Neuromol Med DOI 10.1007/s12017-015-8352-z
ORIGINAL PAPER
KALRN Rare and Common Variants and Susceptibility to Ischemic Stroke in Chinese Han Population Meizheng Dang1 • Zhenzhen Wang1 • Ruyou Zhang2 • Xiaoying Li1 Yanqing Peng1 • Xuesong Han1 • Litao Sun1 • Jiawei Tian1
•
Received: 13 November 2014 / Accepted: 11 April 2015 Ó Springer Science+Business Media New York 2015
Abstract Stroke is the second most common cause of mortality worldwide, and it is a major cause of physical disability. Several genome-wide association studies have yielded numerous common variants which increase the risk of ischemic stroke, including the Kalirin-coding gene, KALRN. KALRN strongly associates with early-onset coronary artery disease and atherosclerosis and plays an important role in stroke in the European population. In this study, we analyzed four KALRN gene SNPs in 503 ischemic stroke patients and 493 control subjects, separating the patients into separate research groups based on comorbidity with hypertension or diabetes and stroke type (atherosis or lacunar and combination type). We found a rare variant of KALRN, rs11712619, that associated with lacunar stroke in the northern Chinese Han population with an average-risk allele frequency 0.009 (OR 2.95, 95 % CI 1.08–8.01, p = 0.028). However, after adjusting for relevant factors, including sex, age, body mass index, dyslipidemia, alcohol consumption, and smoking, this association was not evident. Additionally, the KALRN
Meizheng Dang and Zhenzhen Wang are co-first authors. & Litao Sun [email protected] & Jiawei Tian [email protected] 1
Department of Ultrasound, The 2nd Affiliated Hospital of Harbin Medical University, NO. 246, Xuefu Road, Nangang District, Harbin 150081, Heilongjiang, People’s Republic of China
2
Department of Neurosurgery, The 2nd Affiliated Hospital of Harbin Medical University, NO. 246, Xuefu Road, Nangang District, Harbin 150081, Heilongjiang, People’s Republic of China
variant rs6438833 was associated with ischemic stroke, ischemic stroke comorbid with diabetes, and lacunar stroke after adjusting for the relevant factors (p = 0.046, p = 0.019 and p = 0.046, respectively), which remained significant after 10,000 permutation procedure test (p0 = 0.047, p0 = 0.018 and p0 = 0.048, respectively). The association of these rare and common variants of KALRN with ischemic stroke in northern Chinese Han population offers insight for potential therapeutic research.
Keywords Ischemic stroke Kalirin Association study KALRN isoform
Introduction Stroke is the second most common cause of mortality worldwide and major cause of physical disability (Donnan et al. 2008). Over two-thirds of stroke deaths are in developing countries (Liu et al. 2007). The etiology of ischemic stroke depends on both inherited predisposition and environmental factors. A large number of studies have repeatedly shown that a positive family history is a robust predictor of ischemic stroke (Shea et al. 1984; ten Kate et al. 1982). Although family history has been identified as an important risk factor for stroke, many other factors increase the risk of stroke, including coronary artery disease (CAD), hypertension, diabetes, smoking, drinking, and body mass index (BMI) (Buraczynska et al. 2011; Zhang et al. 2013; Sobiczewski et al. 2013). Several genes have been found to be associated with stroke through a genomewide association study (GWAS), including renalase (Buraczynska et al. 2011; Zhang et al. 2013), BRG1 (Bevan et al. 2012), nitric oxide synthase 1 gene (Manso et al.
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2012), and MMP-9 (Yuan et al. 2013). However, the exact genetic susceptibility to ischemic stroke is still largely undetermined (Manso et al. 2012). A recent study found that the kalirin-coding gene, KALRN, is a novel candidate risk gene for CAD and may be correlated with atherosclerosis and stroke (Wang et al. 2007; Bevan et al. 2012; Wu et al. 2013). KALRN is located on chromosomal 3q21, and its protein product, kalirin, is a guanine nucleotide exchange factor (GEF) abundantly expressed in the CNS that plays a key role in spine/synapse formation in vitro and vivo (Mandela and Ma 2012). Kalirin has many isoforms, one of which is essential for dendritic outgrowth and branching (Jones et al. 2014; Yan et al. 2014). KALRN is also known to play an important role in ischemic signal transduction and is a regulator of dendritic spine remodeling (Beresewicz et al. 2008; Jones et al. 2014). In a recent review, Krug et al. examined the relationship between KALRN and ischemic stroke and indicated that they have an association (Krug et al. 2010). Kalirin undergoes alternative splicing, generating isoform 1 (kalirin-7) or isoform 2 (kalirin-9) (Yan et al. 2014). Kalirin-7 is the most abundantly expressed in CNS and is a critical regulator of dendritic spine remodeling in schizophrenia and Alzheimer’s disease. Kalirin-7 is also associated with ischemic signal transduction (Cahill et al. 2012; Jones et al. 2014; Beresewicz et al. 2008). Kalirin-9 is most abundantly expressed outside the CNS. It may promote smooth muscle cell (SMC) Rac1 activation, migration, and proliferation and leads to CAD or atherosclerosis (Wu et al. 2013). These studies suggest that kalirin isoforms 1 and 2 may be an important risk factor for stroke. Further studies are needed to ascertain the mechanism of its effect in ischemic stroke. In this study, we considered about the relationship between KALRN SNPs with ischemic stroke in 503 ischemic stroke patients and 493 control subjects in the northern Chinese Han population. Increasing evidence suggests that rare or low-frequency variants are associated with multifactorial disease etiology, and the effects of these rare variants tend to be larger than those of higher frequency SNPs (Bodmer and Bonilla 2008). For this reason, we specifically examined four variants of KALRN.
clinical features and computed tomography (CT) and/or magnetic resonance imaging (MRI). Patients with trauma, tumors, or cerebrovascular malformation were excluded from the study. Hypertension was defined according to World Health Organization criteria as a systolic blood pressure of C140 mm Hg or a diastolic blood pressure of C90 mm Hg and/or patients receiving anti-hypertensive treatment (Suarez and Ruilope 1999). Diabetes mellitus was defined by clinical symptoms of hyperglycemia (polyuria, polydipsia, and weight loss), fasting serum glucose C7.0 mmol/l (fasting defined as without calorie intake for at least 8 h), or the use of either insulin or oral hypoglycemic medications (Luke et al. 2009). The data for patients with or without hypertension were analyzed separately, as were patients with ischemic stroke with or without diabetes. The subjects based on the Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria and auxiliary diagnosis (CT or MRI) were further categorized. Control subjects were also enrolled from the Health Medical Center (2nd Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China), for a total of 493 subjects. The unrelated control subjects were gender-matched and geography-matched healthy individuals. All of the biochemical, including blood pressure, glucose, lipids, smoking, and alcohol abuse, was measured by standard laboratory procedures. Ethical approval for conducting this study was granted through the ethics committee of 2nd Affiliated Hospital of Harbin Medical University, and informed consents were signed by all the participants. Candidate SNP Selection Kalirin undergoes alternative splicing, generating isoforms with 1, 2, or 3. We selected four KALRN SNPs, rs17286604, rs7620580, rs11712619, and rs6438833, based on Krug’s study of ischemic stroke in the Portuguese population (Krug et al. 2010). All four of these SNPs are located in introns. SNPs rs17286604 and rs6438833 were mapped in isoform 2, rs7620580 mapped in isoform 3 and rs11712619 mapped in isoform 1. Genomic DNA was extracted from the case–control peripheral blood leukocyte with EDTA using standard procedures and was stored at -20 °C until used for genotype testing.
