Biology subjects

Bhering, L. L.

Publications and source records attributed to Bhering, L. L..

3 recordsLinked to original sources

bioRxiv2025

Unraveling the inbreeding depression patterns in a self-pollinated eucalyptus population

Genetic improvement greatly contributed to the success of the forest industry in Brazil. While past selection efforts in Eucalyptus spp. have yielded satisfactory genetic gains, the response to selection in the last decade has fallen below expectations. Recent research suggests that inbreeding-based selection strategies, well-established in crops such as rice and maize, could be adapted to enhance perennial species such as guava and can be expanded to forest species, such as eucalypt. In this context, 20 elite Eucalyptus spp. genotypes were self-pollinated, producing 30 progenies per family. A total of 600 individuals were planted in an experimental trial and evaluated for growth traits at 3 years of age. Both the progeny and the 20 parent geno-types were genotyped using SNP chips. This research aims to unravel the patterns of inbreeding depression in this S0:1 population of Eucalyptus spp., by examining autozygosity and genomic inbreeding patterns, estimating in-breeding depression (ID) and genetic parameters, and studying the impact of the unbalance between selfed and crossed individuals on the inbreeding depression estimator. The results revealed that dominance variance accounted for a notable portion of the phenotypic variation, demonstrating the significance of non-additive genetic effects in diameter at breast height (DBH). ID was evident, with reductions in DBH observed in most families as ho- mozygosity increased. Simulations highlighted that unbalanced sample sizes of selfed and crossbred individuals could bias estimates of ID. By integrating genomic data and advanced quantitative methods, this study brings new information into the genetic consequences of self-pollination in eucalyptus, offering a foundation for managing inbreeding and enhancing genetic gains in perennial breeding.

genomics↗

bioRxiv2024

Trait prediction through computational intelligence and machine learning applied to soybean (Glycine max) breeding in shaded environments

This study aims to identify more relevant predictors traits, considering different prediction approaches in soybean under different shading levels in the field, using methodologies based on artificial intelligence and machine learning. The experiments were carried out under different shading levels in a greenhouse and in the field, using sixteen cultivars. We have evaluated grain yield, which was used as a response trait, and 22 other attributes as explanatory traits. Three levels of shading were used to restrict photosynthetically active radiation (RPAR): 0%, 25%, and 48%. At full sun level (0% RPAR), the traits that presented better predictive performances using a multilayer perceptron were specific leaf area, plant height and number of pods. In the three levels of shading, the plant height trait exhibited the best performance for the radial base function network. Plant height showed the best predictive efficiency for grain yield at 25% and 48% RPAR, for all machine learning methodologies. Computational intelligence and machine learning methodologies have proven to be efficient in predicting soybean grain yield, regardless of shading level.

genetics↗

bioRxiv2023

A data-driven approach for enhancing forest productivity by accounting for indirect genetic effects

Maintaining the past decades current genetic gains for tree species is a challenging task for foresters and tree breeders due to biotic and abiotic factors. Planting a mixture of genotypes or clonal composites can be an alternative to increase the phytosanitary security and yield of forest plantations. These clonal composites are more complex than monocultures due to inter-genotypic competition and indirect genetic effects that can affect the total heritable variation. This study aims to understand how indirect genetic effects can impact the response to selection and how the stand composition can be used to explore these effects and enhance forest yield. We used a clonally trial of Eucalyptus urophylla x Eucalyptus grandis hybrids in a randomized complete block design with 24 replications, containing a single tree per plot evaluated for mean annual increment at 3 and 6 years. We focus on partitioning the genetic variation of trees into direct and indirect genetic effects based on competition intensity factors. We identified clones as aggressive, homeostatic, and sensitive based on the magnitude of indirect genetic effects. By accounting for indirect genetic effects, for mean annual increment, the total heritability decreased 39 and 44% for 3 and 6 years, respectively. We proposed a workflow that uses the direct and indirect genetic effect to predict the mean value of clonal composite combinations and to select the one with highest yield. Our methodology accounted for spatial variability and interplot competition that can contribute to the total heritable variance and response to selection in forest trials. Based on the models evaluated, the clones are easily classified according to their deviation from the indirect genetic effects mean. Also, we extract useful information to predict different clonal composites compositions, their expected average performance, and define the best recommended combination to be planted in large scale.

genetics↗