Dense genome-wide linkage analysis of rheumatoid arthritis, including covariates

OBJECTIVE: Rheumatoid arthritis (RA) is a heterogeneous disease that exhibits a complex genetic component. Previous RA genome scans confirmed the involvement of the HLA region and generated data on suggestive signals at non-HLA regions, albeit with few overlaps in findings between studies. The present study was undertaken to detect potential RA gene regions and to estimate the number of true RA gene regions, taking into account the heterogeneity of RA, through performance of a dense genome scan. METHODS: In a study of 88 French Caucasian families (105 RA sibpairs), 1,088 microsatellite markers were genotyped (3.3-cM genome scan), and a multipoint model-free linkage analysis was performed. The statistical assessment of the results relied on 10,000 computer simulations. A covariate-based multipoint model-free linkage analysis was performed on the locations of regions with suggestive evidence for linkage. RESULTS: Involvement of the HLA region was strongly confirmed (P = 6 x 10(-5)), and 19 non-HLA regions showed suggestive evidence for linkage (P < 0.05); 9 of these overlapped with regions suggested in other published RA genome scans. A routine 12-cM genome scan with the same families would have detected only 7 of the 19 regions, including only 4 of the 9 overlapping regions. From the 10,000 computer simulations, we estimated that 8 +/- 4 regions (mean +/- SD) were true-positives. RA covariate-based analysis provided additional linkage evidence for 3 regions, with age at disease onset, erosions, and HLA-DRB1 shared epitope as covariates. CONCLUSION: The results of this study provide evidence of 19 non-HLA RA gene regions, with an estimate of 8 +/- 4 as true-positives, and provide additional evidence for 3 regions from covariate-based analysis.     

A unified framework for linkage and association analysis of quantitative traits

We give a unified treatment of the statistical foundations of population based association mapping and of family based linkage mapping of quantitative traits in humans. A central ingredient in the unification involves the efficient score statistic. The discussion focuses on generalized linear models with an additional illustration of the Cox (proportional hazards) model for age of onset data. We give analytic expressions for noncentrality parameters and show how they give qualitative insight into the loss of power that occurs if the scientist's assumed genetic model differs from nature's "true" genetic model. Issues to be studied in detail in the future development of this approach are discussed.     

Immunogenetic heterogeneity of rheumatoid arthritis.

Association of HLA-DR4/Dw4 with rheumatoid arthritis (RA) is well established, but conflicting data exist on a possible association with the severity of the disease, including its extra-articular manifestations. In order to investigate whether a subgroup of RA is preferentially associated with DR4, HLA typing was performed in two groups of patients with severe extra-articular manifestations (Felty's syndrome and histologically proved leucocytoclastic vasculitis), patients with severe joint destruction (seropositive and seronegative), a group with only mild joint destruction, and in healthy controls. The frequency of HLA-DR4 was significantly raised in all patient groups compared with that in healthy controls. The two groups with severe extra-articular manifestations, however, both had a DR4 frequency of 92%, which was significantly (p = 0.002) higher than the 62.7% found in the remaining patients. No significant differences were observed between severe or mild joint destruction and seropositivity or seronegativity in the groups without the above-mentioned extra-articular manifestations. From these data we concluded that DR4 is preferentially associated with severe extra-articular disease manifestations of RA. This observation provides an immunogenetic basis for the disease heterogeneity and for the immunological analogy between RA and leprosy.     

Dense genome‐wide linkage analysis of rheumatoid arthritis,including covariates

Objective

Rheumatoid arthritis (RA) is a heterogeneous disease that exhibits a complex genetic component. Previous RA genome scans confirmed the involvement of the HLA region and generated data on suggestive signals at non‐HLA regions, albeit with few overlaps in findings between studies. The present study was undertaken to detect potential RA gene regions and to estimate the number of true RA gene regions, taking into account the heterogeneity of RA, through performance of a dense genome scan.

Methods

In a study of 88 French Caucasian families (105 RA sibpairs), 1,088 microsatellite markers were genotyped (3.3‐cM genome scan), and a multipoint model‐free linkage analysis was performed. The statistical assessment of the results relied on 10,000 computer simulations. A covariate‐based multipoint model‐free linkage analysis was performed on the locations of regions with suggestive evidence for linkage.

