Association between KCNQ2, TCF4 and RGS18 polymorphisms and silent brain infarction based on whole‑exome sequencing
- Authors:
- Published online on: February 5, 2020 https://doi.org/10.3892/mmr.2020.10975
- Pages: 1973-1983
Abstract
Introduction
Silent brain infarction (SBI) is a cerebral infarction that is verified clinically by brain imaging (1). The occurrence of SBIs is influenced by multiple genetic and environmental factors (2). In an aging society, as in the case of Japan, medical prevention of SBI is important for preventing vascular dementia (3). SBI, a risk factor for stroke, is more frequently found with the advent of modern MRI technology, and the most direct consequence of SBI is a symptomatic stroke (4). For this reason, researchers have worked to demonstrate the relevance of SBI to stroke through comparative studies (5–10). As SBI damages the brain without causing identifiable symptoms, the risk of subsequent transient ischemic attacks and major strokes increases (11). SBI detection could provide more information concerning ischemic tolerance because it yields conclusive results for imaging (12). However, few detailed studies on SBI have been performed to date, opening a timely and necessary opportunity for SBI research.
Next-generation sequencing (NGS) technologies, such as whole-genome sequencing and whole-exome sequencing (WES), are useful for detecting and discovering new variants that can account for a number of heritable diseases and disorders (13–15). In particular, WES focuses on the coding region (i.e., the exons) of the genome, which corresponds to approximately 2.5% of the human genome, and identifies rare or common variants associated with a disorder or phenotype (16). Using WES, variants that were associated with SBI risk were selected based on the following criteria: Fisher's exact test (P<0.05), Hardy-Weinberg equilibrium (HWE) >0.05 and minor allele frequency (MAF) >0.01 (Table SI). Based on these analyses, four single nucleotide polymorphisms (SNPs) were investigated, including potassium voltage-gated channel subfamily Q member 2 (KCNQ2 rs73146513 A>G), transcription factor 4 (TCF4 rs9957668 T>C) and regulator of G-protein signaling 18 (RGS18 rs4329489 A>G and rs4454527 A>G).
KCNQ2 encodes a transmembrane potassium channel that is a member of the acetylcholinergic pathway (17). Pathogenic mutations of KCNQ2 are associated with epilepsy (17) and pre-eclampsia (18), suggesting that KCNQ2 is important in neuronal signaling and artery development. Furthermore, previous findings demonstrated that KCNQ2 expression and function were associated with pathways involving neurotransmitters, and cerebral arterial development and maintenance (19–22). TCF4 encodes a transcription factor that is also known as immunoglobulin transcription factor 2 (ITF-2) (23). ITF-2 binds to the immunoglobulin enhancer µ-E5/κ-E2 motif to initiate transcription of its target genes (23). TCF4 is primarily involved in the neurological development of the fetus during pregnancy and initiates neural differentiation by binding to DNA (24). TCF4 gene variants have been reported to be associated with Pitt-Hopkins syndrome (25) and epileptic encephalopathy (26). Finally, RGS18 is involved in the G-protein signaling pathway and is a member of the regulator of the G-protein signaling family (27). RGS18 hydrolyzes G protein and thereby plays an important role in the cell signaling pathway (27,28). Genetic variants of RGS18 occur in metabolic disorders and are associated with numerous diseases (29). Specifically, previous fjndings have demonstrated an associated between RGS18 variants and platelet aggregation, hemostasis and thrombosis (29,30).
In the present study, four polymorphisms were investigated, namely KCNQ2 rs73146513 A>G, TCF4 rs9957668 T>C, RGS18 rs4329489 A>G and RGS18 rs4454527 A>G, based on WES data and their association with the risk of SBI. All four SNPs were located in non-coding regions and no functional differences due to these polymorphisms have been reported. Despite these reports, in the present study NGS analysis revealed that these four SNPs had MAF >0.01, genotype frequencies >0.05 and HWE P>0.05. Importantly, the MAFs were significantly enriched in patients with SBI with a P-value from Fisher's exact test of <0.05. Therefore, TCF4 was selected as the subject of the case-control study. The correlations of each polymorphism were investigated, alone and in combination, with SBI in a Korean population. The present results suggested the possibility of a biomarker for SBI through these key polymorphisms in the Korean population.
Materials and methods
Ethics statement
All study protocols of participants were reviewed and approved by The Institutional Review Board of CHA Bundang Medical Center and followed the recommendations of The Declaration of Helsinki. Study subjects were recruited by the CHA Bundang Medical Center from the South Korean provinces of Seoul and Gyeonggi-do between June 2000 and December 2010. The Institutional Review Board of CHA Bundang Medical Center approved this genetic study in June 2000 and informed consent was obtained from the study participants.
