The primary goal of plant genetics and breeding research is to investigate the relationship between genetic variation and phenotypic traits, and subsequently enhance crop yield, quality, and stress resistance through targeted improvement. Traditional genotyping techniques, such as restriction fragment length polymorphism (RFLP) and simple sequence repeat (SSR), are difficult to meet the needs of genome-wide fine research because of the limited number of markers, low flux, and high cost.
Genotyping-by-sequencing (GBS), as a simplified genome technology based on next-generation sequencing (NGS), simplifies the complexity of the genome, targets specific regions, and combines with high-throughput sequencing to achieve efficient detection of mutation sites such as massive single-nucleotide polymorphisms (SNPs) in the whole genome. It does not depend on the known genome sequence, and has the characteristics of low cost and high resolution. It has significantly broken through the limitations of traditional technology and has been widely used in many fields such as plant genetic diversity analysis, high-density genetic map construction, gene mapping, molecular marker-assisted breeding, and so on.
The article discusses how GBS overcomes limitations of traditional genotyping techniques and its wide applications in plant genetic diversity analysis, high-density marker development, and expanding utility in plant genetics, along with future prospects.
GBS-Driven Exploration of Plant Genetic Diversity
Plant genetic diversity is the basis for species to adapt to environmental changes and resist biological stress, and it is also an important gene resource for crop improvement. GBS technology has become the core tool to analyze plant genetic diversity because of its advantages of being cost-effective and low-cost.
GBS can efficiently detect mutation sites such as SNP in the whole genome by digesting and sequencing specific regions of the genome, which provides a massive molecular marker for analyzing the genetic structure of plant populations. In the study of natural populations, GBS technology can accurately distinguish the genetic differentiation degree of different geographical populations.
In the genetic variation mining of cultivated and wild crops, GBS shows unique advantages. Through the GBS analysis of maize cultivars and grass (their wild ancestors), the researchers found that about 10% of the genome regions had a strong selection effect during the cultivation process, and these regions contained key genes related to yield traits, revealing the role of artificial selection in shaping genetic diversity.
GBS technology also provides molecular evidence for the detection of interspecific hybridization events. In the study of citrus plants, the population structure analysis based on GBS confirmed that sweet orange was a hybrid of pomelo and citrus, and the contribution ratio of the parental genome in offspring was determined. This method overcomes the limitations of traditional morphological identification and provides accurate molecular marker support for the study of species origin and evolution.
Barley GBS validation using a single DHline (oWB003) (Elshire et al., 2011)
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GBS for High-Density Marker Development and Gene Mapping
High-density molecular markers are the basis of gene mapping, genetic map construction, and marker-assisted breeding. By simplifying the strategy of genome sequencing, GBS technology can rapidly develop a large number of SNP markers and significantly improve the resolution of plant genetic research.
SNP Discovery in Non-Model Plants
In marker development, GBS does not need to know genome information in advance, especially for non-model plants. Taking perennial fruit trees as an example, their genome is huge, and there are many repetitive sequences, and the development cost of traditional markers is high. Using GBS technology, researchers developed more than 100,000 SNP markers in apples at one time, covering more than 50 times as many traditional SSR markers, and the markers were evenly distributed in the genome. These markers are not only used for genetic map construction, but also provide a high-density marker set for genome-wide association study (GWAS).
High-Resolution QTL Mapping in Rice
GBS shows high efficiency in the construction of a genetic map. In the study of Arabidopsis thaliana, the marker density of the genetic map based on GBS reached 10 SNPs per cm, which was nearly 10 times higher than that of the traditional map, and the mapping interval of quantitative trait loci (QTL) was reduced to less than 1Mb. In crops, the high-density map constructed by GBS analysis of rice recombinant inbred line (RIL) population successfully located the QTL controlling tillering number in the region containing three candidate genes, which laid the foundation for subsequent functional verification.
Genetic diversity and population structure of 192 soybean MDP lines (Kim et al., 2022)
GWAS Applications in Disease Resistance
In the field of gene mapping, the combination of GBS and GWAS has become the mainstream method to analyze complex traits. In the GWAS study of maize plant height traits, 500 natural population materials were genotyped by GBS, and eight SNP loci were identified, two of which were located near the known plant height regulation genes. In the study of wheat powdery mildew resistance, GWAS assisted by GBS technology not only verified the reported disease resistance gene Pm21 but also found three new disease resistance sites, which provided new marker resources for disease resistance breeding.