Results

Involvement of the HLA region was strongly confirmed (P = 6 × 10⁻⁵), and 19 non‐HLA regions showed suggestive evidence for linkage (P < 0.05); 9 of these overlapped with regions suggested in other published RA genome scans. A routine 12‐cM genome scan with the same families would have detected only 7 of the 19 regions, including only 4 of the 9 overlapping regions. From the 10,000 computer simulations, we estimated that 8 ± 4 regions (mean ± SD) were true‐positives. RA covariate–based analysis provided additional linkage evidence for 3 regions, with age at disease onset, erosions, and HLA–DRB1 shared epitope as covariates.

Conclusion

The results of this study provide evidence of 19 non‐HLA RA gene regions, with an estimate of 8 ± 4 as true‐positives, and provide additional evidence for 3 regions from covariate‐based analysis.

     

Identifying subclasses of patients with rheumatoid arthritis through cluster analysis

Nonhierarchical cluster analysis was used to classify 92 patients with rheumatoid arthritis drawn from a community rheumatology practice into 5 groups on the basis of biochemical measures and disease indices. The major differentiating variables were the number of active joints, number of damaged joints, overall disease activity, extraarticular complications, and history of joint surgery. Although the 5 subclasses were equivalent on measures of psychological functioning, they differed systematically on such health outcome measures as mobility, physical activity, and dexterity. Relationships between the taxonomy produced through cluster analysis and conventional classifications are discussed, and directions for further investigation are noted.     

HLA: linkage with rheumatoid arthritis or seropositivity

HLA haplotype sharing was compared in sibships from multicase families with rheumatoid arthritis (RA), subdivided by rheumatoid factor status, to investigate the claim that HLA is linked more to RA severity than susceptibility. Considerable deviation from expected (Mendelian) inheritance towards greater sharing of inherited parental haplotypes was observed in the sibships as a whole and when subdivided according to the serological status of the sibship's members. Further, there was no evidence that linkage was stronger in the seropositive concordant than in the other sibships. Linkage was also demonstrated between HLA and seropositivity even in sibships where not all members expressed clinical RA. These results, therefore, do not support the existence of genetic heterogeneity between seropositive and seronegative RA, a possibility previously suggested from population studies of antigen associations.     

Immunogenetic heterogeneity of seronegative rheumatoid arthritis and the antiperinuclear factor.

HLA typing was carried out in 132 patients with rheumatoid arthritis (RA) with long term follow up, and special attention was focused on rheumatoid factor negative patients. The patients were divided into four groups: 55 patients with a seropositive RA and a positive antiperinuclear factor (group A); 39 seropositive patients but a negative antiperinuclear factor (group B); 14 patients consistently seronegative for 3-28 years (mean 11.8 years) but positive for antiperinuclear factor (group C); patients consistently negative for 3-28 years (mean 11.8) and also negative for antiperinuclear factor (group D). The prevalence of HLA-DR4 was 31/55 (56%), 29/39 (74%), 10/14 (71%), and 9/24 (37%) for groups A, B, C, and D respectively, and in all groups was significantly higher than in 277 healthy controls (55/277, 20%). No significant difference was found between seropositive (groups A and B) and seronegative (groups C and D) patients, but groups A, B, and C had higher prevalences than group D. It is concluded that in seronegative RA HLA-DR4 is preferentially associated with the antiperinuclear factor positive group.     

Evaluation through imaging of early rheumatoid arthritis

Radiography is the most widely utilized imaging modality for measuring joint damage in early rheumatoid arthritis. Conventional radiography is, however, insensitive for showing bone erosions and is totally unsuitable for assessing synovial inflammation. The recognition of these limitations has led to intense interest in ultrasonography and magnetic resonance imaging for assessing synovitis and bone erosions. Magnetic resonance imaging allows detection of bone marrow edema, which is considered to be a very early marker of inflammation as well as a "forerunner" of erosions.     

Whole-genome linkage analysis of rheumatoid arthritis susceptibility loci in 252 affected sibling pairs in the United Kingdom

