetic 

variation across the entire genome. Unlike targeted sequencing approaches, WGR enable researchers to capture 
the complete genome sequence of an individual, thereby providing the most comprehensive dataset for genetic 
studies [28]. WGR was instrumental in mapping the genetic architecture of complex traits, enabling the 
identification of SNPs, structural variants, insertion-deletions, and other genomic alterations across both coding 
and noncoding regions [29]. 

The ability to detect both common and rare variants of WGR is particularly beneficial for crops with complex 
genomes or limited reference data [30]. A high depth of coverage of entire genomes can reveal subtle genetic 
differences that may contribute to important agronomic traits such as disease resistance [31]. For instance, Cook, 
et al. [32] reported resistance to soybean cyst nematodes (SCN), and Maron, et al. [33] revealed that a higher 
copy number of the MATE1 gene is linked to superior AI tolerance in maize. This high resolution makes WGR 
particularly useful for GS and GWAS because it offers specific genomic markers for agronomic traits across the 
entire genome [34]. 

However, despite its high performance, WGR remains more expensive and data-intensive than those by 
reduced-representation sequencing (RRS) methods such as GBS and Restriction site-associated (RAD) Seq [35]. 
WGR provides much more comprehensive genome coverage than that by RRS but also comes at a higher cost in 
both sequencing and data processing. RRS methods selectively sequence portions of the genome, which makes 
them more cost-effective and faster to analyze [35]. 

A major challenge with WGR is managing the depth of coverage necessary for accurate variant calling, 
particularly when distinguishing rare variants or completing structural variations. For example, in species with 
large repetitive genomes, aligning sequenced reads to a reference genome can be computationally intensive and 
prone to errors if coverage is insufficient [36]. Despite these challenges, as sequencing costs gradually decreased 
and computational analyses have developed, WGR has become accessible, especially for crops where detailed 
genomic information is required for selection [31]. 

 

3.3. SNP Array 
With the development of Next-Generation Sequencing (NGS) technology, large-scale sequencing enabled the 

discovery of genetic variants. The identification of large numbers of SNPs in crops was accompanied by the 



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development of high-throughput genotyping tools, such as SNP arrays, which allow for the simultaneous 
genotyping of thousands to millions of molecular markers [37]. Currently, more than 40 K SNPs array tools that 
play a crucial role in accelerating the rate of genetic gains in crop breeding have been developed [38-40]. The 
major crops include wheat (35, 90, and 820 K) [41-43] rice (44 and 50 K) [44, 45] maize (50 and 600 K) [2, 46] 
and cotton (35 and 63 K) [47]. Such high-throughput and easy to genotyping SNP arrays can cost-effectively and 
quickly proceed with GWAS, quantitative trait locus (QTL) mapping, and GS [48-50]. 

In addition to their cost-effectiveness and high-throughput capacity, SNP arrays offer several advantages for 
crop-breeding programs. They provide excellent reproducibility and consistency across different platforms and 
studies Hiraoka, et al. [51] and Boichard, et al. [52] which is critical for large-scale breeding initiatives where data 
comparability is key. Furthermore, allowing precise and accurate genotyping with a high call rate and low error 
rate, it is suitable for crops with complex genomes [37]. Accordingly, SNP arrays for particular species were 
developed and can be utilized diversely across different populations, studies, and purposes.  

The conflict with the prominent functions of SNP arrays and the development of novel SNP arrays associated 
with agronomic traits can be restricted by the reference population or cost [31]. Although SNP arrays are effective 
for detecting known variants, they are designed based on reference populations, which can limit their ability to 
detect novel or rare alleles in other populations [53]. If the genetic diversity of a reference population is not 
representative, SNP arrays may miss important variants in different breeding lines or crops. Consequently, the 
development of a novel SNP array can be time- and cost-intensive due to the identification of new genetic variants 
with agronomic traits in other reference populations and validation tests to determine whether they are useful [54]. 

Table 2 presents the advantages and limitations of various genotyping methods. 
 
Table 2. Advantages and limitations for each various genotyping methods. 

