Root and shoot traits responses to phosphorus deficiency and QTL analysis at seedling stage using introgression lines of rice

Phosphorous （P） deficiency is a major restraint factor for crop production and plants have developed several mechanisms to adapt to low P stress. In this study, a set of 271 introgression lines （ILs） were used to characterize the responses of seedlings to low P availability and to identify QTLs for root traits, biomass, and plant height under P-deficiency and P-sufficiency conditions. Plant height, total dry weight, shoot dry weight, and root number were inhibited under P-deficiency, whereas maximum root length （MRL） and root-shoot ratio （RS） were induced by P-deficiency stress. Relative MRL （RMRL, the ratio of MRL under P-deficiency to MRL under P-sufficiency con- dition） and relative RS （RRS） were used to evaluate P-deficiency tolerance at the seedling stage. A total of 24 additive QTLs and 29 pairs of epistatic QTLs were detected, but only qRN4 was detected in both conditions. This suggested that different mechanisms may exist in both P supply levels. QTLs for adaptive traits （RMRL, RRS, RRV, and RRDW） and qRN4 consistently expressed to increase trait stability may contribute to P-deficiency tolerance. Twelve intervals were cluster regions of QTLs for P-deficiency tolerance, and one QTL （qRRSS） showed pleiotropic effects on P-deficiency tolerance and drought tolerance. These interesting QTLs can be used in marker-assisted breeding through the target ILs.     

Combining spatial and phylogenetic eigenvector filtering in trait analysis

Aim To analyse the effects of simultaneously using spatial and phylogenetic information in removing spatial autocorrelation of residuals within a multiple regression framework of trait analysis. Location Switzerland, Europe. Methods We used an eigenvector filtering approach to analyse the relationship between spatial distribution of a trait (flowering phenology) and environmental covariates in a multiple regression framework. Eigenvector filters were calculated from ordinations of distance matrices. Distance matrices were either based on pure spatial information, pure phylogenetic information or spatially structured phylogenetic information. In the multiple regression, those filters were selected which best reduced Moran's I coefficient of residual autocorrelation. These were added as covariates to a regression model of environmental variables explaining trait distribution. Results The simultaneous provision of spatial and phylogenetic information was effectively able to remove residual autocorrelation in the analysis. Adding phylogenetic information was superior to adding purely spatial information. Applying filters showed altered results, i.e. different environmental predictors were seen to be significant. Nevertheless, mean annual temperature and calcareous substrate remained the most important predictors to explain the onset of flowering in Switzerland; namely, the warmer the temperature and the more calcareous the substrate, the earlier the onset of flowering. A sequential approach, i.e. first removing the phylogenetic signal from traits and then applying a spatial analysis, did not provide more information or yield less autocorrelation than simple or purely spatial models. Main conclusions The combination of spatial and spatio‐phylogenetic information is recommended in the analysis of trait distribution data in a multiple regression framework. This approach is an efficient means for reducing residual autocorrelation and for testing the robustness of results, including the indication of incomplete parameterizations, and can facilitate ecological interpretation.     

Incorporation of genotype effects into animal model evaluations when only a small fraction of the population has been genotyped

The method of Israel and Weller (Estimation of candidate gene effects in dairy cattle populations. Journal of Dairy Science 1998, 81, 1653-1662) to estimate quantitative trait locus (QTL) effects when only a small fraction of the population was genotyped was investigated by simulation. The QTL effect was underestimated in all cases, but bias was greater for extreme allelic frequencies, and increased with the number of generations included in the simulations. Apparently, as the fraction of animals with inferred genotypes increases, the genotype probabilities tend to 'mimic' the effect of relationships. Unbiased estimates of QTL effects were derived by a modified 'cow model' without the inclusion of the relationship matrix on simulated data, even though only a small fraction of the population was genotyped. This method yielded empirically unbiased estimates for the effects of the genes DGAT1 and ABCG2 on milk production traits in the Israeli Holstein population. Based on these results, an efficient algorithm for marker-assisted selection in dairy cattle was proposed. Quantitative trait loci effects are estimated and subtracted from the cows' records. Genetic evaluations are then computed for the adjusted records. Animals are then selected based on the sum of their polygenic genetic evaluations and QTL effects. This scheme differs from a traditional dairy cattle breeding scheme in that all bull calves were considered candidates for selection. At year 10, total genetic gain was 20% greater by the proposed algorithm as compared to the selection based on a standard animal model for a locus with a substitution effect of 0.5 phenotypic standard deviations. The proposed method is easy to apply, and all required software are 'on the shelf.' It is only necessary to genotype breeding males, which are a very small fraction of the entire population. The method is flexible with respect to the model used for routine genetic evaluation. Any number of genetic markers can be easily incorporated into the algorithm, and the reduction in genetic gain due to incorrect QTL determination is minimal.     

