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the Department of Medical Biosciences (S.N.-A., P.L., A.K.N., T.J., S.A.E., B.H., D.H.), Division of Medical and Clinical Genetics, and the Department of Public Health and Clinical Medicine (P-G.W., B.S., K.A.), Ume University, Ume, Sweden.
Abstract
Background and Purpose— Recent Icelandic studies have demonstrated linkage for common forms of stroke to chromosome 5q12 and association between phosphodiesterase4D (PDE4D) and ischemic stroke. Using a candidate region approach, we wanted to test the validity of these findings in a different population from northern Sweden.
Methods— A total of 56 families with 117 affected individuals were included in the linkage study. Genotyping was performed with polymorphic microsatellite markers with an average distance of 4.5 cM on chromosome 5. In the association study, 275 cases of first-ever stroke were included together with 550 matched community controls. Polymorphisms were tested individually for association of PDE4D to stroke.
Results— Maximum allele-sharing lod score in favor of linkage was observed at marker locus D5S424 (lod score=2.06; P=0.0010). Conditional logistic regression calculations revealed no significant association of ischemic stroke to the defined at-risk allele in PDE4D (odds ratio, 1.1; 95% confidence interval, 0.84 to 1.45). A protective effect may though be implied for 2 of the polymorphisms analyzed in PDE4D.
Conclusions— Using a candidate region approach in a set of stroke families from northern Sweden, we have replicated linkage of stroke susceptibility to the PDE4D gene region on chromosome 5q. Association studies in an independent nested case-control sample from the same geographically located population suggested that different alleles confer susceptibility/protection to stroke in the Icelandic and the northern Swedish populations.
Key Words: genetics phosphodiesterase inhibitors stroke
Introduction
The common forms of stroke, cerebral infarction and primary intracerebral hemorrhage, constitute 90% of all stroke cases, with the majority being ischemic.1 Genetic components in human stroke has been implicated in several studies, including both twin studies2,3 and family studies.4,5 Animal models also suggest that susceptibility to ischemic stroke is influenced by genetic factors.6 In several rare monogenic forms of cerebrovascular diseases, the genetic components have been identified.7,8 For common forms of stroke, recent studies of Icelandic patients have demonstrated linkage to 5q12 and association between phosphodiesterase4D (PDE4D) and ischemic stroke.9,10 A suggested role of PDE4D in stroke pathogenesis is that PDE4D expression can influence the second messenger c-AMP, an important signal transduction molecule with many cellular functions, including smooth muscle cell proliferation.11–13 To test the validity of these findings in a different population, we studied a family-based sample consisting of 56 nuclear and extended families, including 117 patients with ischemic or hemorrhagic stroke and a nested case-control sample, including 275 patients with ischemic or hemorrhagic stroke and 550 matched community controls, from the 2 northernmost counties of Sweden.