Genotyping method Advantages Limitations 

GBS 

Cost-effective and high-throughput Limited genome coverage 

Generates dense marker sets across the genome Imputation needed  

Suitable without full reference Relies on restriction enzyme sites 

SNP Array 

High consistency Limited to pre-selected SNPs  

Low error rates  May miss novel or rare variants  

Fast and high-throughput genotyping  

Whole-genome 

sequencing (WGS) 

Complete genome data High cost 

Detects novel variants Data-intensive storage needed 

Includes non-coding regions  

WGR 

Full variant detection High cost 

Detailed genetic mapping Complex analysis requirements 

Best with reference genome  

 

4. Limitation of Application in Korea 
As described in the ‘Advantages of the GS’, it has significant advantages over traditional MAS or phenotypic 

selection, primarily because it enables breeders to capture small genetic effects involved in complex traits or 
quantitative traits [14]. GS is now widely adopted in crop breeding programs globally for major crops, such as rice, 
wheat, and maize. Cui, et al. [55] reported the GS for ten agronomic traits, whereas Huang, et al. [56] focused on 
disease resistance in rice. In maize and Wheat, many researchers are working to maximize breeding efficiency 
through GS by following breeding strategies of the International Maize and Wheat Improvement Center 
(CIMMYT) [18, 57-59]. In addition to rice, wheat, and maize, other major crops such as soybean and cotton have 
also seen extensive applications of GS in breeding programs. For example, in soybean, researchers developed high-
density SNP arrays, such as Soy50K and SoySNP arrays, to facilitate the identification of key traits such as seed 
composition, flooding tolerance, and disease resistance. Advancements in soybean genomics accelerated breeding 
for improved yield and stress resilience [60, 61]. Similarly, in cotton, genomic resources were employed to 
improve fiber quality [62]. 

In Korea, studies increasingly focused on the application of GS in various major crops. Kang, et al. [7] 
conducted a study using the Korean wheat (K-wheat) core collection to optimize the GS for key agricultural traits. 
Similarly, Kim, et al. [63] developed a customized 580 K chip array to enhance GS and GWAS in rice. Kang, et al. 
[7] and Kim, et al. [63] emphasized the importance of tailoring GS tools to local crop varieties and specific traits 
of interest. In wheat, Kang et al. focused on optimizing GS for traits related to adaptability and yield, whereas Kim 
et al. addressed key productivity and quality traits in rice. These efforts led to significant advancements in the use 
of high-throughput genotyping platforms to improve the precision and efficiency of breeding programs for major 
crops in Korea, ultimately contributing to the development of more resilient and high-yield varieties adapted to 
local agricultural conditions [7, 63]. Genetic diversity analyses and GS were actively pursued for other major 
crops in Korea. For example, Cho, et al. [64] conducted soybean research focusing on identifying key adaptive 
traits using whole-genome sequencing data. They analyzed soybean accessions from various countries, including 
Korea, China, Japan, and the United States, and found that Korean accessions revealed significant genetic diversity. 

Minor crops, similar to those of major crops, have significantly high nutritional value and require GS to 
improve their productivity and adaptability. However, in Korea, research on these crops faces challenges due to 
their relatively low domestic demand, which makes funding and research initiatives difficult to prioritize. Despite 
their importance in enhancing agricultural diversity and nutrition, research in Korea traditionally focused on major 
crops such as rice, wheat, and soybean. 

Ha, et al. [65] conducted a detailed genomic study on mung bean, providing essential genetic resources by 
identifying quantitative trait loci (QTLs) associated with important traits, such as drought tolerance and pod 
maturity. However, even with these foundational genetic resources, GS has not yet been widely applied in mung 
bean breeding programs. Similarly, Kim, et al. [66] reviewed the current genomic resources available for mung 



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bean, emphasizing the need for further GS development to optimize breeding for traits such as disease resistance 
and seed quality [66]. 

Similarly, sesame, another important minor crop, has seen advances in genetic studies, particularly in the 
identification of key loci associated with complex traits, such as disease resistance. Asekova, et al. [67] combined 
QTL mapping and GWAS to identify candidate genes related to Phytophthora blight resistance in sesame. They 
identified SNP markers on chromosome 10 associated with resistance to Phytophthora nicotianae. These findings 
highlighted the potential use of genetic markers to improve disease resistance in sesame through breeding 
strategies. 

However, despite the discovery of numerous QTLs and genetic variants related to various agronomic traits, the 
application of GS has not progressed in minor crops. The primary challenges are limited domestic demand and 
high costs associated with GS implementation, which have significantly hindered the development of 
comprehensive GS programs for these crops [14, 68]. Although the genetic groundwork is in place, including 
significant progress in marker discovery and the mapping of complex traits, the practical implementation of GS in 
Korea remains a challenge [14].  

 

5. Future Challenges 

To overcome the current challenges of GS for minor crops in Korea, cost-effective genotyping methods such as 
GBS or custom SNP chip arrays offer a viable solution. GBS is a cost-effective, high-throughput method for 
generating genome-wide markers, making it a popular tool for GS, particularly in species with limited genomic 
resources [20]. Customized SNP arrays also offer a targeted approach, allowing breeders to focus on known 
genetic variants relevant to specific traits [69]. However, even cost-effective methods face limitations when applied 
to minor crops because the scale of research and available resources remain insufficient to justify the high cost of 
customized chip development. 

