In the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs.

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.

Maternal nutrition has been highlighted as one of the main factors affecting intra-uterine environment. The increase in nutritional requirements by beef cows during late gestation can cause nutritional deficiency in the fetus and impact the fetal regulation of genes associated with myogenesis and immune response. Forty days before the expected calving date, cows were assigned to one of two diets: 100% (control) or 70% (restricted group) of the daily energy requirement. Muscle samples were collected from 12 heifers and 12 steers, and blood samples were collected from 12 steers. The objective of this work was to identify and to assess the biological relevance of differentially expressed genes (DEG) in the skeletal muscle and blood of beef calves born from cows that experienced [or not] a 30% energy restriction during the last 40 days of gestation. A total of 160, 164, and 346 DEG (q-value< 0.05) were identified in the skeletal muscle for the effects of diet, sex, and diet-by-sex interaction, respectively. For blood, 452, 1392, and 155 DEG were identified for the effects of diet, time, and diet-by-time interaction, respectively. For skeletal muscle, results based on diet identified genes involved in muscle metabolism. In muscle, from the 10 most DEG down-regulated in the energy-restricted group (REST), we identified 5 genes associated with muscle metabolism and development: SLCO3A1, ATP6V0D1, SLC2A1, GPC4, and RASD2. In blood, among the 10 most DEG, we found genes related to response to stress up-regulated in the REST after weaning, such as SOD3 and INO80D, and to immune response down-regulated in the REST after vaccination, such as OASL, KLRF1, and LOC104968634. In conclusion, maternal energy restriction during late gestation may limit the expression of genes in the muscle and increase expression in the blood of calves. In addition, enrichment analysis showed that a short-term maternal energy restriction during pregnancy affects the expression of genes related to energy metabolism and muscle contraction, and immunity and stress response in the blood. Therefore, alterations in the intra-uterine environment can modify prenatal development with lasting consequences to adult life.

Gene expression time series (GETS) analysis aims to characterize sets of genes according to their longitudinal patterns of expression. Due to the large number of genes evaluated in GETS analysis, an useful strategy to summarize biological functional processes and regulatory mechanisms is through clustering of genes that present similar expression pattern over time. Traditional cluster methods usually ignore the challenges in GETS, such as the lack of data normality and small number of temporal observations. Independent Component Analysis (ICA) is a statistical procedure that uses a transformation to convert raw time series data into sets of values of independent variables, which can be used for cluster analysis to identify sets of genes with similar temporal expression patterns. ICA allows clustering small series of distribution-free data while accounting for the dependence between subsequent time-points. Using temporal simulated and real (four libraries of two pig breeds at 21, 40, 70 and 90 days of gestation) RNA-seq data set we present a methodology (ICAclust) that jointly considers independent components analysis (ICA) and a hierarchical method for clustering GETS. We compare ICAclust results with those obtained for K-means clustering. ICAclust presented, on average, an absolute gain of 5.15% over the best K-means scenario. Considering the worst scenario for K-means, the gain was of 84.85%, when compared with the best ICAclust result. For the real data set, genes were grouped into six distinct clusters with 89, 51, 153, 67, 40, and 58 genes each, respectively. In general, it can be observed that the 6 clusters presented very distinct expression patterns. Overall, the proposed two-step clustering method (ICAclust) performed well compared to K-means, a traditional method used for cluster analysis of temporal gene expression data. In ICAclust, genes with similar expression pattern over time were clustered together.