In addition, GBS technology plays an important role in linkage disequilibrium (LD) analysis. By calculating the LD attenuation distance of different populations, the resolution of gene mapping and the population size of association analysis can be determined. The LD attenuation distance of Arabidopsis natural population is about 10kb, while that of the maize tropical population is 100kb, which provides key parameters for GWAS experimental design of different crops.
Distribution of GBS SNP markers in the Oregon Wolfe Barley (OWB) bin map (Poland et al., 2012)
Key Applications of GBS in Accelerating Plant Breeding
Traditional plant breeding relies on phenotypic selection, which has a long cycle and low efficiency. GBS technology has significantly accelerated the breeding process by providing accurate genotype information and has become the core tool of molecular breeding.
Marker-Assisted Selection (MAS) Efficiency
Marker-assisted selection (MAS) is the most direct application of GBS in breeding. In rice breeding for resistance to rice blast, the SNP marker closely linked with the disease-resistant gene Pi9 developed by GBS can be used for early screening of hybrid offspring at the seedling stage, shortening the breeding cycle by 2-3 years. In wheat quality breeding, by detecting the markers related to gluten content through GBS, lines with high gluten content can be quickly screened out, and the selection efficiency can be improved by more than 40%.
Distribution of the GBS markers on 21 linkage groups (Yang et al., 2017)
In backcross breeding, the background selection function of GBS technology greatly improves the breeding accuracy. In the process of introducing the disease-resistant gene of wild tomato into cultivated species, it takes 6-8 generations for traditional backcrossing to restore the genetic background of cultivated species. Using GBS to detect the whole genome background of each backcross population can make the genetic background recovery rate reach more than 98% within 3-4 generations, while retaining the target disease-resistant gene region and significantly reducing the linkage burden.
Heterosis Prediction via Genetic Distance
GBS shows unique value in heterosis prediction. The yield performance of maize hybrids is closely related to the genetic distance between parents. The yield potential of hybrid combinations can be predicted by calculating the genetic distance between inbred lines through GBS. The results show that the accuracy of predicting heterosis based on the genetic distance of GBS is 75%, which is 60% lower than the cost of traditional combining ability measurement, and provides an efficient tool for hybrid selection.
In population improvement, recurrent selection assisted by GBS technology significantly improved the genetic gain of the population. In the improvement of soybean stress tolerance population, the population after each round of selection was genotyped by GBS, and the frequency change of stress tolerance loci was tracked, so that the allele frequency related to stress tolerance increased from the initial 30% to 70%, and the population stress tolerance phenotype increased by 25% on average. This method realizes the accurate correlation between genotype and phenotype, and accelerates the polymerization of excellent genes.
Application of GBS markers in faba bean genetics (Zhang et al., 2024)
GBS in Improving Special Agronomic Traits of Plants
The special agronomic traits of crops (such as stress resistance, quality, growth period, etc.) are important goals of breeding, and GBS technology provides strong support for analyzing the genetic basis of these traits and realizing accurate improvement.
In the aspect of stress resistance improvement, GBS technology has been widely used in crop drought resistance, salt tolerance, and disease resistance research. In maize drought-resistant breeding, the recombinant inbred lines under drought stress were genotyped by GBS, and five QTLs for controlling leaf curl were located, one of which contained the gene encoding dehydration response protein. The yield of maize was increased by 15% based on the marker selection of this QTL.
In the improvement of quality traits, GBS technology promotes the precise regulation of crop nutrients and processing quality. Amylose content in rice is a key trait that affects the eating quality. The near-isogenic lines were analyzed by GBS, and the major QTL controlling this trait was located. The candidate gene was the Wx gene, and the amylose content of the lines selected by this marker was stable in the high-quality range of 15%-20%.
Growth period regulation is the key for crops to adapt to different ecological regions, and GBS technology provides an efficient means for analyzing the genetic basis of growth period. In the study of the flowering period of soybean, GBS was used to genotype varieties from different latitudes, and two new genes related to photoperiod sensitivity were found. Their allelic variation combinations can make soybeans mature normally in different latitudes.
Read depth of GBS reads covering genomic loci produced by the various restriction enzyme (RE) combinations (Zhang et al., 2024)
In addition, GBS technology also plays an important role in improving abiotic stress tolerance. In the study of potato cold tolerance, two QTLs related to low-temperature saccharification were located by genotyping the cold-resistant and sensitive populations through GBS. The reducing sugar content of the screened lines decreased by 40% after low-temperature storage, which significantly improved the processing quality.