A promising approach to address the various challenges in minor crops, where GS cannot play a critical role, 
the multi-species SNP array is proposed, where a single chip can be used across multiple species, significantly 

lowering the costs per species. For example, Silva‐Junior, et al. [70] developed a flexible multi-species 60 K SNP 

chip for Eucalyptus, demonstrating its utility across 12 species and its potential to extend multi-species genotyping 
for practical breeding applications in closely related species. Similarly, a multispecies SNP array was designed for 
Colossoma macropomum (tambaqui) and Piaractus mesopotamicus (pacu), providing an efficient genomic tool for 
both species, thereby reducing costs and enhancing genetic research in aquaculture [71]. Furthermore, multi-
species SNP arrays were developed for applications in both plant and aquaculture, covering species such as Rubus 

spp. (raspberry and blackberry), Leptospermum scoparium (mānuka), Chrysophrys auratus (Australasian snapper), 
and Pseudocaranx georgianus (silver trevally). These arrays facilitate comparative genetic studies across species, 
enabling researchers to optimize genetic resources for use in breeding programs and conservation and addressing 
important economic and ecological goals in both fields [72]. Table 3 presents the criteria used for SNP selection in 
the development of multi-species SNP arrays for each species, detailing sequencing methods, call rates, minor allele 
frequencies (MAF), and final SNP counts, along with their references. 

 
Table 3. Summary of criterions of SNPs selection for multi-species SNP array. 

Species Sequencing method Call rate MAF Final SNPs Reference 

Eucalyptus spp. WGR >97.2% >0.01 60,904 
Silva‐Junior, et al. 

[70] 
Rubus spp. GBS, WGS 

96.4 
>0.05 

12,723 

Montanari, et al. 
[73] 

Manuka WGR 9,002 
Snapper Illumina Novaseq 

98.3 
18,489 

Trevally WGS 20,234 
Tambaqui 

RAD-seq >97% >0.01 
23,739 Mastrochirico-

Filho, et al. [71] Pacu 23,768 

 
Multispecies SNP arrays offer several advantages beyond cost-effectiveness for GS in minor crops. First, they 

enable researchers to capture a broader range of genetic diversity by including polymorphisms conserved across 
multiple species, allowing for more comprehensive assessments of genetic variation, and aiding in the study of 
population structure and evolutionary dynamics. This diversity also strengthens genomic prediction models, 
especially when resources for unique species-specific arrays are limited, making multispecies SNP arrays ideal for 
minor or underfunded crops [73, 74]. 

Furthermore, multispecies SNP arrays support data integration across species, facilitating meta-analyses and 
improving the statistical power of genetic predictions. This approach is particularly beneficial for breeding 
programs because it enables researchers to perform cross-species comparisons more effectively, thereby enhancing 
the understanding of complex traits influenced by environmental interactions [75]. Additionally, by integrating 
genomic data across species, multi-species arrays can significantly increase the accuracy and power of predictions 
for agronomic traits such as disease resistance and yield, as pooling diverse datasets strengthens the reliability of 
genetic predictions and accelerates breeding cycles [72]. 

Conclusively, multi-species SNP arrays present an optimal approach for implementing GS in minor crops and 
provide a cost-effective and robust solution. This approach allows the application of a single SNP chip across 
multiple species, thereby significantly reducing the expenses associated with developing species-specific 
genotyping platforms. For minor crops, which often face funding and resource constraints, this method is especially 
practical, as it not only reduces costs, but also captures a wider range of genetic diversity. Such diversity is 
instrumental in improving the accuracy and statistical power of genetic predictions, enabling a more effective 
selection of complex traits across diverse environmental conditions. Figure 1 illustrates the process of SNP 
selection for multi-species SNP arrays, while Figure 2 presents the overall flowchart for the development and 
application of these arrays in genomic selection. 

 



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Figure 1. Process of SNPs selection for Multi-species SNP array. 

 

 
Figure 2. Overall flowchart of development and application of Multi-species SNP Array. 

 

6. Conclusions 
Summarily, multispecies SNP arrays provide an effective and economical approach for advancing GS in minor 

crops, enhancing genetic diversity capture, and enabling the robust selection of complex traits, thereby supporting 
sustainable agricultural productivity and adaptability across diverse environments. 
 

