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Dissecting the heterogeneity of rheumatoid arthritis through linkage analysis of quantitative traits.

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ARTHRITIS & RHEUMATISM
Vol. 56, No. 1, January 2007, pp 58–68
DOI 10.1002/art.22325
© 2007, American College of Rheumatology
Dissecting the Heterogeneity of Rheumatoid Arthritis Through
Linkage Analysis of Quantitative Traits
Lindsey A. Criswell,1 Wei V. Chen,2 Damini Jawaheer,3 Raymond F. Lum,1 Mark H. Wener,4
Xiangjun Gu,2 Peter K. Gregersen,5 and Christopher I. Amos2
Objective. To dissect the heterogeneity of rheumatoid arthritis (RA) through linkage analysis of quantitative traits, specifically, IgM rheumatoid factor (IgMRF) and anti–cyclic citrullinated peptide (anti-CCP)
autoantibody titers.
Methods. Subjects, 1,002 RA patients from 491
multiplex families recruited by the North American RA
Consortium, were typed for 379 microsatellite markers.
Anti-CCP titers were determined based on a secondgeneration enzyme-linked immunosorbent assay, and
IgM-RF levels were quantified by immunonephelometry. We used the Merlin statistical package to
perform nonparametric quantitative trait linkage
analysis.
Results. For each of the quantitative traits, evidence of linkage, with logarithm of odds (LOD) scores of
>1.0, was found in 9 regions. For both traits, the
strongest evidence of linkage was for marker D6S1629
on chromosome 6p (LOD 14.02 for anti-CCP and LOD
12.09 for RF). Six other regions with LOD scores of >1.0
overlapped between the 2 traits, on chromosomes
1p21.1, 5q15, 8p23.1, 16p12.1, 16q23.1, and 18q21.31.
Evidence of linkage to anti-CCP titer but not to RF titer
was found in 2 regions (chromosomes 9p21.3 and
10q21.1), and evidence of linkage to RF titer but not to
anti-CCP titer was found in 2 regions (chromosomes
5p15.2 and 1q42.3). Several covariates were significantly
associated with 1 or both traits, and linkage analysis
exploring the covariate effects revealed striking effects
of sex in modulating linkage signals for several chromosomal regions. For example, sex had a striking impact
on the linkage results for both quantitative traits on
chromosome 6p (P ⴝ 0.0007 for anti-CCP titer and P ⴝ
0.0012 for RF titer), suggesting a sex–HLA region
interaction.
Conclusion. Analysis of quantitative components of RA is a promising approach for dissecting
the genetic heterogeneity of this complex disorder.
These results highlight the potential importance of sex or
other covariates that may modulate some of the genetic
effects that influence the risk of specific disease manifestations.
Supported by the NIH (grants K24-AR-02175 and ES-09912).
The North American Rheumatoid Arthritis Consortium data collection was supported by the National Arthritis Foundation and the NIH
(grants N01-AR-72232 and R01-AR-44222 from the National Institute
of Allergy and Infectious Diseases and the National Institute of
Arthritis and Musculoskeletal and Skin Diseases). These studies were
performed in part in the General Clinical Research Center, Moffitt
Hospital, University of California, San Francisco, with funds provided
by the NIH National Center for Research Resources (grant 5-M01RR-00079 from the USPHS).
1
Lindsey A. Criswell, MD, MPH, Raymond F. Lum, MPH:
University of California, San Francisco; 2Wei V. Chen, MS, Xiangjun
Gu, MS, Christopher I. Amos, PhD: M. D. Anderson Cancer Center,
Houston, Texas; 3Damini Jawaheer, PhD: University of California,
Los Angeles; 4Mark H. Wener, MD: University of Washington,
Seattle; 5Peter K. Gregersen, MD: Robert S. Boas Center for Genomics and Human Genetics, and Feinstein Institute for Medical Research, North Shore–Long Island Jewish Health System, Manhasset,
New York.
Dr. Gregersen is a member of the Abbott Scholar Scientific
Review Board and receives consulting fees (more than $10,000) from
Abbott.
