Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

Correlation of plasma IGF-I concentrations and growth rate in aquacultured finfish: a tool for assessing the potential of new diets

A recently developed radioimmunoassay (RIA) for measuring insulin-like growth factor (IGF-I) in a variety of fish species was used to investigate the correlation between growth rate and circulating IGF-I concentrations of barramundi (Lates calcarifer), Atlantic salmon (Salmo salar) and Southern Bluefin tuna (Thunnus maccoyii). Plasma IGF-I concentration significantly increased with increasing ration size in barramundi and IGF-I concentration was positively correlated to growth rates obtained in Atlantic salmon (r2=0.67) and barramundi (r2=0.65) when fed a variety of diet formulations. IGF-I was also positively correlated to protein concentration (r2=0.59). This evidence suggested that measuring IGF-I concentration may provide a useful tool for monitoring fish growth rate and also as a method to rapidly assess different aquaculture diets. However, no such correlation was demonstrated in the tuna study probably due to seasonal cooling of sea surface temperature shortly before blood was sampled. Thus, some recommendations for the design and sampling strategy of nutritional trials where IGF-I concentrations are measured are discussed

Characterization of disease resistance gene candidates of the nucleotide binding site (NBS) type from banana and correlation of a transcriptional polymorphism with resistance to Fusarium oxysporum f.sp. cubense race 4

Most plant disease resistance (R) genes encode proteins with a nucleotide binding site and leucine-rich repeat structure (NBS-LRR). In this study, degenerate primers were used to amplify genomic NBS-type sequences from wild banana (Musa acuminata ssp. malaccensis) plants resistant to the fungal pathogen Fusarium oxysporum formae specialis (f. sp.) cubense (FOC) race 4. Five different classes of NBS-type sequences were identified and designated as resistance gene candidates (RGCs). The deduced amino acid sequences of the RGCs revealed the presence of motifs characteristic of the majority of known plant NBS-LRR resistance genes. Structural and phylogenetic analyses grouped the banana RGCs within the non-TIR (homology to Toll/interleukin-1 receptors) subclass of NBS sequences. Southern hybridization showed that each banana RGC is present in low copy number. The expression of the RGCs was assessed by RT-PCR in leaf and root tissues of plants resistant or susceptible to FOC race 4. RGC1, 3 and 5 showed a constitutive expression profile in both resistant and susceptible plants whereas no expression was detected for RGC4. Interestingly, RGC2 expression was found to be associated only to FOC race 4 resistant lines. This finding could assist in the identification of a FOC race 4 resistance gene.

The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials

Modeling of cultivar x trial effects for multienvironment trials (METs) within a mixed model framework is now common practice in many plant breeding programs. The factor analytic (FA) model is a parsimonious form used to approximate the fully unstructured form of the genetic variance-covariance matrix in the model for MET data. In this study, we demonstrate that the FA model is generally the model of best fit across a range of data sets taken from early generation trials in a breeding program. In addition, we demonstrate the superiority of the FA model in achieving the most common aim of METs, namely the selection of superior genotypes. Selection is achieved using best linear unbiased predictions (BLUPs) of cultivar effects at each environment, considered either individually or as a weighted average across environments. In practice, empirical BLUPs (E-BLUPs) of cultivar effects must be used instead of BLUPs since variance parameters in the model must be estimated rather than assumed known. While the optimal properties of minimum mean squared error of prediction (MSEP) and maximum correlation between true and predicted effects possessed by BLUPs do not hold for E-BLUPs, a simulation study shows that E-BLUPs perform well in terms of MSEP.

The climate change risk management matrix for the grazing industry of northern Australia

The complexity, variability and vastness of the northern Australian rangelands make it difficult to assess the risks associated with climate change. In this paper we present a methodology to help industry and primary producers assess risks associated with climate change and to assess the effectiveness of adaptation options in managing those risks. Our assessment involved three steps. Initially, the impacts and adaptation responses were documented in matrices by ‘experts’ (rangeland and climate scientists). Then, a modified risk management framework was used to develop risk management matrices that identified important impacts, areas of greatest vulnerability (combination of potential impact and adaptive capacity) and priority areas for action at the industry level. The process was easy to implement and useful for arranging and analysing large amounts of information (both complex and interacting). Lastly, regional extension officers (after minimal ‘climate literacy’ training) could build on existing knowledge provided here and implement the risk management process in workshops with rangeland land managers. Their participation is likely to identify relevant and robust adaptive responses that are most likely to be included in regional and property management decisions. The process developed here for the grazing industry could be modified and used in other industries and sectors. By 2030, some areas of northern Australia will experience more droughts and lower summer rainfall. This poses a serious threat to the rangelands. Although the impacts and adaptive responses will vary between ecological and geographic systems, climate change is expected to have noticeable detrimental effects: reduced pasture growth and surface water availability; increased competition from woody vegetation; decreased production per head (beef and wool) and gross margin; and adverse impacts on biodiversity. Further research and development is needed to identify the most vulnerable regions, and to inform policy in time to facilitate transitional change and enable land managers to implement those changes.

Correlation between NIRS generated and chemically measured feed quality data in barley (Hordeum vulgare), and potential use in QTL analysis identification

A study was performed to investigate the value of near infrared reflectance spectroscopy (NIRS) as an alternate method to analytical techniques for identifying QTL associated with feed quality traits. Milled samples from an F6-derived recombinant inbred Tallon/Scarlett population were incubated in the rumen of fistulated cattle, recovered, washed and dried to determine the in-situ dry matter digestibility (DMD). Both pre- and post-digestion samples were analysed using NIRS to quantify key quality components relating to acid detergent fibre, starch and protein. This phenotypic data was used to identify trait associated QTL and compare them to previously identified QTL. Though a number of genetic correlations were identified between the phenotypic data sets, the only correlation of most interest was between DMD and starch digested (r = -0.382). The significance of this genetic correlation was that the NIRS data set identified a putative QTL on chromosomes 7H (LOD = 3.3) associated with starch digested. A QTL for DMD occurred in the same region of chromosome 7H, with flanking markers fAG/CAT63 and bPb-0758. The significant correlation and identification of this putative QTL, highlights the potential of technologies like NIRS in QTL analysis.

Atypical correlation of otolith strontium : calcium and barium : calcium across a marine–freshwater life history transition of a diadromous fish

Variation in strontium (Sr) and barium (Ba) within otoliths is invaluable to studies of fish diadromy. Typically, otolith Sr : Ca is positively related to salinity, and the ratios of Ba and Sr to calcium (Ca) vary in opposite directions in relation to salinity. In this study of jungle perch, Kuhlia rupestris, otolith Sr : Ca and Ba : Ca, however, showed the same rapid increase as late-larval stages transitioned directly from a marine to freshwater environment. This transition was indicated by a microstructural check mark on otoliths at 35–45 days age. As expected ambient Sr was lower in the fresh than the marine water, however, low Ca levels (0.4 mg L–1) of the freshwater resulted in the Sr : Ca being substantially higher than the marine water. Importantly, the otolith Sr : Ba ratio showed the expected pattern of a decrease from the marine to freshwater stage, illustrating that Sr : Ba provided a more reliable inference of diadromous behaviour based on prior expectations of their relationship to salinity, than did Sr : Ca. The results demonstrate that Ca variation in freshwaters can potentially be an important influence on otolith element : Ca ratios and that inferences of marine–freshwater habitat use from otolith Sr : Ca alone can be problematic without an understanding of water chemistry.