Marker Assisted Breeding: A paradigm shift in crop improvement research
Author: Dr. Kiran B. Gaikwad, Scientist, Division of Genetics, IARI, New Delhi
Molecular markers commonly referred as DNA markers are numerous in number and their discovery represents a milestone in genetics as they provide the capacity for complete coverage of crop genome. These markers show Mendelian inheritance, are stably inherited, have pleiotropic effect and are unaffected by the environment and express at all developmental stages. Majority of DNA polymorphisms are selectively neutral. The word neutral means that unlike morphological markers, these variations do not show themselves in the phenotype and each might be the result of a simple point mutation, DNA insertion/deletion event or change in repeat copy number at some hyper-variable DNA or microsatellite. Several types of molecular markers which have been developed and are being used in plant sciences research are restriction fragment length polymorphism (RFLP), sequence tagged sites (STS), expressed sequence tags (ESTs),simple sequence repeats (SSRs), randomly amplified polymorphic DNA (RAPDs), amplified fragment length polymorphism (AFLP), single nucleotide polymorphism (SNPs) etc. These markers are of great value in applying genetic technologies to crop improvement such as determining genetic diversity, DNA fingerprinting of crop germplasms, marker assisted selection, gene pyramiding, QTL mapping, map based cloning of important genes and monitoring introgression from exotic / wild species germplasms.
Recent progresses in the areas of molecular biology and plant genomics have the potential to initiate a new Green Revolution (Dubcovsky 2004). However, these discoveries need to be implemented in new cultivars to realize that potential. The controversy about transgenic crops has delayed the incorporation of alien genes into plants and significantly increased the cost to develop and release transgenic crops. Fortunately, biotechnology has provided additional tools that do not require the use of transgenic crops to revolutionize plant breeding. Progress in molecular genetics has resulted in development of DNA markers, which can be helpful in marker aided selection (MAS) strategies for cultivar development. Also detailed genetic maps of DNA markers have been constructed for most of major crop plants (Peterson et al 1996). These molecular markers can be used as chromosome landmarks to facilitate the selection of chromosome segments including useful agronomic traits during the breeding process. Other areas of DNA markers may have a potential impact in classical plant breeding include assessment of genetic diversity, strain identification and plant variety identification and plant variety protection, accelerated introgression and mapping complex traits relevant to crop improvement.
Detection of Quantitative Trait Loci (QTL)
QTL analysis is based on the principle of detecting an association between phenotype and the genotype of marker. The principle of QTL analysis was first applied to map a QTL for seed size in bean (Sax 1923). Most of the agronomically important traits including yield, nutritional quality are quantitatively inherited and show continuous variation (Allard 1960, Hallauer and Miranda 1988).The systematic utilization of these principles becomes simpler with the availability of molecular markers. The regions controlling various traits can be dissected and studied as discrete entities that show normal Mendelian inheritance. In the past 10 years, there have been numerous reports on the use of molecular-based methods to detect, map and characterize the loci responsible for quantitative traits in crops such as tomato (Eshed et al 1996, Frary et al 2004), wheat (Huang et al 2003), barley (Schmalenbach et al 2009), soybean (Wang et al 2004), bean (Blair et al 2006) capsicum (Rao et al 2003) and rice (Xiao et al 1998, Luo et al 2012). With the availability of dense molecular maps and user friendly QTL mapping softwares, we can explore these important aspects (Semel et al 2006). Now it is possible to locate the genomic regions contributing to a complex trait. We can assess the magnitude of effect, interaction amongst QTL, between QTL and environment, genetic basis and the cause and effect relationship between various traits controlled by them. Physiological effects of yield QTL and the major genes underlying yield-enhancing QTL have also been identified (Fridman et al 2004, He et al 2006, Reddy et al 2007).
Marker assisted introgression of QTL for yield improvement
The principles of QTL analysis were first applied to map a QTL for seed size in bean (Sax, 1923). The systematic utilization of these principles became simpler with the availability of molecular markers such as restriction fragment length polymorphism (RFLPs) in 1980 (Botstein et al 1980). The regions controlling various traits can be dissected and studied as discrete entities that show normal Mendelian inheritance. The advent of molecular marker technology in quantitative genetics greatly facilitates the study of complex traits and has made it possible to dissect the polygenes controlling such traits into individual Mendelian factors. Using molecular linkage maps and quantitative trait loci (QTL) mapping technology, it is possible to estimate the number of loci governing a particular trait of agronomic importance and to determine their map positions in the genome (Tanksley 1993). The identification of such genomic regions/QTL governing traits of agronomic importance can create a base for rapid, detailed, and direct genetic manipulation of them through marker-assisted selection (MAS).