Subjects and Methods Genotyping of Kalirin SNPs Study Subjects The case subjects consisted of 503 unrelated new ischemic stroke patients, consecutively enrolled through the same center (Neurology Department, 2nd Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China). All ischemic stroke patients were diagnosed according to
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The genotyping experiments were performed by using the ligase detection reaction (LDR). The target DNA sequences were amplified using a multiplex PCR method with specific primers. The primers for rs17286604 genotyping are forward 50 -CTTTTCCTCATGTGGAAGGC-30 and reverse 50 -CTT TGACGTGATAACCACCC-30 ; for rs7620580 are forward
Neuromol Med
50 -CACACTAAGCAGACAGTCCA-30 and reverse 50 -GG ACCCAGAGCCTTTTTACA-30 ; for rs11712619 are forward 50 -GCAGCAAAGCAATTGGTAAC-30 and reverse 50 -CCACAAGGCACACAAATAATC-30 ; and for rs6438 833 are forward 50 -AGTGCTTTGCTGAGTGTACC-30 and reverse 50 -GGTAATTATAAACACAACAGC-30 . The target DNA sequences were amplified in a 20-ll reaction. The PCR was carried out in the Gene Amp PCR system 9600 (PerkinElmer): the initial denaturation at 95 °C for 2 min, followed by PCR 35 cycles with denaturation at 94 °C, annealing at 50 °C, extension at 72 °C for 30 s, and final extension at 72 °C for 10 min. The PCR products were then sequenced with the ABI sequencer 377, and the results were analyzed with Genemapper software. The LDR was performed in a final volume of 10 ll reaction mixture containing 19 buffer, 1 ll of probe mix (2 pmol/ll each probe), 0.05 ll of 2U ligase (New England Biolabs, Ipswich, MA, USA), and 4 ll of 100 ng/ll PCR products. The fluorescent products of LDR were distinguished by an ABI sequencer 377 and analyzed the results with Genemapper software. The quality of genotyping was controlled using blinded blood duplicates. Physiological and Biochemical Index Collection and Analysis The physiological and biochemical information, including age, sex, systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), glucose (Glu), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), smoking, and alcohol consumption, were collected from patient records. The statistical analysis was performed with SPSS V20.0 (SPSS, Institute Inc). The continuous variable indices were analyzed by the Student’s t test and the categorical variables by the Chi-square test. Allele, Genotype Association Multivariable Logistic Regression Analysis SNPtest software for Linux (http://mathgen.stats.ox.ac.uk/ genetics_software/snptest/snptest.html) was used to look for associations of polymorphisms with stroke using the following models: dominant (major allele homozygotes vs. heterozygotes ? minor allele homozygotes), recessive (major allele homozygotes ? heterozygotes vs. minor allele homozygotes), heterozygous (homozygote for the minor allele ? homozygote for the major allele vs. heterozygote), and log-additive (major allele homozygotes vs. heterozygotes vs. minor allele homozygotes). The inheritance models were adjusted for sex, age, body mass index (BMI), SBP, DBP, glucose, dyslipidemia, alcohol consumption, and smoking. The allele and genotype were
compared in the stroke patients and control subjects, as well as among the subgroups for comorbidity with hypertension and diabetes, as well as by different ischemic subtypes. Allele frequencies and genotype distribution were compared between groups using a Chi-square test of independence with a 2 9 2 contingency. A p value was considered statistically significant at p \ 0.05. Where appropriate, the odds ratios (OR) with corresponding 95 % confidence intervals (CI) were calculated. Ten thousand permutations were performed for each model to correct the multiple test. A p \ 0.05 was considered to be significant.
Results Cohort Description The clinical characteristics of all study participants, including 503 stroke patients and 493 control patients, are shown in Table 1. Similar proportion of male and females were included in the study; however, because stroke is a late-onset disease, the average age for the study group is greater than that of the controls. Additionally, many of the risk factors for stroke were also elevated as compared to controls, including SBP, DBP, TC, TG, LDL, GLU, smoking, drinking, and BMI, while HDL levels were lower. Out of the 503 case subjects, 369 subjects had hypertension, and 141 subjects had diabetes. Quality Control and Genotype Description The polymorphism sites of selected SNP markers are listed in Table 2. The genotype quality control and Hardy– Weinberg equilibrium (HWE) were tested using Haploview (Cambridge, MA 02141, USA). The SNPs had no Mendelian error, and the genotype calling rate in 147 quality control samples was 100 %. All SNPs passed the Hardy– Weinberg equilibrium test for both stroke and control subjects (data not shown). Association Study and Logistic Regression Analysis Results The allele and genotype frequencies of KALRN SNPs in ischemic stroke and controls are shown in Table 2, along with the chromosomal location of the four SNPs and minor allele frequency. The minor allele frequencies of rs17286604 and rs11712619 were 0.015 and 0.009, respectively. The estimated risk analysis of these polymorphisms in stroke patients and controls was tested according to four models (dominant, recessive, heterozygous, and additive) and adjusted for stroke risk factors (Table 2). After adjusting for sex, age, body mass index (BMI), SBP, DBP, Glu, dyslipidemia, alcohol
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Neuromol Med Table 1 Clinical characteristics of the 503 stroke patients and 493 controls in the study
Control subjects (n = 493)
Stroke patients (n = 503)
p \0.001
Age (years)
53.08 ± 8.63
58.83 ± 10.25
Men (%)
300 (60.7)
332 (65.9)
BMI (kg/m2)
23.41 ± 3.29
24.80 ± 3.34
0.105 \0.001
SBP (mmHg)
115.75 ± 11.43
148.00 ± 24.04
\0.001
DBP (mmHg)
78.92 ± 6.40
89.61 ± 13.33
\0.001
Glu (mmol/l)
5.11 ± 2.07
6.77 ± 3.02
\0.001
TC (mmol/l)
4.39 ± 0.58
5.31 ± 3.38
\0.001
TG (mmol/l)
0.99 ± 0.31
1.89 ± 1.48
\0.001
HDL (mmol/l)
1.46 ± 0.28
1.28 ± 0.46
\0.001
LDL (mmol/l)
2.67 ± 0.48
2.87 ± 0.82
\0.001
Smoking [n (%)]
120 (24.29)
244 (48.41)
\0.001
Alcohol intake [n (%)]
134 (27.12)
184 (36.50)
0.002
Hypertension [n (%)]
0 (0)
370 (73.41)
Diabetes [n (%)]
0 (0)
142 (28.17)
Cardiopathy [n (%)]
0 (0)
68 (13.49)
Mean ± standard deviation BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, Glu glucose, TC total cholesterol, TG triglycerides, LDL low-density lipoprotein, HDL highdensity lipoprotein
Table 2 Association of KALRN SNPs in the controls and stroke cases Genotype
Controls
Cases
rs17286604
Position
MAF
124233370
0.015
SNP model
Unadjusted OR [95 % CI]
p
1.12 [0.54–2.31]
Adjusted OR [95 %]
Adjusted p
p0
1.12 [0.54–2.31]
CC
479
487
Dominant
0.75
0.597
0.6
CT
14
16
Recessive
–
–
–
TT
0
0
Heterozygous
0.75
–
–
Additive
0.75
0.597
0.6
0.787 0.54
0.79 0.54
rs7620580
124326456
AA AG
296 174
285 191
GG
23
27
rs11712619
0.233
1.12 [0.91–1.37] Dominant Recessive
124300955
1.12 [0.91–1.37] 0.11 –
Heterozygous
0.11
0.822
0.83
Additive
0.11
0.979
1
0.009
2.13 [0.80–5.64]
2.13 [0.80–5.64]
CC
487
490
Dominant
0.11
0.267
0.27
CT
6
13
Recessive
–
–
–
TT
0
0
Heterozygous
0.11
–
–
Additive
0.11
0.267
0.271
rs6438833 AA
124206748 4
0.081
1.33 [0.96–1.85]
1.33 [0.96–1.85]
2
Dominant
0.39
0.302
0.303
Recessive
0.09
0.091
0.095
Heterozygous
0.12
0.084
0.086
Additive
0.07
0.045
0.047
AT
83
67
TT
406
434
The adjusted p values were calculated from logistic regression analysis and were adjusted for sex, age, body mass index (BMI), dyslipidemia, alcohol consumption, and smoking. Bold numbers indicate significant associations. The p0 was calculated using 10,000 permutations for each model to correct the multiple test. Dashes ‘‘–’’ indicate data missing MAF Minor allele frequency, OR odds ratio, CI confidence interval, p p value
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consumption, and smoking, under additive models, rs6438833 was associated with an increased risk of ischemic stroke when compared to controls (OR 1.33, 95 % CI 0.96–1.85, p = 0.045). After performing 10,000 permutations, the p value was still significant (p0 = 0.047). The data were further analyzed to determine the allele and genotype frequencies of KALRN SNPs in controls and ischemic stroke with or without diabetes (Table 3). After adjusting for sex, age, body mass index (BMI), SBP, DBP, glucose, dyslipidemia, alcohol consumption, and smoking, under the recessive model, heterozygous model, and additive model, the rs6438833 polymorphism was associated with increased risk of stroke with diabetes when compared to healthy controls (OR 1.58, 95 % CI 0.92–2.70, p = 0.019). After performing 10,000 permutations, the recessive model and heterozygous model were still significant (p0 = 0.019 and p0 = 0.018, respectively). However, there was no association of this polymorphism in stroke without diabetes. In addition to diabetes, we also analyzed the possible confounding variable of hypertension. The genotype and allele of the four SNPs for patients with or without hypertension are summarized in Table 4. After adjusting for sex, age, body mass index (BMI), SBP, DBP, glucose, dyslipidemia, alcohol consumption and smoking, neither the genotype nor the allele frequencies of the polymorphisms were significantly different between the stroke with or without hypertension when compared to control subjects. The results of the association studies of genotype and allele of the four SNPs between the TOAST subgroup and control subjects are summarized in Table 5. Under the dominant model, heterozygous model, and additive model, the rare variant rs11712619 (MAF = 0.009) was showed a significant association with lacunar stroke as compared with controls (OR 2.95, 95 % CI 1.08–8.01, p = 0.028). However, after adjusting for age, sex, BMI, SBP, DBP, glucose, TC, TG, HDL, LDL, smoking, and alcohol intake, this association was no longer significant. 10,000 permutations test were performed, the results were also no statistical significance. For the variant rs6438833, even after adjusting for age, sex, BMI, SBP, DBP, glucose, TC, TG, HDL, LDL, smoking, and alcohol intake, under the recessive model, this variant was significantly different between control patients and those with a lacunar stroke (OR 1.51, 95 % CI 1.02–2.23, p = 0.047) and remained significant after the permutation test (p0 = 0.048).