Expanding GBS Utility in Plant Genetics
With the development of technology, the application of GBS has expanded from traditional genotyping to many fields, which has promoted the development of plant genetics research to higher precision and wider dimensions.
Multi-Omics Integration
In the integration of multi-omics, the combination of GBS with transcriptomics and metabonomics provides a systematic perspective for analyzing complex traits. In the study of maize grain development, the correlation analysis between GBS genotype data and grain transcriptome data identified 12 key genes regulating starch synthesis, and the expression of three genes was significantly correlated with starch content. In the study of secondary metabolism in Arabidopsis thaliana, the integrated analysis of GBS and metabonomic data revealed the genetic regulatory network of the flavonoid synthesis pathway and found two new regulatory genes.
In the research of non-model plant genetics, GBS technology breaks the limitation of a lack of genomic information. In the medicinal plant Salvia miltiorrhiza Bunge, 200 population materials were genotyped by GBS, and the first high-density genetic map was constructed, and the QTL for controlling the content of salvianolic acid B was located, which provided a marker resource for improving the quality of Salvia miltiorrhiza Bunge. In the study of the rare and endangered plant Taxus chinensis, the genetic structure of its wild population was analyzed by GBS technology, and three subgroups with extremely low genetic diversity were found, which provided a scientific basis for formulating targeted protection measures.
Epigenetic Insights via Methylated GBS
In epigenetic research, GBS derivative techniques (such as methylated GBS) provide a new method for analyzing epigenetic variation. GBS analysis of rice methylation showed that 10% of CG loci in the genome changed in methylation level under low temperature stress, and some of them were located in the promoter region of the antifreeze gene, indicating that epigenetic regulation played an important role in stress resistance. In Arabidopsis thaliana, GBS technology combined with chromatin immunoprecipitation sequencing (ChIP-seq) reveals the association pattern between histone modification and SNP variation, which provides a new idea for the study of the interaction between epigenetics and genetic variation.
Example of placement of GBS SNP markers into genetic bins of the double haploid mapping populations (Poland et al., 2012)
Germplasm Fingerprinting
GBS technology is widely used in germplasm resources identification and management. Establishing the fingerprint database of crop core germplasm resources based on GBS can realize the accurate identification and traceability of germplasm resources. GBS analysis of rice core collection constructed a fingerprint with 100,000 SNPs, and successfully distinguished 2,000 varieties with similar morphology, which provided molecular evidence for germplasm resources protection and intellectual property rights protection.
Conclusion
GBS technology has become the core tool of plant genetics and breeding research, but its application in complex genome crops (such as polyploids) still faces challenges, such as repeated sequence interference and genotypic calling accuracy. In the future, with the combination of long-read and long sequencing technology and GBS and the optimization of bioinformatics tools, the resolution and efficiency of GBS will be further improved.
At the same time, the combination of GBS and artificial intelligence is expected to achieve accurate prediction from genotype to phenotype, and promote plant breeding to enter an intelligent era. Against the background of agricultural sustainable development, GBS technology will play a greater role in crop stress-resistant breeding, efficient utilization of resources, and variety improvement, and provide key technical support for ensuring food security.
References:
- Elshire RJ, Glaubitz JC, Sun Q, et al. "A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species." PLoS One. 2011 6(5): e19379 https://doi.org/10.1371/journal.pone.0019379
- Kim DG, Lyu JI., et al. "Identification of Loci Governing Agronomic Traits and Mutation Hotspots via a GBS-Based Genome-Wide Association Study in a Soybean Mutant Diversity Pool." International Journal of Molecular Sciences. 2022 23(18): 10441 https://doi.org/10.3390/ijms231810441
- PolandJ A, Brown PJ, Sorrells ME, Jannink JL. "Development of High-Density Genetic Maps for Barley and Wheat Using a Novel Two-Enzyme Genotyping-by-Sequencing Approach." PLoS ONE. 2012 7(2): e32253 https://doi.org/10.1371/journal.pone.0032253
- Yang Z, Chen Z, Peng Z, Yu Y, Liao M, Wei S. "Development of a high-density linkage map and mapping of the three-pistil gene (Pis1) in wheat using GBS markers." BMC Genomics. 2017 18(1): 567 https://doi.org/10.1186/s12864-017-3960-7
- Zhang HL, Fechete LI., et al. "Optimization of Genotyping- by- Sequencing (GBS) for Germplasm Fingerprinting and Trait Mapping in Faba Bean" Legume Science. 2024 6: e254 https://doi.org/10.1002/leg3.254
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