References 
[1] M. A. Augustin et al., "Role of food processing in food and nutrition security," Trends in Food Science & Technology, vol. 56, pp. 115-

125, 2016.  
[2] S. Unterseer et al., "A powerful tool for genome analysis in maize: Development and evaluation of the high density 600 k SNP 

genotyping array," BMC Genomics, vol. 15, pp. 1-15, 2014.  https://doi.org/10.1186/1471-2164-15-823 
[3] T. Begna and T. Begna, "Role and economic importance of crop genetic diversity in food security," International Journal of 

Agricultural Science and Food Technology, vol. 7, no. 1, pp. 164-169, 2021.  https://doi.org/10.17352/2455-815x.000104 
[4] M. Goddard and B. Hayes, "Genomic selection," Journal of Animal Breeding and Genetics, vol. 124, no. 6, pp. 323-330, 2007.  
[5] F. M. Bassi, A. R. Bentley, G. Charmet, R. Ortiz, and J. Crossa, "Breeding schemes for the implementation of genomic selection in 

wheat (Triticum spp.)," Plant Science, vol. 242, pp. 23-36, 2016.  https://doi.org/10.1016/j.plantsci.2015.08.021 
[6] B. Vivek et al., "Use of genomic estimated breeding values results in rapid genetic gains for drought tolerance in maize," The Plant 

Genome, vol. 10, no. 1, p. plantgenome2016.07.0070, 2017.  https://doi.org/10.3835/plantgenome2016.07.0070 
[7] Y. Kang, C. Choi, J. Y. Kim, K. D. Min, and C. Kim, "Optimizing genomic selection of agricultural traits using K-wheat core 

collection," Frontiers in Plant Science, vol. 14, p. 1112297, 2023.  https://doi.org/10.3389/fpls.2023.1112297 

https://doi.org/10.1186/1471-2164-15-823
https://doi.org/10.17352/2455-815x.000104
https://doi.org/10.1016/j.plantsci.2015.08.021
https://doi.org/10.3835/plantgenome2016.07.0070
https://doi.org/10.3389/fpls.2023.1112297


Agriculture and Food Sciences Research, 2024, 11(2): 195-202 

201 
© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 

 

[8] R. Allard, Principles of plant breeding. John Wiley & Sons, Inc, USA, 1999, pp.71-84 
[9] R. Bernardo, Breeding for quantitative traits in plants. Woodbury, MN: Stemma Press 2002. 
[10] B. C. Collard and D. J. Mackill, "Marker-assisted selection: An approach for precision plant breeding in the twenty-first century," 

Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 363, no. 1491, pp. 557-572, 2008.  
https://doi.org/10.1098/rstb.2007.2170 

[11] Y. Xu and J. H. Crouch, "Marker‐assisted selection in plant breeding: From publications to practice," Crop Science, vol. 48, no. 2, pp. 
391-407, 2008.  https://doi.org/10.2135/cropsci2007.04.0191 

[12] T. H. Meuwissen, B. J. Hayes, and M. Goddard, "Prediction of total genetic value using genome-wide dense marker maps," Genetics, 
vol. 157, no. 4, pp. 1819-1829, 2001.  https://doi.org/10.1093/genetics/157.4.1819 

[13] E. L. Heffner, M. E. Sorrells, and J. L. Jannink, "Genomic selection for crop improvement," Crop Science, vol. 49, no. 1, pp. 1-12, 
2009.  

[14] J. A. Bhat et al., "Genomic selection in the era of next generation sequencing for complex traits in plant breeding," Frontiers in 
Genetics, vol. 7, p. 221, 2016. https://doi.org/10.3389/fgene.2016.00221 

[15] E. L. Heffner, A. J. Lorenz, J. L. Jannink, and M. E. Sorrells, "Plant breeding with genomic selection: Gain per unit time and cost," 
Crop Science, vol. 50, no. 5, pp. 1681-1690, 2010.  https://doi.org/10.2135/cropsci2009.11.0662 

[16] J. M. Hickey, T. Chiurugwi, I. Mackay, and W. Powell, "Genomic prediction unifies animal and plant breeding programs to form 
platforms for biological discovery," Nature Genetics, vol. 49, no. 9, pp. 1297-1303, 2017.  https://doi.org/10.1038/ng.3920 

[17] J.-L. Jannink, A. J. Lorenz, and H. Iwata, "Genomic selection in plant breeding: From theory to practice," Briefings in functional 
genomics, vol. 9, no. 2, pp. 166-177, 2010.  

