Rice, a staple food crop, is often subjected to drought and salinity stresses thereby limiting its yield potential. Since there is a cross talk between these abiotic stresses, identification of common and/or overlapping regulatory elements is pivotal for generating rice cultivars that showed tolerance towards them. Analysis of the gene interaction network (GIN) facilitates identifying the role of individual genes and their interactions with others that constitute important molecular determinants in sensing and signaling cascade governing drought and/or salinity stresses. Identification of the various cis-regulatory elements of the genes constituting GIN is equally important. Here, in this study graphical Gaussian model (GGM) was used for generating GIN for an array of genes that were differentially regulated during salinity and/or drought stresses to contrasting rice cultivars (salt-tolerant [CSR11], salt-sensitive [VSR156], drought-tolerant [Vandana], drought-sensitive [IR64]). Whole genome transcriptom profiling by using microarray were employed in this study. Markov Chain completed co-expression analyses of differentially expressed genes using Dynamic Bayesian Network, Probabilistic Boolean Network and Steady State Analysis. A compact GIN was identified for commonly co-expressed genes during salinity and drought stresses with three major hubs constituted by Myb2 transcription factor (TF), phosphoglycerate kinase and heat shock protein (Hsp). The analysis suggested a pivotal role of these genes in salinity and/or drought stress responses. Further, analysis of cis-regulatory elements (CREs) of commonly differentially expressed genes during salinity and drought stresses revealed the presence of 20 different motifs.

Introduction:The liver plays an important role in the haemostatic system as it synthesizes the majority of coagulation factors and fibrinolytic proteins.Aim: The present study was planned to determine the range of haemostatic defects in patients of chronic liver diseases.

Both genetic and environmental factors play roles in hyperuricemia and susceptibility may be modified by functional polymorphisms in folate metabolic genes, such as methylenetetrahydrofolate reductase (MTHFR). Several case control studies investigated association between C677T polymorphism with hyperuricemia but the sample size was small in these studies and the association power was weak. The aim of the present meta-analysis was to evaluate association between MTHFR C677T polymorphism and hyperuricemia. This meta-analysis recruited 6 published studies which were selected by search of electronic databases up to August 2013, including 558 hyperuricemic cases and 912 healthy controls. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the association between MTHFR C677T polymorphism and hypeuricemia susceptibility using fixed effect models. Statistically significant relationship was found between C677T polymorphism and hyperuricemia with all genetic models (Additive model T vs. C: OR=1.

Approximately 300 different types of blood groups are identified so far, the ABO and Rh antigens are still the clinically most significant and genetically most polymorphic of all human blood group systems to date. A total of 200 unrelated individuals from Uttar Pradesh were studied for the phenotype and allele frequency distribution of ABO and Rh (D) blood groups. In total 200 samples analyzed, phenotype B blood type has the highest frequency 36.5% (n=73), followed by O (34.5%; n=69), A (20.5%; n=41) and AB (8.5%; n=17). The O, A and B frequencies were 0.5849, 0.1571 and 0.2580 respectively. The overall phenotypic frequencies of ABO blood groups were B>O>A>AB. The variation in phenotypic frequencies between male and female might be due to small sample size of male sample. The allelic frequency of Rh-negative was 0.2.

Bipolar disorder (BPD) is a psychiatric disease, characterized by the cycles of mania and depression. Several genetic studies investigated BDNF gene Val66Met polymorphism as risk factor for BPD, but results were inconclusive. Therefore, present meta-analysis was performed to reevaluate the BDNF Val66Met polymorphism and BPD association. Four databases (Pubmed, Springer Link, Science Direct and Google Scholar) were searched for eligible studies up to March 31,2018. Pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated to estimate the strength of the association. All statistical analyses were done by MetaAnalyst and Mix program. Forty studies with a total of 28,787 subjects (10,085 cases and 18,702 controls) were included in this meta-analysis. Overall, pooled analysis indicated that there was no significant association between BDNF Val66Met polymorphism and BPD risk under all five genetic models (OR A vs.G =0.99, 95%CI= 0.94-1.03, p=0.49; OR AG vs. GG = 0.1.02, 95%CI= 0.95-1.07, p= 0.57; OR AA vs. GG = 0.98, 95%CI=0.89-1.08, p=0.75; OR AA+AG vs. GG = 1.0, 95%CI= 0.94-1.06, p= 0.89;OR AA vs. AG+GG = 0.96, 95%CI= 0.89-1.05, p= 0.47). Similarly, no significant association was observed in ethnicity based subgroup analysis in both Asian and Caucasian population. However, significant association was found in subtype analysis between BDNF Val66Met and BPDII (OR AA+AG vs. GG = 1.21, 95%CI= 1.06-1.37, p= 0.003) but not with BPDI. These findings suggested that the BDNF Val66Met polymorphism confer no genetic susceptibility to BPD I but risk for BPDII.