Association study between corticotrophin-releasing hormone genomic region (8q13) and rheumatoid arthritis in the Spanish population

A. Julià, D. Gallardo1, F. Vidal1, J. J. De Agustín, P. Barceló, M. Vilardell2 and S. Marsal

Hospital General i Universitari Vall d’Hebron, Unitat de Reumatologia, 1Centre de Transfusions i Banc de Teixits (CTBT), ICS, and 2Hospital General i Universitari Vall d’Hebron, Servei de Medicina Interna, Barcelona, Spain.

Correspondence to: S. Marsal. E-mail: saramarsal{at}eresmas.com


    Abstract
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Objective. To investigate whether the corticotrophin-releasing hormone (CRH) genomic region confers genetic susceptibility to rheumatoid arthritis (RA) in the Spanish population.

Methods. DNA was obtained from 121 simplex RA families and 101 healthy controls, all from Spanish origin. Two microsatellites, CRHRA1 and CRHRA2, located 25 and 20 kb downstream respectively from the CRH gene were examined using a new multiplex design. Linkage disequilibrium (LD) between the markers was assessed and association studies were carried out using the transmission disequilibrium test (TDT) implemented in TRANSMIT.

Results. Both markers are in Hardy-Weinberg equilibrium and there is significant LD between them in the Spanish population. Neither the polymorphic alleles of CRHRA1 and CRHRA2 markers nor their resulting haplotypes were significantly associated to RA. The associated haplotype in the UK population (CRHRA1*10; CRHRA2*14) was undertransmitted in RA patients (12 obs vs 17.43 exp), although the difference is not statistically significant (P > 0.05).

Conclusions. This is the first follow-up study of the association between the CRH genomic region and RA and suggests that the CRH gene may not be involved in the pathogenesis of RA in the Spanish population. Further studies in other populations will help untangle the real contribution of this genomic region to the susceptibility to RA.

KEY WORDS: Rheumatoid Arthritis, susceptibility, association, Transmission Disequilibrium Test, CRH, microsatellite


    Introduction
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by symmetric polyarthritis leading to progressive joint destruction. Although much important work has been carried out, very little is known about RA aetiology. There is, however, strong evidence that genetic factors contribute to the general susceptibility. Twin [1, 2] and family studies [3] have proven the presence of a genetic component, lately reinforced by recent work where disease heritability has been estimated to be ~60% [4]. Besides HLA, which probably accounts for no more than one-third of the genetic susceptibility [5], no other genomic region has consistently been demonstrated to be associated with RA [6].

In order to determine the remaining genetic component several whole-genome scans have been performed using non-parametric linkage approaches in a large number of nuclear families [5, 7, 8] and more recently [9] but, with the exception of DRB1, no other region of linkage has reached the level of statistical significance (P < 2.2 x 10–5) recommended for such approaches [10]. Candidate gene strategy has proven to be more fruitful as demonstrated by two reports where linkage to chromosomal regions has been narrowed down to the locus level through the analysis of several markers tightly linked to the interleukin-1 cluster [11] and to the corticotrophin-releasing hormone (CRH) structural gene [12]. Recently, Fife et al. [13] have confirmed their previous findings with the identification of a second microsatellite marker (CRHRA2) located in the CRH genomic region, close to the initially described CRHRA1 microsatellite (~5 kb). Their demonstration of an association of a single haplotype (CRHRA1*10;CRHRA2*14) and RA in the UK population increased confidence in the role of this region in RA.

CRH is the key factor controlling cortisol secretion by the adrenal gland and thus indirectly has important anti-inflammatory effects. Studies of hypothalamic and peripheral secretion of CRH in RA patients and in animal models yield important evidence suggesting a role of this gene in the ethiopathogenesis of the disease [1416].

The aim of the present study was to provide an independent test of CRH gene association in Spanish RA families. For this purpose we have developed a simple and cost-effective procedure to typify CRHRA1 and CRHRA2 microsatellites. Prior to association analysis we have also estimated the allele frequency distribution of both STRs in the Spanish population.


    Materials and methods
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Patients, family characteristics and controls
Genomic DNA was obtained from 121 simplex RA families, 59 of them belonging to the Sociedad Española de Reumatología (SER) national repository whilst the remaining 62 were recruited from the Unitat de Reumatologia of the Hospital Vall d’Hebron. All the families included in this study were Caucasian and of Spanish origin.

All accessible non-RA sibs were also included in the study independently of parents’ availability (for details see Table 1). All patients satisfied the 1987 American College of Rheumatology modified criteria for RA [17]. Initial optimization of the methodology and the study of allelic distribution of microsatellite markers CRHRA1 and CRHRA2 in our population were performed using 101 unrelated Spanish Caucasian healthy blood donors belonging to Barcelona’s Transfusion Centre and Tissue Bank repository.