Materials and Methods
Subjects
Since 1985, a population-based stroke registry covering patients aged 25 to 74 years has been kept at the northern Sweden MONICA Center. Each case included in the register has been strictly validated according to the World Health Organization (WHO) MONICA criteria.14 A diagnosis of acute stroke was based on clinical presentation. Differentiation of ischemic and hemorrhagic events was based on computed tomography (CT) scans or autopsy, whereas cases lacking unambiguous CT or autopsy data were classified as unspecified stroke. Patients admitted to acute-care hospitals as well as those treated out-of-hospital, fatal and non-fatal, were included.15 This register was used to identify probands for the family-based study. Familial cases of stroke were identified by questionnaires regarding family history of stroke, sent to all patients affected between 1985 and 1996. As a result of this approach, 101 families were ascertained. Families were only included in the study if the probands were born in the counties Norrbotten or Vsterbotten, the 2 northernmost counties in Sweden, and if the families included had at least 1 affected sib pair and at least 1 unaffected sibling. Cases of subarachnoid hemorrhage were excluded. A total of 56 families with 117 affected individuals were included in this study. Most families were nuclear families but extended pedigrees were also identified. The participants were included in the family-based study after obtaining informed consent for access to medical records and donation of blood samples for genetic research. In the association study, all subjects had been participants in population-based cardiovascular risk factor surveys, the MONICA surveys or the Vsterbotten health survey (with design similar to that of the MONICA population surveys). Participants in both the MONICA and the Vsterbotten surveys were asked to donate a blood sample to be stored at the northern Sweden Medical Research Bank for future research and written consent was obtained from all participants. The present study used a nested case-control design including patients affected by stroke that occurred between September 1, 1996 and September 20, 2000. In total, 275 cases of first-ever stroke were included. Two controls for each case were selected from participants in the MONICA or the Vsterbotten surveys. They were matched for sex, age (±2 years), cohort (MONICA or Vsterbotten), and date of health survey (±1 year) and place of domicile. Control subjects were excluded if they had died or had moved away from the MONICA region before the end of the stroke registration period. Both cases and controls were excluded if known from the northern Sweden MONICA incidence registry to have had myocardial infarction or stroke before the health survey or if they had cancer diagnosed during the past 5 years. Procedures for case ascertainment in the nested case-control study are described in detail elsewhere.16 The study was approved by the research Ethics Committee of Ume University and the data-handling procedures by the National Computer Data Inspection Board.
Genotyping
For genotyping, DNA was extracted from 10 mL of donated and EDTA-treated blood using a standard phenol extraction method. Forty-three polymorphic microsatellite markers from ABI Prism Linkage Mapping Set version 2.5 HD5 were used for genotyping in the linkage study. To obtain an average interval between the markers of 4.5 cM, 1 marker was excluded because of uncertain genetic position and 2 markers were added. DNA (30 ng) and reagents to a reaction volume of 7.5 μL/well were pipetted in 96-well plates and performed as multiplex polymerase chain reactions (PCRs) on GeneAmpPCRSystem 9700 (Applied Biosystems). Reagent concentrations and the temperature profile were used as recommended for the Linkage Mapping Set. PCR products were pooled and diluted according to size and fluorescent dye type. Internal size standard (LIZ) and HiDi formamide were added before products were separated and detected on an ABI PRISM 3100 Genetic Analyzer. Data were assembled by 3100 DataCollection software version 1.1. Analysis of results, allele calling, and checks of quality and editing of called genotypes were performed using GeneMapperGenotyping software version 3.0. The genotypes were checked and, if necessary, edited manually. Applied Biosystems supplied all these software. To verify the family relationship and to detect genotype errors, data were checked for Mendelian inheritance using the program Pedcheck.17 For association analysis, we selected 3 single nucleotide polymorphism (SNP) markers based on information available from the public databases, rs1971940 (SNP1), rs716908 (SNP2), and rs294492 (SNP3). Sequences for 2 SNPs, rs12188950 (SNP 4, deCODE SNP 45), rs12153798, (SNP5, deCODE SNP 41) and 1 microsatellite (AC008818-1) were obtained by personal communication from Dr Solveig Gretarsdottir, deCODE, Iceland. We generated SNP genotypes using the TaqMan allelic discrimination method. TaqMan assays and reagents were from Applied Biosystems. PCRs were performed on GeneAmpPCRSystem 9700 PCR program according to the manufacturer’s instructions. ABI PRISM 7900HT Sequence Detection System was used to analyze TaqMan PCR products.
