Perennial plants for biofuel production: Bridging genomics and field research
Abstract
Genomics provides the opportunity to explore the relationships of genes and phenotypes: its operational use in the context of breeding programs through strategies such as genomic selection, promises to foster the development of perennial crops dedicated to biodiesel production by increasing the efficiency of breeding programs and by shortening the length of the breeding cycles.
Development of dedicated perennial crops has been indicated as a strategic action to meet the growing demand for biofuels. Breeding of perennial crops, however, is often time- and resource-consuming. As genomics offers a platform from which to learn more about the relationships of genes and phenotypes, its operational use in the context of breeding programs through strategies such as genomic selection promises to foster the development of perennial crops dedicated to biodiesel production by increasing the efficiency of breeding programs and by shortening the length of the breeding cycles.
Even though coal, oil, natural gas and other fossil fuels represent a finite resource, they currently supply more than 80% of the world's energy [1]. Therefore, alternative sources of energy, especially renewable ones, have been under development in recent years, aiming at reducing fossil fuel dependency. Biofuels in particular are gaining attention due to their reduced CO2 emissions relative to fossil fuels [2]. In such context, worldwide demand for renewable energy has increased considerably, and the need for biofuels should increase even more, especially in developing countries. In these developing countries, biodiesel should play an important role in the near future, providing an opportunity to explore crops grown exclusively to supply oil for biodiesel industrial plants. The most appropriate choice of crops often depends on technical, economical, and socio-environmental aspects of the crop's cultivation, but perennial crops in general, and trees in particular, have shown the most promise. As the demand for biodiesel is constantly increasing, the development of dedicated crops has been suggested as a strategic action. On such a basis, biodiesel production is expected to become much more efficient if not only conversion processes themselves are improved, but also if oil feedstocks are optimized to this end. In that context, genomics offers inumerable technologies for collecting genetic information that could be potentially integrated into the genetic improvement programs of many crops to help the development of cultivars with outstanding performance for biodiesel production. Because genomics offers a platform to learn more about the relationships of genes and phenotypes, the long term goal of the application of genomics to breeding is to bridge genomic information with field research currently underway, with the purpose of developing accurate predictive models. Such models could then be operationally used by breeders to estimate the performance and adaptability of genotypes across locations or ecosystems based on genetic data alone, i.e. without the need of conducting laborious and expensive phenotyping trials at the beginning of the breeding cycle. In the context of a long-lived perennial crop with its long breeding cycles and late-expressing traits, the achievement of such a long-term goal promises to revolutionize selective breeding [3]. Considering that some of the most promising feedstocks for biodiesel production, such as oil palm (Elaeis guineensis), jatropha (Jatropha curcas), macaw palm (Acrocomia aculeate), and pongamia (Pongamia pinnata) are perennial crops, genomic breeding is therefore one of the most promising ways to foster the development of perennial crops dedicated to biodiesel production.
Traditional selective breeding relies on scoring plants based on their observed phenotypical traits to determine their breeding value. In the case of perennial crops, such as the aforementioned crops, phenotypic scoring is often inexact, inefficient, and time-consuming, since most traits are hard to access. In addition, the need for phenotyping the breeding candidates across different productive years and different environments ultimately delays the verification of breeding results on the progeny and the commercial release of improved varieties. Recent developments in next-generation sequencing, however, are now enabling researchers to quickly and cost-effectively perform genotyping-by-sequencing of entire breeding populations to discover genetic markers over an entire genome. Therefore, the use of molecular markers for the selection of the best genotypes in breeding populations under field evaluation has recently emerged as the foundation of “genomic breeding” [4-7]. Initially, molecular markers were used to map quantitative trait loci (QTL) for traits of interest, and to perform marker-assisted selection (MAS). Despite its drawbacks, MAS provided breeders with the first opportunity to bridge genomic information with field research over the long term. As the ability of genotyping thousands to dozens of thousands of molecular markers has improved, researchers have now dedicated themselves to developing comprehensive genetic models to perform MAS on the basis of genome-wide markers instead of searching for statistical associations between commercially important traits and few markers. Genomic selection (GS), as this approach has been called, is one of today's hot topics in plant breeding. It has now been demonstrated that predictive models built on the basis of genome-wide markers allow breeders to obtain higher selective accuracy, even for traits of low heritability. In addition, GS may successfully reduce the time needed to select elite individuals in breeding programs of perennial crops, especially when compared to traditional selection based on phenotypic data [6, 8, 9].
