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Brain, Vol. 127, No. 8, 1717-1722, August 2004
© 2004 Guarantors of Brain
doi: 10.1093/brain/awh193

Association of protein kinase C alpha (PRKCA) gene with multiple sclerosis in a UK population

A. Barton¹, J. A. Woolmore³, D. Ward¹, S. Eyre¹, A. Hinks¹, W. E. R. Ollier², R. C. Strange⁴, A. A. Fryer⁴, S. John², C. P. Hawkins³ and J. Worthington¹

¹ Arthritis Rheumatism Campaign Epidemiology Research Unit and ² Centre for Integrated Genomic Medical Research, University of Manchester, Manchester, ³ Keele Multiple Sclerosis Research Group, Department of Neurology and ⁴ Human Genomics Research Group, Institute of Science and Technology in Medicine, Keele University Medical School, University Hospital of North Staffordshire, Stoke-on-Trent, UK

Correspondence to: Anne Barton, ARC-EU, Stopford Building, University of Manchester, Manchester, UK E-mail: ABarton{at}fs1.ser.man.ac.uk

Received November 26, 2003. Revised March 11, 2004. Accepted March 16, 2004.

	Summary

Top Summary Introduction Patients and methods Results Discussion References

 
Twin, family and adoption studies suggest that susceptibility to multiple sclerosis is substantially mediated by genetic factors. Linkage to human chromosome 17q, homologous to a locus linked to experimental animal models of multiple sclerosis, has been widely replicated and the region likely to harbour a multiple sclerosis susceptibility gene has recently been refined to a 2.5 Mb region of 17q22-24. The candidate multiple sclerosis susceptibility gene, protein kinase C alpha (PRKCA), maps within this interval and association with 35 single-nucleotide polymorphism (SNP) markers, spanning the gene with a median spacing of 7.8 kb, was tested using a case–control approach. Single-marker genotype and estimated haplotype frequencies were compared in UK unrelated cases with multiple sclerosis (n = 184) and healthy controls (n = 340) in order to investigate association with susceptibility to disease. A haplotype of two SNPs mapping to the proximal region of the gene showed evidence for association with susceptibility (Bonferroni-corrected P value = 1.1 x 10^–5). These findings suggest that further investigation of the PRKCA gene is warranted, particularly in cohorts with evidence of linkage to 17q22. Most of the SNPs investigated in this study were intronic and screening to identify disease-associated functional mutations is now required. Our results suggest that the promoter and proximal gene region should be not only included but prioritized in any screening strategy.

Key Words: multiple sclerosis; PRKCA gene; susceptibility

Abbreviations: HLA = human leucocyte antigen; LD = linkage disequilibrium; PRCKA = protein kinase C alpha; SNP = single-nucleotide polymorphism

	Introduction

Top Summary Introduction Patients and methods Results Discussion References

 
Multiple sclerosis is a common inflammatory disease of the CNS characterized by demyelination, gliosis, axonal damage and progressive neurological dysfunction. Twin, family and adoption studies suggest a substantial genetic contribution to susceptibility, with the sibling recurrence risk estimated to be between 20 and 40 (Risch, 1987; Ebers and Sadovnick, 1994; Ebers et al., 1995).

Studies in multiple sclerosis families have identified a number of regions linked to disease which have been replicated in more than one data set (reviewed in (Oksenberg et al., 2001). The region with the most consistent evidence for linkage is HLA (human leucocyte antigen) and a number of studies have confirmed association of multiple sclerosis with HLA-DRB1^* 15, which confers a nearly four-fold relative risk of disease development (Epplen et al., 1997), and with its associated haplotype (DRB1^*1501-DQB1^*0602). Another region which has been widely replicated maps to human chromosome 17q and evidence for linkage to multiple sclerosis has been found in the UK, Scandinavian and Finnish populations, although only in the Finnish population was significant linkage detected (Sawcer et al., 1996; Kuokkanen et al., 1997; Larsen et al., 2000). Thus, evidence for linkage to the region has been reported in different populations and fine mapping studies have refined the region likely to contain the gene to a 2.5 Mb region of 17q22 (Saarela et al., 2002). Furthermore, the homologous region in rats has been linked to experimental autoimmune encephalitis, an experimental model of multiple sclerosis (Jagodic et al., 2001). This rat locus has also been linked to animal models of inflammatory arthritis, and linkage to and association with 17q22-24 has been reported in rheumatoid arthritis families (Lorentzen et al., 1998; Barton et al., 2001). One explanation for this overlap is that the region may harbour an immune regulatory gene that predisposes to a number of autoimmune diseases. The protein kinase C alpha gene (PRKCA) (NM_002737) maps to the interval linked to multiple sclerosis and is a strong candidate immunoregulatory gene because it is involved in T-cell regulation and proliferative responses (Wilkinson and Nixon, 1998). In experimental animal models of both multiple sclerosis and inflammatory arthritis, administration of oral inhibitors of PRKCA protein ameliorate the disease severity effects in the respective models (Wilkinson and Nixon, 1998). Finally, association with markers mapping close to the region harbouring the PRKCA gene have been reported for multiple sclerosis, using transmission disequilibrium testing in Canadian families (Dyment et al., 2001) and whole genome association analysis of pooled DNA samples in UK and German populations (Goedde et al., 2002; Sawcer et al., 2002).