OBJECTIVE: To undertake a systematic whole-genome screen to identify regions exhibiting genetic linkage to rheumatoid arthritis (RA). METHODS: Two hundred fifty-two RA-affected sibling pairs from 182 UK families were genotyped using 365 highly informative microsatellite markers. Microsatellite genotyping was performed using fluorescent polymerase chain reaction primers and semiautomated DNA sequencing technology. Linkage analysis was undertaken using MAPMAKER/SIBS for single-point and multipoint analysis. RESULTS: Significant linkage (maximum logarithm of odds score 4.7 [P = 0.000003] at marker D6S276, 1 cM from HLA-DRB1) was identified around the major histocompatibility complex (MHC) region on chromosome 6. Suggestive linkage (P < 7.4 x 10(-4)) was identified on chromosome 6q by single- and multipoint analysis. Ten other sites of nominal linkage (P < 0.05) were identified on chromosomes 3p, 4q, 7p, 2 regions of 10q, 2 regions of 14q, 16p, 21q, and Xq by single-point analysis and on 3 sites (1q, 14q, and 14q) by multipoint analysis. CONCLUSION: Linkage to the MHC region was confirmed. Eleven non-HLA regions demonstrated evidence of suggestive or nominal linkage, but none reached the genome-wide threshold for significant linkage (P = 2.2 x 10(-5)). Results of previous genome screens have suggested that 6 of these regions may be involved in RA susceptibility.     

Multipoint linkage analysis of a candidate gene locus in rheumatoid arthritis demonstrates significant evidence of linkage and association with the corticotropin-releasing hormone genomic region

OBJECTIVE: Rheumatoid arthritis (RA) is the most common disabling autoimmune disease, affecting approximately 1% of the population. The disease etiology is unknown, but it involves inflammation and immune dysregulation and is influenced by genetic variation at both HLA and other, as-yet-unidentified genetic loci. Corticotropin-releasing hormone (CRH; or corticotropin-releasing factor), a primary regulator of the hypothalamic-pituitary-adrenal axis and a key element in the response to stress and inflammation, is a strong candidate gene for RA. We examined the role of DNA variation across the region containing this gene in multicase families with RA. METHODS: We genotyped fluorescently labeled simple tandem repeat genetic markers from chromosome 8q13 in 295 families with multiple cases of RA. Singlepoint and multipoint nonparametric linkage analysis and association analysis using transmission disequilibrium testing (TDT) were also used. RESULTS: Single-point linkage analysis using a microsatellite within 30 kb of the CRH locus (CRH.PCR at position 8q13) showed a significant excess of allele sharing in 295 United Kingdom RA families with at least 2 affected members (MapMaker/Sibs logarithm of odds [LOD] 1.4; P = 5.5x10(-3); mean identity by descent [ibd] sharing 55.9%). To provide a more detailed linkage map, a multipoint analysis was conducted with an additional 7 dinucleotide microsatellite markers (average heterozygosity 0.75) flanking the CRH locus. Significant linkage was detected over a 22-cM region between D8S285 and D8S530, with the maximum singlepoint LOD score of 1.77 at D8S1723 (MapMaker/Sibs P = 2.2x10(-3); mean ibd sharing 59.3%). Multipoint analysis showed strongest evidence for linkage at the same marker (multipoint LOD 1.78, P = 2.1x10(-3), mean ibd sharing 55.8%). TDT analysis showed significant association at the CRH locus (P = 2.6x10(-3)). CRH has a sibling relative risk of 1.14, and contributes <10% to the sibling relative risk of RA. CONCLUSION: With the exception of HLA, this is the strongest evidence yet of a genetic locus that is both linked to and associated with RA, and provides an avenue for further genetic characterization and potentially novel therapeutic intervention.     

A simplified twenty-eight-joint quantitative articular index in rheumatoid arthritis

We describe a joint index that includes only 28 joints: 10 proximal interphalangeal joints of the fingers, 10 metacarpophalangeal joints, and the wrists, elbows, shoulders, and knees. These joints are evaluated for swelling, tenderness, and limited motion, with the findings scored as abnormal or normal. The 28-joint index is considerably easier to use than traditional methods, and appears to yield as much information in terms of correlation with other measures of clinical status, including hand radiograph scores, American Rheumatism Association functional class, grip strength, walking time, and activities of daily living questionnaire scores (r = 0.25-0.53, P less than 0.001). Simplified joint counts might facilitate acquisition of quantitative articular data in research and clinical rheumatologic settings.     

Dissecting the genetic heterogeneity of gallbladder stone formation

Gallstone disease affects almost 20% of individuals in Westernized countries. As its incidence in the developing countries is rising considerably, currently, it is the second most common gastroenterological condition worldwide. Gallstone formation is driven by an interaction between genetic and environmental risk factors. Previous studies have demonstrated that the genetic background accounts for ~25% of the total disease risk. Linkage and case-control studies of candidate genes and recent genome-wide studies have identified multiple lithogenic genes, in particular the hepatocanalicular cholesterol transporter ABCG5/G8 and the bilirubin conjugating enzyme UGT1A1, as major genetic determinants of gallstones in humans. In this review, we summarize the recent findings related to the genetics of cholelithiasis, update the "inventory" of human lithogenic genes, and relate the genetic studies to the pathobiologic background of the disease. In closing, future applications of genetic testing for gallstone carriers and asymptomatic family members are addressed.     