A series of work have addressed the use of molecular markers in plant breeding programmes. Backcrossing has been a widely used technique in plant breeding for almost a century. Backcrossing is commonly used to incorporate one or a few genes into an adapted or elite variety. The use of molecular markers in backcrossing greatly increases the efficiency of selection. The markers can be used in combination with or to replace screening for the target gene or QTL (foreground selection) and/ or for selecting BC progeny with the greatest proportion of recurrent parent (RP) genome (background selection). With conventional backcrossing, it takes a minimum of six BC generations to recover the recurrent parent and there may still be several donor chromosome fragments linked to the target gene. Using markers, it can be achieved by BC4, BC3, BC2 or even BC1 (Frisch et al 1999), thus saving two to four BC generations. Thus, the use of molecular markers to track the introgressions from cultivated/ wild species has made the process of QTL identification simpler and quicker.
1. Allard R W (1960) Plant Breeding, John Wiley and Sons, New York.
2. Blair M W, Iriarte G and Beebe S (2006) QTL analysis of yield traits in an advanced backcross population derived from a cultivated Andean wild common bean (Haseolus vulgaris L.) cross. Theor Appl Genet 112: 1149-63.
3. Botstein D, White R L, Skolnick M and Davis R W (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphism. American Journal Human Genetics 32: 324-31.
4. Dubcovsky J (2004) Marker assisted selection in public breeding programs: The wheat experience. Crop Sci 44: 1895-98.
5. Eshed T, Gera G and Zamir D C (1996) A genome-wide search for wild-species alleles that increase horticultural yield of processing tomatoes. Theor Appl Genet 93: 877-86.
6. Frary A, Fulton T M, Zamir D and Tanksley S D (2004) Advanced backcross QTL of Lycopersicon esculentum L. pennellii cross and identification of possible orthologs in the solanaceae. Theor Appl Genet 108: 485-96.
7. Fridman E, Carrari F, Liu Y S, Fernie A R and Zamis D (2004) Zoming in on a quantitative trait for tomato yield using interspecific introgression. Science 305: 1786-90.
8. Frisch M, Bohn M and Melchinger A E (1999) Minimum sample size and optimal positioning of flanking markers in marker-assisted backcrossing for transfer of a target gene. Crop Sci 39: 967-75.
9. Hallauer A R and Miranda J B (1988) Quantitative genetics in maize breeding. Iowa State University Press, Ames, Iowa.
10. He G, Luo X, Tian F, Li K, Zhu Z, Su W, Qian X, Fu Y, Wang X, Sun C and Yang J (2006) Haplotype variation in structure and expression of a gene cluster associated with a quantitative trait locus for improved yield in rice. Genome Res 16: 618-26.
11. Huang X Q, Coster H, Ganal M W and Roder M S (2003) Advanced backcross QTl analysis for the identification of quantitative trait loci alleles from wild relative of wheat (Triticum aestivum L.). Theor Appl Genet 106: 1379-89.
12. Luo X, Wu S, Tian F, Xin X, Zha X, Dong X, Fu Y, Wang X, Yang J and Sun C (2011) Identification of heterotic loci associated with yield-related traits in Chinese common wild rice (Oryza rufipogon Griff.). Plant Sci. 181, 14-22.
13. Peterson A H, Tanksley S D and Sorrells M E (1996) DNA markers in plant improvement. Adv Agron 46: 39-90.
14. Rao G U, Chaim A B, Borovsky Y and Peran I (2003) Mapping of yield related QTL in pepper in an interspecific cross Capsicum annuum and Capsicum frutescens. Theor Appl Genet 106: 1457-66.
15. Reddy C S, Babu A P, Swamy B P M and Sarla N (2007) Insight into genes underlying yield enhancing QTLS from O. rufipogon. Rice Genetics Newsetter l 23: 53-5.
16. Sax (1923) The association of size differences with seed coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8: 552-60.
17. Schmalenbach I, Leon J and Pillen K (2009) Identification and verification of QTLS for agronomic traits using wild barley introgression lines. Theor Appl Genet 118: 483-97.
18. Semel Y, Nissenbaum J, Menda N, Zinder M, Krieger U, Issman N, Pleban T, Lippman Z, Gur A and Zamir D (2006) Overdominant quantitative trait loci for yield and fitness in tomato. Proc Natl Acad Sci USA 103: 12981-86.
19. Tanksley S D (1993) Mapping polygenes. Annu Rev Genet 27: 205-33.
20. Wang D, Graef G L, Procopiuk A M and Diers B W (2004) Identifications of putative QTL that underlie yield in interspecific soybean backcross populations. Theor Appl Genet 108: 458-67.
21. Xiao J, Grandillo S, Ahn S N, McCouch S and Tanksley S D (1996) Genes from wild rice improves yield. Nature 384: 223-24.
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