The PRKCA gene is large, containing 17 exons and spanning 0.5 Mb of genomic DNA. Our aim was to investigate the association of the gene in a UK cohort of multiple sclerosis patients using high-density single-nucleotide polymorphism (SNP) markers spanning the gene.

	Patients and methods

Top Summary Introduction Patients and methods Results Discussion References

 
Study design
A case–control approach was used to investigate the association of multiple sclerosis with SNP markers, spanning the PRKCA gene with a median spacing of 7.8 kb. Unrelated cases with multiple sclerosis were compared with population controls for both phenotype and genotype at all loci. A moving window haplotype analysis was performed in HelixTree (Golden Helix, MT, USA) to identify multiple sclerosis susceptibility haplotypes and association with these haplotypes was confirmed using SNPHAP (http://www-gene.cimr.cam.ac.uk/clayton/software/).

Subjects
Multiple sclerosis cases
We recruited a case group of 184 unrelated Northern European Caucasian multiple sclerosis patients in the neurology clinics of the University Hospital, North Staffordshire. All patients had clinically definite multiple sclerosis according to previously published criteria (Poser et al., 1983).

Control patients
Unrelated Northern European Caucasian controls were recruited from healthy volunteers attending the same hospital as the cases with non-inflammatory and non-malignant disorders and from healthy workers recruited locally (n = 180). Healthy individuals were also recruited from general practice (n = 80) or were blood donors (n = 80). Both cases and controls were of UK Caucasoid ethnic origin.

All cases and controls were recruited with ethics committee approval and provided informed consent.

SNP markers selected for investigation
Information regarding SNPs mapping to the PRKCA gene was obtained from the Applied Biosystems website (http://www.appliedbiosystems.com). Thirty-five PRKCA SNPs, spanning the gene at median intervals of 7.8 kb, were tested for association in multiple sclerosis cases and controls.

Genotyping of PRKCA SNPs
Genotyping was performed with Assays-on-Demand (ABI, Warrington, UK) allelic discrimination assays on a Taqman^TM 7700 platform according to the manufacturer's instructions, except that a 5 µl rather than a 25 µl reaction volume was used (http://www.appliedbiosystems.com). Briefly, each PCR reaction contained 15 ng DNA, 2.5 µl Taqman master mix and 0.125 µl of 40x assay mix. PCR was performed using 384-well plates on an ABI 9700 thermal cycler (reaction conditions: 50°C for 2 min, 95°C for 10 min followed by 40 cycles of 95°C for 15 s and 60°C for 1 min). The Taqman 7700 was used to perform plate reading using the allelic discrimination option. A subsample of the case–control cohort were re-genotyped for associated SNPs to establish genotyping error rates.

Statistical analysis
Genotype frequencies for each PRKCA SNP were tested for Hardy–Weinberg equilibrium in the control population. Genotype frequencies for PRKCA SNPs were compared between multiple sclerosis cases and controls using Fisher's exact test.

Linkage disequilibrium (LD) between SNPs was calculated using both D' and r values. A graphical overview of the LD plot was constructed using HelixTree software.

Haplotype analysis can have greater power to detect disease association in two situations: (i) if the SNPs tested are not functional in themselves but in LD with a disease SNP mapping close by (Fallin et al., 2001); (ii) haplotype analysis can increase power in situations where combinations of polymorphisms act in concert to affect gene function (Terry et al., 2000). Haplotypes were estimated and constructed using the EM algorithm implemented in HelixTree software.