Statistical Analysis
We applied multipoint, nonparametric linkage analysis on chromosome 5 for confirmation of linkage to the region of STRK1 using the program Allegro.18 We used the spairs scoring function that assesses identity by descent (IBD) sharing among all pairs of affected individuals within families. This scoring function is suggested to perform well in all types of disease models19 and is also the scoring function used in the Icelandic stroke study.9 The nonparametric link (NPL) Z scores were converted into allele-sharing lod scores using the exponential model described elsewhere.20 When combining the family scores to obtain an overall score, we used a compromise between weighting the families equally and weighting the affected pairs equally, using the "power:0.5" option in Allegro. Allele frequencies were estimated among all individuals according to the algorithm of Merlin.21 We used the marker map of Genethon;22 for comparison, we also performed the analysis based on the deCODE genetic framework map.23 In the latter, interpolations with respect to the physical distances in NCBI genome build 34.3 and the available genetic positions in the deCODE map were made to come up with estimations of markers not included in this map. The probability values reported are computed by comparing the observed allele-sharing lod score with its complete data distribution and are not corrected for multiple testing. To estimate the significance of our findings and to adjust for the number of markers tested in a proper way, we did a simulation study over the candidate region. The simulations were made in Allegro using the same marker frequencies and pedigree structure as in the original analysis but with the assumption of no linkage. To test for association of genotypes and ischemic stroke in the case-control analysis, we calculated genotype-based odds ratios (ORs) with respective 95% confidence intervals (CIs) using conditional logistic regression. Indicator variables for the genotypes were constructed using individuals homozygous for the most common allele as the reference. Calculations were also performed under the assumption of an additive model, assigning the values of 0, 1, and 2 (according to each individual’s number of variant alleles) to a genotype trend variable. All probability values presented are 2-tailed and not corrected for multiple testing. The association analysis in the case-control material was performed using the software package SPSS, version 11.5 (SPSS). For testing association in the family data set, we used the transmission/disequilibrium test within the program TRANSMIT, version 2.5.4, which has the advantage of allowing unknown parental genotypes.24 To avoid for bias using multiple affected individuals within a family in the presence of linkage, we used the robust estimate of the variance of the score vector. Linkage disequilibrium between markers was calculated by D' and R2 statistics in the case-control material using the program ldmax from the GOLD software package.25
Results
Table 1 shows the main characteristics of the stroke cases in the 2 study samples. In the family sample a larger group of cases had an unknown cause for the stroke event, which is explained by the lack of access for CT scans in some of the smaller hospitals in the region during the earlier years. The age of onset was higher in the family sample, which is most likely explained by the ascertainment differences between the samples, whereas the case-control subjects predominantly are from the Vsterbotten survey with an age cutoff at 65 years; the probands in the family sample were included if affected before age 70 and the affected siblings before age 75. Initial nonparametric multipoint linkage analysis of the family-based data set using marker loci distributed over chromosome 5 with an average intermarker distance of 10 cM (applying Genethon map distances) revealed 2 peaks with an allele-sharing lod score >1.0. These peaks were further investigated by running additional markers with an average intermarker distance of 4.5 cM at chromosome 5. As a result, the maximum allele-sharing lod score increased to 2.06 (P=0.0010) at marker D5S424 and to 1.60 (P=0.0033) at marker D5S1969 (Figure 1A). To be able to analyze the results in the frame of the more robust deCODE genetic map, we omitted the markers not available in this map from the analysis, which led to an average intermarker distance of 6.0 cM. As illustrated in Figure 1B, the allele-sharing lod score decreased to 1.86 (P=0.0017) at marker D5S424, whereas it increased to 1.95 (P=0.0014) at marker D5S1969. In the deCODE map, the position of marker D5S1969 is at 68.9 cM, consistent with the findings in the Icelandic genome scan displaying one peak at 69 cM with an allele-sharing lod score of 200.9 When interpolating distances to markers without specified deCODE distances and adding these to the deCODE map, the allele-sharing lod scores changed marginally from the Genethon map, indicating that the differences could be caused by the decreased marker density in the analysis using the deCODE map. Excluding hemorrhagic strokes (n=15), had only a minor effect on the results (Figure 1). Further subphenotyping in the ischemic group was not considered because of the relatively small number of patients with validated carotid or cardiogenic stroke. The simulation study of 1000 random sets yielded a candidate region probability value of 0.01 for the allele-sharing lod score peak of 2.06 at marker D5S424. The candidate region was here assumed to be between the markers D5S1969 and D5S433, spanning over 51.1 cM and 12 markers. Together, these findings replicate previously reported evidence for the location of a locus conferring susceptibility to common forms of stroke at this chromosomal region.9