Discussion KALRN has recently been identified as playing an important role in the development of several cardiovascular diseases, such as CAD and atherosclerosis (Wang et al. 2007; Bevan
et al. 2012). The association of known polymorphisms of the kalirin-coding gene, KALRN, with common diseases in different populations is controversial (Krug et al. 2010; Olsson et al. 2011). In the present study, we analyzed four polymorphisms (rs17286604, rs7620580, rs11712619, and rs6438833) located in the KALRN gene in the Chinese Han population. Previously, these four polymorphisms were associated with ischemic stroke in the Portuguese population (Krug et al. 2010). The results of this study found that in Chinese Han population, two of these polymorphisms, rs11712619 and rs6438833, were associated with risk of stroke development. However, unlike the Portuguese population, rs17286604 and rs11712619 were rare variants in our populations, and rs11712619 was association with ischemic stroke. Interestingly, as compared to those of higher frequency SNPs, the effects of rare variants tend to be larger in common inherited disease (Bodmer and Bonilla 2008; Asimit and Zeggini 2010). We further analyzed the data from the patients with ischemic stroke, examining the impact of complications (diabetes and hypertension) and subtypes (atherosis, lacunar, and combination type) of ischemic stroke. Our results indicated that KALRN has an association with complications and subtypes of ischemic stroke. Specifically, rs11712619 (MAF = 0.009) was associated with lacunar cerebral stroke though after the 10,000 permutation test, and this association was not detected. Rare variants may often have penetrances large enough to justify preventative screening strategies for multifactorial inherited diseases (Bodmer and Bonilla 2008), such as ischemic stroke. Additionally, the SNP rs6438833 was associated with ischemic stroke and lacunar cerebral stroke. Even adjusting for the sex, age, body mass index (BMI), SBP, DBP, Glu, dyslipidemia, alcohol consumption, and smoking, rs6438833 was associated with ischemic stroke, lacunar cerebral stroke, and ischemic stroke with diabetes, and after performing 10,000 permutations, the results were still significant. Diabetes was association with an increased incidence of stroke (Matsushita et al. 2010), and it may share the common pathogenic mechanism: atherosclerosis. The SNPs rs11712619 and rs6438833 were in introns and possibly affect alternative splicing regulation by disrupting exonic splicing enhancers or silencers (VargasAlarcon et al. 2014, 2015). It is suspected that the two SNPs at intronic regions may affect KALRN expression or lead to alternative splicing of kalirin protein, generating isoforms. The SNPs rs11712619 was mapped in isoform 1 and rs6438833 mapped in isoform 2. Kalirin isoform 1 codes for kalirin 7 and isoform 2 codes for kalirin 9 (Yan et al. 2014). Kalirin-7 is involved in ischemic signal transduction; however, although kalirin 7 has been shown to play an important role in postischemic neurodegeneration and repair, the function in ischemic brain is unknown
123
123
14
0
TT
174
23
AG
GG
6
0
CT
TT
406
AT
TT
124
17
0
0
2
139
7
47
87
0
2
139
Stroke with diabetes
0.311
0.156 0.158 0.076
Additive
0.103
Heterozygous
Recessive
Dominant
0.852 0.852
Additive 1.58 [0.92–2.70]
0.852 –
Heterozygous
Recessive
Dominant
0.665 0.806
Heterozygous 1.16 [0.23–5.81]
0.721 0.883
Additive
Recessive
Dominant
0.311 0.11
–
Additive 0.96 [0.69–1.32]
0.49 [0.11–2.19]
Unadjusted OR [95 % CI]
Heterozygous
Recessive
Dominant
SNP model
1.58 [0.92–2.70]
1.16 [0.23–5.81]
0.96 [0.69–1.32]
0.49 [0.11–2.19]
Adjusted OR [95 %]
0.019
0.019
0.019
0.962
0.056
0.056
–
0.056
0.791
0.963
0.766
0.857
0.051
0.051
–
0.051
Adjusted p0
0.509
0.0187
0.0196
0.915
0.639
0.657
–
0.657
0.528
0.966
0.766
0.859
0.593
0.593
–
0.593
p0
309
50
2
0
11
350
20
144
197
0
14
347
Stroke without diabetes
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
SNP model
1.25 [0.88–1.78]
2.52 [0.93–6.86]
1.19 [0.95–1.49]
1.37 [0.65–2.89]
Unadjusted OR [95 % CI]
0.402
0.196
0.232
0.202
0.652
0.06
0.06
–
0.06
0.122
0.17
0.564
0.11
0.402
0.402
–
1.25 [0.88–1.78]
2.52 [0.93–6.86]
1.19 [0.95–1.49]
1.37 [0.65–2.89]
Adjusted OR [95 %]
0.47
0.173
0.292
0.311
0.095
0.095
–
0.095
0.415
0.802
0.398
0.549
0.265
0.265
–
0.265
Adjusted p
0.275
0.179
0.3
0.283
0.283
0.108
–
0.108
0.269
0.798
0.397
0.552
0.275
0.275
–
0.275
p0
The adjusted p values were calculated from logistic regression analysis and were adjusted for sex, age, body mass index (BMI), dyslipidemia, alcohol consumption, and smoking. Bold numbers indicate significant associations. The p0 was calculated using 10,000 permutations for each model to correct the multiple test. Dashes ‘‘–’’ indicate data missing
4
83
AA
rs6438833
487
CC
rs11712619
296
AA
rs7620580
479
CT
Controls
CC
rs17286604
Genotype
Table 3 Association of KALRN SNPs in the controls and stroke patients with or without diabetes
Neuromol Med
14
0
CT
TT
174
23
AG
GG
6
0
CT
TT
406
AT
TT
317
51
1
0
8
361
17
146
206
0
10
359
Stroke with hypertension
0.219 0.119
Additive
0.154
0.278
Heterozygous
Recessive
Dominant
0.279
Additive 1.31 [0.92–1.87]
0.279
–
0.279
Heterozygous
Recessive
Dominant
0.313
Additive 1.78 [0.61–5.17]
0.207
0.962
0.223
Heterozygous
Recessive
Dominant
0.904 0.904
–
0.904
p
Additive 1.12 [0.89–1.40]
0.95 [0.42–2.15]
Unadjusted OR [95 % CI]
Heterozygous
Recessive
Dominant
SNP model
1.31 [0.92–1.87]
1.78 [0.61–5.17]
1.12 [0.89–1.40]
0.95 [0.42–2.15]
Adjusted OR [95 %]
0.808
0.107
0.364
0.06
0.718
0.718
–
0.718
0.438
0.192
0.698
0.271
0.666
0.666
–
0.666
Adjusted p
0.668
0.108
0.372
0.062
0.69
0.74
–
0.74
0.656
0.191
0.697
0.27
0.665
0.665
–
0.665
p0
116
16
1
0
5
128
10
45
78
0
6
127
Stroke no hypertension
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
SNP model
1.40 [0.82–2.36]
3.12 [0.94–10.33]
1.12 [0.81–1.54]
1.60 [0.60–4.21]
Unadjusted OR [95 % CI]
0.194
0.166
0.17
0.945
0.07
0.07
–
0.07
0.467
0.753
0.209
0.771
0.35
0.35
–
0.35
p
1.40 [0.82–2.36]
0.154
3.12 [0.94–10.33]
1.12 [0.81–1.54]
1.60 [0.60–4.21]
Adjusted OR [95 %]
0.28
0.308
0.284
0.753
0.154
0.154
–
0.178
0.658
0.253
0.53
0.409
0.249
0.249
–
0.249
Adjusted p
0.255
0.309
0.291
0.824
0.251
0.178
–
0.257
0.262
0.535
0.417
0.254
0.254
–
0.254
p0
The adjusted p values were calculated from logistic regression analysis and were adjusted for sex, age, body mass index (BMI), dyslipidemia, alcohol consumption, and smoking. The p0 was calculated using 10,000 permutations for each model to correct the multiple test. Dashes ‘‘–’’ indicate data missing
4
83
AA
rs6438833
487
CC
rs11712619
296
AA
rs7620580
479
CC
rs17286604
Controls
Table 4 Association of KALRN SNPs in the controls and stroke patients with or without hypertension
Neuromol Med
123
123
14
0
CT
TT
174
23
AG
GG
6
0
CT
TT
0
TT
174
23
AG
GG
487
6
0
CC
CT
TT
rs11712619
296
AA
rs7620580
479 14
CC CT
2.95 [1.08–8.01]
1.06 [0.83–1.34]
0.25
–
0.26
–
0.26
0.44
0.695 0.25
0.89
0.69
0.76
0.894
0.692
0.768
0.44 0.44
0.437
0.44 –
0.437
0.437 –
101
0
2
103
8
37
60
4 0
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Dominant Recessive
SNP model
1.358 [0.709–2.601]
–
1.260 [0.859–1.849]
0.442 [0.057–3.386]