Address correspondence and reprint requests to Lindsey A.
Criswell, MD, MPH, Division of Rheumatology, 374 Parnassus Avenue, Box 0500, San Francisco, CA 94143. E-mail: Lindsey.
Criswell@ucsf.edu.
Submitted for publication January 13, 2006; accepted in
revised form October 6, 2006.
Rheumatoid arthritis (RA) is a chronic inflammatory disease with the potential to cause substantial
disability, primarily as a result of the erosive and deforming process in joints. The disease is characterized by
a spectrum of clinical and laboratory manifestations,
with resultant variation in the RA phenotype expressed
by affected individuals. Presumably, this phenotypic
heterogeneity reflects differences in underlying disease
58
RA QUANTITATIVE TRAIT LINKAGE ANALYSIS
mechanisms, which might be based, at least in part, on
genetic differences (1,2).
Like most chronic diseases, RA is etiologically
complex, with important contributions from multiple
genetic and environmental factors (3). The most rigorous assessment of RA heritability, which was based on a
quantitative analysis of Finnish and English twins, indicates that 50–60% of the occurrence of RA in twins is
explained by genetic effects (4). Genome-wide screening
in multicase families, which involves the analysis of a
large set of informative, polymorphic markers that are
approximately evenly spaced across the genome, to
identify regions with evidence of linkage to disease, has
been a popular approach for identifying susceptibility
genes. Several full genome screens have now been
performed in families with multiple RA cases (5–10).
Although these studies reveal some overlap in genomic
regions of linkage, as highlighted by a recent metaanalysis (11), overall the results of these individual
studies are notable for their differences. In view of the
phenotypic heterogeneity of RA, it is likely that some of
these differences reflect underlying phenotypic differences in the study populations. Thus, linkage analysis of
more homogeneous subgroups may shed light on these
disparate results and elucidate underlying disease mechanisms with distinct genetic associations.
Although RA has typically been investigated as a
categorical trait, i.e., one that is either present or absent,
other human traits or disorders are more commonly
examined as continuous, or quantitative, traits. Linkage
analyses of quantitative traits are currently of great
interest, in part due to the potentially increased power of
quantitative, compared with qualitative, outcomes (12)
and due to advances in statistical methodology (13). For
example, analyses of asthma-associated phenotypes as
quantitative traits (e.g., levels of serum immunoglobulins) have revealed novel genetic loci for this complex
disorder (14,15).
In the present study, we sought to apply this
powerful methodology to the identification of RA disease loci by performing a genome-wide linkage analysis
of 2 quantitative traits among a large collection of
multiplex RA families recruited by the North American
RA Consortium (NARAC). Specifically, we examined
serum levels of 2 RA-related autoantibodies, IgM rheumatoid factor (IgM-RF) and anti–cyclic citrullinated
peptide (anti-CCP). RF has been used diagnostically
and prognostically in RA for years, as a result of
well-established associations with disease risk and outcome (16). There is also growing interest in anti-CCP
59
titers in RA due to their high specificity (17) and the
fact that these autoantibodies may be detected many
years before RA onset (18). Anti-CCP autoantibodies
are also associated with disease severity, particularly the
development and progression of the erosive process in
joints (19,20).
PATIENTS AND METHODS
Study population. We studied 1,002 affected siblings in
491 multicase RA families recruited as a collaborative effort by
the NARAC (21). The majority of families included a single
sibpair (88.4%), and the remaining families included 3 siblings
(10.4%), 4 siblings (1%), or 6 siblings (0.2%). Details of our
enrollment procedures have been published previously (1), and
clinical and genetic marker data are available at http://
www.naracdata.org/index.asp. Briefly, eligible families had to
meet the following criteria: ⱖ2 siblings satisfied the American
College of Rheumatology (ACR; formerly, the American
Rheumatism Association) 1987 criteria for RA (22), ⱖ1 sibling
had documented erosions on hand radiographs, and ⱖ1 sibling
had disease onset between the ages of 18 and 60 years. The
presence of any other diseases associated with similar articular
symptoms, such as psoriasis or inflammatory bowel disease,
was an exclusionary criterion for families. Informed consent
was obtained from every subject, including all participating
family members, and approval of the local institutional review
board was secured at every recruitment site prior to enrollment.
