Whole-Genome Sequencing of 100 Genomes Identifies a Distinctive Genetic Susceptibility Profile of Qatari Patients with Hypertension

Figure 1. A flowchart representing the genetic resources and bioinformatics methods used in the present research. Thousands of hypertension-related articles were reviewed, as well as the SNP genotyping of 200 Qataris with high blood pressure (A) using text mining (B), statistical analysis (C), and GWAS (D). The text mining analysis included eliminating redundant words, recognizing unique phrases, detecting genes and SNP expressions, and categorizing these genes and SNPs based on their prevalence and redundancy (B). Fifty-three different clinical data of the studied patients were retrieved from QBB and used for statistical analysis (C). The SNP genotyping data were analyzed using a quality control method before being aligned/mapped to the human genome, where discovered SNPs were annotated and their association with high blood pressure was investigated using GWAS analysis (D). The details obtained from text mining and GWAS analysis was analyzed using gene ontology and enrichment analyses (E).

Figure 2. The principal component analysis of hypertension data. The PCA analysis of the 200 Qataris clustering based on 53 different clinical parameters, including blood pressure measurements.

Figure 3. The statistical correlation analysis of hypertension data. The correlation analysis of different clinical parameters from the 200 Qataris obtained from QBB, with blood pressure parameters highlighted in red circles. (A) Graph of a correlation matrix showing positive and negative correlations with significance indicated by p values less than 0.01. (B) A correlation network graph displaying the statistical interaction between various clinical parameters, including blood pressure factors in red.

Figure 4. The chromosomal distribution of the identified SNPs and their association scores with EH. The colored small circles represent SNPs’ location on the human genome and their hypertension association score (−log10 of p value) according to the Fisher’s exact test. Blue and small circles represent non-significant SNPs, while large and red circles represent significant SNPs (A). The number of SNPs according to human chromosome (B). Predicted functional consequences. SNPs are classified into several groups based on their location in and effect on gene structures, which include coding and non-coding regions according to different databases (C).

Figure 5. The genic structure of SNPs associated with EH. (A) The allele frequency vs. FET scores -log10 of the studied SNP variants. Allele frequency of SNP variants (x-axis) vs. FET scores -log10 (y-axis), the SNP genic region (color) and the maximum allele count in studied population (size). (B) The percentage of SNPs distribution across gene structure.

Figure 6. Graphical representation of the location of some SNPs linked to hypertension. The physical location of several SNPs discovered to be associated with hypertension using GWAS analysis.

Figure 7. The gene ontology and PPI analysis of highly frequently occurring genes that are associated with EH in both the literature and our GWAS analysis. The most important gene ontology terms associated with EH (A) and their ontology class (B). The PPI interaction (C) of EH-associated genes found through GWAS and literature analysis separately and together; connecting lines (D) signify the biological interactions.

Figure 8. The disease–gene analysis of the cardiovascular disorders (A) and genes associated with hypertension (B) in the studied Qatari population. The comparison of the presence of various genes (C) and disease (D).

Figure 9. The most frequently mentioned genes in the hypertension literature (A,B). The size of the circle, as well as the numbers included, represent the number of articles that mentioned a genetic link between these genes and EH disorder (A). The most frequently mentioned genes in the hypertension literature and their corresponding biological processes according to the enrichment analysis (C). The histogram depicts the number of hypertension publications that listed these genes (A,B). Biological processes were allocated to hypertension-associated genes (C), with gray bars representing the number of genes found within these processes and colored connections representing genes that contribute to these processes. A colored link is shown between the biological process and the corresponding EH-linked genes, with each gene potentially linked to multiple pathways or vice versa (D).

Figure 10. Comparison between the EH-associated genes identified in the GWAS and text mining analysis.

Figure 11. The clustering analysis of hypertension-associated SNPs (≤4 log-p value) across different ethnic groups. The SNP identity number (SNPID), maximum allele count (AC), EH association log-p value score, chromosome (Chr), overlapped gene (OG), nearest upstream gene (NUG), and nearest downstream gene (NDG).