Camila Ferreira Azevedo

The main approach for improving multiple traits simultaneously is the selection index. The most widely used selection indices are those based on factor analysis, which overcome statistical limitations such as multicollinearity and the reliance on arbitrary weights of the classical Smith–Hazel approach and support multi-environment trials. Nevertheless, the efficiency indices are affected by factors such as genotype number, environment and trait correlation, and heritability. In this study, we simulated different scenarios varying the mentioned factors to evaluate the performance of the Factor-Analysis and Ideotype-Design-Based Index (FAI-BLUP), Multi-trait Genotype–Ideotype Distance Index (MGIDI), and Multi-Trait Stability Index (MTSI). All correlations were positive and constant within each scenario, while the ideotype sought genetic gains for traits in opposite directions. Simulations were conducted using AlphaSimR and FieldSimR, and indices were implemented via the metan package. Results showed that index efficiency was higher in scenarios with larger numbers of genotypes, low-to-moderate trait correlations, and moderate-to-high inter-environment correlations. However, strong correlations among traits, particularly when combined with high heritability, compromise selection index efficiency in scenarios with antagonistic trait objectives. Despite that, the MGIDI consistently outperformed the other indices across most scenarios. Therefore, we emphasize accounting for trait genetic architectures, genotype–trait correlations, and target environment correlations.

Coffee is one of the world's most widely consumed beverages, derived mainly from Coffea arabica , known for its superior flavor, and Coffea canephora (Robusta/Conilon), valued for its resilience and higher caffeine content. Functional breeding aims to develop cultivars that combine productivity and stress tolerance with improved health‐related traits and flavor quality. Because carbohydrate polymers account for more than 50% of green coffee bean dry weight and act as key precursors of aroma and bioactive compounds, we hypothesized that elucidating the genetic architecture underlying carbohydrate metabolites would create new opportunities for molecular breeding in coffee. To test this hypothesis, we integrated cell wall carbohydrate profiling with genomic analyses in a genetically diverse coffee collection encompassing both Coffea canephora and Coffea arabica . Genome‐wide association studies identified 14 major genomic loci associated with variation in cell wall polysaccharide composition. Using cup quality as a functional trait endpoint, we further linked carbohydrate profiles with sensory evaluations and propose a framework for implementing marker‐assisted selection to accelerate flavor improvement in coffee breeding programs.

Genomic selection (GS) estimates the GEBV from genome-wide markers to reduce generation intervals and optimize germplasm selection, which is particularly advantageous for high-cost or late-expressed traits. While models like GBLUP are popular, they assume a polygenic architecture. In contrast, the Bayesian alphabet and machine learning (ML) can accommodate other types of genetic architectures. Given that no single model is universally optimal, stacking ensembles, which train a meta-model using predictions from diverse base learners, emerge as a compelling solution. However, the application of stacking in GS often overlooks non-additive effects. This study evaluated different stacking configurations for genomic prediction across 10 simulated traits, covering additive, dominance, and epistatic genetic architectures. A 5-fold cross-validation scheme was used to assess predictive ability and other evaluation metrics. The stacking approach demonstrated superior predictive ability in all scenarios. Gains were especially pronounced in complex architectures (100 QTLs, h² = 0.3), reaching an 83% increment over the best individual model (BayesA with dominance), and also in oligogenic scenarios with epistasis (10 QTLs, h² = 0.6), with a 27.59% gain. The success of stacking was attributed to two key strategies: base learner selection and the use of robust meta-learners (such as principal component or penalized regression) that effectively handled multicollinearity.

GenomicLand is a free, open-source platform for genomic prediction and genome-wide association studies. Developed in R software using Shiny, it supports diploid and polyploid datasets and integrates statistical learning, Bayesian, machine learning, and neural network methods. GenomicLand provides an accessible environment for researchers, breeders, and students.

This study evaluated statistical dimensionality reduction techniques, Principal Component Analysis (PCA), Sparse PCA (SPCA), Independent PCA (IPCA), deep learning based on autoencoders (AE), and their hybrid combinations, for subpopulation identification in genomic data. High-dimensional single nucleotide polymorphisms (SNP) datasets pose computational challenges and may hinder clustering performance. We compared methods based on clustering agreement with known subpopulation labels using Oryza sativa data (413 genotypes; 44, 100 SNPs). PCA and SPCA exhibited strong performance (Adjusted Rand Index [ARI] = 0.715), with PCA achieving the lowest computational time (4.89 s). The standalone autoencoder performed poorly, likely due to the high-dimensional (p >> n) setting. Among the hybrid approaches, IPCA-AE achieved the highest agreement (ARI = 0.758), demonstrating that combining dimensionality reduction with nonlinear representation learning improves population structure identification.

Wheat is a crop of significant global and Brazilian economic importance. The success of cultivation depends on several factors. Studying genotype by environment (G x E) interaction is fundamental for developing more productive and resilient varieties. Due to the climatic diversity, it is important to understand how weather covariates (WC) interact with the environments under study. To understand the complex relationships between WC and environments, it is possible to perform a graphical analysis based on the singular value decomposition (SVD) of the matrix composed by the values of the WC evaluated in the environments under study. This study proposed graphical analysis to understand the interaction between WC and evaluated environments. We used data from a randomized block design (four replications) assessed 42 wheat genotypes across 10 environments (2 years and 5 Rio Grande do Sul locations). Temperature (°C), relative humidity (%), and precipitation (mm) data were obtained from the NASA POWER project, using the experiment’s years and coordinates. The visualization of the interaction between weather covariates and environments revealed the WC that most influenced the performance of the genotypes. The environment E6 and E8 stood out for their high productivity, mainly associated with precipitation and relative humidity. This tool allows breeders to visualize the interconnection between different cultivation scenarios, obtaining valuable information for more accurate and effective decisions.