Adjusted OR [95 %]
Combination
0.34
Additive p0
0.204
Adjusted p
0.702 0.257
Heterozygous
1.254 [0.56–2.78]
69
Dominant
Recessive
rs17286604
TT
1
9
0.181
Additive 1.358 [0.709–2.601]
0.181
–
Heterozygous
Recessive
0.181
0.235
Additive
Dominant
0.188 –
0.174 0.878
Heterozygous
Recessive
Dominant
0.373
Additive
0.373
p
0.373 1.260 [0.859–1.849]
0.442 [0.057–3.386]
Unadjusted OR [95 %]
Heterozygous
Recessive
Dominant
SNP model
Adjusted OR [95 %]
406
AT
0
0
79
4
34
41
0
1
78
Atherosis
Controls
4
83
AA
rs6438833
487
CC
rs11712619
296
AA
rs7620580
479
CC
rs17286604
Controls
Table 5 Association with genotypes and alleles of 4 SNPs in controls and stroke patients by subgroup
0.74
0.15
0.27
0.45
0.72
0.63
–
0.63
0.74
0.9
0.37
0.744
0.74
0.74
0.5
0.74
0.5
p0
271
1.57 [0.31–7.83]
1.17 [0.83–1.66]
1.34 [0.43–4.13]
39
0
0
11
299
15
115
180
0
11
299
0.595
–
0.595
0.363
0.991
0.237
0.583
0.608
0.608
0.608 –
p
Lacunar
Unadjusted OR[95 %]
0.454
0.15
0.268
0.47
0.589
0.589
–
0.589
0.512
0.909
0.356
0.739
0.739
0.739
–
0.739
Adjusted p
1.57 [0.31–7.83]
1.17 [0.83–1.66]
1.34 [0.43–4.13]
0.71
–
0.71
0.89
0.503
0.598
0.686
0.807
0.807
0.807 –
Adjusted p
1.51 [1.02–2.23]
2.95 [1.08–8.01]
1.06 [0.83–1.34]
1.254 [0.56–2.78]
Unadjusted OR [95 %]
Adjusted OR [95 %]
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
SNP model
0.72
–
0.72
0.8
0.5
0.61
0.68
0.81
0.81
0.81 –
p0
0.03
0.098
0.051
0.048
0.028
0.028
–
0.028
0.612
0.604
0.91
0.579
0.576
0.576
–
0.576
p
Neuromol Med
Heterozygous
Additive
85 0.054
0.44
0.051 406 TT
0.437
Dominant Recessive 1 19 0.62 0.048 0.669 0.047 4 83 AA AT
The adjusted p values were calculated from logistic regression analysis and were adjusted for sex, age, body mass index (BMI), dyslipidemia, alcohol consumption, and smoking. Bold numbers indicate significant associations. The p0 was calculated using 10,000 permutations for each model to correct the multiple test. Dashes ‘‘–’’ indicate data missing
0.89 0.897
0.254
0.756
0.729
0.8
– 0.577 0.887 0.735
1 0.57
0.79 0.71 0.91 [0.55–1.50] 0.595 0.91 [0.55–1.50] Additive 0.44 0.25 1.51 [1.02–2.23] rs6438833
p0 Adjusted p Adjusted OR [95 %] Controls
Table 5 continued
Combination
SNP model
Unadjusted OR[95 %]
p
Adjusted OR [95 %]
Adjusted p
p0
Neuromol Med
(Beresewicz et al. 2008). Kalirin-9 activates Rac1 and PAK1 signaling, which are critical for SMC migration, and prevents NOS2 dimerization, which therefore inhibits NOS2 activity. As NOS2 production of NO reduces SMC proliferation and mitochondrial respiration (Yan and Hansson 1998; Wu et al. 2013), kalirin-9 promotes neointimal hyperplasia leading to atherosclerosis by decreasing NO production, and therefore SMC proliferation (Wu et al. 2013). In conclusion, this study has demonstrated a positive correlation between the rare variant SNP rs11712619 and the common variant SNP rs64388333 in the kalirin gene and ischemic stroke in a Chinese Han population. To confirm the association between ischemic stroke and the rare variant rs11712619, large-scale studies are needed, and more basic research needs to be done to expound the function of kalirin7 in brain ischemic stroke. Understanding the risk of this rare SNP variant may lead to novel insight into the mechanisms and pathogenesis of ischemic stroke. Acknowledgments We are deeply grateful to all study participants. This work was supported by the Natural Science Foundation of Heilongjiang province (No. H201331). Conflict of interest
The authors declared no conflict of interest.
References Asimit, J., & Zeggini, E. (2010). Rare variant association analysis methods for complex traits. Annual Review Genetics, 44, 293–308. Beresewicz, M., Kowalczyk, J. E., & Zablocka, B. (2008). Kalirin-7, a protein enriched in postsynaptic density, is involved in ischemic signal transduction. Neurochemical Research, 33, 1789–1794. Bevan, S., Traylor, M., Adib-Samii, P., Malik, R., Paul, N. L., Jackson, C., et al. (2012). Genetic heritability of ischemic stroke and the contribution of previously reported candidate gene and genomewide associations. Stroke, 43, 3161–3167. Bodmer, W., & Bonilla, C. (2008). Common and rare variants in multifactorial susceptibility to common diseases. Nature Genetics, 40, 695–701. Buraczynska, M., Zukowski, P., Buraczynska, K., Mozul, S., & Ksiazek, A. (2011). Renalase gene polymorphisms in patients with type 2 diabetes, hypertension and stroke. NeuroMolecular Medicine, 13, 321–327. Cahill, M. E., Jones, K. A., Rafalovich, I., Xie, Z., Barros, C. S., Muller, U., & Penzes, P. (2012). Control of interneuron dendritic growth through NRG1/erbB4-mediated kalirin-7 disinhibition. Molecular Psychiatry, 17(1), 99–107. Donnan, G. A., Fisher, M., Macleod, M., & Davis, S. M. (2008). Stroke. Lancet, 371, 1612–1623. Jones, K. A., Eng, A. G., Raval, P., Srivastava, D. P., & Penzes, P. (2014). Scaffold protein x11alpha interacts with kalirin-7 in dendrites and recruits it to Golgi outposts. Journal of Biological Chemistry, 289, 35517–35529. Krug, T., Manso, H., Gouveia, L., Sobral, J., Xavier, J. M., Albergaria, I., et al. (2010). Kalirin: A novel genetic risk factor for ischemic stroke. Human Genetics, 127, 513–523.
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Neuromol Med Liu, M., Wu, B., Wang, W. Z., Lee, L. M., Zhang, S. H., & Kong, L. Z. (2007). Stroke in China: Epidemiology, prevention, and management strategies. Lancet Neurology, 6, 456–464. Luke, M. M., O’Meara, E. S., Rowland, C. M., Shiffman, D., Bare, L. A., Arellano, A. R., et al. (2009). Gene variants associated with ischemic stroke: The cardiovascular health study. Stroke, 40, 363–368. Mandela, P., & Ma, X. M. (2012). Kalirin, a key player in synapse formation, is implicated in human diseases. Neural Plasticity, 2012, 728161. Manso, H., Krug, T., Sobral, J., Albergaria, I., Gaspar, G., Ferro, J. M., et al. (2012). Variants within the nitric oxide synthase 1 gene are associated with stroke susceptibility. Atherosclerosis, 220, 443–448. Matsushita, T., Ashikawa, K., Yonemoto, K., Hirakawa, Y., Hata, J., Amitani, H., et al. (2010). Functional SNP of ARHGEF10 confers risk of atherothrombotic stroke. Human Molecular Genetics, 19, 1137–1146. Olsson, S., Jood, K., Melander, O., Sjogren, M., Norrving, B., Nilsson, M., et al. (2011). Lack of association between genetic variations in the KALRN region and ischemic stroke. Clinical Biochemistry, 44, 1018–1020. Shea, S., Ottman, R., Gabrieli, C., Stein, Z., & Nichols, A. (1984). Family history as an independent risk factor for coronary artery disease. Journal of the American College of Cardiology, 4, 793–801. Sobiczewski, W., Wirtwein, M., Trybala, E., & Gruchala, M. (2013). Severity of coronary atherosclerosis and stroke incidence in 7-year follow-up. Journal of Neurology, 260, 1855–1858. Suarez, C., & Ruilope, L. M. (1999). New guidelines from the World Health Organization and the International Society of Hypertension for the management of hypertension: Toward common guidelines. Revista Espanola de Cardiologia, 52, 381–382. ten Kate, L. P., Boman, H., Daiger, S. P., & Motulsky, A. G. (1982). Familial aggregation of coronary heart disease and its relation to known genetic risk factors. American Journal of Cardiology, 50, 945–953.
