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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Objective

Familial aggregation of fibromyalgia has been increasingly recognized. The goal of this study was to conduct a genome-wide linkage scan to identify susceptibility loci for fibromyalgia.

Methods

We genotyped members of 116 families from the Fibromyalgia Family Study and performed a model-free genome-wide linkage analysis of fibromyalgia with 341 microsatellite markers, using the Haseman-Elston regression approach.

Results

The estimated sibling recurrence risk ratio (λs) for fibromyalgia was 13.6 (95% confidence interval 10.0–18.5), based on a reported population prevalence of 2%. Genome-wide suggestive evidence of linkage was observed at markers D17S2196 (empirical P [Pe] = 0.00030) and D17S1294 (Pe = 0.00035) on chromosome 17p11.2–q11.2.

Conclusion

The estimated sibling recurrence risk ratio (λs) observed in this study suggests a strong genetic component of fibromyalgia. This is the first report of genome-wide suggestive linkage of fibromyalgia to the chromosome 17p11.2–q11.2 region. Further investigation of these multicase families from the Fibromyalgia Family Study is warranted to identify potential causal risk variants for fibromyalgia.

Fibromyalgia is a common chronic pain disorder affecting an estimated 2% of the general population. Fibromyalgia is defined by the American College of Rheumatology (ACR) as widespread pain for at least 3 months and pain on palpation at 11 or more of the 18 tender point sites (1, 2). Although the pathophysiologic mechanisms underlying fibromyalgia are not completely understood (3–5), our group previously reported that fibromyalgia strongly aggregates in families (6), confirming the results of previous preliminary family studies (7–9) and supporting the possible role of genetic factors in the etiology of fibromyalgia (10).

Previous attempts to identify fibromyalgia susceptibility gene(s) have had limited success. Some candidate genes have been tested in population-based association studies, but none has yet been confirmed as a susceptibility locus for fibromyalgia (11, 12). More recently, Smith et al (13), in the largest candidate gene association study of fibromyalgia to date, observed significant differences in allele frequencies between cases and controls for several novel genes: GABRB3 (in the promoter region), TAAR1, GBP1, RGS4, CNR1, and GRIA4. Three of these genes, TAAR1, RGS4, and CNR1, play roles in the modulation of analgesic pathways (13).

Genome-wide association studies (GWAS) have revolutionized the dissection of genetic determinants of many complex traits. To date, GWAS have identified hundreds of robustly replicated loci for common traits (14). However, the identified loci explain only a small portion of the heritable component associated with many complex traits. Clearly, additional loci that can explain a large proportion of the heritable component have not yet been discovered. It has been suggested that one explanation may reside in rare variants that are not captured or are poorly captured by current GWAS designs (15, 16).

Family-based designs such as linkage studies have long been shown to have high power to detect loci with a large effect size. Genetic variants with a large effect size tend to be rare in the population, although such variants are likely more frequent in cohorts of multicase families (17, 18). Because the cost of resequencing and high-dimensional single-nucleotide polymorphism chips has dropped significantly, there has been a resurgence of methods to detect rare variants that could cause complex traits. Recent developments in molecular genomic tools and statistical approaches enable investigators to capture almost exact genetic information about inheritance patterns and allele sharing and to find rare variants of modest-to-large effect using family-based designs (19–21). Some investigators have speculated that rare variants obtained from resequencing of family-based data may show properties different from those obtained from case–control studies (22–24).

In an effort to determine whether such designs can be implemented in investigations of fibromyalgia susceptibility, we conducted an autosomal genome-wide linkage scan to map chromosome loci that may contain rare variants associated with fibromyalgia susceptibility, using a cohort of multicase families from the Fibromyalgia Family Study. To our knowledge, this is the first report of a genome-wide linkage scan focusing on fibromyalgia in a family-based cohort.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Study population.

Multicase families with fibromyalgia were recruited and evaluated at 4 clinical sites: MetroHealth Medical Center/Case Western Reserve University (Cleveland, Ohio), University of Texas Health Sciences Center (San Antonio), University of Illinois College of Medicine at Peoria, and University of Cincinnati College of Medicine (Cincinnati, Ohio). The protocol was approved by the institutional review board at each site. Probands (index cases) were recruited through the rheumatology clinics at each site, by referral from rheumatologists and other physicians, or were self-referred through advertisements and patient support groups. A family was considered eligible if the proband and at least one first-degree relative of the proband met the 1990 ACR criteria for primary fibromyalgia (1). Families were excluded if the proband had a concomitant rheumatic disease, such as rheumatoid arthritis, or another medical explanation for his or her pain symptoms. If the affected first-degree relative of the proband was a parent or offspring, the family was included only if an unaffected female sibling of one of the affected individuals was available. (An unaffected male sibling was considered sufficient if one of the affected individuals in the family was male). If a family was eligible, available parents and female siblings (and male siblings if one of the affected individuals was male) of each affected individual were recruited. Only subjects who were age 12 years or older were included.