Linkage of cytokine genes to rheumatoid arthritis. Evidence of genetic heterogeneity

OBJECTIVE—To investigate linkage of candidate disease susceptibility genes to rheumatoid arthritis (RA) in affected sibling pair families stratified for specific clinical features.
METHOD—Two hundred RA affected sibling pair families were genotyped for informative microsatellite markers mapping within or less than 3cM from: INFα, INFγ, INFβ, IL1α, IL1β, IL1R, IL2, IL6, IL5R, IL8R, BCL2, CD40L, NOS3, NRAMP, α₁ anti-trypsin, and α₁ anti-chymotrypsin, using fluorescence based automated technology. Linkage was examined by defining allele sharing sibling pairs. This was assessed by maximum likelihood—inheritance by descent methods.
RESULTS—An increase in allele sharing was seen for IL5R in female sibling pairs (LOD 0.91, p = 0.03), for INFγ in sibling pairs with an affected male (LOD 0.96, p = 0.03) and most significantly for IL2 in sibling pairs where one or both were persistently seronegative (LOD 1.05, p = 0.02).
CONCLUSION—Weak evidence of linkage of RA to IL5R, IFNγ, and IL2 has been detected in clinical subsets of sibling pairs suggesting that RA is a genetically heterogeneous disease.

Keywords: linkage; rheumatoid arthritis; sibling pairs; cytokine genes     

New susceptibility locus for rheumatoid arthritis suggested by a genome-wide linkage study

Rheumatoid arthritis (RA), the most common autoimmune disease, is associated in families with other autoimmune diseases, including insulin-dependent diabetes mellitus (IDDM). Its genetic component has been suggested by familial aggregation (λs = 5), twin studies, and segregation analysis. HLA, which is the only susceptibility locus known, has been estimated to account for one-third of this component. The aim of this paper was to identify new RA loci. A genome scan was performed with 114 European Caucasian RA sib pairs from 97 nuclear families. Linkage was significant only for HLA (P < 2.510⁻⁵) and nominal for 19 markers in 14 other regions (P < 0.05). Four of the loci implicated in IDDM potentially overlap with these regions: the putative IDDM6, IDDM9, IDDM13, and DXS998 loci. The first two of these candidate regions, defined in the RA genome scan by the markers D18S68-D18S61-D18S469 (18q22–23) and D3S1267 (3q13), respectively, were studied in 194 additional RA sib pairs from 164 nuclear families. Support for linkage to chromosome 3 only was extended significantly (P = 0.002). The analysis of all 261 families provided a linkage evidence of P = 0.001 and suggested an interaction between this putative RA locus and HLA. This locus could account for 16% of the genetic component of RA. Candidate genes include those coding for CD80 and CD86, molecules involved in antigen-specific T cell recognition. In conclusion, this first genome scan in RA Caucasian families revealed 14 candidate regions, one of which was supported further by the study of a second set of families.     

Synovial tissue analysis in rheumatoid arthritis

In rheumatoid arthritis, synovial tissue is easily accessible for systematic analysis. Blind needle biopsy is a simple and safe procedure, but is restricted to smaller tissue samples. Arthroscopic biopsy is also safe but is more complicated as it allows access to most sites in the joint and provides adequate tissue for extensive laboratory investigations. Synovial tissue analysis has been successfully applied to studies of disease mechanisms, response to treatment and prognosis. The immuno-histological features in synovial tissue have consistently reflected disease status. Synovial tissue analysis has been particularly informative in the study of novel therapeutic agents.     

Genome‐wide linkage analysis of quantitative biomarker traits of osteoarthritis in a large,multigenerational extended family

Objective

The genetic contributions to the multifactorial disorder osteoarthritis (OA) have been increasingly recognized. The goal of the current study was to use OA‐related biomarkers of severity and disease burden as quantitative traits to identify genetic susceptibility loci for OA.