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Vargas-Alarcon, G., Posadas-Romero, C., Posadas-Sanchez, R., Martinez-Alvarado, R., Gonzalez-Pacheco, H., Martinez-Sanchez, C., et al. (2015). The variant rs8048002 T [ C in intron 3 of the MHC2TA gene is associated with risk of developing acute coronary syndrome. Cytokine, 71, 268–271. Vargas-Alarcon, G., Posadas-Sanchez, R., Posadas-Romero, C., Gonzalez-Salazar, C., Cardoso-Saldana, G., Martinez-Rios, M. A., et al. (2014). Association of the suppressor of cytokine signaling 1 (SOCS1) gene polymorphisms with acute coronary syndrome in Mexican patients. Molecular Immunology, 62, 137–141. Wang, L., Hauser, E. R., Shah, S. H., Pericak-Vance, M. A., Haynes, C., Crosslin, D., et al. (2007). Peakwide mapping on chromosome 3q13 identifies the kalirin gene as a novel candidate gene for coronary artery disease. American Journal of Human Genetics, 80, 650–663. Wu, J. H., Fanaroff, A. C., Sharma, K. C., Smith, L. S., Brian, L., Eipper, B. A., et al. (2013). Kalirin promotes neointimal hyperplasia by activating Rac in smooth muscle cells. Arteriosclerosis, Thrombosis, and Vascular Biology, 33, 702–708. Yan, Y., Eipper, B. A., & Mains, R. E. (2014). Kalirin-9 and Kalirin12 play essential roles in dendritic outgrowth and branching. Cereb Cortex. doi:10.1093/cercor/bhu182. Yan, Z., & Hansson, G. K. (1998). Overexpression of inducible nitric oxide synthase by neointimal smooth muscle cells. Circulation Research, 82, 21–29. Yuan, M., Zhan, Q., Duan, X., Song, B., Zeng, S., Chen, X., et al. (2013). A functional polymorphism at miR-491-5p binding site in the 30 -UTR of MMP-9 gene confers increased risk for atherosclerotic cerebral infarction in a Chinese population. Atherosclerosis, 226, 447–452. Zhang, R., Li, X., Liu, N., Guo, X., Liu, W., Ning, C., et al. (2013). An association study on renalase polymorphisms and ischemic stroke in a Chinese population. NeuroMolecular Medicine, 15, 396–404.
ORIGINAL PAPER
KALRN Rare and Common Variants and Susceptibility to Ischemic Stroke in Chinese Han Population Meizheng Dang1 • Zhenzhen Wang1 • Ruyou Zhang2 • Xiaoying Li1 Yanqing Peng1 • Xuesong Han1 • Litao Sun1 • Jiawei Tian1
•
Received: 13 November 2014 / Accepted: 11 April 2015 Ó Springer Science+Business Media New York 2015
Abstract Stroke is the second most common cause of mortality worldwide, and it is a major cause of physical disability. Several genome-wide association studies have yielded numerous common variants which increase the risk of ischemic stroke, including the Kalirin-coding gene, KALRN. KALRN strongly associates with early-onset coronary artery disease and atherosclerosis and plays an important role in stroke in the European population. In this study, we analyzed four KALRN gene SNPs in 503 ischemic stroke patients and 493 control subjects, separating the patients into separate research groups based on comorbidity with hypertension or diabetes and stroke type (atherosis or lacunar and combination type). We found a rare variant of KALRN, rs11712619, that associated with lacunar stroke in the northern Chinese Han population with an average-risk allele frequency 0.009 (OR 2.95, 95 % CI 1.08–8.01, p = 0.028). However, after adjusting for relevant factors, including sex, age, body mass index, dyslipidemia, alcohol consumption, and smoking, this association was not evident. Additionally, the KALRN
Meizheng Dang and Zhenzhen Wang are co-first authors. & Litao Sun [email protected] & Jiawei Tian [email protected] 1
Department of Ultrasound, The 2nd Affiliated Hospital of Harbin Medical University, NO. 246, Xuefu Road, Nangang District, Harbin 150081, Heilongjiang, People’s Republic of China
2
Department of Neurosurgery, The 2nd Affiliated Hospital of Harbin Medical University, NO. 246, Xuefu Road, Nangang District, Harbin 150081, Heilongjiang, People’s Republic of China
variant rs6438833 was associated with ischemic stroke, ischemic stroke comorbid with diabetes, and lacunar stroke after adjusting for the relevant factors (p = 0.046, p = 0.019 and p = 0.046, respectively), which remained significant after 10,000 permutation procedure test (p0 = 0.047, p0 = 0.018 and p0 = 0.048, respectively). The association of these rare and common variants of KALRN with ischemic stroke in northern Chinese Han population offers insight for potential therapeutic research.
Keywords Ischemic stroke Kalirin Association study KALRN isoform
Introduction Stroke is the second most common cause of mortality worldwide and major cause of physical disability (Donnan et al. 2008). Over two-thirds of stroke deaths are in developing countries (Liu et al. 2007). The etiology of ischemic stroke depends on both inherited predisposition and environmental factors. A large number of studies have repeatedly shown that a positive family history is a robust predictor of ischemic stroke (Shea et al. 1984; ten Kate et al. 1982). Although family history has been identified as an important risk factor for stroke, many other factors increase the risk of stroke, including coronary artery disease (CAD), hypertension, diabetes, smoking, drinking, and body mass index (BMI) (Buraczynska et al. 2011; Zhang et al. 2013; Sobiczewski et al. 2013). Several genes have been found to be associated with stroke through a genomewide association study (GWAS), including renalase (Buraczynska et al. 2011; Zhang et al. 2013), BRG1 (Bevan et al. 2012), nitric oxide synthase 1 gene (Manso et al.
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2012), and MMP-9 (Yuan et al. 2013). However, the exact genetic susceptibility to ischemic stroke is still largely undetermined (Manso et al. 2012). A recent study found that the kalirin-coding gene, KALRN, is a novel candidate risk gene for CAD and may be correlated with atherosclerosis and stroke (Wang et al. 2007; Bevan et al. 2012; Wu et al. 2013). KALRN is located on chromosomal 3q21, and its protein product, kalirin, is a guanine nucleotide exchange factor (GEF) abundantly expressed in the CNS that plays a key role in spine/synapse formation in vitro and vivo (Mandela and Ma 2012). Kalirin has many isoforms, one of which is essential for dendritic outgrowth and branching (Jones et al. 2014; Yan et al. 2014). KALRN is also known to play an important role in ischemic signal transduction and is a regulator of dendritic spine remodeling (Beresewicz et al. 2008; Jones et al. 2014). In a recent review, Krug et al. examined the relationship between KALRN and ischemic stroke and indicated that they have an association (Krug et al. 2010). Kalirin undergoes alternative splicing, generating isoform 1 (kalirin-7) or isoform 2 (kalirin-9) (Yan et al. 2014). Kalirin-7 is the most abundantly expressed in CNS and is a critical regulator of dendritic spine remodeling in schizophrenia and Alzheimer’s disease. Kalirin-7 is also associated with ischemic signal transduction (Cahill et al. 2012; Jones et al. 2014; Beresewicz et al. 2008). Kalirin-9 is most abundantly expressed outside the CNS. It may promote smooth muscle cell (SMC) Rac1 activation, migration, and proliferation and leads to CAD or atherosclerosis (Wu et al. 2013). These studies suggest that kalirin isoforms 1 and 2 may be an important risk factor for stroke. Further studies are needed to ascertain the mechanism of its effect in ischemic stroke. In this study, we considered about the relationship between KALRN SNPs with ischemic stroke in 503 ischemic stroke patients and 493 control subjects in the northern Chinese Han population. Increasing evidence suggests that rare or low-frequency variants are associated with multifactorial disease etiology, and the effects of these rare variants tend to be larger than those of higher frequency SNPs (Bodmer and Bonilla 2008). For this reason, we specifically examined four variants of KALRN.