[18] J. Crossa et al., "Genomic prediction in CIMMYT maize and wheat breeding programs," Heredity, vol. 112, no. 1, pp. 48-60, 2014.  
https://doi.org/10.1038/hdy.2013.16 

[19] T. Meuwissen, B. Hayes, and M. Goddard, "Genomic selection: A paradigm shift in animal breeding," Animal Frontiers, vol. 6, no. 1, 
pp. 6-14, 2016.  https://doi.org/10.2527/af.2016-0002 

[20] R. J. Elshire et al., "A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species," PloS One, vol. 6, no. 5, 
p. e19379, 2011.  https://doi.org/10.1371/journal.pone.0019379 

[21] J. A. Poland and T. W. Rife, "Genotyping‐by‐sequencing for plant breeding and genetics," The Plant Genome, vol. 5, no. 3, 2012.  
https://doi.org/10.3835/plantgenome2012.05.0005 

[22] J. He, X. Zhao, A. Laroche, Z.-X. Lu, H. Liu, and Z. Li, "Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection 
(MAS) tool to accelerate plant breeding," Frontiers in Plant Science, vol. 5, p. 484, 2014.  https://doi.org/10.3389/fpls.2014.00484 

[23] J. Seeb, G. Carvalho, L. Hauser, K. Naish, S. Roberts, and L. Seeb, "Single-nucleotide polymorphism (SNP) discovery and 
applications of SNP genotyping in nonmodel organisms," Molecular Ecology Resources, vol. 11, 2011.  
https://doi.org/10.1111/j.1755-0998.2010.02979.x 

[24] N. Wang et al., "Genome sequence of dwarf birch (Betula nana) and cross‐species RAD markers," Molecular Ecology, vol. 22, no. 11, 
pp. 3098-3111, 2013.  https://doi.org/10.1111/mec.12131 

[25] R. Ogden et al., "Sturgeon conservation genomics: SNP discovery and validation using RAD sequencing," Molecular Ecology, vol. 
22, no. 11, pp. 3112-3123, 2013.  https://doi.org/10.1111/mec.12234 

[26] S. Pavan, C. Delvento, L. Ricciardi, C. Lotti, E. Ciani, and N. D’Agostino, "Recommendations for choosing the genotyping method 
and best practices for quality control in crop genome-wide association studies," Frontiers in Genetics, vol. 11, p. 447, 2020.  
https://doi.org/10.3389/fgene.2020.00447 

[27] J. C. Glaubitz et al., "TASSEL-GBS: A high capacity genotyping by sequencing analysis pipeline," PloS One, vol. 9, no. 2, p. e90346, 
2014.  

[28] X. Xu and G. Bai, "Whole-genome resequencing: Changing the paradigms of SNP detection, molecular mapping and gene 
discovery," Molecular Breeding, vol. 35, pp. 1-11, 2015.  https://doi.org/10.1007/s11032-015-0240-6 

[29] S. Kumawat et al., "Opportunity and challenges for whole‐genome resequencing‐based genotyping in plants," Genotyping by 
Sequencing for Crop Improvement, pp. 38-51, 2022.  https://doi.org/10.1002/9781119745686.ch3 

[30] T. P. Michael and R. VanBuren, "Building near-complete plant genomes," Current Opinion in Plant Biology, vol. 54, pp. 26-33, 2020.  
https://doi.org/10.1016/j.pbi.2019.12.009 

[31] D. Torkamaneh, B. Boyle, and F. Belzile, "Efficient genome-wide genotyping strategies and data integration in crop plants," 
Theoretical and Applied Genetics, vol. 131, pp. 499-511, 2018.  https://doi.org/10.1007/s00122-018-3056-z 

[32] D. E. Cook et al., "Copy number variation of multiple genes at Rhg1 mediates nematode resistance in soybean," Science, vol. 338, no. 
6111, pp. 1206-1209, 2012.  https://doi.org/10.1126/science.1228746 

[33] L. G. Maron et al., "Aluminum tolerance in maize is associated with higher MATE1 gene copy number," Proceedings of the National 
Academy of Sciences, vol. 110, no. 13, pp. 5241-5246, 2013.  

[34] X. Xu et al., "Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes," 
Nature Biotechnology, vol. 30, no. 1, pp. 105-111, 2012.  https://doi.org/10.1038/nbt.2050 

[35] K. R. Andrews, J. M. Good, M. R. Miller, G. Luikart, and P. A. Hohenlohe, "Harnessing the power of RADseq for ecological and 
evolutionary genomics," Nature Reviews Genetics, vol. 17, no. 2, pp. 81-92, 2016.  https://doi.org/10.1038/nrg.2015.28 

[36] F. J. Sedlazeck, H. Lee, C. A. Darby, and M. C. Schatz, "Piercing the dark matter: Bioinformatics of long-range sequencing and 
mapping," Nature Reviews Genetics, vol. 19, no. 6, pp. 329-346, 2018.  https://doi.org/10.1038/s41576-018-0003-4 

