Advances in the Genetics of Hypertension: The Effect of Rare Variants

Alessia Russo, Cornelia Di Gaetano, Giovanni Cugliari, Giuseppe Matullo
2018 International Journal of Molecular Sciences  
Worldwide, hypertension still represents a serious health burden with nine million people dying as a consequence of hypertension-related complications. Essential hypertension is a complex trait supported by multifactorial genetic inheritance together with environmental factors. The heritability of blood pressure (BP) is estimated to be 30-50%. A great effort was made to find genetic variants affecting BP levels through Genome-Wide Association Studies (GWAS). This approach relies on the "common
more » ... isease-common variant" hypothesis and led to the identification of multiple genetic variants which explain, in aggregate, only 2-3% of the genetic variance of hypertension. Part of the missing genetic information could be caused by variants too rare to be detected by GWAS. The use of exome chips and Next-Generation Sequencing facilitated the discovery of causative variants. Here, we report the advances in the detection of novel rare variants, genes, and/or pathways through the most promising approaches, and the recent statistical tests that have emerged to handle rare variants. We also discuss the need to further support rare novel variants with replication studies within larger consortia and with deeper functional studies to better understand how new genes might improve patient care and the stratification of the response to antihypertensive treatments. Ninety-five percent of hypertensive patients presents a type lacking an obvious identifiable cause (Essential or Primary Hypertension). Investigations of twin and family studies revealed a moderate heritability ranging between 30% and 50% [6, 7] . Hypertension is a heterogeneous disease; besides genetic variation, several factors such as age, sex, and ethnicity influence this trait, in addition to other environmental factors (e.g., lipid levels and obesity). So far, the study of hypertension has mostly been based on Genome-Wide Association Studies (GWAS). GWAS represent a valuable approach to type hundreds of thousands of Single Nucleotide Polymorphisms (SNPs) in very large cohorts. During the last ten years, many studies have been published thanks to the setting of very large consortia, including the International Consortium for Blood Pressure Genome-Wide Association Studies, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) and Global BPgen, the Wellcome Trust Case Control Consortium Studies, the UK Biobank, the PBCHARGE-EchoGen consortium, the CHARGE-HF consortium [8] [9] [10] [11] [12] , leading to the identification of more than 100 SNPs implicated in BP levels, as recently reviewed by Seidel and Scholl [13] . The cause of a complex trait, like essential hypertension, remains elusive if examined in the light of the GWAS results. There has been a step forward compared to the classic GWAS analyses thanks to system genetics approaches and related statistical methods [14] . These approaches use intermediate phenotypes, such as transcript, protein, or metabolite levels, and quantify and integrate them with several traits of interest. Several genes pathways and networks underlying common human diseases have been discovered using systems genetics studies. For example, data derived from GWAS were integrated with expression data to provide a measure of functional variation, i.e., the expression Quantitative Trait Loci (eQTL). When one of these loci is located within ≤1 Mb from the gene encoding the transcript, it is termed a cis-eQTL. When an eQTL affects the expression level of a distal gene, it is called trans-eQTL. Disease susceptibility can be regulated by a plethora of genes controlled by trans-eQTLs which, for this reason, are very informative [15] . Thanks to studies based on rat, mouse, or human cells and tissues, it has been calculated that about 30% of mammalian genes are under the control of eQTLs and they heavily contribute to complex disease susceptibility [16] . Moreover, using comparative genomics between established rat models of hypertension and humans, several studies have shown that human genes found to be associated with hypertension through GWAS, when conserved in the rat, are likely to form both cisand trans-acting eQTLs in multiple tissues [17] . These studies have also taken advantage of a statistical methods known as Weighted Gene Co-Expression Network Analysis (WGCNA) that studies biological networks based on pairwise correlations between variables and is often used to highlight clusters (modules) of highly correlated genes [18] . At the heart of the GWAS-based approaches lies the "common variant-common disease" hypothesis. However, when considering all the detected high-frequency variants in aggregate, the percentage of BP variability explained by genetic variants accounts for only 2-3%. Moreover, blood pressure changes related to different genotypes at these loci are estimated to be modest, approximately 1.0 and 0.5 mmHg for SBP and DBP, respectively [19] . Considering the moderate effects and the scarce genetic control ascribable to high-frequency variants, two possible scenarios came forward: a wrong heritability was estimated, or alleles are more likely to be heterogeneous and uncommon. Furthermore, array-based technologies were infrequently conducted for the detection of causal polymorphisms. These observations implicated strong limits in exploiting GWAS to identify druggable targets with high confidence and supported the idea that rare (frequency < 1%) and uncommon (frequency between 1% and 5%) functional variants may explain a greater fraction of hypertensive individuals. The arrival of Next-Generation Sequencing (NGS) technologies facilitated a shift in focus from common to rare variants and provided the opportunity to unravel the genomic architecture underlying hypertension risk. Along with the development of even more advanced laboratory methodologies, statistical genetic models must also evolve to meet the challenge of using rare variants to link previously unidentified genome loci to BP changes [20, 21] .
doi:10.3390/ijms19030688 pmid:29495593 pmcid:PMC5877549 fatcat:eix5fr6hcrdzrnsaexbe53orfq