Universidade Federal de Viçosa

About: Universidade Federal de Viçosa is a education organization based out in Viçosa, Brazil. It is known for research contribution in the topics: Population & Dry matter. The organization has 16012 authors who have published 26711 publications receiving 353416 citations.

Ecophysiology of coffee growth and production

Abstract: After oil, coffee is the most valuable traded commodity worldwide. In this review we highlighted some aspects of coffee growth and development in addition to focusing our attention on recent advances on the (eco)physiology of production in both Coffea arabica and C. canephora, which together account for 99% of the world coffee bean production. This review is organized into sections dealing with (i) climatic factors and environmental requirements, (ii) root and shoot growth, (iii) blossoming synchronisation, fruiting and cup quality, (iv) competition between vegetative and reproductive growth and branch die-back, (v) photosynthesis and crop yield, (vi) physiological components of crop yield, (vii) shading and agroforestry systems, and (viii) high-density plantings.

Noncoding RNAs, cytokines, and inflammation-related diseases

Abstract: Chronic inflammation is involved in the onset and development of many diseases, including obesity, atherosclerosis, type 2 diabetes, osteoarthritis, autoimmune and degenerative diseases, asthma, periodontitis, and cirrhosis. The inflammation process is mediated by chemokines, cytokines, and different inflammatory cells. Although the molecules and mechanisms that regulate this primary defense mechanism are not fully understood, recent findings offer a putative role of noncoding RNAs, especially microRNAs (miRNAs), in the progression and management of the inflammatory response. These noncoding RNAs are crucial for the stability and maintenance of gene expression patterns that characterize some cell types, tissues, and biologic responses. Several miRNAs, such as miR-126, miR-132, miR-146, miR-155, and miR-221, have emerged as important transcriptional regulators of some inflammation-related mediators. Additionally, little is known about the involvement of long noncoding RNAs, long intergenic noncoding RNAs, and circular RNAs in inflammation-mediated processes and the homeostatic imbalance associated with metabolic disorders. These noncoding RNAs are emerging as biomarkers with diagnosis value, in prognosis protocols, or in the personalized treatment of inflammation-related alterations. In this context, this review summarizes findings in the field, highlighting those noncoding RNAs that regulate inflammation, with emphasis on recognized mediators such as TNF-α, IL-1, IL-6, IL-18, intercellular adhesion molecule 1, VCAM-1, and plasminogen activator inhibitor 1. The down-regulation or antagonism of the noncoding RNAs and the administration of exogenous miRNAs could be, in the near future, a promising therapeutic strategy in the treatment of inflammation-related diseases.

Accuracy of Genomic Selection Methods in a Standard Data Set of Loblolly Pine ( Pinus taeda L.)

Abstract: Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) ridge regression–best linear unbiased prediction (RR–BLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR–BLUP (RR–BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development, and disease-resistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits, Bayes Cπ, Bayes A, and RR–BLUB B had higher predictive ability than RR–BLUP and Bayesian LASSO. Fusiform rust is controlled by few genes of large effect. A limitation of RR–BLUP is the assumption of equal contribution of all markers to the observed variation. However, RR-BLUP B performed equally well as the Bayesian approaches.The genotypic and phenotypic data used in this study are publically available for comparative analysis of genomic selection prediction models.

Genomic selection in forest tree breeding

Abstract: Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size (N e) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by N e and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when N e ≤ 30, while up to 20 markers/cM are necessary for larger N e. Shortening the breeding cycle by 50% with GS provides an increase ≥100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate tree breeding. However, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation.