RESUMEN
The aim of this study was to elucidate the differential gene expression in the RNA sequencing transcriptome of isolated perfused udders collected from 4 slaughtered Holstein × Zebu crossbred dairy cows experimentally inoculated with Streptococcus agalactiae. We studied 3 different statistical tools (edgeR, baySeq, and Cuffdiff 2). In summary, 2 quarters of each udder were experimentally inoculated with Strep. agalactiae and the other 2 were used as a control. Mammary tissue biopsies were collected at times 0 and 3 h after infection. The total RNA was extracted and sequenced on an Illumina HiSeq 2000 (Illumina Inc., San Diego, CA). Transcripts were assembled from the reads aligned to the bovine UMD 3.1 reference genome, and the statistical analyses were performed using the previously mentioned tools (edgeR, baySeq, and Cuffdiff 2). Finally, the identified genes were submitted to pathway enrichment analysis. A total of 1,756, 1,161, and 3,389 genes with differential gene expression were identified when using edgeR, baySeq, and Cuffdiff 2, respectively. A total of 122 genes were identified by the overlapping of the 3 methods; however, only the platelet activation presented a significantly enriched pathway. From the results, we suggest the FCER1G, GNAI2, ORAI1, and VASP genes shared among the 3 methods in this pathway for posterior biological validation.
Asunto(s)
Glándulas Mamarias Animales/microbiología , Mastitis Bovina/genética , ARN/genética , Infecciones Estreptocócicas/veterinaria , Streptococcus agalactiae/fisiología , Animales , Bovinos , Femenino , Genoma , Glándulas Mamarias Animales/metabolismo , Mastitis Bovina/metabolismo , Mastitis Bovina/microbiología , ARN/metabolismo , Análisis de Secuencia de ARN , Infecciones Estreptocócicas/genética , Infecciones Estreptocócicas/metabolismo , Infecciones Estreptocócicas/microbiología , TranscriptomaRESUMEN
Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qτ(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that "best" represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively.
Asunto(s)
Cruzamiento/métodos , Genómica/métodos , Modelos Genéticos , Sitios de Carácter Cuantitativo , Animales , Teorema de Bayes , Marcadores Genéticos/genética , Genotipo , Polimorfismo de Nucleótido Simple , Valor Predictivo de las Pruebas , Análisis de Regresión , Selección GenéticaRESUMEN
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 genome-wide 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.
Asunto(s)
Estudio de Asociación del Genoma Completo/veterinaria , Genómica/métodos , Porcinos/genética , Animales , Teorema de Bayes , Brasil , Análisis Factorial , Predicción , Estudio de Asociación del Genoma Completo/métodos , Genotipo , Análisis Multivariante , Fenotipo , Polimorfismo de Nucleótido Simple , Carácter Cuantitativo HeredableRESUMEN
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.