Table of contents: 2020
Various approaches use gene trees to infer species trees produced from incomplete lineage sorting. Generally, one of these approaches is used to deduce the rooted species tree from a rooted gene tree, or another method can be used to determine the unrooted species tree from an unrooted gene tree. Typically, this unrooted species is then rooted through at least one outgroup. However, in theory, the unrooted gene tree can be used consistently and directly to infer the rooted species tree without using an outgroup. We used an unrooted gene tree with the assumption of a multispecies coalescent model to infer the rooted species tree by using the approximate Bayesian computation (ABC). In certain cases, this could be useful, especially when it is hard to locate a fitting outgroup neglected by gene trees. To address the challenges of increasing the taxa number, an ABC was used to gauge the rooted species tree of a large number of taxa, using an unrooted gene tree to develop the rooted species tree. This is the first ABC application that can handle very large numbers of taxa. Based on the results, the Robinson-Foulds (RF) distance is generally equal to 2 when the species tree is in imbalance. When the shape of the species tree is balanced, the RF distance is normally equal to 0. Out of all shapes of species trees, the most recent one is the most appropriate for every clade.
Genome-Wide Selection (GWS) uses molecular markers to predict the genetic merit of animals and plants. Usually, a high density of molecular markers to predict this genetic merit is used. Thus, statistical methods need to deal with problems of high dimensionality, multicollinearity and computational efficiency. We examined a set of machine learning methods, in particular the tree-based regression methods (Regression Tree, Bagging, Random Forest and Boosting) and evaluated them in relation to predictive ability and bias. Moreover, these methods were compared with the Bayesian Least Absolute Shrinkage and Selection Operator (BLASSO) method, which is routinely used in GWS. For this, we used information of 10 carcass traits in Piau x Commercial pigs. The tree-based regression methods were superior to the BLASSO method, presenting shorter computational times to predict the genetic values of the analyses, especially, the Random Forest and Bagging methods. Furthermore, the predictive abilities of tree-based regression methods were competitive with BLASSO. In terms of bias, the BLASSO underestimated the predictions while tree-based regression methods overestimated the predictions. In addition, among the methods, the Random Forest was the one that obtained the bias value closest to ideal for most of the traits, demonstrating the superiority of this method.
The main polymorphisms linked to dyslipidemia include those of the SMARCA4 gene. We evaluated the association between lipidic profile and SMARCA4 gene polymorphism in 200 military police officers. Real time PCR was used to identify SMARCA4 gene polymorphisms. Among the subjects, 116 had dyslipidemia (case group), of which 94% were males, and 84 presented no dyslipidemia (control group), of which 92% were males. For the SMARCA4 gene polymorphism, 66.4% (77/116) presented the GG genotype, 31.0% (36/116), GT and 2.6% (3/116) TT. Individuals with GG, GT and TT genotypes had LDL cholesterol levels higher than 160 mg/dL, respectively, at frequencies of 28.6, 11.1 and 0%, while total cholesterol higher than 190 mg/dL, was at frequencies of 74.0, 58.3 and 33.3%. Logistic regression analysis to determine the p-values, considering the T allele as dominant, suggested that this allele has the ability to protect the individual from high cholesterol levels (OR, 0.488; 95% CI 0.27 - 0.88; P = 0.0163) and high LDL cholesterol levels (OR 0.277; 95% CI 0.09 - 0.84; P = 0.0230). Absence of the T allele was associated with increased susceptibility to dyslipidemia. Individuals who are homozygous or heterozygous for the T allele are approximately two times more likely to have normal cholesterol levels, and about 3.5 times more likely to have normal LDL levels.
The amount of absorbed phosphorus directly impacts on the growth and grain yield of common bean plants. We evaluated dry matter production at different growth stages and grain yield of common bean genotypes in response to phosphorus availability in a nutrient solution and examined possible associations between these characters. The experiment was carried out in a greenhouse, using a completely randomized design in sub-sub-divided plots. The main plots consisted of five phosphorus concentrations (0.5; 0.9; 1.3; 1.9 and 2.3 mmol.L-1) supplied to plants in a nutrient solution in the growth medium. The sub-plots were composed of two common bean genotypes (Pérola and IPR88 Uirapurú, which are commonly used in Rio Grande do Sul state) and the sub-sub-plots by two growing seasons (fall-winter and spring-summer). In the initial stages, at the first trifoliate leaf and flowering stages, the highest dry mass production occurred in the leaves. As the plants developed, they produced more dry matter in the pods during the pod filling stage, and later in grains, at the maturation stage. Phosphorus concentrations in the nutrient solution between 1.33 and 1.84 mmol.L-1 provided the greatest mass of beans at podfilling and at maturation, the largest number of grains and the greatest grain yield in the two genotypes. The characters dry mass of the leaves, stems and pods in pod filling, dry mass of grains at maturity, number of grains and number of pods are promising for indirect selection criteria.
Recently, carbon nanotubes (CNTs) are gaining attention in the field of agriculture as advanced approaches to minimize toxicity of mycotoxins for crop plants. We examined whether MWCNTs can be used to alleviate genotoxicity and DNA damage induced by ochratoxin A (OTA) in the common bean (Phaseolus vulgaris) by comparing the action of three OTA doses, prior and post-adsorption of OTA on the surface of MWCNTs. The phenotypic parameters, ultrastructure of chloroplasts and nuclei using transmission electron microscopy, and status of nuclear DNA (nDNA) using flow cytometry, comet assay and random amplified polymorphic DNA (RAPD) were used as bioassays. Exposure time was 48 hours. The most effective MWCNT dose (optimal) was 50µg/mL; it enhanced the phenotypic parameters (seed germination and seedling growth, tolerance, and vigor indices), induced unexpected modification of size, shape, external and internal ultrastructure of chloroplasts and nuclei, increased the content of nDNA and genome size, reduced the extent of nDNA damage, and produced a larger number of amplified DNA products and new DNA bands more than the control. Lower and higher MWCNT doses had reductions in these parameters. On the other hand, increases in doses in OTA treatments induced major toxicity in the common bean, leading to strong reductions in all parameters of the bioassays. The MWCNTs served as an adsorbent for OTA and led to alleviation of its toxicity. We conclude that optimal and adsorbent MWCNTs dose could be used as nanocarbon-fertilizer and nanocarbon-mycotoxin to protect crop plants in order to increase crop quality and productivity.