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(Stroke. 2006;37:69.)
© 2006 American Heart Association, Inc.

Original Contributions

Genotype and Haplotype Association Study of the STRK1 Region on 5q12 Among Japanese

A Case-Control Study

Tomohiro Nakayama, MD, PhD; Satoshi Asai, MD, PhD; Naoyuki Sato, BAgr Masayoshi Soma, MD, PhD

From the Divisions of Receptor Biology (T.N., N.S.) and Genomic Epidemiology and Clinical Trials (S.A.), Advanced Medical Research Center, and Division of Nephrology and Endocrinology (M.S.), Department of Medicine, Nihon University School of Medicine, Tokyo, Japan.

Correspondence to Tomohiro Nakayama, MD, Division of Receptor Biology, Advanced Medical Research Center, Nihon University School of Medicine, Ooyaguchi-kamimachi, 30-1 Itabashi-ku, Tokyo 173-8610, Japan. E-mail tnakayam{at}med.nihon-u.ac.jp

	Abstract

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Background and Purpose— Cerebral infarction is thought to be a multifactorial disease that is affected by several environmental factors and genetic variants. Gretarsdottir et al identified a candidate locus (STRK1) for cerebral infarction with a significant logarithm of odds score at 5q12 in whites in 2002 and subsequently identified the PDE4D gene as a susceptibility gene at this locus in 2003. The aims of this haplotype-based case-control study were to confirm, using microsatellite markers and single-nucleotide polymorphisms (SNPs), whether PDE4D is also a susceptibility gene for cerebral infarction in Japanese subjects.

Methods— Cerebral infarction was defined as noncardiogenic ischemic stroke with signs and symptoms lasting >1 month in duration. We genotyped 208 Japanese cerebral infarction patients and 270 non–cerebral infarction controls for 31 SNPs, 3 dinucleotide microsatellites, and 1 tetranucleotide variable number of tandem repeat. Haplotypes were constructed and their frequencies compared between the cerebral infarction patients and the controls.

Results— The haplotype-based case-control study revealed that in addition to the region of the PDE4D gene (P=0.002), another region (P<0.001) also existed within the STRK1 locus.

Conclusions— The region of the PDE4D gene and the other newly detected region within the STRK1 locus were associated with cerebral infarction.

Key Words: case-control studies • cerebral infarction • genetics • haplotypes

	Introduction

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Cerebral infarction is thought to be a heterogeneous multifactorial disease with which several environmental and genetic variants can be associated. These environmental and genetic factors together lead to the development of cerebral infarction.¹ Various susceptibility polymorphisms and mutations exert their effects in a polygenic manner. Epidemiologic studies have suggested a polygenic basis for cerebral infarction.² Identification of cerebral infarction susceptibility genes might enhance prediction of the risk of the disease. However, few genes considered as confirmed susceptibility genes for cerebral infarction have been identified.

Recently, Gretarsdottir et al³ performed a genome-wide scan for susceptibility genes for stroke, which included ischemic and hemorrhagic stroke types, and identified a candidate locus with a significant logarithm of odds (LOD) score at the locus on 5q12 in whites. They named this locus STRK1 because it did not correspond to any other previously known loci. Subsequently, they were able to identify the PDE4D gene as a susceptibility gene within this locus.⁴ However, 2 questions need to be addressed. The first is whether PDE4D is a susceptibility gene for cerebral infarction in Japanese as well as whites, and the second is whether susceptibility genes other than PD4ED are present in the STRK1 susceptibility region.

The aims of the present study were to determine whether PDE4D is a susceptibility gene for cerebral infarction in Japanese subjects using microsatellite markers and single- nucleotide polymorphisms (SNPs) in a haplotype-based case-control study and also to elucidate whether it is the only susceptibility gene in the STRK1 locus on 5q12.