Study population
SBI was diagnosed using the following criteria (2,31): i) Spot areas with a diameter of ≥3 mm in the area supplied by deep penetrating arteries; ii) the same spot areas showing high intensity in T2 and FLAIR (fluid attenuated inversion recovery) images and low intensity in T1 images; iii) absence of neurological symptoms corresponding to MRI lesions; and iv) no history of clinical stroke, including transient ischemic attack. Subjects were also required to be descendants of Koreans living in Seoul or Kyeonggi-do. Patients with SBI had no signs or symptoms of neurological disorders and no clinical history of stroke, including transient ischemic attacks. Patients with a history of stroke or cardiovascular disease were excluded from this study.
Over the same period, 401 control subjects (172 males, 229 females; age range, 20–97 years; mean ± SD, 63.10±11.36) were selected from patients who had visited our hospitals for biochemical tests, electrocardiograms and brain MRIs, and only those without a previous history of myocardial infarction or cerebral vascular disease were included. Control subjects were matched to the SBI group by age and sex. Baseline demographic data and a history of risk factors were obtained from each group. As with the SBI group, subjects with known stroke or cardiovascular disease were excluded. Among the initial 891 participants evaluated, 83 were excluded, leaving 401 controls and 407 cases.
Hypertension was diagnosed using high baseline blood pressure readings (systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg) when one or more antihypertensive medications were being taken or prescribed. Diabetes mellitus was defined as current high fasting plasma glucose levels (≥126 mg/dl) or current treatment with oral hypoglycemic or insulin. Hyperlipidemia was defined as a high serum total cholesterol level (≥240 mg/dl) in the fasting state or a history of antihyperlipidemic treatment. A complete description of the study was provided. Written consent was obtained from all the subjects regarding the provision of information.
WES
WES was performed on samples from 20 control subjects and 20 patients with SBI, they were selected from each total group (controls: 401 individuals and SBI: 407 individuals), considering the sex ratio and mean age of the population. In control subjects, 20 individuals were selected who had no family history of myocardial infarction or cerebral vascular disease. A total of 20 patients with SBI applied the same criteria as the selection criteria of the entire sample group (2,31).
WES was conducted by Macrogen, Inc. The libraries were sequenced on an Illumina HiSeq 2000/2500/4000 instrument, and the analysis was performed using Burrows-Wheeler Alignment Tool (version 0.7.12; bio-bwa.sourceforge.net), Picard (version 1.130; broadinstitute.github.io/picard), Genome Analysis Toolkit (version 3.4.0; gatk.broadinstitute.org/hc/en-us/articles/360035530852-How-should-I-cite-GATK-in-my-own-publications) and SnpEff (version 4.1g) software (32). The annotation databases included dbSNP (142), 100 Genome (Phase 3) (33), ClinVar (05/2015) and ESP (ESP6500SI_V2). The accession number for the data was PRJNA601005. The resulting WES data were used to select SNPs for additional study based on the following criteria: significant Fisher's exact test (P<0.05), HWE >0.05 and MAF >0.01 (Table SI). SNPs found only in control or patient groups were excluded. Of the genes that met these criteria, genes that had been previously implicated in brain diseases were selected for additional study. Genes associated with the platelet formation pathway were also included for additional analysis. To ensure high-quality and consistent experimental results, only genes that met all the criteria were investigated further in this study.
Genotyping
Genomic DNA was extracted from leukocytes using a G-DEX™ Genomic DNA Extraction kit (Intron Biotechnology, Inc.). Genetic polymorphisms were determined by PCR restriction fragment length polymorphism (RFLP) analysis. The PCR primers and PCR conditions for KCNQ2, TCF4 and RGS18 polymorphisms are described below.
The sequences of the KCNQ2 rs73146513 A>G primers were: Forward, 5′-CGGTCACAGTTCCAGACACA-3′ and reverse, 5′-TGGCCCTGCTTGTCTTTCCT-3′. PCR conditions included an initial denaturation at 95°C for 15 min; 35 cycles of denaturation at 95°C for 30 sec, annealing at 61°C for 30 sec and extension at 72°C for 30 sec; and final extension at 72°C for 5 min. The 321 bp PCR product was then digested with 5U DdeI and yielded the following fragments: GG, 321 bp; GA, 321, 256 and 65 bp; and AA, 256 and 65 bp.