Evaluation of the study subjects.

The investigators at each site (LMA, IJR, MBY, MAK), each of whom is experienced with the evaluation of fibromyalgia, conducted the diagnostic assessment of the subjects. The diagnosis of primary fibromyalgia in the proband was confirmed by physical examination, including a tender point examination, and by review of the medical records obtained after releases were signed by the subjects. All recruited family members were evaluated for the characteristic features of fibromyalgia, as defined by the 1990 ACR criteria. A subset of the family members agreed to provide blood samples for the genetic analysis.

Determination of sibling recurrence risk.

To determine the extent of familial aggregation for fibromyalgia in the cohort of multicase families, the sibling recurrence risk (Ks), defined as the proportion of affected siblings among all siblings of affected probands with fibromyalgia, was estimated by using a previously described method (25). The estimate of Ks was adjusted for sampling bias, because families were recruited via an affected individual and because of the varying size of sibships. The single ascertainment strategy (i.e., when the probability of ascertaining a sibship is proportional to the number of affected individuals in that sibship) was used in our sample enrollment procedure. Sibling recurrence risk was calculated using the Olson and Cordell formula for estimating sibling genetic risk parameters corrected for single ascertainment bias (25). In this formula, for ns(a) families with s offspring, a of whom are affected, Ks = equation image, which produces an unbiased estimate for single ascertainment strategies (25). The sibling recurrence risk ratio (λs) for fibromyalgia was calculated according to the formula λs = Ks/K. The population prevalence (K) of fibromyalgia was estimated at 2%, based on a previous study (26).

Genome scanning.

We genotyped 341 markers on 22 autosomal chromosomes by using a Weber panel 8 marker set, which has an average marker spacing of 11 cM. After extracting DNA from the blood samples, we used a fluorescence-based genotyping method for the genome scan, as previously described (27). Briefly, after amplification reactions using fluorescent dye–labeled primers, multiplexed panels of markers were size-separated on an ABI 3700 capillary gel electrophoresis system (Applied Biosystems) by running GeneScan 3.5 software for Windows NT. GeneScan 500 ROX size standards were run in each lane. Along with the in-lane standards, each 96-well plate contained 4 control samples obtained from Centre d'Etude du Polymorphisme Humain (CEPH) Utah residents with ancestry from northern and western Europe (2 samples each from individuals 1331-01 and 1331-02). Sixteen samples were placed in 2 different 96-well plates, and 1 sample was placed in 3 different 96-well plates. The CEPH controls were used to standardize allele calling across plates, with the replicates being the safeguard for allele-calling reliability. The genotype data were collected and analyzed using Genotyper 3.6 software for Windows NT. Initial marker placements were based on Marshfield genetic maps (28), although other placements were also investigated.

Error checking.

First, concordance between duplicate samples was assessed for consistency; inconsistent genotype data were changed to missing values. Second, the allelic data were checked for Mendelian inconsistencies, using the program markerinfo (Statistical Analyses for Genetic Epidemiology [SAGE], version 6.1.0; http://darwin.cwru.edu/sage/) (29). Mendelian-inconsistent genotypes were set to missing for all members of a pedigree, for the purpose of analysis.

Prior to performing the sibling pair–based linkage analysis, we reclassified the sibling pairs in each pedigree according to their likely true relationship, using the program reltest (SAGE, version 6.1.0) (29). The reltest program is based on a Markov model of allele sharing along the chromosomes using genome scan data (30). The probability of misclassification depends on the total length of the genome scan and overall marker informativeness. If 1 or both members of a sibling pair have a high proportion of missing genotypes, their relationship may be misclassified. We reclassified 8 individuals in 4 full sibships as half-siblings, and 2 individuals in 1 full sibship as unrelated. Furthermore, 2 individuals in 1 full sibship were reclassified as monozygotic twins; hence, data for 1 sibling were deleted.

Linkage analysis.

