An Integrated Bioinformatics Approach to Identify Network-Derived Hub Genes in Starving Zebrafish.

The present study was aimed at identifying causative hub genes within modules formed by co-expression and protein-protein interaction (PPI) networks, followed by Bayesian network (BN) construction in the liver transcriptome of starved zebrafish. To this end, the GSE11107 and GSE112272 datasets from the GEO databases were downloaded and meta-analyzed using the MetaDE package, an add-on R package. Differentially expressed genes (DEGs) were identified based upon expression intensity N(µ = 0.2, σ2 = 0.4). Reconstruction of BNs was performed by the bnlearn R package on genes within modules using STRINGdb and CEMiTool. ndufs5 (shared among PPI, BN and COEX), rps26, rpl10, sdhc (shared between PPI and BN), ndufa6, ndufa10, ndufb8 (shared between PPI and COEX), skp1, atp5h, ndufb10, rpl5b, zgc:193613, zgc:123327, zgc:123178, wu:fc58f10, zgc:111986, wu:fc37b12, taldo1, wu:fb62f08, zgc:64133 and acp5a (shared between COEX and BN) were identified as causative hub genes affecting gene expression in the liver of starving zebrafish. Future work will shed light on using integrative analyses of miRNA and DNA microarrays simultaneously, and performing in silico and experimental validation of these hub-causative (CST) genes affecting starvation in zebrafish.

Bayesian threshold analysis of litter size in sheep

Background: Litter size at birth (LSB) is one of the most important economic traits in sheep and could be used in genetic improvement schemes for meat production. LSB is inherently a categorical trait and should be analysed with threshold models. Objective: Bayesian threshold models were used to analyze sheep LSB to estimate genetic parameters. Methods: Data was based on 7,901 LSB records from 14,968 dams and 682 sires collected from 1986 to 2012 at Makouie Sheep Breeding Station in Iran. Means of posterior distributions (MPDs) of LSB's genetic parameters were estimated, and the best-fitted models were selected using the deviance information criterion. Results: In the repeated measurement analysis, the estimated direct and maternal heritabilities, and permanent environmental effect (±SE), according to the best-fitted model (model 5), were 0.01 (0.010), 0.02 (0.014), and 0.01 (0.011), respectively. In the univariate analysis, the best estimates of direct and maternal heritabilities were 0.12 (0.064) and 0.08 (0.045), respectively. An increasing trend for direct and maternal heritabilities was observed in parity 2 (0.15 (0.082) and 0.25 (0.083), respectively). In the bivariate analysis, the best estimates of direct and maternal heritabilities for LSB were 0.03 (0.027) and 0.22 (0.041), respectively. The direct and maternal genetic correlations among parities were 0.25 (0.054) and 0.12 (0.021), respectively. Conclusions: The results showed a considerable influence of environmental factors on LSB in each parity of sheep; also, statistically different genetic parameters (p<0.05) were obtained from one parity to another, indicating the different and large influences of genetic and environmental factors for each parity.

Antecedentes: El tamaño de la camada al nacer (LSB) es inherentemente un rasgo categórico y debe analizarse con modelos de umbral. El LSB es uno de los rasgos de producción de carne más importantes en las ovejas y podría usarse en esquemas de mejora genética para la producción de carne. Objetivo: Se utilizaron modelos de umbral bayesiano para analizar el tamaño de la camada de ovejas al nacer (LSB) y estimar parámetros genéticos. Métodos: Los datos se basaron en 7.901 registros de LSB de 14.968 ovejas y 682 carneros recolectados de 1986 a 2012 en la estación de cría de ovejas Makouie en Irán. Se estimaron las medias de distribuciones posteriores (MPD) de los parámetros genéticos de LSB y se seleccionaron los modelos mejor ajustados utilizando el criterio de información de desviación. Resultados: En los análisis de medición repetida, la heredabilidad materna y directa estimada y el efecto ambiental permanente (±SE), según el modelo mejor ajustado (modelo 5), fueron 0,01 (0,010), 0,02 (0,014) y 0,01 (0,011), respectivamente. En el análisis univariado, las mejores estimaciones de heredabilidad directa y materna fueron 0,12 (0,064) y 0,08 (0,045), respectivamente. Se observó una tendencia creciente de heredabilidades directas y maternas en la paridad 2 (0,15 (0,082) y 0,25 (0,083), respectivamente). En el análisis bivariado, las mejores estimaciones de heredabilidad directa y materna para LSB fueron 0,03 (0,027) y 0,22 (0,041), respectivamente. Las correlaciones genéticas directas y maternas entre partos fueron 0,25 (0,054) y 0,12 (0,021), respectivamente. Conclusiones: Los resultados mostraron una influencia considerable de los factores ambientales sobre el LSB en cada parto de las ovejas; además, se obtuvieron parámetros genéticos estadísticamente diferentes (p<0.05) de un parto a otro, indicando las diferentes y grandes influencias de factores genéticos y ambientales para cada parto en ovejas. Los resultados de este estudio se pueden precisar aún más utilizando datos de SNP de todo el genoma sobre diferentes partes para manejar una amplia gama de problemas relacionados con la interacción del entorno genético del rasgo LSB.