[37] M. W. Ganal et al., "Large SNP arrays for genotyping in crop plants," Journal of Biosciences, vol. 37, no. 5, pp. 821-828, 2012.  
https://doi.org/10.1007/s12038-012-9225-3 

[38] D. Barabaschi et al., "Next generation breeding," Plant Science, vol. 242, pp. 3-13, 2016.  
[39] M. W. Bevan, C. Uauy, B. B. Wulff, J. Zhou, K. Krasileva, and M. D. Clark, "Genomic innovation for crop improvement," Nature, 

vol. 543, no. 7645, pp. 346-354, 2017.  https://doi.org/10.1038/nature22011 
[40] A. Rasheed et al., "Crop breeding chips and genotyping platforms: Progress, challenges, and perspectives," Molecular Plant, vol. 10, 

no. 8, pp. 1047-1064, 2017.  https://doi.org/10.1016/j.molp.2017.06.008 

[41] A. M. Allen et al., "Characterization of a Wheat Breeders’ Array suitable for high‐throughput SNP genotyping of global accessions 
of hexaploid bread wheat (Triticum aestivum)," Plant Biotechnology Journal, vol. 15, no. 3, pp. 390-401, 2017.  
https://doi.org/10.1111/pbi.12635 

[42] Y. Wang et al., "Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery 
mildew," Nature Biotechnology, vol. 32, no. 9, pp. 947-951, 2014.  https://doi.org/10.1038/nbt.2969 

[43] M. O. Winfield et al., "High‐density SNP genotyping array for hexaploid wheat and its secondary and tertiary gene pool," Plant 
Biotechnology Journal, vol. 14, no. 5, pp. 1195-1206, 2016.  https://doi.org/10.1111/pbi.12485 

[44] C.-W. Tung et al., "Development of a research platform for dissecting phenotype–genotype associations in rice (Oryza spp.)," Rice, 
vol. 3, pp. 205-217, 2010.  https://doi.org/10.1007/s12284-010-9056-5 

[45] W. Chen et al., "Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice 
metabolism," Nature Genetics, vol. 46, no. 7, pp. 714-721, 2014.  https://doi.org/10.1038/ng.3007 

[46] M. W. Ganal et al., "A large maize (Zea mays L.) SNP genotyping array: Development and germplasm genotyping, and genetic 
mapping to compare with the B73 reference genome," PloS One, vol. 6, no. 12, p. e28334, 2011.  

[47] A. M. Hulse-Kemp et al., "Development of a 63K SNP array for cotton and high-density mapping of intraspecific and interspecific 
populations of Gossypium spp," G3: Genes, Genomes, Genetics, vol. 5, no. 6, pp. 1187-1209, 2015.  
https://doi.org/10.1534/g3.115.018416 

[48] S. R. McCouch et al., "Open access resources for genome-wide association mapping in rice," Nature Communications, vol. 7, no. 1, p. 
10532, 2016.  

[49] J. Dalton-Morgan et al., "A high-throughput SNP array in the amphidiploid species Brassica napus shows diversity in resistance 
genes," Functional & Integrative Genomics, vol. 14, pp. 643-655, 2014.  https://doi.org/10.1007/s10142-014-0391-2 

https://doi.org/10.1098/rstb.2007.2170
https://doi.org/10.2135/cropsci2007.04.0191
https://doi.org/10.1093/genetics/157.4.1819
https://doi.org/10.3389/fgene.2016.00221
https://doi.org/10.2135/cropsci2009.11.0662
https://doi.org/10.1038/ng.3920
https://doi.org/10.1038/hdy.2013.16
https://doi.org/10.2527/af.2016-0002
https://doi.org/10.1371/journal.pone.0019379
https://doi.org/10.3835/plantgenome2012.05.0005
https://doi.org/10.3389/fpls.2014.00484
https://doi.org/10.1111/j.1755-0998.2010.02979.x
https://doi.org/10.1111/mec.12131
https://doi.org/10.1111/mec.12234
https://doi.org/10.3389/fgene.2020.00447
https://doi.org/10.1007/s11032-015-0240-6
https://doi.org/10.1002/9781119745686.ch3
https://doi.org/10.1016/j.pbi.2019.12.009
https://doi.org/10.1007/s00122-018-3056-z
https://doi.org/10.1126/science.1228746
https://doi.org/10.1038/nbt.2050
https://doi.org/10.1038/nrg.2015.28
https://doi.org/10.1038/s41576-018-0003-4
https://doi.org/10.1007/s12038-012-9225-3
https://doi.org/10.1038/nature22011
https://doi.org/10.1016/j.molp.2017.06.008
https://doi.org/10.1111/pbi.12635
https://doi.org/10.1038/nbt.2969
https://doi.org/10.1111/pbi.12485
https://doi.org/10.1007/s12284-010-9056-5
https://doi.org/10.1038/ng.3007
https://doi.org/10.1534/g3.115.018416
https://doi.org/10.1007/s10142-014-0391-2