Understanding trait relationships is fundamental in soybean breeding because the goal is to maximize simultaneous gains. Standard multi-trait genome-wide association studies (MT-GWAS) identify variants linked to multiple traits but fail to capture phenotypic structures or interrelations. Structural Equation Models (SEM) account for covariances and recursion, enabling the decomposition of single nucleotide polymorphism (SNP) effects into direct or indirect components and identifying pleiotropic regions. We applied SEM to analyze morphology (pod thickness, PT) and yield traits (number of pods, NP; number of grains, NG; hundred-grain weight, HGW). The dataset comprised 96 soybean individuals genotyped with 4070 SNP markers. The phenotypic network was constructed using the hill-climbing algorithm, a class of score-based methods commonly applied to learn the structure of Bayesian networks, and structural coefficients were estimated with SEM. According to coefficient signs, we identified negative interrelationships between NG and HGW, and positive ones between NP and NG, and HGW and PT. NG, HGW, and PT showed indirect SNP effects. We also found loci jointly controlling traits. In total, 46 candidate genes were identified: 7 associated exclusively with NP and 4 associated with NG. An additional 15 genes were common to NP and NG, 3 were common to NP and HGW, 6 were common to NG and HGW, and 11 were common to NP, NG, and HGW. In summary, SEM-GWAS revealed novel relationships among soybean traits, including PT, supporting breeding programs.

Recognizing the interrelationship among variables becomes critical in genetic breeding programs, where the goal is often to optimize selection for multiple traits. Conventional multi-trait models face challenges such as convergence issues, and they fail to account for cause-and-effect relationships. To address these challenges, we conducted a comprehensive analysis involving confirmatory factor analysis (CFA), Bayesian networks (BN), structural equation modeling (SEM), and genome-wide selection (GWS) using data from 195 arabica coffee plants. These plants were genotyped with 21,211 single nucleotide polymorphism markers as part of the Coffea arabica breeding program at UFV/EPAMIG/EMBRAPA. Traits included vegetative vigor (VV), canopy diameter (CD), number of vegetative nodes (NVN), number of reproductive nodes (NRN), leaf length (LL), and yield (Y). CFA established the following latent variables: vigor latent (VL) explaining VV and CD; nodes latent (NL) explaining NVN and NRN; leaf length latent (LLL) explaining LL; and yield latent (YL) explaining Y. These were integrated into the BN model, revealing the following key interrelationships: LLL → VL, LLL → NL, LLL → YL, VL → NL, and NL → YL. SEM estimated structural coefficients, highlighting the biological importance of VL → NL and NL → YL connections. Genomic predictions based on observed and latent variables showed that using VL to predict NVN and NRN traits resulted in similar gains to using NL. Predicting gains in Y using NL increased selection gains by 66.35% compared to YL. The SEM-GWS approach provided insights into selection strategies for traits linked with vegetative vigor, nodes, leaf length, and coffee yield, offering valuable guidance for advancing Arabica coffee breeding programs.

Regional heritability mapping (RHM) employs mixed-model and single-genomic region approaches for Genome-Wide Association Studies (GWAS). Although it utilizes marker groups and possesses a high detection power for identifying markers associated with target phenotypes, the significant linkage disequilibrium (LD) among markers limits the RHM's capacity to estimate the effects of one genomic region simultaneously. This constraint may hinder its capacity to detect complex associations or relationships among various genomic regions. Within a Bayesian framework, RHM can operate with multiple structured covariance matrices and can incorporate several genomic regions into a single model, effectively utilizing the LD present in these regions. In this study, our objectives were: (i) to propose the simultaneous estimation of multiple genomic region effects using a Bayesian model; (ii) to compare the efficiency of this simultaneous estimation with that of the single-region estimation using simulated data, focusing on the detection of significant genomic regions for phenotypes characterized by diverse genetic architectures; and (iii) to demonstrate the applicability of these models in breeding programs, particularly applying them to rice data. The results indicated that the simultaneous estimation of genomic region effects using the Bayesian approach offered greater detection power for more complex traits in simulated data. In the rice dataset, the simultaneous estimation method identified more regions than those previously reported in the literature, as well as newly uncovered genomic regions that deserve further investigation in post-GWAS analyses. This methodology holds promise for exploring and applying new genomic regions associated with target traits.

The objective of this work was to evaluate the performance of the combination of the BayesDπ model for marker selection based on posterior inclusion probability (PIP) and of BayesA in assessing the predictive ability, heritability, and predictive bias for a set of rice phenotypic traits. The Markov chain Monte Carlo algorithm was used for the data analysis. For the calculation of PIP, marker effects were estimated using the BayesDπ method. Subsequently, the ratio between the number of iterations in which each marker had a non-zero effect and the total number of iterations was calculated. The markers were allocated into groups of 2,000, 4,000, 6,000, ..., and 36,901 (entire data set), in descending order of importance. The BayesA method was used to re-estimate the effect of the markers in each group. For comparison purposes, marker effects were also calculated using the BayesA and BayesDπ methods separately. In the proposed model, the PIP proved to be effective in understanding genetic architecture, resulting in a higher predictive ability, as well as in a higher heritability and a lower bias in the selection of the most important markers for genomic prediction compared with the other methods without prior marker selection.