Rare Copy Number Variants Identified Suggest the Regulating Pathways in Hypertension-Related Left Ventricular Hypertrophy

Association of vascular endothelial growth factor (VEGF) protein levels and gene polymorphism with the risk of chronic kidney disease

Vascular endothelial growth factor (VEGF) is a heparin-specific growth factor specific for vascular endothelial cells and induces angiogenesis via binding to vascular endothelial growth factor receptor (VEGFR). Chronic kidney disease (CKD), accompanied by microvascular disease, is recognized as an irreversible reduction of renal function. The effects of VEGF on CKD risk were evaluated in this study. 121 CKD patients and 50 healthy volunteers were evaluated in the current study. Data mining using the China Biological Medicine (CBM) and NCBI/PubMed databases, was performed and applicable investigations were pursued. Pooled mean differences (MD) and pooled odds ratios (OR), with corresponding confidence intervals (CIs), were calculated by meta-analysis. The levels of Scr, BUN and VEGF in the CKD group were significantly higher, when compared with the control group (P < 0.01). For the meta-analysis, thirteen articles and our current study were evaluated. VEGF levels was found to be associated with CKD risk (P < 0.00001). In the sub-group meta-analysis, we found that the pooled MD of VEGF levels was related to the early CKD group, although the difference was not notable. However, the meta-analysis itself indicated that the pooled MD of VEGF levels were in accordance with severe CKD group (P < 0.00001). Furthermore, VEGF +936C/T T allele was not associated with CKD risk (P = 0.69). VEGF levels are apparently associated with CKD risk, especially in more severe CKD. Gene polymorphism analysis indicates that the VEGF +936C/T T allele is not associated with CKD risk.

INDEX-db: The Indian Exome Reference Database (Phase I)

Deep sequencing-based genetic mapping has greatly enhanced the ability to catalog variants with plausible disease association. Confirming how these identified variants contribute to specific disease conditions, across human populations, poses the next challenge. Differential selection pressure may impact the frequency of genetic variations, and thus detection of association with disease conditions, across populations. To understand genotype to phenotype correlations, it thus becomes important to first understand the spectrum of genetic variation within a population by creating a reference map. In this study, we report the development of phase I of a new database of genetic variations called INDian EXome database (INDEX-db), from the Indian population, with an aim to establish a centralized database of integrated information. This could be useful for researchers involved in studying disease mechanisms at clinical, genetic, and cellular levels.

Genomic copy number variation analysis in multiple system atrophy

Genomic variation includes single-nucleotide variants, small insertions or deletions (indels), and copy number variants (CNVs). CNVs affect gene expression by altering the genome structure and transposable elements within a region. CNVs are greater than 1 kb in size; hence, CNVs can produce more variation than can individual single-nucleotide variations that are detected by next-generation sequencing. Multiple system atrophy (MSA) is an α-synucleinopathy adult-onset disorder. Pathologically, it is characterized by insoluble aggregation of filamentous α-synuclein in brain oligodendrocytes. Generally, MSA is sporadic, although there are rare cases of familial MSA. In addition, the frequencies of the clinical phenotypes differ considerably among countries. Reports indicate that genetic factors play roles in the mechanisms involved in the pathology and onset of MSA. To evaluate the genetic background of this disorder, we attempted to determine whether there are differences in CNVs between patients with MSA and normal control subjects. We found that the number of CNVs on chromosomes 5, 22, and 4 was increased in MSA; 3 CNVs in non-coding regions were considered risk factors for MSA. Our results show that CNVs in non-coding regions influence the expression of genes through transcription-related mechanisms and potentially increase subsequent structural alterations of chromosomes. Therefore, these CNVs likely play roles in the molecular mechanisms underlying MSA.