clinical features and computed tomography (CT) and/or magnetic resonance imaging (MRI). Patients with trauma, tumors, or cerebrovascular malformation were excluded from the study. Hypertension was defined according to World Health Organization criteria as a systolic blood pressure of C140 mm Hg or a diastolic blood pressure of C90 mm Hg and/or patients receiving anti-hypertensive treatment (Suarez and Ruilope 1999). Diabetes mellitus was defined by clinical symptoms of hyperglycemia (polyuria, polydipsia, and weight loss), fasting serum glucose C7.0 mmol/l (fasting defined as without calorie intake for at least 8 h), or the use of either insulin or oral hypoglycemic medications (Luke et al. 2009). The data for patients with or without hypertension were analyzed separately, as were patients with ischemic stroke with or without diabetes. The subjects based on the Trial of Org 10172 in Acute Stroke Treatment (TOAST) criteria and auxiliary diagnosis (CT or MRI) were further categorized. Control subjects were also enrolled from the Health Medical Center (2nd Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China), for a total of 493 subjects. The unrelated control subjects were gender-matched and geography-matched healthy individuals. All of the biochemical, including blood pressure, glucose, lipids, smoking, and alcohol abuse, was measured by standard laboratory procedures. Ethical approval for conducting this study was granted through the ethics committee of 2nd Affiliated Hospital of Harbin Medical University, and informed consents were signed by all the participants. Candidate SNP Selection Kalirin undergoes alternative splicing, generating isoforms with 1, 2, or 3. We selected four KALRN SNPs, rs17286604, rs7620580, rs11712619, and rs6438833, based on Krug’s study of ischemic stroke in the Portuguese population (Krug et al. 2010). All four of these SNPs are located in introns. SNPs rs17286604 and rs6438833 were mapped in isoform 2, rs7620580 mapped in isoform 3 and rs11712619 mapped in isoform 1. Genomic DNA was extracted from the case–control peripheral blood leukocyte with EDTA using standard procedures and was stored at -20 °C until used for genotype testing.
Subjects and Methods Genotyping of Kalirin SNPs Study Subjects The case subjects consisted of 503 unrelated new ischemic stroke patients, consecutively enrolled through the same center (Neurology Department, 2nd Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China). All ischemic stroke patients were diagnosed according to
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The genotyping experiments were performed by using the ligase detection reaction (LDR). The target DNA sequences were amplified using a multiplex PCR method with specific primers. The primers for rs17286604 genotyping are forward 50 -CTTTTCCTCATGTGGAAGGC-30 and reverse 50 -CTT TGACGTGATAACCACCC-30 ; for rs7620580 are forward
Neuromol Med
50 -CACACTAAGCAGACAGTCCA-30 and reverse 50 -GG ACCCAGAGCCTTTTTACA-30 ; for rs11712619 are forward 50 -GCAGCAAAGCAATTGGTAAC-30 and reverse 50 -CCACAAGGCACACAAATAATC-30 ; and for rs6438 833 are forward 50 -AGTGCTTTGCTGAGTGTACC-30 and reverse 50 -GGTAATTATAAACACAACAGC-30 . The target DNA sequences were amplified in a 20-ll reaction. The PCR was carried out in the Gene Amp PCR system 9600 (PerkinElmer): the initial denaturation at 95 °C for 2 min, followed by PCR 35 cycles with denaturation at 94 °C, annealing at 50 °C, extension at 72 °C for 30 s, and final extension at 72 °C for 10 min. The PCR products were then sequenced with the ABI sequencer 377, and the results were analyzed with Genemapper software. The LDR was performed in a final volume of 10 ll reaction mixture containing 19 buffer, 1 ll of probe mix (2 pmol/ll each probe), 0.05 ll of 2U ligase (New England Biolabs, Ipswich, MA, USA), and 4 ll of 100 ng/ll PCR products. The fluorescent products of LDR were distinguished by an ABI sequencer 377 and analyzed the results with Genemapper software. The quality of genotyping was controlled using blinded blood duplicates. Physiological and Biochemical Index Collection and Analysis The physiological and biochemical information, including age, sex, systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), glucose (Glu), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), smoking, and alcohol consumption, were collected from patient records. The statistical analysis was performed with SPSS V20.0 (SPSS, Institute Inc). The continuous variable indices were analyzed by the Student’s t test and the categorical variables by the Chi-square test. Allele, Genotype Association Multivariable Logistic Regression Analysis SNPtest software for Linux (http://mathgen.stats.ox.ac.uk/ genetics_software/snptest/snptest.html) was used to look for associations of polymorphisms with stroke using the following models: dominant (major allele homozygotes vs. heterozygotes ? minor allele homozygotes), recessive (major allele homozygotes ? heterozygotes vs. minor allele homozygotes), heterozygous (homozygote for the minor allele ? homozygote for the major allele vs. heterozygote), and log-additive (major allele homozygotes vs. heterozygotes vs. minor allele homozygotes). The inheritance models were adjusted for sex, age, body mass index (BMI), SBP, DBP, glucose, dyslipidemia, alcohol consumption, and smoking. The allele and genotype were
compared in the stroke patients and control subjects, as well as among the subgroups for comorbidity with hypertension and diabetes, as well as by different ischemic subtypes. Allele frequencies and genotype distribution were compared between groups using a Chi-square test of independence with a 2 9 2 contingency. A p value was considered statistically significant at p \ 0.05. Where appropriate, the odds ratios (OR) with corresponding 95 % confidence intervals (CI) were calculated. Ten thousand permutations were performed for each model to correct the multiple test. A p \ 0.05 was considered to be significant.
Results Cohort Description The clinical characteristics of all study participants, including 503 stroke patients and 493 control patients, are shown in Table 1. Similar proportion of male and females were included in the study; however, because stroke is a late-onset disease, the average age for the study group is greater than that of the controls. Additionally, many of the risk factors for stroke were also elevated as compared to controls, including SBP, DBP, TC, TG, LDL, GLU, smoking, drinking, and BMI, while HDL levels were lower. Out of the 503 case subjects, 369 subjects had hypertension, and 141 subjects had diabetes. Quality Control and Genotype Description The polymorphism sites of selected SNP markers are listed in Table 2. The genotype quality control and Hardy– Weinberg equilibrium (HWE) were tested using Haploview (Cambridge, MA 02141, USA). The SNPs had no Mendelian error, and the genotype calling rate in 147 quality control samples was 100 %. All SNPs passed the Hardy– Weinberg equilibrium test for both stroke and control subjects (data not shown). Association Study and Logistic Regression Analysis Results The allele and genotype frequencies of KALRN SNPs in ischemic stroke and controls are shown in Table 2, along with the chromosomal location of the four SNPs and minor allele frequency. The minor allele frequencies of rs17286604 and rs11712619 were 0.015 and 0.009, respectively. The estimated risk analysis of these polymorphisms in stroke patients and controls was tested according to four models (dominant, recessive, heterozygous, and additive) and adjusted for stroke risk factors (Table 2). After adjusting for sex, age, body mass index (BMI), SBP, DBP, Glu, dyslipidemia, alcohol
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Neuromol Med Table 1 Clinical characteristics of the 503 stroke patients and 493 controls in the study