The sequences of the TCF4 rs9957668 T>C primers were: Forward, 5′-TAAACCAAGGCCAAGTCTCCC-3′ and reverse, 5′-GGCCCCTTAAAAGAAAGGCCT-3′. PCR conditions included an initial denaturation at 95°C for 5 min; 35 cycles of denaturation at 95°C for 30 sec, annealing at 63°C for 30 sec and extension at 72°C for 30 sec; and final extension at 72°C for 5 min. The PCR product was digested with NsiI and yielded the following fragments: TT, 356 and 297 bp; TC, 636, 356 and 297bp; and CC, 636 bp.
The sequences of the RGS18 rs4329489 A>G primers were: Forward, 5′-TGTTATCTGTGCCCTTTAACC-3′ and reverse, 5′-ATGATTCACCCCATTTCACTG-3′. PCR conditions included an initial denaturation at 95°C for 15 min; 35 cycles of denaturation at 95°C for 30 sec, annealing at 60°C for 30 sec and extension at 72°C for 30 sec; and final extension at 72°C for 5 min. The 335 bp PCR product was digested with 5U HpyCH4V and yielded the following fragments: AA, 335 bp; AG, 335, 175 and 160 bp; and GG, 175 and 160 bp.
Finally, the sequences of the RGS18 rs4454527 A>G primers were: Forward, 5′-GATTGTCGGTGAGCAAAAGG-3′ and reverse, 5′-CGGGTGTCTTCATGAAACTC-3′. PCR conditions included an initial denaturation at 95°C for 15 min; 35 cycles of denaturation at 95°C for 30 sec, annealing at 58°C for 30 sec and extension at 72°C for 30 sec; and final extension at 72°C for 5 min. The PCR product was digested by 5U NdeI and yielded the following fragments: AA, 265 bp; AG, 265, 232 and 33 bp; and GG, 232 and 33 bp.
To validate RFLP findings, 10% of the PCR assays for each polymorphism were randomly selected, repeated and subjected to DNA sequencing. Sequencing was performed with an ABI 3730×l DNA Analyzer (Applied Biosystems; Thermo Fisher Scientific, Inc.). The concordance of the quality control samples was 100%.
Statistical analysis
To estimate the relative risk of the KCNQ2, TCF4 and RGS18 genotypes for SBI, the odds ratio (OR) and 95% confidence intervals (CIs) were calculated using Fisher's exact test. Case and control subjects were compared using two-sided t-tests for continuous variables and the Chi-square test was used for categorical variables. The adjusted ORs (AORs) for KCNQ2, TCF4 and RGS18 polymorphisms were determined from logistic regression analyses used to adjust for possible confusion, including age, sex, hypertension, diabetes mellitus and hyperlipidemia. P<0.05 was considered statistically significant for all the tests. To solve the multiple comparison problem, post-hoc analyses were performed using false discovery rates. Analyses were performed using GraphPad Prism 4.0 (GraphPad Software, Inc.) and MedCalc version 12.7.1.0 (MedCalc Software bvba; http://www.medcalc.org; 2013). Haplotypes were evaluated using HAPSTAT software version 3.0 (version 3.0; by maximizing the likelihood that properly accounts for phase uncertainly and study design) (34). Genetic interaction analysis was carried out using the multidimensional reduction (MDR) software package (v.2.0), available from www.epistasis.org.
Results
Clinical characteristics
Various clinical characteristics, including homocysteine, folate, vitamin B12, total cholesterol, platelet (PLT), prothrombin time (PT), activated partial thromboplastin time (aPTT) and fibrinogen levels of patients with SBI and controls are summarized in Table I. The patients with SBI had significantly increased of levels of hypertension, homocysteine, total cholesterol, PT, systolic blood pressure (SBP) and diastolic blood pressure (DBP). Patients with SBI also had decreased levels of smoking frequency and vitamin B12 levels.