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TABLE 1. Pedigree features of the simplex families used in the association analysis

 
Polymerase chain reaction (PCR) conditions
In order to amplify CRHRA1 and CRHRA2 microsatellites simultaneously we developed a multiplex PCR using a total of four oligonucleotides, two of which were of new design and named nCRHRA1-forward (5'-ACAAAGAGCAGACCCAGTCC-3') and nCRHRA2-reverse (5'-GTTTGGATTCAGCCTTGTGGTG-3') and the other two were primers CRHRA1-reverse and CRHRA2-forward previously reported by Fife et al. [12, 13]. Forward primers were fluorescently labelled with phosphoramydite dye TET attached to the 5' end (Applied Biosystems, Foster City, CA, USA). PCR solution contained 50 mM KCl, 20 mM Tris-HCl (pH 8.4), 1 mM MgCl2, 200 µM dNTPs, 1 unit Platinum Taq DNA polymerase (Invitrogen, Carlsbad, CA, USA), 50 ng of each primer and 100–150 ng of genomic DNA in a total volume of 25 µl. After initial denaturation at 94°C for 3 min, 27 cycles of 94°C for 20 s, 59°C for 30 s and 72°C for 30 s were performed, followed by a final extension at 72°C for 3 min. Initial optimization of the technique also included visualization of the amplimers by agarose gel electrophoresis and ethidium bromide staining.

Genotyping
Semi-automated analysis of microsatellite genotypes was performed using an ABI PRISM 310 Genetic Analyzer with GENESCAN analysis software, version 2.1 (Applied Biosystems). DNA fragment analysis software, GENOTYPER version 2.5 (Applied Biosystems), was used for allele-size assignment. Sizing of each PCR product was determined in reference to the internal standard TAMRA 50–500 (Invitrogen).

All genotypes were read by two independent investigators to ensure accurate allele assignment. A sample of known genotype was used in all assays to control proper electrophoresis and allele assignment.

Statistical analysis
A set of genetic measurements was calculated from the control population by using the methods implemented in Arlequin software [18]. These include allele and haplotype frequencies, Hardy–Weinberg equilibrium (HWE) testing for each marker and linkage disequilibrium (LD) analysis between both markers. Association analyses were performed using the transmission disequilibrium test (TDT), a family-based method for the determination of preferential transmission of a specific allele or haplotype [19]. The TDT was implemented using the software package TRANSMIT v. 2.5, which uses additional genotype information from unaffected siblings to infer missing parental genotypes and reconstruct multilocus haplotypes [20]. For the present analysis, minimum haplotype frequencies were set at 0.025, i.e. haplotypes which occurred with a frequency of less than 2.5% were pooled. A global {chi}2-test (PGlobal) for transmission distortions was performed across alleles or haplotypes. To account for multiple testing, the Bonferroni method was carried out over haplotypes.


    Results
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 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
Multiplex PCR optimization
According to primer design, expected PCR product sizes should range from 129 bp (one CA repeat) to 175 bp (24 CA repeats) for microsatellite CRHRA1 and from 209 bp (one CA repeat) to 265 bp (29 CA repeats) for microsatellite CRHRA2. Optimization of the multiplex PCR parameters was done using 10 unrelated control DNA samples, finally obtaining intense and specific DNA bands within the expected range as inferred by agarose gel electrophoresis. Subsequent capillary electrophoresis of the multiplex PCR products gave discrete and strong peaks, easily distinguishable from the associated peaks of lower intensity that appear due to the stutter effect inherent to microsatellite analysis (Fig. 1) [21].



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FIG. 1. Allele assignment of microsatellites CRHRA1 (left box) and CRHRA2 (right box).

 
Distribution of CRHRA1 and CRHRA2 alleles in the Spanish population
Table 2 shows the allelic and combined haplotype frequencies of CRHRA1 and CRHRA2 markers in the control population; alleles are named according to the nomenclature proposed by Fife et al. On one hand, CRHRA1 follows a unimodal distribution, where predominant alleles CRHRA1*10 and CRHRA*11 summarize almost 75% of the total. On the other hand, CRHRA2 alleles have a much more sparse distribution, thus being a more polymorphic marker than CRHRA1. When composite haplotype frequencies are estimated via Expectation-Maximization (E-M) algorithm, allelic frequencies are even more evenly distributed, therefore increasing informativeness.


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TABLE 2. Allelic frequency of CRHRA1 and CRHRA2 markers in the Spanish control population calculated separately and estimated together as a haplotype

 
To analyse these data further we assessed HW equilibrium of each of the markers and the level of LD existing between them. There was no deviation from HW expectations at any of the loci and there was significant LD between both markers (100 000 permutations, P < 0.05).