Control subjects (n = 493)
Stroke patients (n = 503)
p \0.001
Age (years)
53.08 ± 8.63
58.83 ± 10.25
Men (%)
300 (60.7)
332 (65.9)
BMI (kg/m2)
23.41 ± 3.29
24.80 ± 3.34
0.105 \0.001
SBP (mmHg)
115.75 ± 11.43
148.00 ± 24.04
\0.001
DBP (mmHg)
78.92 ± 6.40
89.61 ± 13.33
\0.001
Glu (mmol/l)
5.11 ± 2.07
6.77 ± 3.02
\0.001
TC (mmol/l)
4.39 ± 0.58
5.31 ± 3.38
\0.001
TG (mmol/l)
0.99 ± 0.31
1.89 ± 1.48
\0.001
HDL (mmol/l)
1.46 ± 0.28
1.28 ± 0.46
\0.001
LDL (mmol/l)
2.67 ± 0.48
2.87 ± 0.82
\0.001
Smoking [n (%)]
120 (24.29)
244 (48.41)
\0.001
Alcohol intake [n (%)]
134 (27.12)
184 (36.50)
0.002
Hypertension [n (%)]
0 (0)
370 (73.41)
Diabetes [n (%)]
0 (0)
142 (28.17)
Cardiopathy [n (%)]
0 (0)
68 (13.49)
Mean ± standard deviation BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, Glu glucose, TC total cholesterol, TG triglycerides, LDL low-density lipoprotein, HDL highdensity lipoprotein
Table 2 Association of KALRN SNPs in the controls and stroke cases Genotype
Controls
Cases
rs17286604
Position
MAF
124233370
0.015
SNP model
Unadjusted OR [95 % CI]
p
1.12 [0.54–2.31]
Adjusted OR [95 %]
Adjusted p
p0
1.12 [0.54–2.31]
CC
479
487
Dominant
0.75
0.597
0.6
CT
14
16
Recessive
–
–
–
TT
0
0
Heterozygous
0.75
–
–
Additive
0.75
0.597
0.6
0.787 0.54
0.79 0.54
rs7620580
124326456
AA AG
296 174
285 191
GG
23
27
rs11712619
0.233
1.12 [0.91–1.37] Dominant Recessive
124300955
1.12 [0.91–1.37] 0.11 –
Heterozygous
0.11
0.822
0.83
Additive
0.11
0.979
1
0.009
2.13 [0.80–5.64]
2.13 [0.80–5.64]
CC
487
490
Dominant
0.11
0.267
0.27
CT
6
13
Recessive
–
–
–
TT
0
0
Heterozygous
0.11
–
–
Additive
0.11
0.267
0.271
rs6438833 AA
124206748 4
0.081
1.33 [0.96–1.85]
1.33 [0.96–1.85]
2
Dominant
0.39
0.302
0.303
Recessive
0.09
0.091
0.095
Heterozygous
0.12
0.084
0.086
Additive
0.07
0.045
0.047
AT
83
67
TT
406
434
The adjusted p values were calculated from logistic regression analysis and were adjusted for sex, age, body mass index (BMI), dyslipidemia, alcohol consumption, and smoking. Bold numbers indicate significant associations. The p0 was calculated using 10,000 permutations for each model to correct the multiple test. Dashes ‘‘–’’ indicate data missing MAF Minor allele frequency, OR odds ratio, CI confidence interval, p p value
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consumption, and smoking, under additive models, rs6438833 was associated with an increased risk of ischemic stroke when compared to controls (OR 1.33, 95 % CI 0.96–1.85, p = 0.045). After performing 10,000 permutations, the p value was still significant (p0 = 0.047). The data were further analyzed to determine the allele and genotype frequencies of KALRN SNPs in controls and ischemic stroke with or without diabetes (Table 3). After adjusting for sex, age, body mass index (BMI), SBP, DBP, glucose, dyslipidemia, alcohol consumption, and smoking, under the recessive model, heterozygous model, and additive model, the rs6438833 polymorphism was associated with increased risk of stroke with diabetes when compared to healthy controls (OR 1.58, 95 % CI 0.92–2.70, p = 0.019). After performing 10,000 permutations, the recessive model and heterozygous model were still significant (p0 = 0.019 and p0 = 0.018, respectively). However, there was no association of this polymorphism in stroke without diabetes. In addition to diabetes, we also analyzed the possible confounding variable of hypertension. The genotype and allele of the four SNPs for patients with or without hypertension are summarized in Table 4. After adjusting for sex, age, body mass index (BMI), SBP, DBP, glucose, dyslipidemia, alcohol consumption and smoking, neither the genotype nor the allele frequencies of the polymorphisms were significantly different between the stroke with or without hypertension when compared to control subjects. The results of the association studies of genotype and allele of the four SNPs between the TOAST subgroup and control subjects are summarized in Table 5. Under the dominant model, heterozygous model, and additive model, the rare variant rs11712619 (MAF = 0.009) was showed a significant association with lacunar stroke as compared with controls (OR 2.95, 95 % CI 1.08–8.01, p = 0.028). However, after adjusting for age, sex, BMI, SBP, DBP, glucose, TC, TG, HDL, LDL, smoking, and alcohol intake, this association was no longer significant. 10,000 permutations test were performed, the results were also no statistical significance. For the variant rs6438833, even after adjusting for age, sex, BMI, SBP, DBP, glucose, TC, TG, HDL, LDL, smoking, and alcohol intake, under the recessive model, this variant was significantly different between control patients and those with a lacunar stroke (OR 1.51, 95 % CI 1.02–2.23, p = 0.047) and remained significant after the permutation test (p0 = 0.048).
Discussion KALRN has recently been identified as playing an important role in the development of several cardiovascular diseases, such as CAD and atherosclerosis (Wang et al. 2007; Bevan
et al. 2012). The association of known polymorphisms of the kalirin-coding gene, KALRN, with common diseases in different populations is controversial (Krug et al. 2010; Olsson et al. 2011). In the present study, we analyzed four polymorphisms (rs17286604, rs7620580, rs11712619, and rs6438833) located in the KALRN gene in the Chinese Han population. Previously, these four polymorphisms were associated with ischemic stroke in the Portuguese population (Krug et al. 2010). The results of this study found that in Chinese Han population, two of these polymorphisms, rs11712619 and rs6438833, were associated with risk of stroke development. However, unlike the Portuguese population, rs17286604 and rs11712619 were rare variants in our populations, and rs11712619 was association with ischemic stroke. Interestingly, as compared to those of higher frequency SNPs, the effects of rare variants tend to be larger in common inherited disease (Bodmer and Bonilla 2008; Asimit and Zeggini 2010). We further analyzed the data from the patients with ischemic stroke, examining the impact of complications (diabetes and hypertension) and subtypes (atherosis, lacunar, and combination type) of ischemic stroke. Our results indicated that KALRN has an association with complications and subtypes of ischemic stroke. Specifically, rs11712619 (MAF = 0.009) was associated with lacunar cerebral stroke though after the 10,000 permutation test, and this association was not detected. Rare variants may often have penetrances large enough to justify preventative screening strategies for multifactorial inherited diseases (Bodmer and Bonilla 2008), such as ischemic stroke. Additionally, the SNP rs6438833 was associated with ischemic stroke and lacunar cerebral stroke. Even adjusting for the sex, age, body mass index (BMI), SBP, DBP, Glu, dyslipidemia, alcohol consumption, and smoking, rs6438833 was associated with ischemic stroke, lacunar cerebral stroke, and ischemic stroke with diabetes, and after performing 10,000 permutations, the results were still significant. Diabetes was association with an increased incidence of stroke (Matsushita et al. 2010), and it may share the common pathogenic mechanism: atherosclerosis. The SNPs rs11712619 and rs6438833 were in introns and possibly affect alternative splicing regulation by disrupting exonic splicing enhancers or silencers (VargasAlarcon et al. 2014, 2015). It is suspected that the two SNPs at intronic regions may affect KALRN expression or lead to alternative splicing of kalirin protein, generating isoforms. The SNPs rs11712619 was mapped in isoform 1 and rs6438833 mapped in isoform 2. Kalirin isoform 1 codes for kalirin 7 and isoform 2 codes for kalirin 9 (Yan et al. 2014). Kalirin-7 is involved in ischemic signal transduction; however, although kalirin 7 has been shown to play an important role in postischemic neurodegeneration and repair, the function in ischemic brain is unknown