Genotype frequencies
The genotype frequencies of KCNQ2, TCF4 and RGS18 polymorphisms were then compared between patients with SBI and control subjects. AORs were calculated using multivariate logistic regression analyses with respect to age, sex and incidence of hypertension, diabetes mellitus and hyperlipidemia (Table II). The frequencies of KCNQ2, TCF4 and RGS18 genotypes in patients with SBI and control subjects were consistent with the expected frequencies under HWE (P>0.05). It was shown that only the TCF4 rs9957668 T>C polymorphism correlated significantly with SBI prevalence (TT vs. CC: AOR, 1.815, 95% CI, 1.202–2.740; TT vs. TC+CC: AOR, 1.492, 95% CI, 1.066–2.088; TT+TC vs. CC: AOR, 1.454, 95% CI, 1.045–2.023).
 | Table II.Genotype frequency of KCNQ2, TCF4 and RGS18 gene polymorphisms between patients with SBI and control subjects. |
To determine whether combinations of the selected variants were associated with SBI risk, genotype combination frequencies of KCNQ2 rs73146513 A>G, TCF4 rs9957668 T>C, RGS18 rs4329489 A>G and RGS18 rs4454527 A>G were analyzed (Table III). It was found that several genotype combinations were significantly associated with SBI when the SBI group was compared with the control group. Specifically, these combinations included KCNQ2 rs73146513 and TCF4 rs9957668 genotype combinations [AA-TC (AOR, 2.662, 95% CI, 1.274–5.561, P=0.009), AA-CC (AOR, 3.201, 95% CI, 1.387–7.387, P=0.006), AG-CC (AOR, 3.719, 95% CI, 1.766–7.833, P=0.001) and GG-TC (AOR, 3.193, 95% CI, 1.405–7.252, P=0.006)]; KCNQ2 rs73146513 and RGS18 rs4454527 genotype combinations [AA-AG (AOR, 2.174, 95% CI, 1.201–3.935, P=0.010) and GG-AA (AOR, 1.784, 95% CI, 1.013–3.140, P=0.045)]; and a TCF4 rs9957668 and RGS18 rs4329489 genotype combination [CC-AA (AOR, 1.731, 95% CI, 1.036–2.890, P=0.036)]. In contrast, a genotype combination of RGS18 rs4329489 and RGS18 rs4454527 [AG-AA (AOR, 0.504, 95% CI, 0.321–0.791, P=0.003)] was associated with lower SBI prevalence (Table IV). Furthermore, allele combination analyses were conducted to compare patients with SBI and control subjects using the MDR method (Table SII). All data for genotype combination frequencies of KCNQ2 rs73146513 A>G, TCF4 rs9957668 T>C, RGS18 rs4329489 A>G and RGS18 rs4454527 A>G are detailed in Tables SIII and SIV.
| Table III.Allele combinations for the KCNQ2, TCF4 and RGS18 gene polymorphisms in patients with SBI and control subjects by MDR. |
| Table IV.Combined genotype analysis for the KCNQ2, TCF4 and RGS18 gene polymorphisms between patients with SBI and control subjects. |
t-tests were performed between KCNQ2/TCF4 genotype combinations and clinical parameters (Table IV). The comparisons included only genotype combinations that were statistically significant in the analysis detailed in Table IV. Several combined genotypes of KCNQ2 rs73146513 and TCF4 rs9957668, namely, AA-TC, AA-CC, AG-CC and GG-TC, were associated with increased SBI prevalence. In this analysis, the KCNQ2 and TCF4 AA-CC genotype combination correlated with a significant difference in PT and uric acid levels (PT, 12.10±1.38, P=0.043; uric acid, 4.41±1.20, P=0.044), and the GG-TC combination correlated with a significant difference in PT and fasting blood sugar (FBS) levels (PT, 12.14±1.00, P=0.004; FBS, 105.27±23.80, P=0.046). In addition, the association of various clinical parameters and genotypes in patients with SBI, as well as the risk of disease with each SNP under various conditions were analyzed by stratification analyses. These analyses are detailed in Tables SV–SXI but did not yield any significant associations.
Discussion
To date, the risk factors for SBI have been poorly defined. There are reports that SBIs occur more frequently in women than in men (35); however, this hypothesis is not clearly established and few data exist to support the claim. Typical risk factors, such as diabetes and smoking, are also not clearly correlated with SBI risk (35). In fact, the only patient characteristic that has been conclusively classified as an independent risk factor for SBI is hypertension (36). The present study provides data that support classifying diabetes as a risk factor for SBI. Moreover, the SBI risk factors reported here are similar to those reported for symptomatic strokes (37). Identification of genetic risk factors would further the clinical ability to detect and address SBI occurrence prior to the onset of more serious neurological events.