Association analysis in RA families
We tested for preferential transmission of CRHRA1 and CRHRA2 independently and as a haplotype. No association could be identified with either of the two microsatellites (PGlobal = 0.177 and 0.301 for CRHRA1 and CRHRA2, respectively). Results obtained for each locus are shown in Table 3. Although alleles CRHRA1*14 and CRHRA2*15 present distortion in transmission, their P value (0.011 and 0.042, respectively) does not withstand a Bonferroni correction for multiple testing. We then analysed the transmission disequilibrium of both markers as a haplotype, but neither of them was significantly associated with the presence of the disease (see Table 4). When we looked at the associated haplotype in the UK population (CRHRA1*10;CRHRA2*14), we observed that it was under-transmitted in RA patients (12 obs vs 17.43 exp) although statistical significance was not achieved (P = 0.061).


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TABLE 3. Transmission distortion of markers CRHRA1 and CRHRA2 observed in 121 simplex families using TRANSMIT

 

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TABLE 4. Transmission distortion of CRHRA1;CRHRA2 haplotypes observed in 121 simplex families using TRANSMIT

 

    Discussion
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 
In an attempt to answer the question of whether the CRH genomic region is associated with RA in the Spanish Caucasian population we have sampled 121 simplex RA families and performed transmission disequilibrium testing. Our findings do not support the initial observation made by Fife et al. [13] in the UK population. We observed positive association between RA and alleles CRHRA1*14 and CRHRA2*15, but when conservative correction for multiple testing was applied, the significance disappeared. More surprisingly, the associated haplotype in the UK population (CRHRA1*10;CRHRA2*14) seems to be protective in the Spanish population, although this is not significant.

This discrepancy might indicate that these markers are not associated with RA. In fact, when we look at the previously reported results, it is noticeable that the P value of the global test of association in the group of sporadic RA families has not been shown in the data, making comparison between the studies less reliable. It is also worth mentioning that the increase in transmission of the independent alleles CRHRA1*10 and CRHRA2*14 in this group of families is not statistically significant. In this sense, the results in the UK and Spanish simplex families do not seem to differ, since they both lack significant association.

Another possible explanation of the discrepancies between the two studies is the lack of power of our test given the size of the samples assessed. Power approximations indicate that a large sample size is needed to detect association when using TDT in genes that make small contributions to the overall risk of developing the disease [22]. Therefore, one could argue that if we had collected more samples we would have found significant association with one of the CRHRA1;CRHRA2 haplotypes. However, the similar number of simplex families included in both studies (131 UK vs 121 Spanish families) makes the results comparable.

An alternative scenario is that, in fact, they might both be true results. We would therefore be identifying the genetic and population heterogeneity that has also been observed in HLA studies and genome scans. The incidence of RA in the Spanish population is lower when compared with other northern European populations, and it has been found that there are differences in HLA-DRB1 haplotypes associated with susceptibility [23]. So far, genome scans have been performed in families belonging to different populations and their results, except for the HLA genomic region, scarcely overlap [5, 79]. Clinical heterogeneity in RA is also widely recognized. It is certainly assumed that RA groups of patients are not homogeneous or unambiguous in their clinical phenotype. The absence of a diagnostic test for RA makes comparison of patients difficult to such an extent that some authors doubt the validity of pooling RA patients across Europe [24].

Another important issue is if the microsatellites CRHRA1 and CRHRA2 (20 and 25 kb downstream from the CRH gene, respectively) fall within the range of linkage disequilibrium necessary to detect association with the disease relevant variation [25, 26]. Nevertheless, although linkage disequilibrium in this region has not been characterized in the Spanish population, we should bear in mind that these highly polymorphic markers have been used owing to the absence of conclusive results with the previously described CRH promoter region bi-allelic variants in RA patients [27, 28].

Finally, we must also question if TDT is the most appropriate statistical tool for the study of RA susceptibility genes. In the last few years case–control approaches have been undermined in favour of family-based association tests that elicit the influence of a population stratification confounder [19]. However, the development of new methods for assessing the underlying substructure in population samples [29, 30], and the possibility of accumulating larger sample sizes, is giving new confidence in the case–control strategy to detect genes with a modest effect in complex diseases.

This is the first follow-up study to be done of this genomic region with a different set of individuals belonging to a different Caucasian population. To help understand the differences between the two reports, subsequent studies with other populations should be carried out. Case–control studies with a larger number of patients and controls will probably be the most appropriate approach in assessing the genetic contribution of the CRH gene. It would be of great interest to know if there are real differences in the contribution of this gene to RA between populations.


    Acknowledgments
 
We are indebted to Dr A. Balsa and Dr D. Pascual for providing DNA samples from the S.E.R. DNA bank. Also we would like to thank Dr J. Lanchbury for his collaboration and Dr A. Fontdevila for the critical review of the manuscript.

Conflict of interest

The authors have declared no conflicts of interest.


    References
 Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

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Submitted 27 September 2002; Accepted 28 April 2003





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