Using HelixTree, a moving window analysis using two SNPs was undertaken across the region. This method uses the EM algorithm to estimate haplotypes, identifies adjacent SNPs where haplotype distributions differ between cases and controls and assigns a significance level to that difference. Potential shortfalls of this method are that (i) haplotypes estimated with a low probability are included in the analysis, and (ii) only individuals with complete genotype information from which to infer haplotypes are included, making it potentially less powerful. Therefore, the analysis package SNPHAP was also used. This program also uses the EM algorithm but, in addition, the software assigns haplotypes to individuals, provides information about the probability of that assignment and also allows for some missing data—as a result of PCR failure, for example. Two haplotypes are assigned to each individual and individuals in whom haplotypes were assigned with a probability of less than 95% were excluded from further analysis in order to ensure stringency. Each haplotype was coded as a single number and frequencies were then compared between cases and controls as for a multi-allelic marker.

Association with 35 SNPs was investigated in this study. However, as the SNPs are tightly linked, applying Bonferroni correction of 35 is overly conservative as the test assumes independence of loci. The strategy adopted in this study was to calculate significance values using a correction factor of 35 but to present both the uncorrected and corrected results.

	Results

Top Summary Introduction Patients and methods Results Discussion References

 
Patient cohort
Clinical characteristics of the multiple sclerosis patients are shown in Table 1.

View this table: [in this window] [in a new window]   Table 1 Characteristics of the multiple sclerosis cohort

 
SNP markers
The approximate positions of the SNPs are shown in Fig. 1. The median spacing was 7.8 kb (interquartile range 6.6–17.7 kb). Genotype frequencies for one SNP deviated from Hardy–Weinberg expectations at the 5% significance level in the control population (SNP 34, P = 0.02) but this is no more than would be expected by chance.

View larger version (80K): [in this window] [in a new window]   Fig. 1 Pairwise LD plot showing correlation (R) between individual SNPs mapping across the PRKCA gene.

 
Susceptibility
Single—marker analysis
None of the SNPs showed significant evidence for association using single-marker analysis after Bonferroni correction had been applied (Table 2).

View this table: [in this window] [in a new window]   Table 2 Genotype frequencies for 35 SNPs spanning the PRKCA gene in cases and controls

 
LD
Figure 2 shows a graphic representation of the LD plot across the gene. The correlation values between SNPs are presented. Of the 35 SNPs examined, only four show no correlation between themselves and any other SNP (r < 0.3). Hence the SNP genotypes are not independent tests but show correlation between SNPs and the SNPs form haplotypes. For example, SNPs 30, 31 and 32 were in strong LD in both cases and controls, with r values of 0.8–0.9.

View larger version (13K): [in this window] [in a new window]   Fig. 2 Two-marker moving window haplotype analysis of SNPs spanning the PRKCA gene in multiple sclerosis cases versus controls. The graph shows the –log10 of the uncorrected P-value; hence, the more significant association.

 
Haplotype analysis
Using a moving window analysis of two SNPs, association with a haplotype of alleles of SNPs PRKCA^*1_2 (uncorrected P = 2.5x10^–5) and PRKCA^*30_31 (uncorrected P = 5.3 x 10^–3) was detected (Fig. 3). After applying Bonferroni correction, only the PRKCA^*1_2 haplotype remained significantly associated (8.8 x 10^–4). The software programme SNPHAP was used to assign haplotypes to individuals where the probability of that haplotype exceeded 95%. Haplotype frequencies were then compared between cases and controls and for SNPs 1 and 2, the haplotype on which both variant alleles was present occurred more frequently in cases than controls (39.3 versus 30.8%) (Bonferroni corrected P = 1.1 x 10^–5) (Table 3). The odds ratio of the susceptibility haplotype compared with the remaining haplotypes was 1.4, 95% confidence interval 1.1–1.9.

View larger version (5K): [in this window] [in a new window]   Fig. 3 Diagrammatic representation of PRKCA gene showing exons (dark blocks) and approximate SNP positions along the gene.