Methods

In a large multigenerational extended family (n = 350), we measured 5 OA‐related biomarkers: hyaluronan (HA), cartilage oligomeric matrix protein (COMP), N‐propeptide of type IIA collagen (PIIANP), C‐propeptide of type II procollagen (CPII), and type II collagen neoepitope (C2C). Single‐nucleotide polymorphism markers (n = 6,090) covering the whole genome were genotyped using the Illumina HumanLinkage‐12 BeadChip. Variance components analysis, as implemented in the Sequential Oligogenic Linkage Analysis Routines, was used to estimate heritabilities of the quantitative traits and to calculate 2‐point and multipoint logarithm of odds (LOD) scores using a polygenic model.

Results

After adjusting for age and sex, we found that 4 of the 5 biomarkers exhibited significant heritability (PIIANP 0.57, HA 0.49, COMP 0.43, C2C 0.30; P ≤ 0.01 for all). Fourteen of the 19 loci that had multipoint LOD scores of >1.5 were near to or overlapped with previously reported OA susceptibility loci. Four of these loci were identified by more than 1 biomarker. The maximum multipoint LOD scores for the heritable quantitative biomarker traits were 4.3 for PIIANP (chromosome 8p23.2), 3.2 for COMP (chromosome 8q11.1), 2.0 for HA (chromosome 6q16.3), and 2.0 for C2C (chromosome 5q31.2).

Conclusion

Herein, we report the first evidence of genetic susceptibility loci identified by OA‐related biomarkers in an extended family. Our results demonstrate that serum concentrations of PIIANP, HA, COMP, and C2C have substantial heritable components, and using these biomarkers, several genetic loci potentially contributing to the genetic diversity of OA were identified.

     

Whole‐genome linkage analysis of rheumatoid arthritis susceptibility loci in 252 affected sibling pairs in the United Kingdom

Objective

To undertake a systematic whole‐genome screen to identify regions exhibiting genetic linkage to rheumatoid arthritis (RA).

Methods

Two hundred fifty‐two RA‐affected sibling pairs from 182 UK families were genotyped using 365 highly informative microsatellite markers. Microsatellite genotyping was performed using fluorescent polymerase chain reaction primers and semiautomated DNA sequencing technology. Linkage analysis was undertaken using MAPMAKER/SIBS for single‐point and multipoint analysis.

Results

Significant linkage (maximum logarithm of odds score 4.7 [P = 0.000003] at marker D6S276, 1 cM from HLA–DRB1) was identified around the major histocompatibility complex (MHC) region on chromosome 6. Suggestive linkage (P < 7.4 × 10⁻⁴) was identified on chromosome 6q by single‐ and multipoint analysis. Ten other sites of nominal linkage (P < 0.05) were identified on chromosomes 3p, 4q, 7p, 2 regions of 10q, 2 regions of 14q, 16p, 21q, and Xq by single‐point analysis and on 3 sites (1q, 14q, and 14q) by multipoint analysis.

Conclusion

Linkage to the MHC region was confirmed. Eleven non‐HLA regions demonstrated evidence of suggestive or nominal linkage, but none reached the genome‐wide threshold for significant linkage (P = 2.2 × 10⁻⁵). Results of previous genome screens have suggested that 6 of these regions may be involved in RA susceptibility.

     

Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins

OBJECTIVE: Twin concordance data for rheumatoid arthritis (RA) on their own provide only limited insight into the relative genetic and environmental contribution to the disease. We applied quantitative genetic methods to assess the heritability of RA and to examine for evidence of differences in the genetic contribution according to sex, age, and clinical disease characteristics. METHODS: Data were analyzed from 2 previously published nationwide studies of twins with RA conducted in Finland and the United Kingdom. Heritability was assessed by variance components analysis. Differences in the genetic contribution by sex, age, age at disease onset, and clinical characteristics were examined by stratification. The power of the twin study design to detect these differences was examined through simulation. RESULTS: The heritability of RA was 65% (95% confidence interval [95% CI] 50-77) in the Finnish data and 53% (95% CI 40-65) in the UK data. There was no significant difference in the strength of the genetic contribution according to sex, age, age at onset, or disease severity subgroup. Both study designs had power to detect a contribution of at least 40% from the common family environment, and a difference in the genetic contribution of at least 50% between subgroups. CONCLUSION: Genetic factors have a substantial contribution to RA in the population, accounting for approximately 60% of the variation in liability to disease. Although tempered by power considerations, there is no evidence in these twin data that the overall genetic contribution to RA differs by sex, age, age at disease onset, and disease severity.