Biology subjects

Souza, A. P. d.

Publications and source records attributed to Souza, A. P. d..

3 recordsLinked to original sources

bioRxiv2026

Assessing the Role of Marker Density and Minor Allele Frequency on Machine Learning Driven Genomic Selection Accuracy in Grapevine

Although grapevine (Vitis spp.) is among the oldest and most economically significant fruit species globally, its genetic improvement faces major bottlenecks due to long juvenile periods and extended cycles for phenotypic evaluation. In this context, genomic selection (GS) has emerged as an effective alternative to traditional selection, offering a robust framework to optimize breeding programs by significantly reducing generation intervals while enhancing predictive accuracy (PA) in early generations and expected genetic gains (EGGs). Nevertheless, factors such as minor allele frequency (MAF) and population size can significantly affect predictive models, even to the point of making their use unfeasible in breeding programs. In this context, this study evaluated the effect of data dimensionality reduction on GS accuracy by selecting single-nucleotide polymorphisms (SNPs) based on MAF thresholds. The experimental design tested the predictive capacities of four machine learning (ML) algorithms (ElasticNet, K-Neighbors, Support Vector Machine Regression, and XGBoost) alongside the conventional Genomic Best Linear Unbiased Prediction (gBLUP) model. These were validated using three SNP datasets (11,115, 9,494, and 6,100 markers) filtered by MAF levels of 0.05, 0.1, and 0.2 across six genetic traits, and EGGs were compared between conventional breeding and GS via the breeders equation. The results revealed that the ML models exhibited remarkable stability, with no significant differences in PA across the different MAF-based SNP densities, except for berry length, which showed a substantial difference with XGBoost at an MAF of 0.2. Conversely, gBLUP demonstrated high sensitivity to dimensionality reduction, with its performance significantly impacted by MAF filtering across all the traits. These results suggest that compared with traditional GS models that rely on a genomic kinship matrix, ML-based approaches offer greater flexibility in feature reduction. Additionally, compared with chemical traits, morphological traits generally had greater predictive ability. Furthermore, every GS model provided estimated genetic gains superior to traditional breeding, with improvements ranging from an 8.90-fold increase in berry length to a 2.86-fold increase in total soluble solids, confirming that GS integration is promising for enhancing breeding efficiency in grapevines.

genetics↗

bioRxiv2019

Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for dealing with G x E

Several genomic prediction models incorporating genotype x environment (GxE) interactions have recently been developed and used in genomic selection (GS) in plant breeding programs. GxE interactions decrease selection accuracy and limit genetic gains in plant breeding. Two genomic data sets were used to compare the prediction ability of multi-environment GxE genomic models and two kernel methods (a linear kernel (genomic best linear unbiased predictor, GBLUP) (GB) and a nonlinear kernel (Gaussian kernel, GK)) and prediction accuracy (PA) of five genomic prediction models: (1) one without environmental data (BSG); (2) a single-environment, main genotypic effect model (SM); (3) a multi-environment, main genotypic effect model (MM); (4) a multi-environment, single variance GxE deviation model (MDs); and (5) a multi-environment, environment-specific variance GxE deviation model (MDe). We evaluated the utility of GS with 435 rubber tree individuals in two sites and genotyped the individuals with genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were estimated for diameter (DAP) and height (AP) at different ages, with a heritability ranging from 0.59 to 0.75 for both traits. Applying the model (BSG, SM, MM, MDs, and MDe) and kernel method (GBLUP and GK) combinations to rubber tree data showed that models with the nonlinear GK and linear GBLUP kernel had similar PAs. Multi-environment models were superior to single-environment genomic models regardless the kernel (GBLUP or GK), suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. In the best scenario (well-watered (WW / GK), an increase of 6.7 and 8.7 fold of genetic gain can be obtained for AP and DAP, respectively, with multi-environment GS (MM, MDe and MDS) than by conventional genetic breeding model (CBM). Furthermore, GS resulted in a more balanced selection response in DAP and AP and if used in conjunction with traditional genetic breeding programs will contribute to a reduction in selection time. With the rapid advances in and declining costs of genotyping methods, balanced against the overall costs of managing large progeny trials and potential increased gains per unit time, we are hopeful that GS can be implemented in rubber tree breeding programs.

genetics↗

bioRxiv2018

Apomixis-related genes identified from a coexpression network in Paspalum notatum, a Neotropical grass

Apomixis is a highly desirable trait in modern agriculture, due to the maintenance of characteristics of the mother plant in the progeny. However, incorporating it into breeding programs requires a deeper knowledge of its regulatory mechanisms. Paspalum notatum is considered a good model for such studies because it exhibits both sexual and apomictic cytotypes, facilitating the performance of comparative approaches. Therefore, we used comparative transcriptomics between contrasting P. notatum cytotypes to identify novel candidate genes involved in the regulation of the expression of this phenotype. We assembled and characterized a transcriptome from leaf and inflorescence from apomictic tetraploids and sexual diploids/tetraploids of P. notatum accessions, and then assembled a coexpression network based on pairwise correlation between transcripts expression profiles. We identified genes exclusively expressed in each cytotype and differentially expressed genes between pairs of cytotypes. Gene ontology enrichment analyses were performed for the interpretation of data. We de novo assembled 114,306 of reference transcripts. 536 novel candidate genes for the control of apomixis were detected through statistical analyses of expression data, contains in this set, the interactions among genes potentially linked to the apomixis-controlling region, differentially expressed, several genes also already reported in the literature and their neighbors transcriptionally related in the coexpression network. The reference transcriptome obtained in this study represents a robust set of expression data for P. notatum. Additionally, novel candidate genes identified in this work represent a valuable resource for future grass breeding programs.\n\nAuthor SummaryClonal mode of reproduction by seeds is termed apomixis, which results from the failure of gamete formation (meiosis) and fertilization in the sexual female reproductive pathway. The manipulation of seeds production genetically identical to the mother plant bears great promise for agricultural applications, however clarification regarding gene interactions involved in reproductive process is needed. Paspalum is considered a model genus for the analysis of apomixis mechanisms. Here, we describe an overall analysis of the expression profiles of Paspalum notatum transcripts in response to changes in reproductive mode (sexual to apomictic), which allowed us to identify several candidate apomixis genes. Among these, we found genes potentially associated with the apomixis control region, in addition to genes already described in the literature for Paspalum, which highlights the representativeness of assembled transcriptome. For the first time in the literature, we explored the main biological processes involved in controlling the expression of apomictic reproduction based on co-regulatory networks of candidate apomixis genes.

plant biology↗