Maximum likelihood estimates of allele frequencies for each genetic marker were estimated using the program freq (SAGE, version 6.1.0), which explicitly models pedigree structure to obtain unbiased estimates of allele frequencies (29). Multipoint identity-by-descent sharing estimates were calculated for sibling pairs at a spacing of 2 cM, using the program genibd (SAGE, version 6.1.0). Multipoint model-free linkage analyses were performed using the Haseman-Elston regression method (31), implemented in the program sibpal (SAGE, version 6.1.0). The Haseman-Elston linkage test was performed using multipoint identity-by-descent sharing estimates. The W4 weighting scheme (option W4 in sibpal) was used to adjust for non-independence of sibling pairs within a sibship and of squared trait sums and differences, as previously described (32). The reported P values were obtained assuming the usual asymptotic distribution of the likelihood ratio test statistic. In addition to nominal P values, statistical significance was estimated via permutation testing, constructing an approximate empirical distribution of the test statistic. To verify all nominal P values less than 0.00074, which is the Lander and Kruglyak criterion for suggestive linkage (33), we performed up to 100,000 permutations to obtain empirical P (Pe) values.

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

Clinical characteristics and demographics of the study participants.

Members of 116 families in the Fibromyalgia Family Study were evaluated. Among the family members assessed for the presence of fibromyalgia, 342 siblings were included in the assessment of the sibling recurrence risk ratio (λs) (see below). A subset of family members contributed blood samples for the analysis (341 family members, including 257 siblings [203 sibling pairs]). The majority of individuals affected with fibromyalgia were women, which is consistent with the known preponderance of fibromyalgia in women (2). The mean age at fibromyalgia onset was 34.5 years. Characteristics of the family members, including the total group of family members genotyped and the subgroup of siblings who were included in the sibling pair–based linkage analysis, are shown in Table 1.

Table 1. Characteristics of the Fibromyalgia Family Study participants
CharacteristicTotal genotyped sample (n = 341)Siblings (n = 257)
Age, mean ± SD years49.6 ± 13.648.6 ± 11.5
Female sex, no. (%)305 (89)232 (90)
Race, no. (%)  
 Caucasian305 (89)231 (90)
 Hispanic22 (6.5)15 (5.8)
 African American4 (1.2)2 (0.8)
 Mixed8 (2.3)8 (3.1)
 Unknown2 (0.6)1 (0.4)
Fibromyalgia, no. (%)264 (77)198 (77)
 Age at onset, mean ± SD years34.5 ± 13.534.5 ± 13.3

Sibling recurrence risk ratio (λs).

Using the dichotomous definition of fibromyalgia affected (yes/no), we calculated the sibling recurrence risk ratio (λs) with correction for single ascertainment within the 116 families (25). We assumed a population prevalence of 2% (2), and that birth order was interchangeable. The largest family consisted of 13 siblings. Among the 342 siblings (female and male) assessed for fibromyalgia, the observed sibling recurrence risk (Ks) for fibromyalgia was 27.2% (95% confidence interval [95% CI] 22.5–31.9%), which yielded a sibling recurrence risk ratio (λs) of 13.6 (95% CI 10.0–18.5). The sibling recurrence risk (Ks) for only the 194 female siblings was higher (43.8%; 95% CI 36.7–50.8%). However, because the prevalence of fibromyalgia is higher in women than in men in the general population (26), the corresponding sibling recurrence risk ratio (λs) was 12.9 (95% CI 9.4–17.6), which is similar to that observed for the entire data set.

Genome scan results.

Multipoint linkage analysis was performed in the 203 sibling pairs. The results of the genome scan multipoint linkage analysis are shown in Figure 1. Detailed information regarding the most significant marker at each locus with nominal significance (P ≤ 0.01) is shown in Table 2. Five of these loci were located on chromosomes 4q21.21–21.23, 8p21.2, 8q11.23, 9p24.3–24.1, and 17p11.2–q11.2. The most significant markers at these loci were D4S2361, D8S1048, D8S1110, D9S1779, and D17S2196.

thumbnail image

Figure 1. Results of genome-wide multipoint linkage scan for fibromyalgia. For each chromosome, the genetic distance (cM) and –log10 P values are plotted on the x-axis and y-axis, respectively. The broken horizontal line (P = 0.00074) represents the Lander and Kruglyak criterion for suggestive linkage.

