Biology subjects

Dias, K. O. G.

Publications and source records attributed to Dias, K. O. G..

5 recordsLinked to original sources

bioRxiv2025

Predictive Ability of Enviromic Modeling in GxE Interactions for Upland Rice Site Recommendations

Enviromics is an omics approach that investigates a phenomenon using all available environmental information. This study explores the use of enviromic covariates in studies of genotype x environment (GxE) interactions in upland rice in Brazil, utilizing a field trial dataset from 143 locations over 27 years, covering diverse environmental conditions. The platforms WorldClim, NASA POWER, and SoilGrids were used to extract data, resulting in 383 environmental covariates. The objective of this study was to evaluate the use of enviromic kernels to integrate GIS and genetic data for predicting upland rice productivity across Brazil and to determine the optimal number of environmental covariates required to ensure model accuracy and stability. The predictive abilities of the enviromic model peaked with around 81 covariates, stabilizing when all 383 were included, suggesting the importance of a comprehensive dataset for accurate predictions. Analysis reveals that environmental dissimilarities are more critical than geographical distance for genotypic variability, reinforcing the need to consider multiple covariates in predictive models. Heritability mapping revealed spatial variations, with regions of high heritability concentrated in southern Brazil, where genetic selection may be more efficient. The clustering of mega-environments was not efficient, highlighting the complexity of GxE interactions, and confirming that pixel-by-pixel enviromic models are a safer approach for recommending breeding actions for upland rice. This study suggests strategies to improve genotype selection for specific conditions, guiding the expansion of rice cultivation into new agricultural areas in Brazil. The findings also contribute to rice-growing regions worldwide, especially in countries cultivating upland rice under diverse conditions. Structured AbstractO_ST_ABSObjectiveC_ST_ABSThis study aimed to evaluate the predictive ability of enviromic models for site-specific recommendations in upland rice, focusing on genotype x environment (GxE) interactions by integrating environmental and phenotypic data. MethodsA total of 734 field trials conducted between 1995 and 2022 across 143 Brazilian locations were analyzed. Environmental data (383 covariates) were retrieved from WorldClim, NASA POWER, and SoilGrids using GIS-based procedures. Statistical analyses included mixed linear models and Random Forest to correct for design effects, estimate heritability, and perform spatial predictions. ResultsEnvironmental dissimilarity better explained genotypic ranking than geographic distance. Predictive ability plateaued after 81 covariates, but adding more covariates reduced variance and increased model stability, supporting the use of comprehensive environmental data. ConclusionsThe study reinforces the need for detailed environmental characterization and the use of comprehensive enviromic models. Pixel-based predictions are more reliable than broad clustering approaches, supporting the use of virtual trials to optimize breeding strategies and resource allocation.

genetics↗

bioRxiv2023

GIS-FA: An approach to integrate thematic maps, factor-analytic and envirotyping for cultivar targeting

Key message: We propose an enviromics prediction model for cultivar recommendation based on thematic maps for decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and Factor-Analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA that integrates geographic information systems (GIS) techniques, FA models, Partial Least Squares (PLS) regression, and Enviromics to predict phenotypic performance in untested environments. The GIS-FA approach allows: (i) predict the phenotypic performance of tested genotypes in untested environments; (ii) select the best-ranking genotypes based on their over-all performance and stability using the FA selection tools; (iii) draw thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes have superior predicted performance and facilitates/optimizes the cultivar recommendation by utilizing thematic maps.

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↗

bioRxiv2023

Popcorn haploids identified by Navajo phenotype and ploidy level

For popcorn, obtaining and identifying haploids are still challenging steps. We aimed to induce and screen haploids in popcorn using the Navajo phenotype, seedling vigor and ploidy level. We used the Krasnodar Haploid Inducer (KHI) in crosses with 20 popcorn source germplasms and five maize controls. The field trial design was completely randomized, with three replications. We assessed the efficacy of induction and of identification of haploids based on haploidy induction rate (HIR) and false positive and negative rates (FPR and FNR). Additionally, we also measured the penetrance of the Navajo marker gene (R1-nj). All putative haploids classified by R1-nj were germinated together with a diploid sample and evaluated for false positives and negatives based on vigor. Seedlings from 14 females were submitted to flow cytometry to determine the ploidy level. The HIR and penetrance were analyzed by fitting a generalized linear model with a logit link function. The HIR of the KHI, adjusted by cytometry, ranged from 0.0 to 1.2%, with a mean of 0.34%. The average FPR from screening based on the Navajo phenotype were 26.2% and 76.4%, by the vigor and ploidy, respectively. The FNR was zero. The penetrance of R1-nj ranged from 30.8 to 98.6%. The average number of seeds per ear in temperate germplasm (76) was lower than that obtained in tropical germplasm (98). There is induction of haploids in germplasm of tropical and temperate origin. We recommend the selection of haploids associating the Navajo phenotype with a direct method of confirming the ploidy level, such as flow cytometry. We also show that haploid screening based on Navajo phenotype and seedling vigor reduces misclassification. The origin and genetic background of the source germplasm influence the R1-nj penetrance. Because the known inducers are maize, developing doubled-haploid technology for popcorn hybrid breeding requires overcoming the unilateral cross-incompatibility.

genetics↗

bioRxiv2019

Improving yield and fruit quality traits in sweet passion fruit: evidence for genotype by environment interaction and cross-compatibility in selected genotypes

Breeding for yield and fruit quality traits in passion fruits is complex due to the polygenic nature of these traits and the existence of genetic correlations among them. Therefore, studies focused on crop management practices and breeding using modern quantitative genetic approaches are still needed, especially for Passiflora alata, an understudied crop, popular known as the sweet passion fruit. It is assumed to be a self-incompatible species and is highly appreciated for its typical aroma and flavor characteristics. With the aim of estimating the genetic and phenotypic parameters related to fruit traits, our group has already investigated a sweet passion fruit progeny consisting of 100 individuals from which 30 genotypes were selected. In this study, we reevaluated these superior genotypes in three environmental conditions. The results of the multi-environment trial analysis indicate that the genotypes do not behave consistently across these environments, and this was taken into account when selecting genotypes. Pairwise genetic correlations among the fruit traits were evaluated, and different genotype rankings obtained depending on the trait and environment, providing further evidence of genotype by environment interaction. Finally, we used a multiplicative selection index to select 20% of genotypes for weight of pulp and against thickness and weight of the fruit skin. The response to selection was positive for all traits except soluble solids, and the superior (six) genotypes were ranked. The consensus is that open-pollinated populations can be used as commercial varieties in crop species that are sensitive to inbreeding depression or within breeding programs that are not well developed. For these reasons, we performed a complete diallel cross for evaluating the capability of the selected genotypes to be fertilized and set fruits. Most of the crosses were compatible, resulting in over 50% fruit set. It is worth noting that all genotypes produce fruits if used as females, which is essential to guarantee yields in commercial orchards.

plant biology↗