Large-scale GWAS and meta-analyses have been widely used to identify and validate common or novel susceptible gene variants in various medical diseases over the past decade. However, given the overall genotype-phenotype analyses, disease-modifying functional mutations and direct biological relevance to disease have yet to be elucidated completely20. In addition, the heritability of a specific trait cannot be fully explained by common SNPs of intronic or intergenic regions via GWASs that have been targeted to identify common variants in common complex diseases. Accordingly, even if a large number of susceptible loci were identified, a few cases showed their replication in an independent cohort. Thus, few disease-associated variants have been demonstrated in functional in vitro studies or used in treatment21. To overcome these limitations, an updated fine-mapping analysis was performed to identify the variant causality associated with human complex diseases and as a cost-effective genotyping strategy9. To date, many studies underscored the need for ‘feature selection’ to identify relevant “variables” using parametric or non-parametric models. However, feature selection is not a simple challenge and requires substantial genetic investigations. It is important to identify the causal or driver mutations linked to treatment of human complex diseases. The selection of genetic variants from GWAS is uncertain given the strongly correlated SNPs corresponding to a pairwise LD structure at the population level. A fine-mapping analysis facilitates the identification of creditable genetic variants to refine the selection bias such as false-positive variants based on the initial GWAS and to improve the findings of molecular functional studies9. Here, we performed a fine-mapping analysis based on the results of previous IA GWAS using the statistical method developed by Benner et al.14 Our findings may enable the identification of causal variants (true negative) and exclude potential false positives via statistically significant fine-mapping analysis of transformed GWAS results. Therefore, these analytical methods may enable the selection of functional candidate variants based on the molecular mechanisms associated with IA formation.
In this study, we found four causal genes that are potentially linked to IA such as GBA, TCF24, OLFML2A, and ARHGAP32. We speculated that these genetic variants may cause dysfunctional immune response and inflammation in DNA sequences damaged by amino acid substitution or gain- or loss-of-function mutations, which affects the IA formation. GBA located in the exonic regions of 1q22 (rs75822236) was significantly associated with IA12. More specifically, GWAS revealed that the “T” allele of this variant increased the risk of IA.12 In addition, a fine-mapping analysis also revealed a higher level of log10BF (15.06) and PIP (1.0), suggesting that this variant was a true positive for IA. The role of GBA was mainly investigated in Parkinson’s disease (PD) or Gaucher disease (GD), which is a recessive lysosomal storage disorder, and barely investigated in IA. Mata et al.22 reported that GBA mutations and E326K carrier were related to impaired working memory and executive function in patients with PD. In GD, null or severe homozygous mutations of GBA showed little or no human glucocerebrosidase activity23. These findings suggested differences in phenotype due to the various GBA mutations. Kleinloog et al.24 reported enrichment of the lysosomal pathway in ruptured IA compared with UIA based on RNA sequencing analysis of aneurysm wall. Although the lysosomal pathway does not reflect an acute reaction to IA rupture24, it is likely that it is induced by inflammation after bleeding.
OLFML2A and TCF24 showed a protective effect against IA formation with log10BF levels greater than 12 and completed PIPs. However, the relationship between these two genes and IA is still unclear, even though it has been implicated in cardiovascular diseases. Conversely, ARHGAP32 significantly increased the risk of IA with the highest log10BF (20.88) and completed PIP. ARHGAP32 refers to Rho GTPase-activating protein 32 and mediates N-methyl D-aspartate receptor signaling12. The role of ARHGAP32 has been mainly investigated in the regulation of blood pressure. Rho-specific GTPase-activating protein GRAF3 was highly expressed in smooth muscle cells (SMCs) and regulated blood pressure control by inhibiting the contractility of RhoA-mediated SMC25. GRAF3-deficient mice also showed increased blood pressure in response to angiotensin II and endothelin 126. In actual clinical practice, many patients manifest both IA and hypertension. Inci et al.27 reported that the rate of pre-existing hypertension was 43.5% in patients with IA, which was higher than 24.4% in the normal population. Hypertension may contribute to degeneration of the internal elastic lamina, weakening of the vessel wall, and IA formation27. Nevertheless, it is unclear whether the role of ARHGAP32 in IA is mediated indirectly via chronic hypertension or directly via change in vascular tone.
Functional network analyses showed that PSAP was an important gene in the development of IA. The role of PSAP gene was rarely investigated in IA and was mainly studied in PD. Oji et al.28 reported that two SNPs of rs4747203 and rs885828, the intronic regions of the PSAP saposin D domain were linked to PD. PSAP mutation can also result in dopaminergic neurodegeneration and motor decline in mice. Although we did not include patients with PD, a fine-mapping analysis revealed that PD-related genes such as GBA and PSAP may contribute to IA. Lysosomal dysfunction and the resulting lysosomal storage disorder can contribute causally to PD. Putative damaging variants in at least one gene associated with lysosomal storage disorder were observed in most PD patients29. However, lysosomal dysfunction can also be observed in the arterial wall. Lysosomal changes in the vascular SMCs were attributed to the accumulation of excessive substrate levels in the lysosomes of a primate model of atherosclerosis and hypertension30. The excessive sterol accumulation in lysosomes can disrupt the lysosomal function31. Therefore, in this case, it is possible that lysosomal dysfunction may directly affect IA formation or may contribute to IA via atherosclerosis. Hokari et al.32 reported that atherosclerotic factors strongly increased the risk of middle cerebral artery aneurysm compared with paraclinoid aneurysm. After securing aneurysm, consistent statin therapy was significantly correlated with better prognosis.33 Wu et al.34 demonstrated that the autophagy–lysosomal pathway, which entails self-digestion of dysfunctional intracellular components by lysosomal enzymes, was an important pro-survival mechanism after SAH. However, the study investigated the role of the lysosome after SAH development, but not in IA development itself. Therefore, additional studies are needed to investigate the role of candidate variants in lysosomal dysfunction resulting in IA formation via abnormal ECM remodeling in response to hemodynamic stress.
A fine-mapping analysis may enable the detection of mutations involving the candidate genes and thereby contribute to treatment based on genome-based precision medicine. Identifying the causal variants beyond GWAS provides a crucial blueprint in predicting disease risk and preventing IA as well as metabolic diseases and stroke35. To the best of our knowledge, this was the first fine-mapping analysis based on the results of a GWAS of IA. Nevertheless, the interpretation of the results may have some limitations. First, the sample size was relatively somehow small to discovery more functional mutations to achieve a sufficient statistical power, even though four common mutations have been detected in our study. Second, variables regarding aneurysm location and size were not considered in the analysis. Microscopic analysis of the aneurysm during actual surgery often reveals atherosclerotic changes in the aneurysm wall, especially in elderly patients with larger aneurysm. Accordingly, GWAS and subsequent fine-mapping analysis should be performed together given the metabolic status of a large number of patients with IA.
In summary, fine-mapping analysis robustly identified four functional mutations of causal candidate genes, such as GBA, TCF24, OLFML2A, and ARHGAP32 that have associated with IA. Mutations in these genes show play roles in immune and inflammatory systems according to our literature review and functional annotations. Their mutations suggest a possible polygenic inheritance of IA formation. Finally, our study will provide more informative and replicable causal susceptibilities to IA including four mutations in the second stage GWAS.