We next investigated if the reported association of ischemic stroke to the PDE4D gene10 could also be replicated in a nested case cohort from northern Sweden. For this we excluded all hemorrhagic cases, together with their controls. We performed a directed association study and included the microsatellite marker and the 2 SNPs reported by Gretarsdottir et al10 to display alleles with the strongest association to stroke. We also included 3 additional SNPs in the PDE4D gene that was genotyped before the Icelandic report of association of ischemic stroke to the PDE4D gene. The positions of the SNPs and the microsatellite marker in the PDE4D gene are shown in Figure 2. The markers used were found to be in Hardy-Weinberg equilibrium using 2 goodness-of-fit tests in the control group. Genotype frequencies and ORs of all the PDE4D markers analyzed are shown in Table 2. For 2 of the markers analyzed, conditional logistic regression calculations revealed association with P<0.05. SNP3 displayed an OR of 0.68 (95% CI, 0.48 to 0.96) and the "B" allele (–4 bp compared with the shortest allele of CEPH 1347- 02) in AC008818-1 displayed an OR of 0.69 (95% CI, 0.49 to 0.98), assuming an additive model in both cases. Although when correcting for the number of markers and alleles tested probability values did not reach formal significance levels, this observation remains interesting. No significant association to the Icelandic defined at-risk allele 0 of AC008818-1, here denoted as allele C, was obtained in this study (OR, 1.1; 95% CI, 0.84 to 1.45) (Table 2). SNP4 and SNP5 were found to be in strong linkage disequilibrium (LD) with D' and R2 being close to 1. In addition, the microsatellite marker AC008818-1 (allele C) showed LD to SNP4 and SNP5, with D' values equal to 1, but with low R2 values (Table 3). These linkage disequilibrium data are consistent with those of the Icelandic study. Linkage disequilibrium data for all genotyped markers in the PDE4D gene are shown in Table 3. LD coverage is not complete because the association study primarily aimed to test the previously reported most significant disease-associated genetic markers: SNP 4, SNP 5, and AC008818-1. These markers were also analyzed for association to stroke in the family-based material, but no significant values for association were obtained (Table 4). Our data are unlikely to be confounded by population stratification because cases and controls were matched not only by sex, age, cohort, and date of health survey but also for place of domicile. All samples originate from the same geographical region in northern Sweden. The discrepancies between our data and those reported previously by Gretarsdottir et al10 may be because of population differences with alternative genotypes in this region contributing to stroke susceptibility in the northern Sweden population.
Discussion
In conclusion, we have replicated the previous findings of the location of a susceptibility locus for common forms of stroke on chromosome 5q. The linkage analysis performed on families from northern Sweden and the linkage analysis of Icelandic stroke families reveal 2 peaks in the region of interest (Gretarsdottir, personal communication). This could suggest that 2 or more genes conferring susceptibility to common stroke are located in this chromosomal region. Further analysis of genes other than the PDE4D should be performed to conclusively evaluate this possibility. Extensive fine-mapping studies and association studies of candidate genes in one of these regions in the Icelandic study10 have identified the PDE4D gene as the prime candidate for stroke susceptibility. The association study in the family-based material and in the nested case-control study from northern Sweden did not provide formal evidence in favor of association to this gene using a limited number of polymorphic markers. There are several indications that alternative genotypes may be contributing disease susceptibility in this population and additional SNP genotyping is planned. These data give further support for the benefits of using isolated populations for analysis of complex traits and suggest that genetic factors identified in this manner may be more general in nature than previously suspected.
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