Lack of an association between TSC gene Arg904Gln polymorphisms and essential hypertension risk based on a meta-analysis

Elevated blood pressure: Our family's fault? The genetics of essential hypertension

AIM

To provide an updated review on current genetic aspects possibly affecting essential hypertension (EH), and to further elucidate their role in EH.

METHODS

We searched for genetic and epigenetic factors in major studies associated with EH between Jan 2008-Oct 2013 using PubMed. We limited our search to reviews that discussed mostly human studies, and were accessible through the university online resource. We found 11 genome wide association studies (GWAS), as well as five methylation and three miRNA studies that fit our search criteria. A distinction was not made between genes with protective effects or negative effects, as this article is only meant to be a summary of genes associated with any aspect of EH.

RESULTS

We found 130 genes from the studies that met our inclusion/exclusion criteria. Of note, genes with multiple study references include: STK39, CYP17A1, MTHFR-NPPA, MTHFR-NPPB, ATP2B1, CSK, ZNF652, UMOD, CACNB2, PLEKHA7, SH2B3, TBX3-TBX5, ULK4, CSK-ULK3, CYP1A2, NT5C2, CYP171A, PLCD3, SH2B3, ATXN2, CACNB2, PLEKHA7, SH2B3, TBX3-TBX5, ULK4, and HFE. The following genes overlapped between the genetic studies and epigenetic studies: WNK4 and BDKRB2. Several of the identified genes were found to have functions associated with EH. Many epigenetic factors were also correlated with EH. Of the epigenetic factors, there were no articles discussing siRNA and its effects on EH that met the search criteria, thus the topic was not included in this review. Among the miRNA targets found to be associated with EH, many of the genes involved were also identified in the GWAS studies.

CONCLUSION

Genetic hypertension risk algorithms could be developed in the future but may be of limited benefit due to the multi-factorial nature of EH. With emerging technologies, like next-generation sequencing, more direct causal relationships between genetic and epigenetic factors affecting EH will likely be discovered creating a tremendous potential for personalized medicine using pharmacogenomics.

Particle swarm optimization algorithm for analyzing SNP-SNP interaction of renin-angiotensin system genes against hypertension

Most non-significant individual single nucleotide polymorphisms (SNPs) were undiscovered in hypertension association studies. Their possible SNP-SNP interactions were usually ignored and leaded to missing heritability. In present study, we proposed a particle swarm optimization (PSO) algorithm to analyze the SNP-SNP interaction associated with hypertension. Genotype dataset of eight SNPs of renin-angiotensin system genes for 130 non-hypertension and 313 hypertension subjects were included. Without SNP-SNP interaction, most individual SNPs were non-significant difference between the hypertension and non-hypertension groups. For SNP-SNP interaction, PSO can select the SNP combinations involving different SNP numbers, namely the best SNP barcodes, to show the maximum frequency difference between non-hypertension and hypertension groups. After computation, the best PSO-generated SNP barcodes were dominant in non-hypertension in terms of the occurrences of frequency differences between non-hypertension and hypertension groups. The OR values of the best SNP barcodes involving 2-8 SNPs were 0.705-0.334, suggesting that these SNP barcodes were protective against hypertension. In conclusion, this study demonstrated that non-significant SNPs may generate the joint effect in association study. Our proposed PSO algorithm is effective to identify the best protective SNP barcodes against hypertension.