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Table 2. Genetic locations and multipoint P values for markers demonstrating evidence of linkage (nominal P ≤ 0.01) in the fibromyalgia genome scan*
ChromosomeMarkerCytogenetic locationPosition, cMPosition on HG19, kbNominal P
  • *

    The Haseman-Elston method was used to determine nominal P values. P values less than 0.00074 represent the Lander and Kruglyak criterion for genome-wide suggestive linkage. HG19 = Human Genome 19.

4D4S32434q21.2188∼80,9320.0058
4D4S23614q21.2393∼85,0050.0045
8D8S10488p21.247.5∼26,8110.0061
8D8S11108q11.2360.5∼53,1810.0092
9D9S17799p24.30∼5160.0075
9D9S21699p24.114∼5,2000.0094
17D17S219617p11.244∼17,2640.00033
17D17S129417q11.250∼28,3820.00048

We observed one chromosome region with a multipoint nominal P value less than 0.00074, which is the criterion for genome-wide suggestive linkage proposed by Lander and Kruglyak (33), on chromosome 17p11.2–q11.2 (Table 3). Chromosome 17p11.2–q11.2 showed evidence of suggestive linkage to fibromyalgia in a 10-cM region spanning marker D17S2196 (nominal P = 0.00033; position 44 cM) to marker D17S1294 (nominal P = 0.00048; position 50 cM) (Figure 2). The best signal on chromosome 17p11.2–q11.2 was at marker D17S2196 (nominal P = 0.00033; Pe = 0.00030).

Table 3. Genetic locations and multipoint P values for chromosome 17 locus demonstrating genome-wide suggestive linkage for fibromyalgia*
MarkerCytogenic locationPosition, cMPosition on HG19, kbNominal PEmpirical P
  • *

    The Haseman-Elston method was used to determine nominal P values. Up to 100,000 permutations were performed to obtain empirical P values. P values less than 0.00074 represent the Lander and Kruglyak criterion for genome-wide suggestive linkage.

40.00.000710.00044
42.00.000430.00036
D17S219617p11.244.0∼17,2640.000330.00030
46.00.000250.00020
48.00.000290.00026
D17S129417q11.250.0∼28,3820.000480.00035
thumbnail image

Figure 2. Details of multipoint linkage scan for fibromyalgia on chromosome (Chr) 17. P = 0.00074 represents the Lander and Kruglyak criterion for suggestive linkage.

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We converted the P values to logarithm of odds (LOD) scores using the method described by Nyholt (34). The P value for the best signal on chromosome 17p11.2–q11.2 at marker D17S2196 (nominal P = 0.00033) is equivalent to a LOD score of 2.52, which is greater than the LOD score of 2.2 proposed by Lander and Kruglyak for genome-wide suggestive linkage (33).

DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

To our knowledge, the present study is the first genome-wide linkage scan for fibromyalgia in a cohort of multicase families. To determine whether there was excess risk among family members, we computed the sibling recurrence risk ratio (λs) in the studied cohort of multicase families. The estimated sibling recurrence risk ratio (λs) of 13.6 suggests a strong genetic component of fibromyalgia, confirms previous reports that fibromyalgia aggregates in families (6, 10), and is consistent with λs values reported for other complex disorders to which multiple genetic and environmental factors likely contribute (35).

By using model-free linkage methods, we detected one major locus for fibromyalgia on the chromosome 17p11.2–q11.2 region and several possible minor loci on other chromosomal regions. It is notable that the chromosome 17p11.2–q11.2 region coincides with the map coordinate for 2 potential candidate genes for fibromyalgia: the serotonin transporter gene (SLC6A4) and the transient receptor potential vanilloid channel 2 gene (TRPV2).

Several converging lines of biologic, pharmacologic, and genetic evidence suggest that SLC6A4 is an attractive candidate gene for fibromyalgia susceptibility (5, 6, 36–43). The human serotonin transporter protein is encoded by a single gene (SLC6A4 [LocusLink ID: 6532]) mapped to chromosome 17q11.1–q12.12. A functional polymorphism (5-HTTLPR) in the 5′ regulatory region of SLC6A4 involves 2 major alleles, termed S (short) and L (long), that correspond to the presence of 14 or 16 repeat units of a 20–23-bp incomplete repeat (44). The short allele was observed to reduce transcription efficiency for SLC6A4, resulting in decreased gene expression and serotonin uptake in lymphoblast cell lines (44). Association studies of this functional polymorphism and clinical pain syndromes such as fibromyalgia have generated conflicting results (36, 42, 43, 45). Further studies are needed to elucidate the role of SLC6A4 variants in the etiology of fibromyalgia.

