Improving Drought Resistance via Marker-Assisted Selection

Several factors limit the possibility of obtaining reliable QTL data and, most importantly, their deployment in breeding programs through MAS (Tuberosa et al. 2007). Among such factors, the environment dependence of QTL expression is of utmost importance in order to obtain reproducible data and effectively assess the value of a particular QTL. This aspect is particularly relevant for stress tolerance traits since the effect of the same QTL can markedly differ according to the prevailing environmental conditions (Collins et al. 2008). Although many studies have described QTLs that influence tolerance to drought, MAS has so far contributed marginally to the release of drought-resistant cultivars. Improving crop performance under waterlimiting conditions via MAS may also require considering QTLs for tolerance to abiotic (e.g. high boron) and biotic (e.g. nematodes) factors that impair root growth and functions. A common feature of cereal responses to drought near flowering and during early stages of seed growth is a reduction of reproductive fertility due to partial sterility and/or early abortion. This loss of fertility has been attributed to different factors acting alone and more likely on reproductive fertility. The QTL approach attempts to dissect out the genetic and physiological components affecting source-sink relationships under abiotic stress and to what extent these may influence yield (Miralles and Slafer 2007). Major QTLs for seed weight and grain yield at different moisture conditions have been identified in durum wheat (Maccaferri et al. 2008) and are being introgressed in different genetic backgrounds. In bread wheat, Fleury et al. (2010) have implemented a strategy where a specific environment is targeted and appropriate germplasm adapted to the chosen environment is selected, based on extensive definition of the morpho-physiological and molecular mechanisms of tolerance of the parents. This information was then used to create structured populations and develop models for QTL analysis, MAS and positional cloning.

Future Perspectives

Increasing attention has been devoted to the use of crop modeling for elucidating the genetic basis of genotype × management × environment (G × M × E) interaction at the level of the entire genotype and, more recently, at the level of single loci (Ludwig and Asseng 2010; Richards et al. 2010; Tardieu and Tuberosa 2010; van Eeuwijk et al. 2010; Parent and Tardieu 2014). The objective is to predict, via modeling, yield differences among genotypes grown under different environmental conditions (Cooper et al. 2009; Tardieu and Tuberosa 2010). The benefits accrued by modeling studies are expected to increase as the complexity of the genetic control of traits increases provided it is possible to account for the effects of genetic interactions for predicting trait variation (Cooper et al.2009). Ultimately, modeling aims to predict the best combinations of QTL alleles able to optimize yield. The main underlying assumption of the modelling approach is that yield and other functionally complex traits can be analyzed and improved by dissecting it into simpler processes, and then by re-assembling such processes to reconstruct via modelling higher order of plant functionality and ultimately yield itself. Models have been used to generate an index of the climatic environment (e.g. of drought stress) for breeding program trials. In wheat grown in northern Australia, this has shown that mid-season drought generates large genotype by environment interaction (Chapman 2008).

With only a few exceptions as listed above, the vast majority of loci that affect crop yield per se have a rather small effect, particularly under drought conditions. Therefore combining the favorable alleles by MAS to achieve a significant improvement quickly becomes impractical and would excessively constrain the potential for achieving yield gain due to the action of other loci. In this case, MAS for mapped QTLs (Randhawa et al. 2013) can be replaced by genome-wide selection (Bernardo 2010; Storlie and Charmet 2013). Nowadays, genome selection is facilitated by the vailability of large numbers of markers, particularly Single Nucleotide Polymorphysms (SNPs; Wang et al. 2014) that are amenable to high-throughput profiling at very low cost.


The release of cultivars better adapted to a broader range of environmental conditions will become an increasingly important goal of breeding projects worldwide. Compared to conventional breeding practices, the contribution in this direction of molecular breeding has somehow fallen short of expectations (Blum 2014; Tuberosa et al. 2014). Nonetheless, genomics approaches and sequence-based breeding will expedite the dissection of the genetic basis of abiotic stress tolerance while providing unprecedented opportunities to tap into wild relatives of wheat. To what extent this will actually impact the release of improved cultivars will largely depend on a more complete and comprehensive understanding of the adaptive response of crops to abiotic stress and our capacity to integrate this information into breeding programs via modeling or other approaches such as genomic selection. In view of the complexity of yield, particularly under drought, we foresee that genomic selection will provide the most effective way to raise the yield potential to the levels required to keep up with the fast-increasing demand in food worldwide. However, MAS will remain a valid option for major loci (genes and/or QTLs) as long as their effects will be sufficiently predictable and economically viable (Tuberosa and Pozniak 2014). Additionally, QTL cloning will become a more routine activity thanks to a more widespread utilization of high-throughput, accurate phenotyping (Tuberosa 2012), sequencing and the identification of suitable candidate genes via 'omics' profiling. Ultimately, reducing wheat vulnerability to drought will require a multidisciplinary and integrated approach that will eventually allow breeders to more effectively select drought-resistant cultivars.

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