Genomic selection is conceptually defined as the simultaneous selection based on hundreds or thousands of markers, which densely cover the entire genome so that all genes or QTLs of a given quantitative trait are expected to be in linkage disequilibrium (LD) with at least one of the markers [10, 11]. Then, the markers in LD with QTLs of major and minor effects explain together almost the totality of the genetic variation of the trait being considered. Therefore, the fundamental difference between MAS and GS impacting the effectiveness of these two selection tools is scale. But, the single most important characteristic of GS over MAS is that while MAS is based on linkage maps created using LD mapping, i.e. determining the correlation of markers and phenotypic traits within a given population, GS is based on dense sets of genome-wide markers genotyped on the entire breeding population. This allows GS to be operationalized in the context of breeding programs in a much more straightforward way. Moreover, after having its genetic effects estimated from phenotypic data in a sample of the population under selection, GS allows breeders to select the best genotypes of its breeding population with high accuracy, exclusively based on the marker data set [7]. This is the closest we have ever been to achieving the long term goal of bridging genomic information with field research currently underway, with the purpose of developing accurate predictive models. Because of these advances, GS has attracted the attention of many annual [10] and perennial crop breeders [6, 12].
Despite many species-specific aspects of a technical nature, breeding of perennial crops by traditional schemes typically relies on a series of successive field tasks (Fig. 1) that involves: (i) a pre-breeding phase, in which germplasm collections are dissected to establish breeding populations based on genetic information and individual performance; (ii) a breeding phase on which best trees in the breeding population are recombined to form large progenies, which are field-evaluated for a number of traits of interest for posterior selection of the best individuals based either on their breeding value or genetic value; and finally (iii) an elite variety development phase, in which the individuals previously selected based on their genetic value are propagated for the establishment of clonal trials, so that elite clones are selected, based on their performance, to be commercially released as an elite variety. Individuals selected during the breeding phase based on their breeding value will form a new breeding population so as to advance the overall breeding scheme. In case of perennial crops, such a scheme is likely to span several years (10 to 14 years in the case of jatropha and 18 to 20 years in the case of oil palm).
General overview of perennial crop breeding.
Based upon theoretical studies and practical considerations, GS is likely to increase the efficiency of breeding programs by shortening the length of the breeding cycle. Actually, the progeny testing phase could potentially be omitted, since with GS in hand, breeders will be able to perform early selection for yet-to-be observed phenotypes at the seedling stage (Fig. 2). This early selection would then allow the selected individuals to be immediately propagated, if micro-propagation protocols are available, for the immediate establishment of optimized clonal trials with several years of anticipation, compared to a classical breeding scheme. As the selection response is inversely proportional to the breeding cycle length, if the time needed to complete a breeding generation is reduced, the selection response per time unit may be drastically increased (by as much as 50%), as has been theoretically and experimentally demonstrated [6]. In operational terms, it has been demonstrated via simulation studies that for oil palm, for example, GS can be more effective in terms of cost and time reduction than phenotype-based selection, as breeders are theoretically able to perform four breeding cycles in the same timespan that usually would accommodate only two cycles when breeding is performed traditionally [8, 12]. Besides shortening breeding cycles, early selection may also allow breeders to increase selection intensity as the newly developed genotyping-by-sequencing approaches allow them to have an enormous number of plants quickly and cost-effectively genotyped for thousands of markers (Fig. 2). On the other hand, progeny trials are usually limited in size in the case of perennial crop breeding due to economics and operational aspects, which reduce the number of traits of interest that can be scored given the number of individuals. Therefore, with GS, breeders should be able to reduce their investment in field-testing (restricting it to optimized clonal trials), saving time and resources and also improving the selection precision for traits of low heritability.
Comparison of traditional and GS-based breeding, showing the possibility of saving time and resources.
Another advantage of coupling GS with traditional field research is the possibility of simultaneously carrying out selection for several traits in large numbers of individuals. Due to the costs involved in establishing, maintaining, and phenotyping large progeny trials for several traits of low heritability, it is today virtually impossible for any breeding program to complete a rigorous assessment of the most important traits for all trees in a progeny trial.
Based on the aforementioned considerations, the prospects of GS applied to breeding of oilseed feedstock for biodiesel production are very promising. Both simulation studies and experimental reports point in this direction. By allowing breeders to operationally bridge genomic information with field research, application of GS in breeding schemes of crops such as oil palm and jatropha is probably one of the best ways to foster the domestication and development of perennial crops dedicated to biodiesel production.
Acknowledgements
The authors declare no commercial or financial conflict of interest.
