Biology subjects

Guerra, W.

Publications and source records attributed to Guerra, W..

5 recordsLinked to original sources

bioRxiv2025

WISER: an innovative and efficient method for correcting population structure in omics-based prediction and selection

This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and having the potential to improve association studies. Unlike existing approaches that correct for population structure, WISER provides a generalized framework applicable across diverse experimental setups, species, and omics datasets, including single nucleotide polymorphisms (SNPs), metabolomics, and near-infrared spectroscopy (NIRS) used as phenomic predictors. Central to WISER is the concept of whitening, a statistical transformation that removes correlations between variables and standardizes their variances. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby minimizing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISERs potential to advance omics analyses and related research fields by capturing stronger genetic signals.

genetics↗

bioRxiv2025

WISER: an innovative and efficient method for correcting population structure in omics-based selection and association studies

This work introduces WISER (whitening and successive least squares estimation refinement), an innovative and efficient method designed to enhance phenotype estimation by addressing population structure. WISER outperforms traditional methods such as least squares (LS) means and best linear unbiased prediction (BLUP) in phenotype estimation, offering a more accurate approach for omics-based selection and association studies. Unlike existing approaches which correct for population structure, WISER offers a generalized framework that can be applied across diverse experimental setups, species, and omics datasets, such as single nucleotide polymorphisms (SNPs), near-infrared spectroscopy (NIRS), and metabolomics. Within its framework, WISER extends classical methods that use eigen-information as fixed-effect covariates to correct for population structure, by relaxing their assumptions and implementing a true whitening matrix instead of a pseudo-whitening matrix. This approach corrects fixed effects (e.g., environmental effects) for the genetic covariance structure embedded within the experimental design, thereby removing confounding factors between fixed and genetic effects. To support its practical application, a user-friendly R package named wiser has been developed. The WISER method has been employed in analyses for genomic prediction and heritability estimation across four species and 33 traits using multiple datasets, including rice, maize, apple, and Scots pine. Results indicate that genomic predictive abilities based on WISER-estimated phenotypes consistently outperform the LS-means and BLUP approaches for phenotype estimation, regardless of the predictive model applied. This underscores WISERs potential to advance omics analyses and related research fields by capturing stronger genetic signals.

genetics↗

bioRxiv2024

Integrative multi-environmental genomic prediction in apple

Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, non-additive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning. However, the application of multi-environmental genomic prediction in apple remained limited, likely due to the challenge of building multi-environmental datasets and structurally complex models. Here, we applied efficient statistical and deep learning models for multi-environmental genomic prediction of eleven apple traits with contrasting genetic architectures by integrating genomic- and enviromic-based model components. Incorporating genotype-by-environment interaction effects into statistical models improved predictive ability by up to 0.08 for nine traits compared to the benchmark model. This outcome, based on Gaussian and Deep kernels, shows these alternatives can effectively substitute the standard G-BLUP. Including non-additive effects slightly improved predictive ability by up to 0.03 for two traits, but enviromic-based effects resulted in no improvement. The deep learning approach achieved the highest predictive ability for three traits with simpler genetic architectures, outperforming the benchmark by up to 0.10. Our results demonstrate that the tested statistical models capture genotype-by-environment interactions particularly well, and the deep learning models efficiently integrate data from diverse sources. This study will foster the adoption of multi-environmental genomic prediction to select apple cultivars adapted to diverse environmental conditions, providing an opportunity to address climate change impacts.

plant biology↗

bioRxiv2021

Genetic architecture and genomic prediction accuracy of apple quantitative traits across environments

Implementation of genomic tools is desirable to increase the efficiency of apple breeding. The apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic prediction accuracy, and studying genotype by environment interactions (GxE). Here we show contrasting genetic architecture and genomic prediction accuracies for 30 quantitative traits across up to six European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic prediction accuracies of 0.18-0.88 were estimated using single-environment univariate, single-environment multivariate, multi-environment univariate, and multi-environment multivariate models. The GxE accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency.

genomics↗

bioRxiv2019

Prediction of fruit texture with training population optimization for efficient genomic selection in apple

Texture plays a major role in the determination of fruit quality in apple. Due to its physiological and economic relevance, this trait has been largely investigated, leading to the fixation of the major gene PG1 controlling firmness in elite cultivars. To further improve fruit texture, the targeting of an undisclosed reservoir of loci with minor effects is compelling. In this work, we aimed to unlock this potential with a genomic selection approach by predicting fruit acoustic and mechanical features as obtained with a TA.XTplus texture analyzer in 537 individuals genotyped with 8,294 SNP markers. The best prediction accuracies following cross-validations within the training set (TRS) of 259 individuals were obtained for the acoustic linear distance (0.64). Prediction accuracy was further improved through the optimization of TRS size and composition according to the test set. With this strategy, a maximal accuracy of 0.81 was obtained when predicting the synthetic trait PC1 in the family Gala x Pink Lady. We discuss the impact of genetic relatedness and clustering on trait variability and predictability. Moreover, we demonstrated the need for a comprehensive dissection of the complex texture phenotype and the potentiality of using genomic selection to improve fruit quality in apple. HighlightA genomic selection study, together with the optimization of the training set, demonstrated the possibility to accurately predict texture sub-traits valuable for the amelioration of fruit quality in apple.

genetics↗