Biology subjects

Ferrao, F.

Publications and source records attributed to Ferrao, F..

3 recordsLinked to original sources

bioRxiv2024

SimpleMating: R-package for prediction and optimization of breeding crosses using genomic selection

Selecting parents and crosses is a critical step for a successful breeding program. The ability to design crosses with high means that will maintain genetic variation in the population is the goal for long-term applications. Herein, we describe a new computational package for mate allocation in a breeding program. SimpleMating is a flexible and open-source R package originally designed to predict and optimize breeding crosses in crops with different reproductive systems and breeding designs. Divided into modules, SimpleMating first estimates the cross performance (criterion), such as mean parental average, cross total genetic value, and/or usefulness of a set of crosses. The second module implements an optimization algorithm to maximize a target criterion while minimizing next-generation inbreeding. The software is flexible, allowing users to specify the desired number of crosses, maximum/minimum number of crosses per parent, and the maximum value of the parent relationship for creating crosses. As an outcome, SimpleMating generates a mating plan from the target parental population using single or multi-trait criteria. As example, we implemented and tested SimpleMating in a simulated maize breeding program obtained through stochastic simulations. The crosses designed via SimpleMating showed large genetic mean over time (up to 22% more genetic gain than conventional genomic selection programs, with lower genetic diversity decrease over time), supporting the use of this tool, as well as the use of data-driven decisions in applied breeding programs.

genetics↗

bioRxiv2019

Effects of moderate and high intensity isocaloric aerobic training upon microvascular reactivity and myocardial oxidative stress in rats

Systemic and central cardiovascular adaptations may vary in response to chronic exercise performed with different intensities and volumes. This study compared the effects of aerobic training with different intensities but equivalent volume upon microvascular reactivity in cremaster muscle and myocardial biomarkers of oxidative stress in Wistar rats. After peak oxygen uptake (VO2peak) assessment, rats (n=24) were assigned into three groups: moderate-intensity exercise training (MI); high-intensity exercise training (HI); sedentary control (SC). Treadmill training occurred during 4 weeks, with exercise bouts matched by the energy expenditure (3.0-3.5 Kcal). Microvascular reactivity was assessed in vivo by intravital microscopy in cremaster muscle arterioles, while biomarkers of oxidative stress and eNOS expression were quantified at left ventricle and at aorta, respectively. Similar increasing vs. sedentary control group (SC) occurred in moderate intensity training group (MI) and high-intensity training group (HI) for endothelium-dependent vasodilation (10-4M: MI: 168.7%, HI: 164.6% vs. SC: 146.6%, P=0.0004). Superoxide dismutase (SOD) (HI: 0.13 U/mg vs. MI: 0.09 U/mg and SC: 0.06 U/mg; P=0.02), glutathione peroxidase (GPX) (HI: 0.00038 U/mg vs. MI: 0.00034 U/mg and SC: 0.00024 U/mg; P=0.04), and carbonyl protein content (HI: 0.04 U/mg vs. MI: 0.03 U/mg and SC: 0.01 U/mg; P=0.003) increased only in HI. No difference across groups was detected for catalase (CAT) (P=0.12), Thiobarbituric acid reactive substances (TBARS) (P = 0.38) or eNOS expression in aorta (P=0.44). In conclusion, higher exercise intensity induced greater improvements in myocardium antioxidant defenses, while gains in microvascular reactivity appeared to rely more on exercise volume than intensity.

physiology↗

bioRxiv2018

Genomic prediction of autotetraploids; influence of relationship matrices, allele dosage, and continuous genotyping calls in phenotype prediction

Estimation of allele dosage in autopolyploids is challenging and current methods often result in the misclassification of genotypes. Here we propose and compare the use of next generation sequencing read depth as continuous parameterization for autotetraploid genomic prediction of breeding values, using blueberry (Vaccinium corybosum spp.) as a model. Additionally, we investigated the influence of different sources of information to build relationship matrices in phenotype prediction; no relationship, pedigree, and genomic information, considering either diploid or tetraploid parameterizations. A real breeding population composed of 1,847 individuals was phenotyped for eight yield and fruit quality traits over two years. Analyses were based on extensive pedigree (since 1908) and high-density marker data (86K markers). Our results show that marker-based matrices can yield significantly better prediction than pedigree for most of the traits, based on model fitting and expected genetic gain. Continuous genotypic based models performed as well as the current best models and presented a significantly better goodness-of-fit for all traits analyzed. This approach also reduces the computational time required for marker calling and avoids problems associated with misclassification of genotypic classes when assigning dosage in polyploid species. Accuracies are encouraging for application of genomic selection (GS) for blueberry breeding. Conservatively, GS could reduce the time for cultivar release by three years. GS could increase the genetic gain per cycle by 86% on average when compared to phenotypic selection, and 32% when compared with pedigree-based selection.

genomics↗