Accelerating Plant Breeding with GBS-Based Marker-Assisted Selection

The core goal of modern plant breeding is to efficiently and accurately introduce favorable genes controlling important agronomic traits into an excellent genetic background. The emergence of genotyping-by-sequencing (GBS) technology has greatly accelerated this process. By generating tens of thousands of genome-wide single-nucleotide polymorphism (SNP) markers in one analysis, GBS can not only quickly construct high-density genetic maps, accurately locate quantitative trait loci (QTLs) and major genes, but also completely change the implementation paradigm of marker-assisted selection (MAS), backcross breeding, and gene mapping.

In this paper, the application of GBS in linkage mapping to marker-trait association analysis will be systematically expounded, and how to optimize the design of MAS and backcross scheme will be explained in detail, and the strategy of realizing multi-gene efficient polymerization will be discussed in depth. Finally, through a typical case of staple crops, it shows how GBS can significantly shorten the breeding cycle and improve the selection efficiency in actual breeding.

From Linkage Mapping to Marker-Trait Association: Identifying Causal Loci

On the stage of gene mapping, GBS plays a revolutionary role, which pushes the traditional linkage analysis to an unprecedented level of high resolution and efficiency.

Construction of A High-density Genetic Linkage Map

In parental populations (such as F, RILs, and DH lines), GBS can explore and type thousands to tens of thousands of SNP markers at one time.

• A. Database:
  • a) By GBS analysis of the whole isolated population and its parents, a high-density genotype matrix covering the whole genome can be obtained.
• B. Atlas construction process:
  • a) Marker filtering: Firstly, SNP with high deletion rate, serious segregation, and redundancy is eliminated.
  • b) Grouping and sequencing: Using software (such as JoinMap, MSTmap) to assign SNP markers to different linkage groups based on Mendel's segregation law and exchange rate, and determine their linear arrangement order on linkage groups.
  • c) Graph distance calculation: According to the recombination rate between markers, the genetic function (such as the Kosambi function) is used to convert the recombination rate into genetic graph distance (cM).
• C. Advantages of the GBS map:
  • a) Ultra-high density: The mark spacing can be reduced to 1cM or even lower, which greatly improves the drawing accuracy.
  • b) Uniform genome coverage: Compared with traditional SSR or AFLP markers, SNP markers are distributed more evenly, which can effectively reduce the blind spots in the genome.
  • c) Directly related to physical location: For species with reference genomes, GBS-SNP markers can be easily compared to physical locations to form a comprehensive genetic-physical map, which lays a solid foundation for map-based cloning.

Mapping of QTL

A high-density genetic map is a blueprint for high-precision QTL mapping.

• A. Methods:
  • a) The constructed genetic map was combined with multi-point phenotypic data for many years, and the whole genome was scanned by statistical methods such as IntervalMapping or CompositeIntervalMapping to find the genome interval significantly related to the target traits.
• B. Breakthrough brought by GBS:
  • a) Higher resolution: Dense markers greatly reduce the confidence interval of QTL, from the traditional 10-20cM to several cM or even smaller, which greatly reduces the range of candidate genes for subsequent map-based cloning.
  • b) Detection of minor QTLs: High-density markers improve the ability to detect minor QTLs that contribute little to traits but have cumulative effects, which is very important for quantitative trait improvement controlled by multiple genes.
  • c) Epiepistatic interaction analysis: Sufficient marker density makes it possible to systematically analyze epistatic interactions among different QTLs in the whole genome, so as to understand the genetic structure of traits more deeply.
• C. An effective supplement to correlation analysis:
  • a) Although a genome-wide association study (GWAS) is usually carried out in natural populations, the positioning results of GBS in parents' own populations can be mutually verified with GWAS. The key QTL located in one population can be verified in another population, which increases the reliability of the results and accelerates the determination of candidate genes.

Phenotypic analysis of salt tolerance at the seedling stage of the two parents (Diouf et al., 2017)

Designing and Implementing Marker-Assisted Selection and Backcrossing Schemes

GBS upgraded marker-assisted selection from a directional operation relying on a few markers to a panoramic precision breeding covering the whole genome.

Prospect selection: Accurate Tracking of Target Genes

• Prospect selection refers to the screening of offspring individuals by using molecular markers closely linked with target genes (such as disease resistance gene R) in backcross or pedigree breeding to ensure the successful introduction of target genes.
• The role of GBS: GBS can find SNP markers located very close to the flank of the target gene (that is, markers in the linkage imbalance block). These markers can be used as ideal tools for foreground selection. Compared with the traditional tedious process of developing specific markers, GBS can provide a large number of alternative flank markers in one analysis, which makes the foreground selection more accurate and reliable and minimizes the chain burden.

