Wednesday, August 15, 2007
K K Vinod
Classical plant breeding is primarily of phenotypic selection of superior individuals, among segregating progenies following a hybridization, induced variability created through mutation, polyploidisation etc., and from a native outbreeding mixture of individuals. Though the idea seems simple, success of choosing a right kind of genotype is often hindered by genotype x environment interaction and the genetic nature of trait of interest itself. In addition to testing procedures for selection of target traits in target environments are difficult, unreliable and expensive due to the nature of the traits themselves like biotic and abiotic stresses.
Latest advents in molecular marker technology, using tiny DNA fragments, that can distinguish individuals with slightest genetic variation, and unaffected by the environments, became a handy tool in providing information on selecting individuals possessing target trait genes. These DNA fragments are known as DNA markers. These markers can be established through various molecular marker systems viz., restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), microsatellites or simple sequence repeats (SSR), inter-simple sequence repeat (ISSR), retrotransposon based polymorphisms, sequence characterized amplified regions (SCAR), sequence related amplified polymorphisms (SRAP), single nucleotide polymorphisms (SNP) etc. Each of these systems has its own merits and demerits.
Molecular markers which are stable, unique and abundant can help us in study the linkage among themselves relating to their positions in the target genome. When subjected to classical genetic analysis based on the segregating pattern of markers among individual segregating progenies obtained by crossing two homozygous individuals (purelines) which are genetically different, linkage distance between each of the segregating markers can be determined and drawn on a linkage map. When many markers are employed in this attempt covering the entire genome, we will be able to reconstruct individual linkage groups or chromosomes of that particular genome. This framework of chromosomes will provide us the information where individual markers are located and in consultation with classical cytogenetic maps we can assign individual chromosome designations to the constructed linkage groups. These populations used for molecular map construction are called mapping populations.
Further extending the marker segregation pattern and the linkage disequilibrium among themselves, on to the phenotypic segregation of quantitative traits, help us in identifying markers that closely follow the pattern of segregation of the target traits. The simplest possible analysis for this is to carryout the simple regression between marker and phenotype segregation pattern. Alternatively we can use simple factor analysis of variance (ANOVA). This analysis is called single marker analysis (SMA). Extending further these analysis the model can be allowed to include more than one marker interacting, so that we can identify marker-by-marker interaction significantly influencing the phenotype. These markers can either be on the same chromosome (linked) or on different chromosome (epistatic). Locations of these markers which significantly influence the phenotype is called quantitative trait loci (QTL). Locations on these markers can be traced to the molecular linkage map, thereby the genomic location of the QTL.
However, one may be very conscious while referring the QTL as the gene responsible for the trait. Actually QTLs are putative locations of genes responsible for influencing the trait. Actual gene may, therefore, be away from this location or at this location. Nevertheless, the function how these genes influence the trait is also unknown. Besides, there may be different degree of influence of QTLs to the traits. Some QTLs which show larger and conspicuous influence are called major QTLs, and those with minor effects are called minor QTLs.
Further extending concept of linkage analysis on multipoint mapping, the methods called interval mapping is designed. There are two types viz., simple interval mapping (SIM) and composite interval mapping (CIM). Compare to SIM, CIM is statistically robust and help in predicting more accurate QTL positions. The procedures of interval mapping include extensive step-wise regression and predictions based on maximum likelihood ratios and/or best linear unbiased prediction (BLUP). Mixed model mapping incorporating models of additive x additive, additive x epistatic interactions and phenotyping under varying environments are also employed currently. Many statistical predictions and sub-sampling are done using Bayesian methods or jackknifing procedures.
Location of QTLs, help us in closely looking at the chromosomal locations using closely placed markers. This is known as fine mapping of the target genomic locations, and once the exact location of the QTL is identified this genomic location can be sequenced to see whether this location code for a known or unknown protein that influence the trait. By doing this the exact influence of the gene located at this QTL can be understood. If found extensively useful this QTL can be cloned and used for further genetic engineering programmes.