123
123
14
0
TT
174
23
AG
GG
6
0
CT
TT
406
AT
TT
124
17
0
0
2
139
7
47
87
0
2
139
Stroke with diabetes
0.311
0.156 0.158 0.076
Additive
0.103
Heterozygous
Recessive
Dominant
0.852 0.852
Additive 1.58 [0.92–2.70]
0.852 –
Heterozygous
Recessive
Dominant
0.665 0.806
Heterozygous 1.16 [0.23–5.81]
0.721 0.883
Additive
Recessive
Dominant
0.311 0.11
–
Additive 0.96 [0.69–1.32]
0.49 [0.11–2.19]
Unadjusted OR [95 % CI]
Heterozygous
Recessive
Dominant
SNP model
1.58 [0.92–2.70]
1.16 [0.23–5.81]
0.96 [0.69–1.32]
0.49 [0.11–2.19]
Adjusted OR [95 %]
0.019
0.019
0.019
0.962
0.056
0.056
–
0.056
0.791
0.963
0.766
0.857
0.051
0.051
–
0.051
Adjusted p0
0.509
0.0187
0.0196
0.915
0.639
0.657
–
0.657
0.528
0.966
0.766
0.859
0.593
0.593
–
0.593
p0
309
50
2
0
11
350
20
144
197
0
14
347
Stroke without diabetes
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
SNP model
1.25 [0.88–1.78]
2.52 [0.93–6.86]
1.19 [0.95–1.49]
1.37 [0.65–2.89]
Unadjusted OR [95 % CI]
0.402
0.196
0.232
0.202
0.652
0.06
0.06
–
0.06
0.122
0.17
0.564
0.11
0.402
0.402
–
1.25 [0.88–1.78]
2.52 [0.93–6.86]
1.19 [0.95–1.49]
1.37 [0.65–2.89]
Adjusted OR [95 %]
0.47
0.173
0.292
0.311
0.095
0.095
–
0.095
0.415
0.802
0.398
0.549
0.265
0.265
–
0.265
Adjusted p
0.275
0.179
0.3
0.283
0.283
0.108
–
0.108
0.269
0.798
0.397
0.552
0.275
0.275
–
0.275
p0
The adjusted p values were calculated from logistic regression analysis and were adjusted for sex, age, body mass index (BMI), dyslipidemia, alcohol consumption, and smoking. Bold numbers indicate significant associations. The p0 was calculated using 10,000 permutations for each model to correct the multiple test. Dashes ‘‘–’’ indicate data missing
4
83
AA
rs6438833
487
CC
rs11712619
296
AA
rs7620580
479
CT
Controls
CC
rs17286604
Genotype
Table 3 Association of KALRN SNPs in the controls and stroke patients with or without diabetes
Neuromol Med
14
0
CT
TT
174
23
AG
GG
6
0
CT
TT
406
AT
TT
317
51
1
0
8
361
17
146
206
0
10
359
Stroke with hypertension
0.219 0.119
Additive
0.154
0.278
Heterozygous
Recessive
Dominant
0.279
Additive 1.31 [0.92–1.87]
0.279
–
0.279
Heterozygous
Recessive
Dominant
0.313
Additive 1.78 [0.61–5.17]
0.207
0.962
0.223
Heterozygous
Recessive
Dominant
0.904 0.904
–
0.904
p
Additive 1.12 [0.89–1.40]
0.95 [0.42–2.15]
Unadjusted OR [95 % CI]
Heterozygous
Recessive
Dominant
SNP model
1.31 [0.92–1.87]
1.78 [0.61–5.17]
1.12 [0.89–1.40]
0.95 [0.42–2.15]
Adjusted OR [95 %]
0.808
0.107
0.364
0.06
0.718
0.718
–
0.718
0.438
0.192
0.698
0.271
0.666
0.666
–
0.666
Adjusted p
0.668
0.108
0.372
0.062
0.69
0.74
–
0.74
0.656
0.191
0.697
0.27
0.665
0.665
–
0.665
p0
116
16
1
0
5
128
10
45
78
0
6
127
Stroke no hypertension
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
SNP model
1.40 [0.82–2.36]
3.12 [0.94–10.33]
1.12 [0.81–1.54]
1.60 [0.60–4.21]
Unadjusted OR [95 % CI]
0.194
0.166
0.17
0.945
0.07
0.07
–
0.07
0.467
0.753
0.209
0.771
0.35
0.35
–
0.35
p
1.40 [0.82–2.36]
0.154
3.12 [0.94–10.33]
1.12 [0.81–1.54]
1.60 [0.60–4.21]
Adjusted OR [95 %]
0.28
0.308
0.284
0.753
0.154
0.154
–
0.178
0.658
0.253
0.53
0.409
0.249
0.249
–
0.249
Adjusted p
0.255
0.309
0.291
0.824
0.251
0.178
–
0.257
0.262
0.535
0.417
0.254
0.254
–
0.254
p0
The adjusted p values were calculated from logistic regression analysis and were adjusted for sex, age, body mass index (BMI), dyslipidemia, alcohol consumption, and smoking. The p0 was calculated using 10,000 permutations for each model to correct the multiple test. Dashes ‘‘–’’ indicate data missing
4
83
AA
rs6438833
487
CC
rs11712619
296
AA
rs7620580
479
CC
rs17286604
Controls
Table 4 Association of KALRN SNPs in the controls and stroke patients with or without hypertension
Neuromol Med
123
123
14
0
CT
TT
174
23
AG
GG
6
0
CT
TT
0
TT
174
23
AG
GG
487
6
0
CC
CT
TT
rs11712619
296
AA
rs7620580
479 14
CC CT
2.95 [1.08–8.01]
1.06 [0.83–1.34]
0.25
–
0.26
–
0.26
0.44
0.695 0.25
0.89
0.69
0.76
0.894
0.692
0.768
0.44 0.44
0.437
0.44 –
0.437
0.437 –
101
0
2
103
8
37
60
4 0
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Dominant Recessive
SNP model
1.358 [0.709–2.601]
–
1.260 [0.859–1.849]
0.442 [0.057–3.386]
Adjusted OR [95 %]
Combination
0.34
Additive p0
0.204
Adjusted p
0.702 0.257
Heterozygous
1.254 [0.56–2.78]
69
Dominant
Recessive
rs17286604
TT
1
9
0.181
Additive 1.358 [0.709–2.601]
0.181
–
Heterozygous
Recessive
0.181
0.235
Additive
Dominant
0.188 –
0.174 0.878
Heterozygous
Recessive
Dominant
0.373
Additive
0.373
p
0.373 1.260 [0.859–1.849]
0.442 [0.057–3.386]
Unadjusted OR [95 %]
Heterozygous
Recessive
Dominant
SNP model
Adjusted OR [95 %]
406
AT
0
0
79
4
34
41
0
1
78
Atherosis
Controls
4
83
AA
rs6438833
487
CC
rs11712619
296
AA
rs7620580
479
CC
rs17286604
Controls
Table 5 Association with genotypes and alleles of 4 SNPs in controls and stroke patients by subgroup
0.74
0.15
0.27
0.45
0.72
0.63
–
0.63
0.74
0.9
0.37
0.744
0.74
0.74
0.5
0.74
0.5
p0
271
1.57 [0.31–7.83]
1.17 [0.83–1.66]
1.34 [0.43–4.13]
39
0
0
11
299
15
115
180
0
11
299
0.595
–
0.595
0.363
0.991
0.237
0.583
0.608
0.608
0.608 –
p
Lacunar
Unadjusted OR[95 %]
0.454
0.15
0.268
0.47
0.589
0.589
–
0.589
0.512
0.909
0.356
0.739
0.739
0.739
–
0.739
Adjusted p
1.57 [0.31–7.83]
1.17 [0.83–1.66]
1.34 [0.43–4.13]
0.71
–
0.71
0.89
0.503
0.598
0.686
0.807
0.807
0.807 –
Adjusted p
1.51 [1.02–2.23]
2.95 [1.08–8.01]
1.06 [0.83–1.34]
1.254 [0.56–2.78]
Unadjusted OR [95 %]
Adjusted OR [95 %]
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
Additive
Heterozygous
Recessive
Dominant
SNP model
0.72
–
0.72
0.8
0.5
0.61
0.68
0.81
0.81
0.81 –
p0
0.03
0.098
0.051
0.048
0.028
0.028
–
0.028
0.612
0.604
0.91
0.579
0.576
0.576
–
0.576
p
Neuromol Med
Heterozygous
Additive
85 0.054
0.44
0.051 406 TT
0.437
Dominant Recessive 1 19 0.62 0.048 0.669 0.047 4 83 AA AT
The adjusted p values were calculated from logistic regression analysis and were adjusted for sex, age, body mass index (BMI), dyslipidemia, alcohol consumption, and smoking. Bold numbers indicate significant associations. The p0 was calculated using 10,000 permutations for each model to correct the multiple test. Dashes ‘‘–’’ indicate data missing
0.89 0.897
0.254
0.756
0.729
0.8
– 0.577 0.887 0.735
1 0.57
0.79 0.71 0.91 [0.55–1.50] 0.595 0.91 [0.55–1.50] Additive 0.44 0.25 1.51 [1.02–2.23] rs6438833
p0 Adjusted p Adjusted OR [95 %] Controls
Table 5 continued
Combination
SNP model
Unadjusted OR[95 %]
p
Adjusted OR [95 %]
Adjusted p
p0
Neuromol Med
(Beresewicz et al. 2008). Kalirin-9 activates Rac1 and PAK1 signaling, which are critical for SMC migration, and prevents NOS2 dimerization, which therefore inhibits NOS2 activity. As NOS2 production of NO reduces SMC proliferation and mitochondrial respiration (Yan and Hansson 1998; Wu et al. 2013), kalirin-9 promotes neointimal hyperplasia leading to atherosclerosis by decreasing NO production, and therefore SMC proliferation (Wu et al. 2013). In conclusion, this study has demonstrated a positive correlation between the rare variant SNP rs11712619 and the common variant SNP rs64388333 in the kalirin gene and ischemic stroke in a Chinese Han population. To confirm the association between ischemic stroke and the rare variant rs11712619, large-scale studies are needed, and more basic research needs to be done to expound the function of kalirin7 in brain ischemic stroke. Understanding the risk of this rare SNP variant may lead to novel insight into the mechanisms and pathogenesis of ischemic stroke. Acknowledgments We are deeply grateful to all study participants. This work was supported by the Natural Science Foundation of Heilongjiang province (No. H201331). Conflict of interest
The authors declared no conflict of interest.
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