Background Selection: Accelerating Recovery of Recurrent Parent Genome

• Background selection is a more revolutionary link in backcross breeding. Its purpose is to screen out the individuals whose whole genome background is closest to the excellent recurrent parent (RP), except for the target gene region, from the offspring carrying the target gene.
• Limitations of traditional methods: Before GBS, due to the limited number of markers, background selection could only roughly check a few chromosome regions, and the process of restoring the RP genome is slow and uncertain.
• Paradigm change brought by GBS: Genome-wide SNP markers produced by GBS make genome-wide background selection a reality.
• Working principle: By GBS analysis of backcross generations (such as BC₂F₁), the percentage of genotype similarity between each individual and RP at all SNP sites can be calculated.
• Visualization: With the genome visualization tool, the genome regression map of each candidate individual can be generated, which directly shows which chromosome segments are from RP and which are from donor parents (DP).
• Efficient screening: Breeders can accurately select those individuals who carry donor fragments in the target gene region and are closest to RP in the rest of the genome for the next cross or self-crossing. This can improve the efficiency of rapid recovery of the RP genome in the backcross population several times.

Scheme Optimization and Breeding Cycle Shortening

• The GBS-MAS scheme, which combines foreground and background selection, can greatly shorten the 6-8 generations required by traditional backcrossing to 3-4 generations. This not only saves time but also significantly reduces the risk of beneficial gene loss caused by random segregation, making the breeding process completely controllable and predictable.

L of DRR17B displaying high grain number under field conditions at IIRR, Panicles of ILs along with donor and recurrent parents (Balachiranjeevi et al., 2018)

Pyramiding Multiple Genes: A GBS-Driven Strategy for Durable Trait Stacking

In the face of complex biological stress (such as evolving pathogenic bacteria) or pursuing multiple quality improvements, it is key to cultivate breakthrough varieties by aggregating multiple beneficial genes from different sources into the same excellent variety. GBS is an ideal platform to realize this strategy of gene superposition or gene pyramid.

Strategic Challenge

Without genome-wide information, it is extremely difficult to aggregate multiple genes (especially those with similar or recessive phenotypes). Traditional phenotypic identification can not distinguish the existence of a single gene, and it is difficult for limited markers to track all target sites at the same time and ensure the Excellence of the background.

GBS-driven Precise Aggregation Process

• Step 1: Identify target sites and develop diagnostic markers: firstly, analyze donor parents with different resistance/trait sources by GBS, accurately locate each major gene /QTL, and develop or find SNP markers co-separated from them.
• Step 2: Multi-parent hybridization design: through a complex hybridization scheme, the target genes of multiple donor parents are gradually introduced into a common excellent recipient parent.
• Step 3: Genome-wide tracking and selection: GBS analysis is performed on large-scale populations in isolated generations (such as F or BCnF₂). One-time analysis can achieve:
• Simultaneous prospect selection: Confirm whether the individual carries all the target genes at the same time. GBS data can directly display the genotype at each target site.
• Synchronous background selection: Evaluate the whole genome background of the individual to ensure that it is as close as possible to the excellent recipient parents.
• Identification of recombination events: Identify recombinants with smaller donor fragments that have been exchanged near the target gene, thus further reducing linkage burden.

Advantages and Output

Through gene polymerization driven by GBS, breeders can create varieties with multiple disease-resistant genes, thus providing lasting and broad-spectrum resistance, or cultivate a perfect variety with high yield, high quality, and stress resistance at the same time. This method greatly reduces the uncertainty of selection and makes it possible to improve multiple complex traits at the same time.

Computer simulations to optimize MABS approach in wheat (Randhawa et al., 2009)

Case Study: Rapid Improvement of Disease Resistance in a Staple Cereal Crop

In order to show the power of GBS in actual combat, we use a highly typical case to quickly cultivate new varieties resistant to wheat straw rust by GBS-MAS.