	Methods

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Subjects
Patients and control subjects from the northern area of Tokyo were recruited for the case-control study. Patients were selected among those who were admitted at our hospital (Nihon University Hospital in Tokyo) or community hospitals in Tokyo between 1995 and 2005. Control subjects were selected from among the outpatients at our hospital during the same period. More than 80% of the 500 subjects we approached consented to participate in the study. Therefore, given that the subjects were selected according to the criteria for case-control studies, this investigation cannot be considered a population-based study. The study group consisted of 208 patients (126 men and 82 women; mean age 66.0±12.4 years, ranging from 29 to 99 years) diagnosed with cerebral infarction by computed tomography or MRI. All patients had neurological deficits that persisted for 1 month. A total of 270 subjects without cerebral infarction (141 men and 129 women; mean age 66.1±5.8 years, ranging from 50 to 98 years) were used as control subjects. Ages of the control subjects exceeded 50 years, but there was no significant difference between the cerebral infarction patients and the controls. Control subjects had vascular risk factors such as hypertension, diabetes mellitus, or hypercholesterolemia but no cerebrovascular disease. Hypertension was defined as having a sitting systolic blood pressure >160 mm Hg, diastolic blood pressure >100 mm Hg, or both on 3 occasions within 2 months after the first medical examination⁵ or current use of an antihypertensive drug because of a history of arterial hypertension. Diagnosis of diabetes mellitus was based on the World Health Organization (WHO) criteria. Hyperlipidemia was defined as plasma total cholesterol >6.5 mmol, plasma triglycerides >2 mmol, or current use of a lipid-lowering drug in addition to a confirmed diagnosis of hyperlipidemia.⁶ Smokers were defined as current or former smokers, whereas nonsmokers were defined as subjects with no history of previous or current smoking. History of smoking was recorded and current smokers included individuals who had stopped smoking <1 year before enrollment. History of alcohol use was recorded with habitual consumers defined as individuals who had 2 alcoholic beverages per day.⁷ Individuals with a proven cause of cardioembolism such as recent myocardial infarction, valvular heart disease, and arrhythmia including atrial fibrillation were excluded from the cerebral infarction and control groups. Informed consent was obtained from each participant according to a protocol approved by the human studies committee at Nihon University.⁸

Biochemical Analysis
Plasma concentrations of total and high-density lipoprotein cholesterol and serum concentrations of creatinine and uric acid were measured as described previously.⁷

Genotyping of Microsatellite Markers and SNPs
Blood samples were collected from all participants, and genomic DNA was extracted from peripheral blood mononuclear cells using standard procedures.⁹

The entire STRK1 locus is located between the D5S407 and D5S647 microsatellite markers. We therefore selected 3 microsatellite markers including 2 within the STRK1 locus that were obtained using PRISM linkage mapping set HD-5 (Applied Biosystems). The primer sets supplied with this kit are suitable for accurate genotyping. The distance between neighboring microsatellite markers was an average of 4.5 cM, and the average heterozygosity was 0.76. The primers were dye-conjugated with FAM, HEX, or NED (Applied Biosystems). Polymerase chain reaction (PCR) amplification was performed using 15 ng of genomic DNA, 3 µL True Allele PCR Premix (containing PCR buffer, MgCl₂, dNTP, and AmpliTaq Gold; Applied Biosystems), and 0.33 µL of primer mix, to give a total reaction volume of 5 µL. PCR amplification consisted of an initial step at 95°C for 12 minutes to activate the AmpliTaq Gold; 10 cycles of denaturation at 94°C for 15 s, annealing at 55°C for 15 s, and extension at 72°C for 30 s. This was followed by 20 cycles of denaturation at 89°C for 15 s, annealing at 55°C for 15 s, and extension at 72°C for 30 s, with a final step at 72°C for 10 minutes. PCR amplification was performed using Gene Amp PCR 9600 thermocycler (Applied Biosystems). Products from up to 8 different PCRs were diluted 1:10 (1:5 for products labeled with HEX and NED) and pooled. The pooled products (1 µL) were then mixed with GS-500 LIZ internal size standard (0.5 µL) and diluted 1:6 with HI-DI formamide (Applied Biosystems). The pooled reactions were denatured at 95°C for 3 minutes and were loaded on an ABI 3700 DNA analyzer (Applied Biosystems). Fluorescent signals of fragments from the reactions with various fluorescent dyes were recorded and analyzed using GeneScan software version 2.1 (Applied Biosystems), and the sizes of the fluorescent peaks were estimated by referencing the in-lane size standards. Marker allele calling was performed using Genotyper software version 2 (Applied Biosystems). In addition, we performed a manual surveillance for all of the genotypes.¹⁰

To determine the associations between the markers and cerebral infarction, we selected 29 SNPs in the STRK1 locus (supplemental Figure I, available online at http://stroke.ahajournals.org). Based on the website from the National Center for Biotechnology Information SNP database or the Applied Biosystems–Celera Discovery System (CDS) database, we chose SNPs that had a minor allele frequency >18%. SNP Assays-on-Demand kits (Applied Biosystems) were used for the determinations. The CDS was used to confirm the SNPs via the accession number. Genotypes were determined using TaqMan PCR.¹¹

To examine whether PDE4D was the susceptibility gene for cerebral infarction in Japanese subjects, 2 SNPs (SNP45 and SNP83) and 1 tetranucleotide variable number of tandem repeat (VNTR; AC008818-1) were also genotyped because they had been reported previously to be susceptibility variants in the PDE4D gene.⁴ X allele has been defined in a previous report.⁴