Agriculture and Food Sciences Research, 2024, 11(2): 195-202 

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© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 

 

[50] W. E. Clarke et al., "A high-density SNP genotyping array for Brassica napus and its ancestral diploid species based on optimised 
selection of single-locus markers in the allotetraploid genome," Theoretical and Applied Genetics, vol. 129, pp. 1887-1899, 2016.  
https://doi.org/10.1007/s00122-016-2746-7 

[51] Y. Hiraoka, S. P. Ferrante, G. A. Wu, C. T. Federici, and M. L. Roose, "Development and assessment of SNP genotyping arrays for 
citrus and its close relatives," Plants, vol. 13, no. 5, p. 691, 2024.  https://doi.org/10.3390/plants13050691 

[52] D. Boichard et al., "Design of a bovine low-density SNP array optimized for imputation," PloS One, vol. 7, no. 3, p. e34130, 2012.  
https://doi.org/10.1371/journal.pone.0034130 

[53] J. Lachance and S. A. Tishkoff, "SNP ascertainment bias in population genetic analyses: Why it is important, and how to correct 
it," Bioessays, vol. 35, no. 9, pp. 780-786, 2013.  https://doi.org/10.1002/bies.201300014 

[54] J. A. Tennessen, T. D. O'Connor, M. J. Bamshad, and J. M. Akey, "The promise and limitations of population exomics for human 
evolution studies," Genome Biology, vol. 12, pp. 1-7, 2011.  https://doi.org/10.1186/gb-2011-12-9-127 

[55] Y. Cui et al., "Hybrid breeding of rice via genomic selection," Plant Biotechnology Journal, vol. 18, no. 1, pp. 57-67, 2020.  
https://doi.org/10.1111/pbi.13170 

[56] M. Huang et al., "Use of genomic selection in breeding rice (Oryza sativa L.) for resistance to rice blast (Magnaporthe oryzae)," 
Molecular Breeding, vol. 39, pp. 1-16, 2019.  https://doi.org/10.1007/s11032-019-1023-2 

[57] S. A. Atanda et al., "Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program," Theoretical and 
Applied Genetics, vol. 134, pp. 279-294, 2021.  https://doi.org/10.1007/s00122-020-03696-9 

[58] C. Guzman et al., "Wheat quality improvement at CIMMYT and the use of genomic selection on it," Applied & translational 
genomics, vol. 11, pp. 3-8, 2016.  https://doi.org/10.1016/j.atg.2016.10.004 

[59] P. Juliana et al., "Genomic selection for grain yield in the CIMMYT wheat breeding program—status and perspectives," Frontiers 
in Plant Science, vol. 11, p. 564183, 2020.  https://doi.org/10.3389/fpls.2020.564183 

[60] A. Tyagi, Z. A. Mir, M. A. Almalki, R. Deshmukh, and S. Ali, "Genomics-assisted breeding: A powerful breeding approach for 
improving plant growth and stress resilience," Agronomy, vol. 14, no. 6, p. 1128, 2024.  
https://doi.org/10.3390/agronomy14061128 

[61] J. Petereit et al., "Genetic and genomic resources for soybean breeding research," Plants, vol. 11, no. 9, p. 1181, 2022.  
https://doi.org/10.3390/plants11091181 

[62] C. Shen et al., "Population genomics unravels the characteristic relationship between introgression and geographical distribution in 
upland cotton," Agronomy, vol. 13, no. 7, p. 1781, 2023.  https://doi.org/10.3390/agronomy13071781 

[63] K.-W. Kim, B. Nawade, J. Nam, S.-H. Chu, J. Ha, and Y.-J. Park, "Development of an inclusive 580K SNP array and its application 
for genomic selection and genome-wide association studies in rice," Frontiers in Plant Science, vol. 13, p. 1036177, 2022.  
https://doi.org/10.3389/fpls.2022.1036177 

[64] Y. Cho et al., "Whole-genome sequencing analysis of soybean diversity across different countries and selection signature of Korean 
soybean accession," G3: Genes, Genomes, Genetics, vol. 14, no. 8, p. jkae118, 2024.  https://doi.org/10.1093/g3journal/jkae118 

