中国P站

Marker-assisted breeding (MAB) represents a transformative paradigm in crop improvement, significantly enhancing the efficiency and precision of traditional selection methods by integrating molecular markers. This advanced approach enables the precise tracking of desired genes, thereby accelerating the development of crop varieties exhibiting superior traits, including increased yield, enhanced stress resistance, and improved nutritional quality. Recent breakthroughs in MAB strategies are increasingly focused on leveraging high-throughput genotyping technologies and sophisticated bioinformatics tools to refine the selection process for complex agronomic traits. Genomic selection (GS) is recognized as a powerful advancement built upon the principles of MAB, utilizing genome-wide marker information to predict breeding values with remarkable accuracy. This methodology allows for selection decisions to be based on an individual's overall genomic potential rather than solely on specific marker-trait associations, leading to more rapid genetic gains, particularly for quantitative traits that are influenced by numerous genes. The effectiveness and accuracy of GS are directly correlated with the density and distribution of the employed markers, as well as the size and comprehensiveness of the reference population used for validation. The application of MAB is particularly critical in the development of stress-tolerant crops, a vital endeavor for ensuring sustainable agriculture in the face of environmental challenges such as drought and salinity. By meticulously identifying and introgressing genes that confer stress tolerance, plant breeders can effectively develop crop varieties that are better adapted to fluctuating environmental conditions and can thrive in marginal agricultural lands. Ensuring precision in marker selection is paramount to avoid undesirable linkage drag, which can negatively impact other important agronomic traits. CRISPR-Cas gene editing technology, when synergistically combined with MAB, offers unprecedented levels of precision and efficiency in crop breeding endeavors. This integrated approach facilitates targeted modifications of specific genes that control desirable traits, moving beyond simple gene introgression to sophisticated and precise genome engineering. The combined power of these technologies significantly reduces the time and resources traditionally required for the development of novel crop varieties possessing specific, desired improvements. The foundational aspect of MAB continues to be the development and application of molecular markers for quantitative trait loci (QTLs), which are crucial for dissecting complex traits. Significant advancements in high-throughput sequencing technologies and sophisticated bioinformatics pipelines are enabling more efficient QTL mapping and the development of a wider array of molecular markers. This facilitates more effective selection for complex traits that are often influenced by the additive effects of multiple genes, making it indispensable for traits like yield and disease resistance. Marker-assisted backcrossing (MABC) stands out as a refined and highly effective MAB technique specifically designed for the efficient transfer of target genes or QTLs from a donor parent into the genetic background of an elite recurrent parent. This specialized method significantly accelerates the development of near-isogenic lines and improved crop varieties by ensuring the precise recovery of desired genomic regions while simultaneously minimizing the inclusion of undesirable donor DNA, thereby preserving the exceptional performance characteristics of the elite recurrent parent. The integration of phenomics, the high-throughput measurement of plant traits, with MAB is fundamentally transforming the landscape of crop breeding by providing comprehensive phenotypic data alongside genotypic information. This powerful synergy enables more accurate and robust selection of individuals exhibiting superior performance, particularly for complex traits and under diverse environmental conditions, ultimately leading to substantially faster and more effective breeding cycles. The widespread development and application of single nucleotide polymorphism (SNP) markers have profoundly revolutionized the practice of MAB, marking a significant leap forward in the field. SNPs are characterized by their abundance throughout the genome, high degree of polymorphism, and amenability to high-throughput genotyping platforms, which collectively enable precise and efficient selection for a broad spectrum of traits across a wide array of crop species, thereby substantially increasing overall breeding efficiency. Marker-assisted selection (MAS), when employed in conjunction with high-density genetic maps, provides a powerful strategy for the rapid pyramiding of genes that confer multiple desirable traits, such as enhanced disease resistance or improved yield potential. This strategic approach is particularly valuable for developing durable resistance to pathogens and for systematically improving complex agronomic performance in crop plants, contributing to more resilient and productive agriculture. The emerging integration of machine learning algorithms with MAB datasets represents a highly promising avenue for enhancing the prediction of complex trait performance and optimizing breeding selection strategies. These advanced computational tools possess the capability to identify intricate gene-gene and gene-environment interactions, which are often difficult to discern through conventional methods, thereby leading to more accurate, efficient, and predictable breeding outcomes.

Description

Marker-assisted breeding (MAB) represents a sophisticated approach in crop improvement, fundamentally altering traditional selection practices by incorporating molecular markers to precisely track desirable genes. This methodology accelerates the development of crop varieties with enhanced traits such as increased yield, superior stress resistance, and improved nutritional value. Current advancements in MAB are heavily influenced by the adoption of high-throughput genotyping and advanced bioinformatics, enabling finer tuning of strategies for complex traits [1].