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Linkage Disequilibrium Analysis and the Haplotype-Based Case-Control Study
Based on the genotype data of the genetic variations, linkage disequilibrium (LD) analysis was performed using SNPAlyze software (limited version 4.1; Dynacom Co, Ltd)¹³ Haplotype frequencies were estimated by SNPAlyze software (version 3.2; Dynacom) using the expectation maximization algorithm.¹² Although the software was suitable for haplotype-based case-control studies using a maximum of 10 SNPs, it was not so for studies using microsatellites. Given that the online haplotype database showed all haplotype blocks on 5q12 to have been constructed by SNPs located within 1000 kbp in Japanese subjects (Project Ensemble), the pair-wise LD analysis of this study was performed using SNP pairs separated by <1000 kbp. Absolute D' values (|D'|) 0.3 were used to assign SNP locations to 1 haplotype block. Tagged SNPs were selected by omitting 1 SNP in SNP pairs showing r²0.5 for each haplotype block. Despite being limited to 3 variants, the haplotype-based case-control study that used mixed SNP and microsatellite data were performed using the SNPAlyze software (limited version 4.1).¹³

Statistical Analysis
Clinical data represented as mean±SD were first tested by ANOVA followed by Fisher’s protected least significant difference test; P values <0.05 indicated a significant difference (Dr. SPSS II; SPSS Japan Inc).¹⁴ Hardy–Weinberg equilibrium was assessed by ² analysis (Dr. SPSS II).

The overall distribution of alleles between the cerebral infarction patients and controls was analyzed by ² goodness-of-fit test using 2x2 contingency tables; P values <0.05 were considered significant (Dr. SPSS II).¹⁵

In the haplotype-based case-control study, the threshold value for haplotype frequency that was included in the analysis was set at 2%.^16–19 All haplotype frequencies below the threshold value were excluded from the analysis, and P values <0.05/n were considered significant after correcting for the number of comparisons made (Bonferroni correction).¹³

	Results

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The characteristics of the study participants are shown in Table 1. Cerebral infarction and age-matched control groups were used to assess polymorphism association between and within sex-specific groups in the total study cohort.

View this table: [in this window] [in a new window]   TABLE 1. Characteristics of Study Participants

Genotyping was successful for >97% of the samples using the 3 microsatellites, 1 VNTR, and 31 SNPs from the 478 study subjects. Although Gretarsdottir et al reported that SNP45 was one of the variants consistent with the susceptibility haplotype associated with cerebral infarction, none of the Japanese subjects in our experiment were heterogeneous for SNP45. The expected frequencies for each genotype for all 3 microsatellites and the remaining 30 SNPs in the control group were in Hardy–Weinberg equilibrium (data not shown). There was no association with any of the polymorphisms examined (after correcting for multiple comparisons; Table 2). The distribution of genotype data are shown in supplemental Table I.

View this table: [in this window] [in a new window]   TABLE 2. Association Study of Microsatellites and SNPs

View this table: [in this window] [in a new window]   TABLE 2. (Continued)

View this table: [in this window] [in a new window]   TABLE 2. (Continued)

View this table: [in this window] [in a new window]   TABLE 2. (Continued)

View this table: [in this window] [in a new window]   TABLE I. Genotype Distribution of SNPs

View this table: [in this window] [in a new window]   TABLE I. (Continued)

LD patterns are illustrated by their |D'| and r² values (supplemental Table II). Seven haplotype blocks were found in the STRK1 locus (Table 2). Tagged SNPs were selected in each haplotype block based on the pair-wise LD analysis. The haplotype-based case-control study that was performed for each block using tagged SNPs revealed that there were 2 significantly different haplotype blocks between the cerebral infarction and control groups (Table 2). Because 7 haplotype blocks were compared, P values <0.05/7=0.0071 were considered significant. The first block consisted of SNP83, SNP8, and VNTR within the PDE4D gene (P=0.002). The second block consisted of SNPs 18, 19, 20, and 21 (P<0.001). These P values also became significant after performing Bonferroni corrections.

View this table: [in this window] [in a new window]   TABLE II. The Pair-Wise LD Analysis Using SNPs

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SNP45, SNP83, VNTR, and the haplotype map built with SNP45 as well as the VNTR have all been reported previously to be associated with cerebral infarction.⁴ SNP45 was not heterogenous, and the allelic distribution of SNP83 differed from that observed in whites.⁴ The C allele was prevalent in 52.0% of the white control subjects but only in 11.8% of the Japanese control subjects. We performed our haplotype-based case-control study using SNP83, SNP8, and the VNTR because SNP45 was not polymorphic in our study population. The overall distribution of the haplotypes showed a significant difference between the cerebral infarction and control groups (²=21.4; P value=0.002). The results for individual haplotypes showed that the frequency of the c-g-X haplotype was significantly higher in the cerebral infarction group compared with the controls (Table 3), whereas the T-g-X haplotype was significantly less frequent in the cerebral infarction group than in the controls.

