The potential of microarrays to assist shrimp breeding and production: a review

The shrimp aquaculture industry is a relatively new livestock industry, having developed over the past 30 years. Thus, it is poised to take advantage of new technologies from the outset of selective breeding programs. This contrasts with long established livestock industries, where there are already highly specialised breeds. This review focuses specifically on the potential application of microarrays to shrimp breeding. Potential applications of microarrays in selective breeding programs are summarised. Microarrays can be used as a rapid means to generate molecular markers for genetic linkage mapping, and genetic maps have been constructed for yeast, Arabidopsis and barley using microarray technology. Microarrays can also be used in the hunt for candidate genes affecting particular traits, leading to development of perfect markers for these traits (i.e. causative mutations). However, this requires that microarray analysis be combined with genetic linkage mapping, and that substantial genomic information is available for the species in question. A novel application of microarrays is to treat gene expression as a quantitative trait in itself and to combine this with linkage mapping to identify quantitative trait loci controlling the levels of gene expression; this approach may identify higher level regulatory genes in specific pathways. Finally, patterns of gene expression observed using microarrays may themselves be treated as phenotypic traits in selection programs (e.g. a particular pattern of gene expression might be indicative of a disease tolerant individual). Microarrays are now being developed for a number of shrimp species in laboratories around the world, primarily with a focus on identifying genes involved in the immune response. However, at present, there is no central repository of shrimp genomic information, which limits the rate at which shrimp genomic research can be progressed. The application of microarrays to shrimp breeding will be extremely limited until there is a shared repository of genomic information for shrimp, and the collective will and resources to develop comprehensive genomic tools for shrimp.

Determining the effective dimensionality of the genetic variance-covariance matrix

Determining the dimensionality of G provides an important perspective on the genetic basis of a multivariate suite of traits. Since the introduction of Fisher's geometric model, the number of genetically independent traits underlying a set of functionally related phenotypic traits has been recognized as an important factor influencing the response to selection. Here, we show how the effective dimensionality of G can be established, using a method for the determination of the dimensionality of the effect space from a multivariate general linear model introduced by AMEMIYA (1985). We compare this approach with two other available methods, factor-analytic modeling and bootstrapping, using a half-sib experiment that estimated G for eight cuticular hydrocarbons of Drosophila serrata. In our example, eight pheromone traits were shown to be adequately represented by only two underlying genetic dimensions by Amemiya's approach and factor-analytic modeling of the covariance structure at the sire level. In, contrast, bootstrapping identified four dimensions with significant genetic variance. A simulation study indicated that while the performance of Amemiya's method was more sensitive to power constraints, it performed as well or better than factor-analytic modeling in correctly identifying the original genetic dimensions at moderate to high levels of heritability. The bootstrap approach consistently overestimated the number of dimensions in all cases and performed less well than Amemiya's method at subspace recovery.

Short- and long-term consequences of thermal variation in the larval environment of anurans

To survive adverse or unpredictable conditions in the ontogenetic environment, many organisms retain a level of phenotypic plasticity that allows them to meet the challenges of rapidly changing conditions. Larval anurans are widely known for their ability to modify behaviour, morphology and physiological processes during development, making them an ideal model system for studies of environmental effects on phenotypic traits. Although temperature is one of the most important factors influencing the growth, development and metamorphic condition of larval anurans, many studies have failed to include ecologically relevant thermal fluctuations among their treatments. We compared the growth and age at metamorphosis of striped marsh frogs Limnodynastes peronii raised in a diurnally fluctuating thermal regime and a stable regime of the same mean temperature. We then assessed the long-term effects of the larval environment on the morphology and performance of post-metamorphic frogs. Larval L. peronii from the fluctuating treatment were significantly longer throughout development and metamorphosed about 5 days earlier. Frogs from the fluctuating group metamorphosed at a smaller mass and in poorer condition compared with the stable group, and had proportionally shorter legs. Frogs from the fluctuating group showed greater jumping performance at metamorphosis and less degradation in performance during a 10-week dormancy. Treatment differences in performance could not be explained by whole-animal morphological variation, suggesting improved contractile properties of the muscles in the fluctuating group.

Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits-phenology, osmotic adjustment, transpiration efficiency, stay-green-and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.

DNA markers linked to yield, yield components, and morphological traits in autotetraploid lucerne (Medicago sativa L.)