A new floating sensor array to detect electric near fields of beating heart preparations

A new flexible sensor for in vitro experiments was developed to measure the surface potential, Φ, and its gradient, E (electric near field), at given sites of the heart. During depolarisation, E describes a vector loop from which direction and magnitude of local conduction velocity θ can be computed. Four recording silver electrodes (14 μm × 14 μm) separated by 50 μm, conducting leads, and solderable pads were patterned on a 50 μm thick polyimide film. The conductive structures, except the electrodes, were isolated with polyimide, and electrodes were chlorided. Spacer pillars mounted on the tip fulfil two functions: they keep the electrodes 70 μm from the tissue allowing non-contact recording of Φ and prevent lateral slipping. The low mass (9.1 mg) and flexibility (6.33 N/m) of the sensor let it easily follow the movement of the beating heart without notable displacement. We examined the electrodes on criteria like rms-noise of Φ, signal-to-noise ratio of Φ and E, maximum peak-slope recording dΦ/dt, and deviation of local activation time (LAT) from a common signal and obtained values of 24–28 μV, 46 and 41 dB, 497–561 V/s and no differences, respectively. With appropriate data acquisition (sampling rate 100 kHz, 24-bit), we were able to record Φ and to monitor E and θ on-line from beat-to-beat even at heart rates of 600 beats/min. Moreover, this technique can discriminate between uncoupled cardiac activations (as occur in fibrotic tissue) separated by less than 1 mm and 1 ms.     

Prediction of IBD based on population history for fine gene mapping

A novel multiple regression method (RM) is developed to predict identity-by-descent probabilities at a locus L (IBD_L), among individuals without pedigree, given information on surrounding markers and population history. These IBD_L probabilities are a function of the increase in linkage disequilibrium (LD) generated by drift in a homogeneous population over generations. Three parameters are sufficient to describe population history: effective population size (Ne), number of generations since foundation (T), and marker allele frequencies among founders (p). IBD_L are used in a simulation study to map a quantitative trait locus (QTL) via variance component estimation. RM is compared to a coalescent method (CM) in terms of power and robustness of QTL detection. Differences between RM and CM are small but significant. For example, RM is more powerful than CM in dioecious populations, but not in monoecious populations. Moreover, RM is more robust than CM when marker phases are unknown or when there is complete LD among founders or Ne is wrong, and less robust when p is wrong. CM utilises all marker haplotype information, whereas RM utilises information contained in each individual marker and all possible marker pairs but not in higher order interactions. RM consists of a family of models encompassing four different population structures, and two ways of using marker information, which contrasts with the single model that must cater for all possible evolutionary scenarios in CM.     

Requirement of tryptophan hydroxylase during development for maturation of sensorimotor gating

Deficits in sensorimotor gating, a function to focus on the most salient stimulus, could lead to a breakdown of cognitive integrity, and could reflect the "flooding" by sensory overload and cognitive fragmentation seen in schizophrenia. Sensorimotor gating emerges at infancy, and matures during childhood. The mechanisms that underlie its development are largely unclear. Here, we screened the mouse genome, and found that tryptophan hydroxylase (TPH) is implicated in the maturation of sensorimotor gating. TPH, an enzyme involved in the biosynthesis of serotonin, proved to be required only during the weaning period for maturation of sensorimotor gating, but was dispensable for its emergence. Proper serotonin levels during development underlie the mature functional architecture for sensorimotor gating via appropriate actin polymerization. Thus, maintaining proper serotonin levels during childhood may be important for mature sensorimotor gating in adulthood.     

Near-periodic substitution and the genetic variance induced by environmental change

We investigate a model that describes the evolution of a diploid sexual population in a changing environment. Individuals have discrete generations and are subject to selection on the phenotypic value of a quantitative trait, which is controlled by a finite number of bialleic loci. Environmental change is taken to lead to a uniformly changing optimal phenotypic value. The population continually adapts to the changing environment, by allelic substitution, at the loci controlling the trait. We investigate the detailed interrelation between the process of allelic substitution and the adaptation and variation of the population, via infinite population calculations and finite population simulations. We find a simple relation between the substitution rate and the rate of change of the optimal phenotypic value.     

Relative effects of phylogeny,biological characters and environments on leaf traits in shrub biomes across central Inner Mongolia,China

Aims Understanding the drivers for leaf traits is critical to improving our predictions on ecosystems' responses to global changes. Geographic patterns of leaf traits are shaped by phylogenetic, biological and environmental factors simultaneously. However, till now few studies have examined how these factors influenced leaf traits together, and how their effects differed at the within- and among-site levels.Methods We sampled leaf traits from a 1100 km shrub-biome transect across central Inner-Mongolia, including leaf mass per area (LMA), mass-based photosynthetic rate, nitrogen (N) and phosphorus (P) concentrations. We examined the effects of phylogenetic, biological (height and growth rate) and environmental (climate and soil) factors on leaf traits with mixed-model analyses of variance. Variation partitioning method was used to separate the joint and independent effects of these three types of factors.Important findings (i) Climate and soil fertility (total or available nutrient concentrations) together explained 11.4–41.4% of among-site variations, with remarkable difference among traits. (ii) Height and height growth rate together explained 0.4–31.9% of trait variations (mostly among-site variations). Our results could only weakly support the ability of leaf traits as predictors for whole-plant growth. (iii) LMA was negatively related to height, which was consistent with the resource-use strategy hypothesis but inconsistent with the hypotheses proposed for coexisting trees, suggesting that the LMA–height relationship is shaped by rather different mechanisms between the within- and among-communities levels. (iv) The variation partitioning analysis showed that, the relationships between leaf traits and biological characters largely reflected the differences in both leaf traits and biological characters among species that occupying different sites. The relative importance of phylogenetic, biological and environmental factors differed remarkably among leaf traits, between the within- and among-communities levels, and between different biomes. (v) Our results strongly suggest the necessity of examining the three types of factors simultaneously, and at both the within- and among-communities levels, for a better understanding of the drivers for leaf traits patterns.