Molecular tools in plant breeding

“There are no miracles in agriculture production. Nor is there such a thing as a miracle variety of wheat, rice, or maize…to cure all ills of a stagnant, traditional agriculture.”

Dr. Norman Borlaug

In 1970 Dr. Normal Borlaug won the Noble Peace Prize for his work promoting the Green Revolution throughout the developing world. The dramatic increase in yield was through the exploitation of dwarfing genes in conjunction with disease resistance rooted on interdisciplinary approach. The initiation of this revolution happened in CIMMYT where with 16 years efforts he developed two semi-dwarf disease-resistant high-yielding wheat varieties, Pitic62 and Penjamo62. The multiline varieties were developed after testing several pure lines with different resistant genes through separate backcrossing and phenotypic selection in multiple environments.

The time, land, and labor resources used in traditional breeding pipeline using phenotypic selection are significantly reduced in modern plant breeding through molecular tools. With the advent of molecular markers, selection of lines with targeted traits can be done genetically with more accuracy and efficiency. Molecular breeding uses several breeding strategies such as marker-assisted selection (MAS)⁷ and genomic selection (GS)⁴. MAS allows evaluating the presence of favorable alleles in breeding population in effectively selecting individuals with major genes or quantitative trait loci (QTL) with large effects. For quantitative traits with multiple loci involved, GS is the efficient method for selection.

Statistical tools are the foundation of molecular breeding strategies. These tools take advantage of the linkage between the molecular marker and the chromosomal location of the gene(s) or locus(loci) governing the trait(s) of interest. The presence of favorable alleles of the gene is indicated by the marker genes and thus molecular breeding tools indirectly select the individuals having the trait(s) of interest. In MAS, individual lines are selected through significance testing in step-wise regression methods. MAS is useful when fewer genes are associated with the trait of interest. GS can be used for selection of individuals for complex traits contributed by genome wide loci with small or moderate effects using regression or classification techniques without a significance threshold.

Following are some examples where MAS and GS are used in plant breeding: MAS helped in reducing the cost and facilitating the handling of materials for additional trait evaluations while selecting for fusarium wilt resistant chickpea lines ²; sweet and quality protein maize composite is developed with higher content of essential amino acids and vitamins with o2 allele pyramided with sh2 allele using MAS⁸; the traditional long-term recurrent selection and wild species introgression projects in Virginia-type peanut cultivars is revolutionized by the genomics-assisted breeding allowing swift response to changing biotic and abiotic threats ultimately developing superior cultivars⁵; rapid cycle recurrent GS increased the genetic gain for grain yield in wheat on evaluating through three cycles of recombination of bi-parents³.

Glossary

Quantitative trait loci (QTL) are genetic regions that influence phenotypic variation of a complex trait, often through genetic interactions with each other and the environment. These are commonly identified through a statistical genetic analysis known as QTL mapping⁶.

Linkage refers to the closeness of genes or other DNA sequences to one another on the same chromosome. The closer two genes or sequences are to each other on a chromosome, the greater the probability that they will be inherited together. (https://www.genome.gov/genetics-glossary/Linkage).

Recurrent selection is a term coined by Fred H. Hull of the Florida Agricultural Experiment Station, Gainesville, Florida, USA, in 1945 for a cyclical improvement method aimed at concentrating desirable alleles in a population through a process of detecting better individuals of a population¹.

References

Bosland, Paul W., and Derek W. Barchenger. 2024. “Resistance: Classical Breeding Methods.” In Breeding Disease-Resistant Horticultural Crops, 195–204. Elsevier. https://doi.org/10.1016/B978-0-443-15278-8.00008-5.
Castro, Patricia, Cristina Caballo, Alejandro Carmona, Teresa Millan, Juan Gil, José V. Die, Inmaculada Izquierdo, and Josefa Rubio. 2024. “Efficient Single Nucleotide Polymorphism Marker-Assisted Selection to Fusarium Wilt in Chickpea.” Plants 13 (3): 436. https://doi.org/10.3390/plants13030436.
Dreisigacker, Susanne, Paulino Pérez-Rodríguez, Leonardo Crespo-Herrera, Alison R Bentley, and José Crossa. 2023. “Results from Rapid-Cycle Recurrent Genomic Selection in Spring Bread Wheat.” Edited by A Lipka. G3: Genes, Genomes, Genetics 13 (4): jkad025. https://doi.org/10.1093/g3journal/jkad025.
Meuwissen, T. H., B. J. Hayes, and M. E. Goddard. 2001. “Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps.” Genetics 157 (4): 1819–29.
Newman, Cassondra S., Ryan J. Andres, Ramey C. Youngblood, Jacqueline D. Campbell, Sheron A. Simpson, Steven B. Cannon, Brian E. Scheffler, Andrew T. Oakley, Amanda M. Hulse-Kemp, and Jeffrey C. Dunne. 2023. “Initiation of Genomics-Assisted Breeding in Virginia-Type Peanuts through the Generation of a de Novo Reference Genome and Informative Markers.” Frontiers in Plant Science 13 (January): 1073542. https://doi.org/10.3389/fpls.2022.1073542.
Powder, Kara E. 2020. “Quantitative Trait Loci (QTL) Mapping.” In eQTL Analysis, edited by Xinghua Mindy Shi, 2082:211–29. Methods in Molecular Biology. New York, NY: Springer US. https://doi.org/10.1007/978-1-0716-0026-9_15.
Sax, Karl. 1923. “The Association of Size Differences with Seed-Coat Pattern and Pigmentation in <i> Phaseolus Vulagaris <i>.” Genetics 8 (6): 552–60. https://doi.org/10.1093/genetics/8.6.552.
Sharma, Pratibha, Vivek Sharma, Harcharan S. Dhaliwal, and Rahul Kumar. 2024. “Introgression of Opaque2 Allele into Sweetcorn Composite through Marker-Assisted Selection.” Cereal Research Communications, January. https://doi.org/10.1007/s42976-023-00483-2.