Biology subjects

Yassue, R. M.

Publications and source records attributed to Yassue, R. M..

6 recordsLinked to original sources

bioRxiv2024

Complementary approaches to dissect late leaf rust resistance in an interspecific raspberry population

Over the last ten years, global raspberry production has increased by 47.89%, based on the red species (Rubus idaeus). However, the black raspberry species (Rubus occidentalis), although less consumed, is resistant to one of the most important diseases for the crop, the late leaf rust caused by Acculeastrum americanum fungus, to which the red ones are susceptible. In this context, genetic resistance is the most sustainable way to control the disease, mainly because there are no registered fungicides for late leaf rust in the crop in Brazil. Therefore, the aim was to understand the genetic architecture that controls resistance to late rust in raspberries. For that, we used an interspecific diversity panel between the cited above species, two different statistical approaches to associate the phenotypes to the markers (GWAS and copula graphical models), and two phenotyping methodologies from the first to the seventeenth day after inoculation (high-throughput phenotyping with a multispectral camera and traditional phenotyping by disease severity scores). Our findings indicate that a locus of higher effect possibly controls the resistance to late leaf rust, as both GWAS and the network suggested the same marker. Furthermore, a candidate defense-related gene cluster is close to this marker. Finally, the best stage to evaluate for disease severity is thirteen days after inoculation, confirmed by both traditional and high-throughput phenotyping. Although the network and GWAS indicated the same higher effect genomic region, the network identified other different regions complementing the genetic control comprehension.

genetics↗

bioRxiv2022

Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice

The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better understanding of the impact of higher night temperature on single grain at whole genome level is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature conditions of genotypes, and metabolites and single nucleotide polymorphisms to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes alone could be used to classify control and high night temperature conditions with high accuracy using random forest or extreme gradient boosting. The best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. Metabolic prediction was most effective for grain width, resulting in the highest prediction performance. Genomic prediction performed better than metabolic prediction. Integrating metabolites and genomics simultaneously in a prediction model slightly improved prediction performance. We did not observe a difference in prediction between the control and high night temperature conditions. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. Our results showed that, in addition to single nucleotide polymorphisms, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of high night temperature responses and regression modeling of grain size-related phenotypes in rice.

genetics↗

bioRxiv2022

Genome-wide association analysis of hyperspectral reflectance data to dissect growth-related traits genetic architecture in maize under inoculation with plant growth-promoting bacteria

Plant growth-promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth-related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome-wide association analyses to examine maize growth-related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386-1,021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome-wide association analysis as potential markers for growthrelated traits under PGPB inoculation. Eight SNPs were detected, which were associated with manually measured and hyperspectral phenotypes. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multi-phenotype genome-wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral-based phenotyping for studying maize growth-related traits in response to PGPB inoculation.

genetics↗

bioRxiv2022

Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data

Recent technological advances in high-throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyper-spectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize lines with or without plant growth-promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1021 nm and 13,826 single-nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth-related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyper-spectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral-based phenotyping for management and phenomic prediction studies. Core ideasO_LIHyperspectral reflectance data can classify plant growth-promoting bacteria inoculation status C_LIO_LIPhenomic prediction performs better than genomic prediction depending on the target phenotype C_LIO_LIAutoML is a promising approach for automating hyperparameter tuning for classification and prediction C_LI

genetics↗

bioRxiv2021

A low-cost greenhouse-based high-throughput phenotyping platform for genetic studies: a case study in maize under inoculation with plant growth-promoting bacteria

Greenhouse-based high-throughput phenotyping (HTP) presents a useful approach for studying novel plant growth-promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize cultivars exhibiting highly stable symbiosis with PGPB, greenhouse-based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen-limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth. Phenotypic correlations between the image-derived phenotypes and manual measurements were at least 0.6. The genomic heritability estimates of the image-derived phenotypes ranged from 0.23 to 0.54. Moderate-to-strong genomic correlations between the plant biomass index and shoot dry mass (0.24-0.47) and between HTP-based plant height and manually measured plant height (0.55-0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low-cost HTP platform for large-scale genetic and management studies to capture plant growth. Core ideasO_LIA low-cost greenhouse-based HTP platform was developed. C_LIO_LIImage-derived phenotypes presented moderate to high genomic heritabilities and correlations. C_LIO_LIPlant growth-promoting bacteria can improve plant resilience under nitrogen-limited conditions. C_LI

genetics↗

bioRxiv2020

CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction

Usually, the comparison among genomic prediction models is based on validation schemes as Repeated Random Subsampling (RRS) or K-fold cross-validation. Nevertheless, the design of training and validation sets has a high effect on the way and subjectiveness that we compare models. Those procedures cited above have an overlap across replicates that might cause an overestimated estimate and lack of residuals independence due to resampling issues and might cause less accurate results. Furthermore, posthoc tests, such as ANOVA, are not recommended due to assumption unfulfilled regarding residuals independence. Thus, we propose a new way to sample observations to build training and validation sets based on cross-validation alpha-based design (CV-). The CV- was meant to create several scenarios of validation (replicates x folds), regardless of the number of treatments. Using CV-, the number of genotypes in the same fold across replicates was much lower than K-fold, indicating higher residual independence. Therefore, based on the CV- results, as proof of concept, via ANOVA, we could compare the proposed methodology to RRS and K-fold, applying four genomic prediction models with a simulated and real dataset. Concerning the predictive ability and bias, all validation methods showed similar performance. However, regarding the mean squared error and coefficient of variation, the CV- method presented the best performance under the evaluated scenarios. Moreover, as it has no additional cost nor complexity, it is more reliable and allows the use of non-subjective methods to compare models and factors. Therefore, CV- can be considered a more precise validation methodology for model selection.

genomics↗