• A. Background:
  • a) Wheat straw rust is a devastating disease, and the new pathogen race Ug99 poses a serious threat to global wheat production. None of the existing main cultivars is resistant to Ug99. Scientists have identified a new major anti-Ug99 gene, Sr72, in a wild emmer wheat. The task of breeding is to quickly introduce the Sr72 gene into the high-yield but disease-resistant wheat variety Fengshou 5.
• B. Bottleneck of traditional methods:
  • a) It is estimated that it will take 7-8 backcross generations to obtain a new strain with nearly the same agronomic traits and disease resistance as Fengshou 5, depending on phenotypic identification (pathogen inoculation is required, which is greatly influenced by the environment and has a long cycle) and background selection of a few markers.
• C. Implementation of GBS-MAS scheme:
  • a) The donor (carrying Sr72) and recurrent parent Fengchan No.5 were sequenced by GBS.
  • b) Through comparative analysis, three highly linked SNP markers were identified within 0.5cM of the flanking area of the Sr72 gene for foreground selection.
  • c) At the same time, 5000 SNP markers covering the whole genome were obtained for background selection.
• D. Backcross and selection process:
  • a) BCF generation: Backcross between (donor× Fengchan No.5) F and Fengchan No.5 was carried out to obtain a 200 BCF population.
  • b) GBS analysis: GBS was performed on 200 BC₁F₁ plants.
• E. Screening:
  • a) Prospect selection: All individuals who are heterozygous (indicating that they carry Sr72) at three foreground marker sites, about 100 strains, were screened out.
  • b) Background selection: Among these 100 strains, the genome similarity between each strain and Fengshou 5 was calculated. Select the top 5 individuals with the highest similarity (for example, > 92%).
  • c) BCF generation: Back-cross with Fengchan No.5 with the best five selected plants to obtain a 300 BCF population. Repeat GBS analysis. At this time, the background recovery rate is expected to be higher because of another round of cross. The top 3 individuals with heterozygous foreground and background similarity > 96% were screened out.
  • d) BCF generation: Let the selected BCF plants self-cross to obtain the BCF population. GBS analysis showed that the single plant was homozygous and disease-resistant at the Sr72 locus, and the similarity between the whole genome background and Fengshou 5 was more than 98%. Theoretically, these plants are very close to Fengshou 5, but they carry a small donor fragment containing Sr72.
• F. Results and benefits:
  • a) Time benefit: The whole process is shortened from 7-8 generations in the traditional method to only 3 generations (BCF → BCF → BCF), and the breeding cycle is shortened by more than 50%.
  • b) Accuracy: The new strain finally bred was named "High Yield Anti-rust No.1". Its agronomic characters, yield, and quality were not significantly different from those of Fengchan 5, but it showed high resistance to Ug99.
  • c) Economic benefit: The rapid introduction of this variety has effectively responded to the threat of stem rust in time and ensured food security and farmers' income. The analysis cost of GBS is much lower than the possible economic loss caused by a disease outbreak, and the input-output ratio is extremely high.

Impact of selective amplification on the number and depth of coverage of SNPs (Sonah et al., 2013)

Conclusion

By providing a large number of genome-wide SNP markers, GBS technology has been deeply integrated into the core process of modern breeding, realizing the fundamental transformation from blind selection to visual selection, from single trait selection to genome-wide comprehensive selection. It greatly improves the accuracy of QTL mapping, innovates the efficiency of marker-assisted selection and backcross breeding, and makes the complex task of multi-gene polymerization clear and controllable.

In the future, with the further reduction of GBS cost and the automation and intelligence of the data analysis process, it will become the standard configuration of all large-scale breeding projects. Combining it with GenomicSelection, Qualcomm phenotypes, and gene editing will build an unprecedented precision breeding 4.0 system. Within this system, GBS, as the basic provider of genotypic data, will continue to play an indispensable role, continuously promote the rapid development of crop breeding in a more efficient, accurate, and predictable direction, and inject strong impetus into the sustainable prosperity of global agriculture.

FAQ

1. What core role does GBS play in modern plant breeding?

GBS generates thousands of genome-wide SNP markers, accelerating high-density genetic map construction, QTL mapping, MAS optimization, and multi-gene polymerization.

2. How does GBS shorten the traditional backcross breeding cycle?

By combining GBS-driven foreground (track target genes) and background (recover recurrent parent genome) selection, it cuts 6-8 generations to 3-4.

3. Can GBS help with multi-gene stacking for plant traits?

Yes. GBS identifies target gene markers, supports multi-parent hybridization, and enables genome-wide tracking to stack multiple beneficial genes (e.g., disease resistance, high yield).

4. What advantages does GBS's genetic map have over traditional markers (e.g., SSR)?

GBS maps have ultra-high density (≤1cM), uniform genome coverage (fewer blind spots), and direct links to physical locations (aids map-based cloning).

References

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2. C H B, S BN, V AK, et al. "Marker-assisted pyramiding of two major, broad-spectrum bacterial blight resistance genes, Xa21 and Xa33 into an elite maintainer line of rice, DRR17B." PLoS One. 2018 13(10): e0201271.
3. Randhawa HS, Mutti JS, Kidwell K, Morris CF, Chen X, Gill KS. "Rapid and targeted introgression of genes into popular wheat cultivars using marker-assisted background selection." PLoS One. 2009 4(6): e5752.
4. Sonah H, Bastien M, Iquira E, et al. "An improved genotyping by sequencing (GBS) approach offering increased versatility and efficiency of SNP discovery and genotyping." PLoS One. 2013 8(1): e54603.