To address the genetic risk of SBI, previously reported genetic variants were analyzed by NGS analysis and selected variants of interest based on MAF and genotype frequencies in SBI and control subjects. A variant set of three genes were constructed (Table SI) and confirmed these three genes in a large sample of patients with SBI and control subjects. Through validation, false positives and errors were eliminated, and the remaining four SNPs were analyzed. In the present study, the associations between KCNQ2 rs73146513 A>G, TCF4 rs9957668 T>C, RGS18 rs4329489 A>G and RGS18 rs4454527 A>G polymorphisms and SBI prevalence were examined. It was found that TCF4 rs9957668 T>C genotypes were strongly associated with SBI susceptibility. In particular, the TCF4 rs9957668 T>C CC genotype was found to be approximately 7% higher in patients with SBI than in control subjects. The C allele in TCF4 rs9957668 T>C had an AOR of 1.325 after adjusting for several SBI risk factors, which included age, sex, hypertension, diabetes mellitus and hyperlipidemia. This result indicated that the risk of SBI was increased approximately 1.3 times in patients with this polymorphism. Therefore, TCF4 rs9957668 T>C may be a specific polymorphism that indicates susceptibility to SBI. Based on these data, it is suggested that the TCF4 rs9957668 T>C genotype may contribute to the occurrence of SBI and should be considered during diagnosis of the disease.
In addition, the TCF4 rs9957668 T>C polymorphism may be an important diagnostic indicator of psychosis in patients, because SBI increases the risk of stroke and dementia (37). In the WES results (Table SI), the OR of TCF4 rs9957668 T>C was 6.261, indicating high sensitivity to SBI. However, as the number of samples increased, the OR of TCF4 rs9957668 T>C decreased approximately 1.6 times. This phenomenon raises some important questions concerning methodologies that are based on WES results and the importance of providing answers to these questions in articles reporting such data. In fact, it was confirmed that, although the OR of TCF4 rs9957668 T>C changed, the overall outcome remained consistent. Thus, these results can be reported with confidence in our hypothesis based on WES results.
In addition to single variant effects, the present study also investigated the effect of SNP combinations on the prevalence of SBI. As TCF4 rs9957668 was independently associated with SBI risk, several genotype combinations were analyzed and significant associations were found. In these combinatorial analyses, the ORs were low and the probability of statistical significance was also reduced due to the small sample size. Nevertheless, several genotype combinations of TCF4 rs9957668 and KCNQ2 rs73146513 increased the risk of SBI. Naturally, TCF4 rs9957668 is likely to play a substantial role in these associations, but it is important to note that the combination of KCNQ2 and TCF4 increased the association with SBI by ~2–3 times compared to TCF4 rs9957668 alone. Such analyses may be a useful basis for predicting disease risk due to genetic variation.
A number of previous studies have linked TCF4 to intelligence, schizophrenia and endothelial dystrophy (38–40). However, evidence regarding its association with cerebrovascular diseases, such as stroke or SBI, is lacking. Therefore, it must be acknowledged that there are some limitations of the present study. First, it needs to be confirmed whether the susceptibility to SBI that is associated with the TCF4 rs9957668 T>C polymorphism is specific to Koreans or applies to other ethnic groups as well. If similar results are found in other races, this raises the possibility that TCF4 rs9957668 T>C could be a more general biomarker for SBI. Second, it remains to be clarified whether there is a correlation between schizophrenia and SBI. If the pathogenesis of schizophrenia is associated with the occurrence of SBI, this represents a new research direction for genes known to be related to this disease. One report out of Japan suggests that there has been an increase in the proportion of SBI and cerebral infarction in patients with psychosis relative to controls (37). Based on this report, it was postulated that an association between SBI and patients with psychosis could be identified. Third, the number of patients in this study with the TCF4 rs9957668 T>C polymorphism was very low, thereby limiting the ability to implicate it as a biological indicator of SBI. Generally, smaller study populations tend to have an increased error rate because statistical power is limited. Thus, it is important to have a large, representative cohort of patients to support the results found. Finally, SBI control studies of SNPs other than rs9957668 T>C polymorphism in the TCF4 gene are required to confirm specificity. Studies of other polymorphisms in the same gene could play a crucial role in interpretation of this and other results. If results from multiple studies conflict with each other, it will influence confidence in the results reported here.
It is acknowledged that these limitations may affect confidence in the present results. Therefore, the aim of future studies is to address these limitations, allowing clearer conclusions to be drawn. If future studies indicate that the TCF4 pathway is critical in the pathogenesis of SBI, it may suggest that modulating TCF4 gene expression and/or activity could facilitate prevention or early treatment of SBI. Finally, more epidemiological studies are needed to clarify the causal relationship between TCF4 polymorphisms and the prevalence of SBI, and meta-analyses of heterogeneous populations should be conducted.