However, more useful and practical approach a breeder is interested in is using the QTL information and the looking for presence of them in a population using the linked markers, help him in selecting the target QTLs carrying individuals which will in turn contain the target trait itself. This procedure of selection is called marker assisted selection (MAS). However, marker assisted selection is not much confined to QTLs as it can be extended to any molecular marker linked to any major gene. Best examples are single genes conferring resistance to diseases. Also there can be the involvement of more than one major genes to which MAS can be targeted. Another avenue MAS is extensively used is the selection among the transgene derived populations. Here, when the MAS is exercised on the target trait directly (herbicide tolerance) it is called foreground selection and when done on the marker trait (antibiotic resistance) it is called background selection.
The success of MAS depends on location markers with respect to the gene of interest. Three kinds of relationships are common, (i) marker lie within the gene, which is most favourable situation for positive selection (ii) marker is in linkage disequilibrium with the gene of interest in the whole population, where there will be the tendency of the marker to inherit closely with the gene of interest, and (iii) the marker in linkage equilibrium with the gene of interest, in which case the success of MAS is unpredictable. Another challenge in MAS using QTL is the interaction of QTL with the target environments. QTL x environment (QE) interaction is a serious problem in MAS. Here it is more prudent to look for environment specific QTL or widely adapted QTLs depending upon the objective of the selection programme.
Marker assisted selection in rice
MAS has been successfully employed in rice crop. Successful marker assisted screening and selection for root traits (Price and Curtois, 1999) resulted in better drought tolerance in upland rice. Selection was done based on RFLP and SSR markers for QTLs that determined root traits. Successful MAS based backcrossing also was done to transfer early season drought resistance and aroma from a japonica variety Azucena to Kalinga III, a high yielding height grain quality indica variety (Steel et al., 2002). Participatory plant breeding using MAS bulks and purelines were successfully carried out in backcross progenies of Kalinga III x Azucena, following schemes given below:
Another approach was to develop purelines out of the BC3 population and evaluating them for the presence of individual root QTLs and also for the combination of more than one QTLs. As many as four root QTLs were pyramided in successful lines.
A great advantage of these selections is that many are done at farmers holdings and selections were done by the farmers themselves. Predominantly lines selected by the farmers were accumulating the targeted QTLs confirming the success of MAS. As many as 24 lines from upland, 12 from medium upland and 16 lines from lowland conditions were selected by these participatory plant breeding approach using MAS from crosses using Kalinga III as one parent, and IR 64, Radha 32, IR 36 and Vandana as other parent (Steele et al., 2002).
Many successful attempts for MAS in rice is reviewed in Babu et al., (2004). These include resistances to blast, bacterial blight, rice tungro virus, gall midge, brown plant hopper and green leaf hopper, tolerance to submergence and salt accumulation, wide compatibility, temperature sensitive male sterility, garin aroma, amylase content, photoperiod sensitivity, semi-dwarf stature and shattering tolerance.
Babu, R., Nair, S.K., Prasanna, B.M., and Gupta, H.S. (2004) Integrating marker-assisted selection in crop breeding – Prospects and challenges. Current Science, 87: 607-619.
Steele, K.A., Virk, D.S., Prasad, S.C., Kumar, R., Singh, D.N., Gangeswar, J.S., and Witcombe. J.R. (2002) Combining PPB and marker assisted selection: Strategies and experiences in rice. In: Quality Science in participatory plant breeding. Workshop at IPGRI, Sept 30- Oct 4, 2002,Rome, Italy.
Price, A.H., and Curtois, B. (1999) Mapping QTLs associated with drought resistance in rice: Problems, progress and prospects. Plant Growth Regulation, 29: 123-133.
Price, A.H., Steele, K.A., Moore, B.J., and Wyn-Jones, G. (2002) Upland rice grown in soil filles chambers and exposed to contrasting water deficit regimes. II. Mapping QTLs for root morphology and distribution. Field Crops Research, 76:25-43.