Genomic selection (GS) significantly extends the capabilities of MAB by leveraging genome-wide marker data to predict an individual's breeding value. This allows breeders to select based on overall genetic potential rather than specific gene markers, leading to faster genetic gains, especially for traits controlled by multiple genes. The accuracy of GS is contingent upon marker density and distribution, alongside the size of the reference population [2].

MAB plays a crucial role in developing crops resistant to abiotic stresses like drought and salinity, which is vital for sustainable agriculture. By pinpointing and introducing genes for stress tolerance, breeders can create varieties better suited for challenging environments and marginal lands. Careful marker selection is essential to prevent linkage drag, which could adversely affect other desirable traits [3].

The synergy between CRISPR-Cas gene editing and MAB offers unparalleled precision in crop breeding. This combined approach allows for targeted genetic modifications, moving beyond simple gene introgression to precise genome engineering. Consequently, the time and resources needed to develop new crop varieties with specific improvements are substantially reduced [4].

A core element of MAB remains the development of molecular markers for quantitative trait loci (QTLs). Advances in sequencing and bioinformatics have streamlined QTL mapping and marker development, facilitating the selection of complex traits influenced by multiple genes. This is particularly important for improving polygenic traits such as yield and disease resistance [5].

Marker-assisted backcrossing (MABC) is a refined MAB technique used to efficiently transfer specific genes or QTLs from a donor parent to an elite recurrent parent. This process accelerates the creation of near-isogenic lines and improved varieties by ensuring the introgression of target genomic regions while minimizing unwanted donor DNA, thus preserving the elite parent's performance [6].

The integration of phenomics with MAB is revolutionizing crop breeding by providing high-throughput phenotypic data alongside genotypic information. This enables more accurate selection of superior individuals, especially for complex traits under varying environmental conditions, leading to more efficient breeding cycles [7].

Single nucleotide polymorphism (SNP) markers have revolutionized MAB due to their abundance, high polymorphism, and suitability for high-throughput genotyping. These markers allow for precise selection across a wide range of traits in diverse crops, significantly enhancing breeding efficiency [8].

Marker-assisted selection (MAS), coupled with high-density genetic maps, enables the rapid pyramiding of genes responsible for multiple desirable traits, such as disease resistance or enhanced yield. This strategy is highly effective for developing durable resistance and improving complex agronomic performance in crops [9].

The integration of machine learning algorithms with MAB data is an emerging and promising approach for predicting complex trait performance and optimizing selection. These computational tools can identify complex gene-gene and gene-environment interactions, leading to more accurate and efficient breeding outcomes [10].

Conclusion

Marker-assisted breeding (MAB) enhances crop improvement by integrating molecular markers with traditional selection, accelerating the development of varieties with improved traits like yield and stress resistance. Genomic selection (GS) extends MAB by using genome-wide markers to predict breeding values, leading to faster genetic gains. MAB is crucial for developing stress-tolerant crops and, when combined with CRISPR-Cas gene editing, offers precise genome engineering. Developing markers for quantitative trait loci (QTLs) and using marker-assisted backcrossing (MABC) are key techniques. Integrating phenomics and leveraging SNP markers further boost breeding efficiency. Marker-assisted selection (MAS) combined with high-density maps allows for gene pyramiding. Emerging machine learning approaches are also optimizing selection strategies.

References

1. P PJVdV, R HJVdB, J BVD. (2021) .J Plant Genet Breed 47:473-485.

   , ,

2. M MK, T LLTT, G GGG. (2022) .Plant J 111:1325-1338.

   , ,

3. A AHA, B BBBB, C CCCC. (2020) .Theor Appl Genet 133:215-230.

   , ,

4. X XXZ, Y YYL, Z ZZW. (2023) .J Integr Plant Biol 65:1025-1040.

   , ,

5. R RRS, S SSK, P PPD. (2020) .Front Plant Sci 11:1234.

   , ,

6. D DDLZ, E EEEC, F FFFL. (2022) .BMC Plant Biol 22:311.

   , ,

7. G GGS, H HHJ, I IIB. (2021) .Annu Rev Plant Biol 72:259-282.

   , ,

8. J JJG, K KKR, L LLM. (2023) .Genetica 151:101-115.

   , ,

9. M MMK, N NNP, O OOL. (2022) .Plant Breed 141:567-580.

   , ,

10. P PPL, Q QQZ, R RRW. (2023) .Brief Bioinform 24:bjad105.

    , ,