Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

How to assess the temporal dynamics of landscape connectivity in ever-changing landscapes: a literature review

Landscape Ecology - Landscape connectivity plays a key role in determining the persistence of species inhabiting fragmented habitat patches. In dynamic landscapes, most studies measure connectivity...     

Correction to: Coupling landscape graph modeling and biological data: a review

Landscape Ecology - In the original publication of the article, the sixth author name has been misspelt. The correct name is given in this Correction. The original article has been corrected.     

A new standard area diagram set for assessment of severity of soybean rust improves accuracy of estimates and optimizes resource use

Soybean rust (SBR), caused by Phakopsora pachyrhizi, is the most important yield-damaging fungal disease of soybean due to severe reduction in healthy leaf area and acceleration of leaf fall. In experimental research, SBR severity is estimated visually aided/trained by a standard area diagram (SAD) developed and validated during the mid-2000s (Old SAD). In this study, we propose a new SAD set for SBR with six true-colour diagrams following linear increments (c. 15% increments) amended with four additional diagrams at low (<10%) severities, totalling 10 diagrams (0.2%, 1%, 3%, 5%, 10%, 25%, 40%, 55%, 70%, and 84%). For evaluation, 37 raters were split into two groups. Each assessed severity in a 50-image sample (0.25%–84%), first unaided and then using either the Old SAD or the New SAD. Accuracy, precision, and reliability of estimates improved significantly relative to unaided estimates only when aided by the New SAD (accuracy >0.95). Low precision (<0.78) and a trend of underestimation with an increase in severity were the main issues with the Old SAD, which did not differ from unaided estimates. Simulation to evaluate the impact of the errors by different methods on hypothesis tests, showed that the new SAD was more powerful for detecting the smallest difference in mean control (e.g., 70% vs. 65% disease reduction) than the Old SAD; the latter required a 2-fold increase in sample size to achieve the same power. There is a need to improve some SADs, taking advantage of new knowledge and technology to increase accuracy of the estimates, and to optimize both resource use efficiency and management decisions.     

Using landscape graphs to delineate ecologically functional areas

Context

Landscape graphs are widely used to model connectivity and to support decision-making in conservation planning. Compartmentalization methods applied to such graphs aim to define clusters of highly interconnected patches. Recent studies show that compartmentalization based on modularity is suitable, but it applies to non-weighted graphs whereas most landscape graphs involve weighted nodes and links.

Objectives

We propose to adapt modularity computation to weighted landscape graphs and to validate the relevance of the resulting compartments using demographic or genetic data about the patches.

Methods

A weighted adjacency matrix was designed to express potential fluxes, associating patch capacities and inter-patch distances. Eight weighting scenarios were compared. The statistical evaluation of each compartmentalization was based on Wilks’ Lambda. These methods were performed on a grassland network where patches are documented by annual densities of water voles in the Jura massif (France).

Results

The scenarios in which patch capacity is assigned a small weight led to the more relevant results, giving high modularity values and low Wilks’ Lambda values. When considering a fixed number of compartments, we found a significant negative correlation between these two criteria. Comparison showed that compartments are ecologically more valid than graph components.

Conclusions

The method proposed is suitable for designing ecologically functional areas from weighted landscape graphs. Maximum modularity values can serve as a guide for setting the parameters of the adjacency matrix.

     

Use of genomic and phenotypic selection in lines for prediction of testcrosses in maize II: grain yield and plant traits

Plant breeders have been trying to predict the performance of hybrids based on their parental performance. One application of molecular markers is its use in selection. The objectives were to map quantitative trait loci (QTL) and verify its congruence in maize lines and in their testcrosses and verify the possibility to select testcrosses from the predicted means of the lines by using information from markers. Two-hundred and fifty six lines and the testcrosses of these lines with two testers were evaluated in six environments, considering grain yield, plant lodging, days to anthesis and silking, anthesis-silking interval, plant and ear height and ear placement. QTL were mapped in the lines and in testcrosses and the predicted means of the lines were computed based on QTL effects and in all markers of the genome. The congruence of QTL detected in the lines and testcrosses were small for all traits. The correlations between the predicted means of the lines and the phenotypic means of the testcrosses ranged from low for grain yield to moderate for cycle and stature traits. The highest coincidences of the lines and selected testcrosses were observed for cycle and stature traits and the lowest for grain yield. Even by using molecular markers information, it is only possible to predict the testcrosses performance from the lines information to less complex traits and with reduced dominance effect. For complex traits and with pronounced dominance effect, information of markers must be obtained directly in the testcrosses, so they can be used for selection.     

LED-Induced Chlorophyll Fluorescence Spectral Analysis for the Early Detection and Monitoring of Cadmium Toxicity in Maize Plants

Chlorophyll fluorescence spectral analysis permits detection, monitoring, and evaluation of abiotic stresses upon healthy plants using illumination of a light source in the UV?CVIS spectral range. This technique indirectly assesses the amount of physiological stress caused by photosynthetic damage, specifically damage to photosystem II, in plants. The objective of this study was to detect the toxicity of cadmium in maize plants via spectral analysis of chlorophyll fluorescence. The analysis is noninvasive and nondestructive and is used to follow the temporal evolution of changes in the chlorophyll content and physiological state of Zea mays L. seedlings under cadmium stress. Conventional techniques were also used to evaluate the dry matter production and Cd accumulation in plant leaves. Plants exhibited a notable reduction in dry matter production and chlorophyll levels with the administration of increasing doses of Cd in the nutrient solution. The fluorescence analysis was sensitive to changes caused by Cd in maize plants, detecting damage caused by different treatments before visual symptoms were observed. This technique has a practical application and produces rapid results that can be used in the evaluation of Cd-induced stress in plants and the detection of areas contaminated by this element.