In conclusion, an association between the prevalence of SBI with the KCNQ2 rs73146513 A>G, TCF4 rs9957668 T>C, RGS18 rs4329489 A>G and RGS18 rs4454527 A>G polymorphisms has been demonstrated in a Korean population. These results suggested that TCF4 polymorphism may contribute to SBI and be used as a potential biomarker to evaluate SBI risk.
Supplementary Material
Supporting Data
Acknowledgements
Not applicable.
Funding
The present study was partially supported by National Research Foundation of Korea Grants funded by the Korean Government (grant nos. NRF-2016R1D1A1B03930141 and NRF-2017R1D1A1 B03030110) and by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare (grant no. HI18C19990200), Republic of Korea.
Availability of data and materials
The datasets generated and/or analyzed during the current study are available in the NCBI SRA repository (www.ncbi.nlm.nih.gov/sra/PRJNA601005).
Authors' contributions
NKK and OJK were involved in study conception and design, and gave the final approval of the version to be published. JOK, HWK, HSP, JK and JHS were involved in data acquisition and analysis. JOK, KOL, HSP, DO and NKK interpreted the data for the work. JOK and HWK drafted the work. JOK and KOL revised the manuscript.
Ethics approval and consent to participate
All study protocols of participants were reviewed and approved by The Institutional Review Board of CHA Bundang Medical Center and followed the recommendations of the Declaration of Helsinki. The Institutional Review Board of CHA Bundang Medical Center approved this genetic study in June 2000 and informed consent was obtained from the study participants.
Patient consent for publication
Written consent was obtained from all subjects regarding the provision of information.
Competing interests
The authors declare that they have no competing interests.
Glossary
Abbreviations
Abbreviations:
|
AOR |
adjusted odds ratio |
|
aPTT |
activated partial thromboplastin time |
|
DBP |
diastolic blood pressure |
|
HWE |
Hardy-Weinberg equilibrium |
|
ITF-2 |
immunoglobulin transcription factor 2 |
|
KCNQ2 |
potassium voltage-gated channel subfamily Q member 2 |
|
MAF |
minor allele frequency |
|
MDR |
multifactorial demention reduction |
|
MRI |
magnetic resonance imaging |
|
NGS |
next-generation sequencing |
|
PLT |
platelet |
|
PT |
prothrombin time |
|
RGS18 |
regulator of G-protein signaling 18 |
|
SBI |
silent brain infarction |
|
SBP |
systolic blood pressure |
|
TCF4 |
transcription factor 4 |
|
WES |
whole exome sequencing |
References
|
Kim SH, Shin DW, Yun JM, Lee JE, Lim JS, Cho BL, Kwon HM and Park JH: Kidney dysfunction and silent brain infarction in generally healthy adults. J Neurol Sci. 379:89–93. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Cho Y, Kim JO, Lee JH, Park HM, Jeon YJ, Oh SH, Bae J, Park YS, Kim OJ and Kim NK: Association of reduced folate carrier-1 (RFC-1) polymorphisms with ischemic stroke and silent brain infarction. PLoS One. 10:e01152952015. View Article : Google Scholar : PubMed/NCBI | |
|
Nakaguchi H, Matsuno A, Okubo T and Hoya K: Relationship between silent brain infarction and amount of daily coffee consumption in middle age. J Stroke Cerebrovasc Dis. 25:1678–1682. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Gupta A, Giambrone AE, Gialdini G, Finn C, Delgado D, Gutierrez J, Wright C, Beiser AS, Seshadri S, Pandya A, et al: Silent brain infarction and risk of future stroke: A systematic review and meta-analysis. Stroke. 47:719–725. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Hong SH, Park HM, Ahn JY, Kim OJ, Hwang TS, Oh D and Kim NK: ACE I/D polymorphism in Korean patients with ischemic stroke and silent brain infarction. Acta Neurol Scand. 117:244–249. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Jeon YJ, Kim OJ, Kim SY, Oh SH, Oh D, Kim OJ, Shin BS and Kim NK: Association of the miR-146a, miR-149, miR-196a2, and miR-499 polymorphisms with ischemic stroke and silent brain infarction risk. Arterioscler Thromb Vasc Biol. 33:420–430. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Kim MH, Moon JS, Park SY, An SA, Kim OJ, Kim NK and Oh SH: Different risk factor profiles between silent brain infarction and symptomatic lacunar infarction. Eur Neurol. 65:250–256. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Kim OJ, Lee JH, Choi JK, Oh SH, Hong SH, Oh D and Kim NK: Association between tumor necrosis factor-alpha (−308G→A and −238G→A) polymorphisms and homocysteine levels in patients with ischemic strokes and silent brain infarctions. Cerebrovasc Dis. 30:483–490. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Oh SH, Kim NK, Kim SH, Kim JK, Kim HS, Kim WC and Kim OJ: The prevalence and risk factor analysis of silent brain infarction in patients with first-ever ischemic stroke. J Neurol Sci. 293:97–101. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Park SY, An SA, Lee HB, Kim Y, Kim NK, Kim SH, Kim OJ and Oh SH: Different impact of hyperhomocysteinemia on cerebral small vessel ischemia and cervico-cerebral atherosclerosis in non-stroke individuals. Thromb Res. 131:e12–e16. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Kunikata H, Aizawa N, Kudo M, Mugikura S, Nitta F, Morimoto R, Iwakura Y, Ono Y, Satoh F, Takahashi H, et al: Relationship of ocular microcirculation, measured by laser speckle flowgraphy, and silent brain infarction in primary aldosteronism. PLoS One. 10:e01174522015. View Article : Google Scholar : PubMed/NCBI | |
|
Kwon HS, Kim YS, Park HH, Choi H, Lee KY, Lee YJ, Heo SH, Chang DI and Koh SH: Increased VEGF and decreased SDF-1α in patients with silent brain infarction are associated with better prognosis after first-ever acute lacunar stroke. J Stroke Cerebrovasc Dis. 24:704–710. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Fanning JP, Wong AA and Fraser JF: The epidemiology of silent brain infarction: A systematic review of population-based cohorts. BMC Med. 12:1192014. View Article : Google Scholar : PubMed/NCBI | |
|
Hoefler G: Next-generation sequencing in diagnostics and clinical research. Pathobiology. 84:289–291. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Malhotra S, Singh S and Sarkar S: Whole genome variant analysis in three ethnically diverse Indians. Genes Genomics. 40:497–510. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Zare F, Dow M, Monteleone N, Hosny A and Nabavi S: An evaluation of copy number variation detection tools for cancer using whole exome sequencing data. BMC Bioinformatics. 18:2862017. View Article : Google Scholar : PubMed/NCBI | |
|
Duan H, Peng J, Kessi M and Yin F: De novo KCNQ2 mutation in one case of epilepsy of infancy with migrating focal seizures that evolved to infantile spasms. Child Neurol Open. 5:2329048X187677382018. View Article : Google Scholar : PubMed/NCBI | |
|
Wei X, Zhang Y, Yin B, Wen J, Cheng J and Fu X: The expression and function of KCNQ potassium channels in human chorionic plate arteries from women with normal pregnancies and pre-eclampsia. PLoS One. 13:e01921222018. View Article : Google Scholar : PubMed/NCBI | |
|
Bonham LW, Evans DS, Liu Y, Cummings SR, Yaffe K and Yokoyama JS: Neurotransmitter pathway genes in cognitive decline during aging: Evidence for GNG4 and KCNQ2 genes. Am J Alzheimers Dis Other Demen. 33:153–165. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Goodwill AG, Fu L, Noblet JN, Casalini ED, Sassoon D, Berwick ZC, Kassab GS, Tune JD and Dick GM: KV7 channels contribute to paracrine, but not metabolic or ischemic, regulation of coronary vascular reactivity in swine. Am J Physiol Heart Circ Physiol. 310:H693–H704. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Ng FL, Davis AJ, Jepps TA, Harhun MI, Yeung SY, Wan A, Reddy M, Melville D, Nardi A, Khong TK, et al: Expression and function of the K+ channel KCNQ genes in human arteries. Br J Pharmacol. 162:42–53. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Spagnoli C, Salerno GG, Iodice A, Frattini D, Pisani F and Fusco C: KCNQ2 encephalopathy: A case due to a de novo deletion. Brain Dev. 40:65–68. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Kolligs FT, Nieman MT, Winer I, Hu G, Van Mater D, Feng Y, Smith IM, Wu R, Zhai Y, Cho KR, et al: ITF-2, a downstream target of the Wnt/TCF pathway, is activated in human cancers with beta-catenin defects and promotes neoplastic transformation. Cancer Cell. 1:145–155. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Henthorn P, McCarrick-Walmsley R and Kadesch T: Sequence of the cDNA encoding ITF-2, a positive-acting transcription factor. Nucleic Acids Res. 18:6781990. View Article : Google Scholar : PubMed/NCBI | |
|