[65] J. Ha et al., "A near‐complete genome sequence of mungbean (Vigna radiata L.) provides key insights into the modern breeding 
program," The Plant Genome, vol. 14, no. 3, p. e20121, 2021.  https://doi.org/10.1002/tpg2.20121 

[66] S. K. Kim, R. M. Nair, J. Lee, and S.-H. Lee, "Genomic resources in mungbean for future breeding programs," Frontiers in Plant 
Science, vol. 6, p. 626, 2015.  https://doi.org/10.3389/fpls.2015.00626 

[67] S. Asekova et al., "An integrated approach of QTL mapping and genome-wide association analysis identifies candidate genes for 
phytophthora blight resistance in sesame (Sesamum indicum L.)," Frontiers in Plant Science, vol. 12, p. 604709, 2021.  
https://doi.org/10.3389/fpls.2021.604709 

[68] N. Budhlakoti et al., "Genomic selection: A tool for accelerating the efficiency of molecular breeding for development of climate-
resilient crops," Frontiers in Genetics, vol. 13, p. 832153, 2022.  https://doi.org/10.3389/fgene.2022.832153 

[69] D. Iamartino et al., "Design and validation of a 90K SNP genotyping assay for the water buffalo (Bubalus bubalis)," PloS One, vol. 
12, no. 10, p. e0185220, 2017.  https://doi.org/10.1371/journal.pone.0185220 

[70] O. B. Silva‐Junior, D. A. Faria, and D. Grattapaglia, "A flexible multi‐species genome‐wide 60K SNP chip developed from pooled 
resequencing of 240 Eucalyptus tree genomes across 12 species," New Phytologist, vol. 206, no. 4, pp. 1527-1540, 2015.  
https://doi.org/10.1111/nph.13322 

[71] V. A. Mastrochirico-Filho et al., "Development of a multi-species SNP array for serrasalmid fish Colossoma macropomum and 
Piaractus mesopotamicus," Scientific Reports, vol. 11, no. 1, p. 19289, 2021.  https://doi.org/10.1038/s41598-021-98885-x 

[72] G. Keeble-Gagnère et al., "Novel design of imputation-enabled SNP arrays for breeding and research applications supporting 
multi-species hybridization," Frontiers in Plant Science, vol. 12, p. 756877, 2021.  https://doi.org/10.3389/fpls.2021.756877 

[73] S. Montanari et al., "A multiplexed plant–animal SNP array for selective breeding and species conservation applications," G3: Genes, 
Genomes, Genetics, vol. 13, no. 10, p. jkad170, 2023.  https://doi.org/10.1093/g3journal/jkad170 

[74] K. D. Rasal et al., "Single-Nucleotide Polymorphism (SNP) array: An array of hope for genetic improvement of aquatic species and 
fisheries management," Blue Biotechnology, vol. 1, no. 1, p. 3, 2024.  https://doi.org/10.1186/s44315-024-00004-8 

[75] A. Jighly, H. Benhajali, Z. Liu, and M. E. Goddard, "MetaGS: An accurate method to impute and combine SNP effects across 
populations using summary statistics," Genetics Selection Evolution, vol. 54, no. 1, p. 37, 2022.  https://doi.org/10.1186/s12711-022-
00725-7 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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https://doi.org/10.1007/s00122-016-2746-7
https://doi.org/10.3390/plants13050691
https://doi.org/10.1371/journal.pone.0034130
https://doi.org/10.1002/bies.201300014
https://doi.org/10.1186/gb-2011-12-9-127
https://doi.org/10.1111/pbi.13170
https://doi.org/10.1007/s11032-019-1023-2
https://doi.org/10.1007/s00122-020-03696-9
https://doi.org/10.1016/j.atg.2016.10.004
https://doi.org/10.3389/fpls.2020.564183
https://doi.org/10.3390/agronomy14061128
https://doi.org/10.3390/plants11091181
https://doi.org/10.3390/agronomy13071781
https://doi.org/10.3389/fpls.2022.1036177
https://doi.org/10.1093/g3journal/jkae118
https://doi.org/10.1002/tpg2.20121
https://doi.org/10.3389/fpls.2015.00626
https://doi.org/10.3389/fpls.2021.604709
https://doi.org/10.3389/fgene.2022.832153
https://doi.org/10.1371/journal.pone.0185220
https://doi.org/10.1111/nph.13322
https://doi.org/10.1038/s41598-021-98885-x
https://doi.org/10.3389/fpls.2021.756877
https://doi.org/10.1093/g3journal/jkad170
https://doi.org/10.1186/s44315-024-00004-8
https://doi.org/10.1186/s12711-022-00725-7
https://doi.org/10.1186/s12711-022-00725-7

