Plateau essential hypertension is a common chronic harmful disease of permanent residents in plateau areas. Studies have shown some single nucleotide polymorphisms (SNPs) associations with hypertension, but few have been verified in plateau area-lived people. In this paper, we examined some hypertension-related gene loci to analyze the relationship between risk SNPs and plateau essential hypertension in residents in Qinghai-Tibet plateau area. We screened hypertension-related SNPs from the literature, Clinvar database, GHR database, GTR database, and GWAS database, and then selected 101 susceptible SNPs for detection. Illumina MiSeq NGS platform was used to perform DNA sequencing on the blood samples from 185 Tibetan dwellings of Qinghai, and bioinformatic tools were used to make genotyping. Genetic models adjusted by gender and age were used to calculate the risk effects of genotypes. Four known SNPs as well as a new locus were found associated with PHE, which were rs2493134 (AGT), rs9349379 (PHACTR1), rs1371182 (CYP2C56P-PRPS1P1), rs567481079 (CYP2C56P-PRPS1P1), and chr14:61734822 (HIF1A). Among them, genotypes of rs2493134, rs9349379, and rs567481079 were risk factors, genotypes of rs1371182 and chr14:61734822 were protective factors. The rs2493134 in AGT was found associated with an increased risk of the plateau essential hypertension by 3.24-, 3.24-, and 2.06-fold in co-dominant, dominant, and Log-additive models, respectively. The rs9349379 in PHACTR1 is associated with a 2.61-fold increased risk of plateau essential hypertension according to the dominant model. This study reveals that the alleles of AGT, HIF1A, and PHACTR1 are closely related to plateau essential hypertension risk in the plateau Tibetan population.
Background
The critical step in analyzing gene expression data is to divide genes into co-expression modules using module detection methods. Clustering algorithms are the most commonly employed technique for gene module detection. To obtain gene modules with great biological significance, the choice of an appropriate similarity measure methodology is vital. However, commonly used similarity measurement may not fully capture the complexities of biological systems. Hence, exploring more informative similarity measures before partitioning gene co-expression modules remains important.
Results
In this paper, we proposed a Dual-Index Nearest Neighbor Similarity Measure (DINNSM) algorithm to address the above issue. The algorithm first calculates the similarity matrix between genes using Pearson correlation or Spearman correlation. Then, nearest neighbor measurements are constructed based on the similarity matrix. Finally, the similarity matrix is reconstructed. We tested the six similarity measurement methods (Pearson correlation, Spearman correlation, Euclidean distance, maximum information coefficient, distance correlation, and DINNSM) by using four clustering algorithms: K-means, Hierarchical, FCM, and WGCNA on three independent gene expression datasets. The cluster evaluation was based on four indices: the Silhouette index, Calinski-Harabaz index, Adjust-Biological homogeneity index, and Davies-Bouldin index. The results showed that DINNSM is accurate and can get biologically meaningful gene co-expression modules.
Conclusions
DINNSM is better at revealing the complex biological relationships between genes and helps to obtain more accurate and biologically meaningful gene co-expression modules.
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