de Pontual L, Mathieu Y, Golzio C, Rio M, Malan V, Boddaert N, Soufflet C, Picard C, Durandy A, Dobbie A, et al: Mutational, functional, and expression studies of the TCF4 gene in Pitt-Hopkins syndrome. Hum Mutat. 30:669–676. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Amiel J, Rio M, de Pontual L, Redon R, Malan V, Boddaert N, Plouin P, Carter NP, Lyonnet S, Munnich A, et al: Mutations in TCF4, encoding a class I basic helix-loop-helix transcription factor, are responsible for Pitt-Hopkins syndrome, a severe epileptic encephalopathy associated with autonomic dysfunction. Am J Hum Genet. 80:988–993. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Park IK, Klug CA, Li K, Jerabek L, Li L, Nanamori M, Neubig RR, Hood L, Weissman IL and Clarke MF: Molecular cloning and characterization of a novel regulator of G-protein signaling from mouse hematopoietic stem cells. J Biol Chem. 276:915–923. 2001. View Article : Google Scholar : PubMed/NCBI | |
|
Yowe D, Weich N, Prabhudas M, Poisson L, Errada P, Kapeller R, Yu K, Faron L, Shen M, Cleary J, et al: RGS18 is a myeloerythroid lineage-specific regulator of G-protein-signalling molecule highly expressed in megakaryocytes. Biochem J. 359:109–118. 2001. View Article : Google Scholar : PubMed/NCBI | |
|
Alshbool FZ, Karim ZA, Vemana HP, Conlon C, Lin OA and Khasawneh FT: The regulator of G-protein signaling 18 regulates platelet aggregation, hemostasis and thrombosis. Biochem Biophys Res Commun. 462:378–382. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Louwette S, Van Geet C and Freson K: Regulators of G protein signaling: Role in hematopoiesis, megakaryopoiesis and platelet function. J Thromb Haemost. 10:2215–2222. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Kim JO, Jeon YJ, Kim OJ, Oh SH, Kim HS, Shin BS, Oh D, Kim EJ, Cho YK and Kim NK: Association between common genetic variants of α2A-, α2B- and α2C-adrenoceptors and the risk of silent brain infarction. Mol Med Rep. 9:2459–2466. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Cingolani P, Platts A, Wang L, Coon M, Nguyen T, Wang L, Land SJ, Lu X and Ruden DM: A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 6:80–92. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
1000 Genomes Project Consortium, ; Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, et al: A global reference for human genetic variation. Nature. 526:68–74. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Lin DY, Hu Y and Huang BE: Simple and efficient analysis of disease association with missing genotype data. Am J Hum Genet. 82:444–452. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Vermeer SE, Den Heijer T, Koudstaal PJ, Oudkerk M, Hofman A and Breteler MM; Rotterdam Scan Study, : Incidence and risk factors of silent brain infarcts in the population-based Rotterdam Scan Study. Stroke. 34:392–396. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Kim MN, Kim JO, Lee SM, Park H, Lee JH, Rim KS, Hwang SG and Kim NK: Variation in the Dicer and RAN genes are associated with survival in patients with hepatocellular carcinoma. PLoS One. 11:e01622792016. View Article : Google Scholar : PubMed/NCBI | |
|
Kanzaki T, Uju Y, Sekine K, Ishii Y, Yoshimi T, Yasui R, Yasukawa A, Sato M, Okamoto S, Hisaoka T, et al: Increased silent brain infarction accompanied with high prevalence of diabetes and dyslipidemia in psychiatric inpatients: A cross-sectional study. Prim Care Companion CNS Disord. 2015 Mar 26;2015 (Epub ahead of print). doi: 10.4088/PCC.14m01713. View Article : Google Scholar : PubMed/NCBI | |
|
Quednow BB, Brzózka MM and Rossner MJ: Transcription factor 4 (TCF4) and schizophrenia: Integrating the animal and the human perspective. Cell Mol Life Sci. 71:2815–2835. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Rao BS, Tharigopala A, Rachapalli SR, Rajagopal R and Soumittra N: Association of polymorphisms in the intron of TCF4 gene to late-onset Fuchs endothelial corneal dystrophy: An Indian cohort study. Indian J Ophthalmol. 65:931–935. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Sepp M, Vihma H, Nurm K, Urb M, Page SC, Roots K, Hark A, Maher BJ, Pruunsild P and Timmusk T: The intellectual disability and schizophrenia associated transcription factor TCF4 is regulated by neuronal activity and protein kinase A. J Neurosci. 37:10516–10527. 2017. View Article : Google Scholar : PubMed/NCBI |