 

View this table: [in this window] [in a new window]   Table 3 Haplotype frequencies for SNPs 1 and 2 estimated using SNPHAP

 
Genotyping error
The genotyping error rate was determined by regenotyping SNP 1 and SNP 2 in a subset of cases and controls; overall it was found to be <0.01%.

	Discussion

Top Summary Introduction Patients and methods Results Discussion References

 
We have found evidence for association of the PRKCA gene with susceptibility to multiple sclerosis. This work follows on from previous results of linkage and association analysis in multiple sclerosis families. Three studies in multiple sclerosis families have reported evidence for linkage to markers mapping to 17q22 (Sawcer et al., 1996; Kuokkanen et al., 1997; Larsen et al., 2000) and the locus has been refined to a 2.5 Mb region of 17q22 in a Finnish population (Saarela et al., 2002). Furthermore, association with SNPs mapping within the central part of this region has been reported in the same Finnish families (Saarela et al., 2003). The PRKCA gene maps to this interval and is a strong candidate gene as the protein product plays a crucial role in determining the magnitude of the T-cell proliferative response upon T-cell activation and it appears to be the major PRKC isoform involved in regulation of interleukin 2 receptor expression (reviewed in Wilkinson and Nixon, 1998). Furthermore, in experimental animal models of multiple sclerosis, administration of oral inhibitors of PRKCA has been shown to ameliorate the disease effects (Wilkinson and Nixon, 1998).

As understanding of patterns of LD across the genome has improved, it has become clear that testing one or two SNPs mapping to a candidate gene is insufficient to exclude association with that gene. The PRKCA gene is large (17 exons, 0.5 Mb genomic sequence) and we used SNP markers spanning the gene with a median interval of 7.8 kb to investigate association indirectly using LD analysis. This permitted a thorough examination of association with susceptibility to disease. However, even with this SNP density, we were unable to exclude association with regions of the gene where SNPs were more sparsely spaced. Using a moving window haplotype approach, a haplotype of two SNPs mapping to the 5' end of the gene, PRKCA^*1_2, showed evidence for association with susceptibility. It is unlikely that either is causal as neither shows association by allele or genotype when analysed separately. It is more likely that they define a haplotype on which the disease mutation is present and found more frequently in cases with multiple sclerosis. This will require further screening of the PRKCA gene, particularly the promoter and proximal gene region, to identify further polymorphisms that may contribute to disease susceptibility directly as well as functional studies to determine the role of these mutations in disease development.

A large number of SNP markers have been tested in this study. This was necessary in order to systematically investigate association with the PRKCA gene, which is itself large. Some associations may have occurred due to chance alone (false positive). In order to account for this, it has been proposed that associations should be corrected for the multiple tests undertaken. However, this will reduce the power to detect association and may result in an elevated false negative rate (type 2 error) (Perneger, 1998). Furthermore, we have demonstrated that the SNP genotypes are not independent, as correlation values between SNPs are high. Hence, applying Bonferroni correction is overly conservative, but it should be noted that the PRKCA^*1_2 susceptibility haplotype remained significantly associated despite this.

In summary, we report evidence for association of the PRKCA gene with susceptibility to multiple sclerosis. The effect of the gene on disease is likely to be modest as the current study is sufficiently large to exclude a major genetic effect (80% power to exclude a genotypic relative risk of 2.0 at the 5% significance level, assuming a dominant mode of inheritance and allele frequencies of 10–50%). We have found that a haplotype mapping to the 5' portion of the gene is associated with susceptibility to multiple sclerosis, and this is unlikely to have arisen by chance. Hence, the PRKCA gene warrants further investigation, particularly in cohorts with evidence of linkage to the 17q22 region. Most of the SNPs investigated in this study were intronic and screening of the gene to identify disease-associated functional mutations is now required. Our results suggest that the promoter and proximal gene region should be prioritized rather than simply included in any screening strategy.

	Acknowledgements

 
Funding for this work was received from the MRC, the Wellcome Trust, the ARC and the Keele Multiple Sclerosis Research Group.

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This Article Abstract FREE Full Text (PDF) All Versions of this Article: 127/8/1717    most recent awh193v1 Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Archive Download to citation manager Search for citing articles in: ISI Web of Science (12) Request Permissions Disclaimer Google Scholar Articles by Barton, A. Articles by Worthington, J. Search for Related Content PubMed PubMed Citation Articles by Barton, A. Articles by Worthington, J. Social Bookmarking          What's this?

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