View this table: [in this window] [in a new window]   TABLE 3. Individual Haplotype Frequencies in the Haplotype Block Consisting of SNP83, 8, and VNTR

Distribution of individual haplotypes in the second block and their P values are shown in Table 4. Results of the individual haplotypes indicated that 4 haplotypes exhibited significant differences between the cerebral infarction and control groups.

View this table: [in this window] [in a new window]   TABLE 4. Individual Haplotype Frequencies in the Haplotype Block Consisting of SNPs18, 19, 20, and 21

	Discussion

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The foundation for human studies examining putative causative genes that may be involved in cerebral infarction is based on a candidate gene approach. This involves selecting a functionally relevant gene to study and subsequently investigating its association with the cerebral infarction phenotype. Candidate genes in cerebral infarction research are chosen mainly for their role in the risk of stroke or vascular reactivity and brain response after insult. Stroke candidate genes fall into 5 main groups: renin-angiotensin system,²⁰ NO production,²¹ lipid metabolism,²² hemostasis,²³ and homocysteine metabolism.²⁴ However, the candidate regions are quite varied and the requisite assay analyses differ for each investigation.

A genome-wide scan was performed by Gretarsdottir et al in 2002 in an Icelandic population of 476 stroke patients and 438 relatives. The authors were able to identify a stroke susceptibility locus, STRK1, and mapped it to human chromosome 5ql2.³ A broad but rigorous definition of the phenotype was documented, and hemorrhagic stroke, ischemic stroke, and transient ischemic attack were all included to map a locus for common stroke. The LOD score at the chromosome 5 locus increased from an initial score of 2.00 to 3.39 after genotyping of 45 additional markers over the identified 45-cM region. Furthermore, linkage analysis undertaken using an even higher marker density resulted in an LOD score of 4.40. This study was therefore the first to successfully map a major locus for stroke by combining genealogy, a large population from which patients with broadly defined stroke were selected, and allele-sharing methods. Together, these considerations make this study one of the most comprehensive analyses of genetic predisposition to stroke published to date. If indeed the susceptibility genes or polymorphisms can be completely elucidated, these results will have a considerable impact on the medical community because stroke is one of the major public health concerns in the world today. With this goal in mind, we began our initial experiments in 2002. After Gretarsdottir et al further identified the PDE4D gene as a susceptibility gene for this locus in 2003,⁴ we decided to attempt to confirm whether this PDE4D gene was also a susceptibility gene in Japanese subjects.

Candidate polymorphisms that were possible major risk factors for myocardial infarction in Japanese subjects were identified previously in a large-scale case-control study that used 92 788 gene-based SNP markers from the entire human genome.²⁵ The results of this study confirmed the value of whole genome scanning using SNPs for the identification of susceptibility genes of multifactorial diseases. Therefore, we designed a case-control study using SNPs and microsatellites in the susceptibility locus. In our study, the cerebral infarction patients and control subjects were selected based on considerably stricter criteria than those used in the Gretarsdottir et al study by excluding transient ischemic attack. To do this, cerebral infarction was defined as noncardiogenic ischemic stroke with signs and symptoms lasting >1 month in duration. Inclusion of the various subtypes of stroke may have increased the false-negative results in their case-control study.

Very recently, 2 groups independently reported that the PDE4D gene was not associated with stroke.^26,27 Not surprisingly, comparison of different studies produces different results,²⁸ and one explanation for these disparities is that different analytical methods, genetic maps, and markers are used. Furthermore, striking differences in incidence, prevalence, and the clinical patterns among different ethnic populations reported in several epidemiological studies support this view.

Our results suggest that there may be a susceptibility region other than that of the PDE4D gene within the locus in Japanese subjects. The region of the PDE4D gene may be the susceptibility region for cardioembolic stroke, whereas the region that we identified may be the susceptibility region for noncardiogenic ischemic stroke. Further studies are needed to isolate other susceptibility genes and determine the confounding between these 2 regions.

	Acknowledgments

 
This work was supported by a grant from the Ministry of Education, Science and Culture of Japan (High-Tech Research Center, Nihon University) and a research grant from the Alumni Association of Nihon University School of Medicine. We would like to thank K. Sugama for her technical assistance.

Received July 26, 2005; revision received September 20, 2005; accepted October 17, 2005.

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