We have mapped and identifed DNA markers linked to morphology, yield, and yield components of lucerne, using a backcross population derived from winter-active parents. The high-yielding and recurrent parent, D, produced individual markers that accounted for up to 18% of total yield over 6 harvests, at Gatton, south-eastern Queensland. The same marker, AC/TT8, was consistently identified at each individual harvest, and in individual harvests accounted for up to 26% of the phenotypic variation for yield. This marker was located in linkage group 2 of the D map, and several other markers positively associated with yield were consistently identified in this linkage group. Similarly, markers negatively associated with yield were consistently identified in the W116 map, W116 being the low-yielding parent. Highly significant positive correlations were observed between total yield and yield for harvests 1-6, and between total yield and stem length, tiller number, leaf yield/plant, leaf yield/5 stems, stem yield/plant, and stem yield/5 stems. Highly significant QTL were located for all these characters as well as for leaf shape and pubescence.

Predator-induced phenotypic plasticity in tadpoles: extension or innovation?

Phenotypic plasticity, the ability of a trait to change as a function of the environment, is central to many ideas in evolutionary biology. A special case of phenotypic plasticity observed in many organisms is mediated by their natural predators. Here, we used a predator-prey system of dragonfly larvae and tadpoles to determine if predator-mediated phenotypic plasticity provides a novel way of surviving in the presence of predators (an innovation) or if it represents a simple extension of the way noninduced tadpoles survive predation. Tadpoles of Limnodynastes peronii were raised in the presence and absence of predation, which then entered a survival experiment. Induced morphological traits, primarily tail height and tail muscle height, were found to be under selection, indicating that predator-mediated phenotypic plasticity may be adaptive. Although predator-induced animals survived better, the multivariate linear selection gradients were similar between the two tadpole groups, suggesting that predator-mediated phenotypic plasticity is an extension of existing survival strategies. In addition, nonlinear selection gradients indicated a cost of predator-induced plasticity that may limit the ability of phenotypic plasticity to enhance survival in the presence of predators.

Studying phenotypic evolution using multivariate quantitative genetics

Quantitative genetics provides a powerful framework for studying phenotypic evolution and the evolution of adaptive genetic variation. Central to the approach is G, the matrix of additive genetic variances and covariances. G summarizes the genetic basis of the traits and can be used to predict the phenotypic response to multivariate selection or to drift. Recent analytical and computational advances have improved both the power and the accessibility of the necessary multivariate statistics. It is now possible to study the relationships between G and other evolutionary parameters, such as those describing the mutational input, the shape and orientation of the adaptive landscape, and the phenotypic divergence among populations. At the same time, we are moving towards a greater understanding of how the genetic variation summarized by G evolves. Computer simulations of the evolution of G, innovations in matrix comparison methods, and rapid development of powerful molecular genetic tools have all opened the way for dissecting the interaction between allelic variation and evolutionary process. Here I discuss some current uses of G, problems with the application of these approaches, and identify avenues for future research.

Trait physiology and crop modelling to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. We explore whether a crop growth and development modelling framework can link phenotype complexity to underlying genetic systems in a way that strengthens molecular breeding strategies. We use gene-to-phenotype simulation studies on sorghum to consider the value to marker-assisted selection of intrinsically stable QTLs that might be generated by physiological dissection of complex traits. The consequences on grain yield of genetic variation in four key adaptive traits – phenology, osmotic adjustment, transpiration efficiency, and staygreen – were simulated for a diverse set of environments by placing the known extent of genetic variation in the context of the physiological determinants framework of a crop growth and development model. It was assumed that the three to five genes associated with each trait, had two alleles per locus acting in an additive manner. The effects on average simulated yield, generated by differing combinations of positive alleles for the traits incorporated, varied with environment type. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages with gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies. We simulated a marker-assisted selection (MAS) breeding strategy based on the analyses of gene effects. When marker scores were allocated based on the contribution of gene effects to yield in a single environment, there was a wide divergence in rate of yield gain over all environments with breeding cycle depending on the environment chosen for the QTL analysis. It was suggested that knowledge resulting from trait physiology and modelling would overcome this dependency by identifying stable QTLs. The improved predictive power would increase the utility of the QTLs in MAS. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate QTLs.