Biology subjects

Dias, K. O. d. G.

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

3 recordsLinked to original sources

bioRxiv2024

Genotyping-By-Sequencing and DNA array for genomic prediction in soybean oil composition

Soybean oil is intended for various purposes, such as cooking oil and biodiesel. The oil composition changes the shelf life, palatability, and how healthy this oil is for the human diet. Genomic selection jointly uses these traits, phenotypes, and markers from one of the available genotyping platforms to increase genetic gain over time. This study aims to evaluate the impact of different genotyping platforms, DNA arrays, and Genotyping-by-Sequencing (GBS) on genomic selection in relation to the composition of fatty acids in soybean oil and total oil content. We used different quality control parameters, such as heterozygote rate, minor allele frequency, and missing data rate in ten combinations, and two prediction models, BayesB and BRR. To compare the impact of the genotyping approaches, we calculated the principal components analysis from the kinship matrices, the SNP density, and the traits prediction accuracies for each approach. Principal component analysis showed that the DNA array explained better the population genetic architecture. On the other hand, prediction accuracies varied between the different genotyping platforms and only GBS was affected under different quality control parameters. Although the DNA array has important and well-studied polymorphisms for soybeans and is stable, it also has ascertainment bias. GBS, although not stable and requires more robust quality control, can discover alleles specific to the population under study. As soybean oil is used for different functions and the fatty acid profiles are different for each objective, the work constitutes a critical study and direction for improving the composition of soybean oil.

genetics↗

bioRxiv2024

Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions

Complementing phenotypic traits and molecular markers with high-dimensional data such as climate and soil information is becoming a common practice in breeding programs. This study explored new ways to integrate non-genetic information in genomic prediction models using machine learning (ML). Using the multi-environment trial data from the Genomes To Fields initiative, different models to predict maize grain yield were adjusted using various inputs: genetic, environmental, or a combination of both, either in an additive (genetic-and-environmental; G+E) or a multiplicative (genotype-by-environment interaction; GEI) manner. When including environmental data, the mean predictive ability of machine learning genomic prediction models increased from 7-9% over the well-established Factor Analytic Multiplicative Mixed Model (FA) among the three cross-validation scenarios evaluated. Moreover, using the G+E model was more advantageous than the GEI model given the superior, or at least comparable, predictive ability, the lower usage of computational memory and time, and the flexibility of accounting for interactions by construction. Our results illustrate the flexibility provided by the ML framework, particularly with feature engineering. We show that the featured engineering stage offers a viable option for envirotyping and generates valuable information for machine learning-based genomic prediction models. Furthermore, we verified that the genotype-by-environment interactions may be considered using tree-based approaches without explicitly including interactions in the model. These findings support the growing interest in merging high-dimensional genotypic and environmental data into predictive modeling. Key messageIncorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to model genotype-by-environment interactions.

genetics↗

bioRxiv2021

Leveraging probability concepts for genotype by environment recommendation

Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for distinct environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process towards cultivar recommendation in multi-environment trials.

genetics↗