The 17p11.2–q11.2 chromosome region also coincides with map coordinates for TRPV2 (46–48). There are compelling data supporting the contribution of other members of the TRPV subfamily, including TRPV-1, TRPV-4, and the ankyrin transmembrane protein TRPA-1, to pain (specifically pathologic pain associated with inflammatory and neuropathic states) (46). TRPV2 may play a role in mediating pain, but more investigation is required to understand the role of TRPV2 in pain biology and chronic pain disorders such as fibromyalgia.

However, the goal of a genome-wide linkage study is to explore all possible loci that may be involved in the causation of fibromyalgia, and any discussion about the role of SLC6A4 or TRPV2 in fibromyalgia is merely speculative. Furthermore, the region contains more than 100 other genes, and further molecular analyses including sequencing of genes on the chromosome 17p11.2–q11.2 region are warranted to identify additional variants for genetic testing.

Several loci on 4q21.21–21.23, 8p21.2, 8q11.23, and 9p24.3–24.1 were identified by this genome scan. Whether mutations in these weaker loci are critical components in fibromyalgia etiology, or whether these loci act as modifiers, is a topic for further investigation. The majority of the loci identified in the genome scan do not coincide with map coordinates for previously postulated candidate genes for fibromyalgia, such as HTR2A (13q14–q21) (49), COMT (22q11.21) (50), DRD4 (11p15.5) (51), HLA region (52, 53), or susceptibility genes identified in a recent candidate gene association study (13).

Whole-exome sequencing (WES) is a comprehensive and unbiased survey of numerous types of genetic changes in the protein-coding regions of the genome. Whole-genome sequencing (WGS) is a similarly all-inclusive survey of virtually the entire coding and noncoding genome of an individual. One of the primary uses of next-generation sequence data is to improve our understanding of the correlation between phenotypes and genotypes (54, 55). Genetic studies have shown a very high correspondence between the presence of rare, clinically meaningful mutations and acute disorder states, which can be identified through WES or WGS in both small and large families. For example, Bainbridge et al (56) performed WGS in a pair of dizygotic twins with dopa-responsive dystonia, in order to identify a mutation in the sepiapterin reductase gene. Because our study included 116 families enriched for affected pairs, we can similarly set up deep sequencing contrasts from targeted enrichment of the 17p11.2–q11.2 region to WES and WGS to identify rare and common variants that may play a role in fibromyalgia etiology. The rapid advancement of these technologies will enable us to explore many hypotheses simultaneously.

Several limitations of this study should be considered. First, the findings should be considered preliminary based on the sample size of 203 affected sibling pairs. However, the identified loci contain 2 biologic candidate genes (SLC6A4 and TRPV2), suggesting that these loci deserve further attention in future studies. Second, there is heterogeneity in the phenotypic expression of fibromyalgia. Future studies will examine the effect of phenotypic covariates in the analysis. Third, some genes are transcribed based on their parental origin, such that only 1 of the 2 alleles (either paternal or maternal) is expressed in a process known as genomic imprinting (57). To dissect parent-of-origin effects, parental genotypes and preferably collection of extended families will be required. In the current study, the family structures were not suitable for such an analysis. However, in future models of fibromyalgia, parent-of-origin effects for the candidate genes will need to be considered.

In conclusion, we detected genome-wide suggestive linkage to the chromosome 17p11.2–q11.2 region in a cohort of multicase families from the Fibromyalgia Family Study. Further comprehensive sequencing analyses of the 17p11.2–q11.2 chromosome region in multicase families are warranted to identify potential causal genetic risk variants for fibromyalgia.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Drs. Arnold and Iyengar had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Arnold, Fan, Russell, Yunus, Khan, Kushner, Olson, Iyengar.

Acquisition of data. Arnold, Fan, Russell, Yunus, Khan, Olson, Iyengar.

Analysis and interpretation of data. Arnold, Fan, Yunus, Iyengar.

Acknowledgements

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES

We would like to thank the following members of the Scientific Advisory Committee who provided valuable advice during the study: Robert Bennett, MD, Harvey Moldofsky, MD, Daniel J. Clauw, MD, and Christopher Amos, PhD. We also thank the staff at the National Institutes of Health (National Institute of Arthritis and Musculoskeletal and Skin Diseases) for their support. We would like to acknowledge our research staff at each of the investigator sites. Finally, we thank the patients and their family members for their participation in this study.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. AUTHOR CONTRIBUTIONS
  7. Acknowledgements
  8. REFERENCES