Bayesian Forecasting Of Temporal Gene Expression By Using An Autoregressive Panel Data Approach

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, respectively, for each cluster. The percentage of credibility intervals that contained the true values of gene expression for a future time was ~90%. The methodology performed well, providing a valid forecast of gene expression values by fitting an autoregressive panel data model. This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data. © FUNPEC-RP.152CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico#APQ 00825, FAPEMIG, Fundação de Amparo à Pesquisa do Estado de Minas GeraisFUNARBE, Fundação Arthur BernardesCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach

We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, respectively, for each cluster. The percentage of credibility intervals that contained the true values of gene expression for a future time was ~90%. The methodology performed well, providing a valid forecast of gene expression values by fitting an autoregressive panel data model. This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs

The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genomewide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: “weight”, “fat”, “loin”, and “performance”. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor

Influência da posição e da profundidade de semeadura na emergência de plântulas de açaí (Euterpe oleracea Mart. - Arecaceae) Influence of the position and the depth of sowing in the emergency of açai (Euterpe oleracea Mart. - Arecaceae) seedlings

O objetivo deste trabalho foi determinar a posição e a profundidade de semeadura mais adequadas para a emergência de plântulas de açaizeiro. As sementes foram colocadas para germinar nas profundidades de 0; 3 e 6 cm e nas seguintes posições: sementes com a rafe perpendicular à superfície do substrato e poro germinativo para cima, rafe perpendicular e poro germinativo para baixo, rafe paralela à superfície e poro germinativo para baixo e rafe paralela à superfície e poro germinativo para cima. A emergência de plântulas de açaizeiro com a rafe perpendicular à superfície do substrato e poro germinativo para cima é a mais adequada, pois proporciona igual porcentagem e menor tempo médio de emergência. Profundidades iguais ou superiores a 3 cm são inadequadas para semeadura de Euterpe oleracea Mart.<br>The present work had the objective to determine the most suitable depth and position of sowing for the emergency of açaizeiro seedlings. The seeds were placed to germinate at the depths of 0; 3 and 6 cm and in the following positions: with the raphe perpendicular to the surface of the substratum and germinative aperture up, the raphe perpendicular and germinative aperture down, the raphe parallel to the low surface and germinative aperture down, and raphe parallel to the surface and germinative aperture up. The emergency of açaizeiro seedlings with rafe perpendicular to the surface of the substratum and germinative aperture up is the most suitable, as it provides an equal percentage and minor average time of emergency. Depths equal or superior to 3 cm are inadequate for sowing of Euterpe oleracea Mart