Antecedentes: O tamanho da ninhada ao nascer (LSB) é inerentemente uma característica categórica e deve ser analisada com modelos de limiar. LSB é uma das características mais importantes de produção de carne em ovinos e pode ser usado em esquemas de melhoramento genético para a produção de carne. Objetivo: Modelos de limiar bayesiano foram usados para analisar o tamanho da ninhada de ovelhas ao nascer (LSB) para estimar parâmetros genéticos. Métodos: Os dados foram baseados em 7.901 registros LSB de 14.968 ovelhas e 682 carneiros coletados de 1986 a 2012 na Estação de Criação de Ovinos Makouie no Irã. Médias de distribuições posteriores (MPDs) dos parâmetros genéticos de LSB foram estimadas e os modelos mais bem ajustados foram selecionados usando o critério de informação de desvio. Resultados: Nas análises de medidas repetidas, as herdabilidades diretas e maternas estimadas e o efeito do ambiente permanente (±SE), de acordo com o modelo mais bem ajustado (modelo 5), foram 0,01 (0,010), 0,02 (0,014) e 0,01 (0,011), respectivamente. Na análise univariada, as melhores estimativas das herdabilidades direta e materna foram 0,12 (0,064) e 0,08 (0,045), respectivamente. Uma tendência crescente para as herdabilidades direta e materna foi observada na paridade 2 (0,15 (0,082) e 0,25 (0,083), respectivamente). Na análise bivariada, as melhores estimativas de herdabilidades direta e materna para LSB foram 0,03 (0,027) e 0,22 (0,041), respectivamente. As correlações genéticas diretas e maternas entre os partos foram 0,25 (0,054) e 0,12 (0,021), respectivamente. Conclusões: Os resultados mostraram uma influência considerável dos fatores ambientais na LSB em cada paridade de ovelhas; também, parâmetros genéticos estatisticamente diferentes (p<0,05) foram obtidos de uma paridade para outra, indicando as diferentes e grandes influências de fatores genéticos e ambientais para cada paridade em ovinos. Os resultados deste estudo podem ser ainda mais definidos usando dados SNPs de todo o genoma em diferentes partes para lidar com uma ampla gama de problemas relacionados à interação do ambiente genético do traço LSB.

Constraint-Based, Score-Based and Hybrid Algorithms to Construct Bayesian Gene Networks in the Bovine Transcriptome.

Bayesian gene networks are powerful for modelling causal relationships and incorporating prior knowledge for making inferences about relationships. We used three algorithms to construct Bayesian gene networks around genes expressed in the bovine uterus and compared the efficacies of the algorithms. Dataset GSE33030 from the Gene Expression Omnibus (GEO) repository was analyzed using different algorithms for hub gene expression due to the effect of progesterone on bovine endometrial tissue following conception. Six different algorithms (grow-shrink, max-min parent children, tabu search, hill-climbing, max-min hill-climbing and restricted maximum) were compared in three higher categories, including constraint-based, score-based and hybrid algorithms. Gene network parameters were estimated using the bnlearn bundle, which is a Bayesian network structure learning toolbox implemented in R. The results obtained indicated the tabu search algorithm identified the highest degree between genes (390), Markov blankets (25.64), neighborhood sizes (8.76) and branching factors (4.38). The results showed that the highest number of shared hub genes (e.g., proline dehydrogenase 1 (PRODH), Sam-pointed domain containing Ets transcription factor (SPDEF), monocyte-to-macrophage differentiation associated 2 (MMD2), semaphorin 3E (SEMA3E), solute carrier family 27 member 6 (SLC27A6) and actin gamma 2 (ACTG2)) was seen between the hybrid and the constraint-based algorithms, and these genes could be recommended as central to the GSE33030 data series. Functional annotation of the hub genes in uterine tissue during progesterone treatment in the pregnancy period showed that the predicted hub genes were involved in extracellular pathways, lipid and protein metabolism, protein structure and post-translational processes. The identified hub genes obtained by the score-based algorithms had a role in 2-arachidonoylglycerol and enzyme modulation. In conclusion, different algorithms and subsequent topological parameters were used to identify hub genes to better illuminate pathways acting in response to progesterone treatment in the bovine uterus, which should help with our understanding of gene regulatory networks in complex trait expression.

Variation in autosomal and sex-linked genetic effects for growth traits in Markhoz goat using multivariate animal models.

The present study was conducted to estimate autosomal and sex-linked genetic parameters by restricted maximum likelihood method using four different multivariate models for growth traits in Markhoz goat. Data were collected over a period of 23 years (1992-2014) from the breeding station of Markhoz goat. The studied growth traits were birth weight (BW), weaning weight (WW), 6-month weight (6WT), 9-month weight (9WT), and 12-month weight (12WT). The best model was determined by Akaike's information criterion. Based on the best model, direct autosomal heritabilities of 0.18, 0.10, 0.12, 0.22, and 0.24 were obtained for BW, WW, 6WT, 9WT, and 12WT, respectively. On the other hand, estimates of direct sex-linked heritability for BW, WW, 6WT, 9WT, and 12WT were 0.02, 0.01, 0.002, 0.03, and 0.04, respectively. The maternal permanent environmental component was remarkable for growth traits. Direct autosomal additive genetic correlations among the traits were positive and varied from 0.11 between BW and 9WT to 0.95 between BW and WW. The positive correlations were also estimated for direct sex-linked additive genetic effect except between BW-6WT (- 0.07) and WW-6WT (-0.19). The result of this study indicated that sex-linked chromosome effect had more considerable influence at post-weaning traits.

WITHDRAWN:Identification of bovine uterine transcriptomic hub genes using Bayesian network algorithms.

Ahead of Print article withdrawn by publisher.