Phenotype characterization. Basic demographic and
clinical information, including ethnic background, age at onset
of RA symptoms, and exposure to tobacco smoke, was collected from all RA patients by telephone. Confirmation of RA
diagnosis, including which ACR criteria were fulfilled, was
obtained from patients’ rheumatologists. At the time of study
enrollment, RA patients were examined to document the
extent of joint involvement and functional disability, using
validated RA instruments (23,24). Radiographs of the hands
and wrists were also obtained to document the presence and
extent of joint involvement.
RF and anti-CCP testing. Serum samples collected at
study entry were tested for IgM-RF and anti-CCP autoantibodies in the Immunology Division of the Department of
Laboratory Medicine at the University of Washington. RF
testing was performed using a latex-enhanced nephelometric
assay (Behring Diagnostics, San Jose, CA) in which human and
rabbit IgG–coated latex beads are used as antigen. This assay
was calibrated to the World Health Organization international
standard for RF (25). RF values ⬍12 IU were considered
negative and were assigned a quantitative value of 11 for
analysis. Anti-CCP titers were determined based on a secondgeneration enzyme-linked immunosorbent assay (Inova Diagnostics, San Diego, CA). Anti-CCP values exceeding the upper
limit of linearity were rounded off to that value. Thus, all
anti-CCP values ⬎210 were assigned a quantitative value of
210 for analysis.
Microsatellite marker genotyping. All families were
genotyped for 379 microsatellite markers from the Marshfield Set 8A Combo List with additional markers in certain
60
regions, such as the HLA complex (6). These markers provided ⬃10 cM genome-wide coverage and consisted of 47
panels. Reaction conditions were standard for all markers
used, as described in the Marshfield polymerase chain reaction (PCR) protocol (http://research.marshfieldclinic.org/
genetics/sets/combo.html). Details of our methods for microsatellite genotyping in these families have been described
previously (8).
The genetic relationships among individuals were
checked using Relative (26), resulting in the identification of
29 half-sibling pairs and 4 unrelated pairs who were initially
reported to be full siblings. Consistency in allele inheritance
within families was then verified using the PedCheck program
(27). The summary error rate, which includes the number of
genotypes that were eliminated to resolve Mendelian inconsistencies, was 1.12%. Whenever a Mendelian inconsistency was
found, we applied a conservative approach of removing the
genotypes of all individuals whose genotypes caused the inconsistency. The data were also checked for the presence of
monozygotic twin pairs, and the 8 pairs identified (based on
identical genotypes) were eliminated from the analysis. Testing
for Hardy-Weinberg equilibrium of the microsatellite markers
was also performed, using the method developed by Guo and
Thompson (28), and implemented with the SAS Allele procedure (SAS Genetics, Cary, NC). Based on these results, we
repeated the linkage analysis for genomic regions containing
ⱖ1 microsatellite marker with significant evidence of HardyWeinberg disequilibrium (at a genome-wide significance level
of P ⬍ 0.000013). Finally, Simwalk (29) was used to verify
intermarker distances against the published maps used, as
previously reported (8).
HLA–DRB1 genotyping. Broad-level HLA–DRB1 typing for the allele groups DRB1*01 through DRB1*18, and
high-resolution DRB1*04 typing were accomplished by initial
PCR amplification of groups of alleles using biotinylated PCR
primers, followed by hybridization to immobilized sequencespecific oligonucleotide probes in a linear array format. Positive hybridization reactions were detected using a streptavidin–
horseradish peroxidase conjugate and a soluble colorless
substrate, 3,3⬘,5,5⬘-tetramethylbenzidine (30). A computer
algorithm based on the sequence-specific oligonucleotide
probe hybridization pattern and the Anthony Nolan 1999
HLA sequence database (http://www.ebi.ac.uk/imgt/hla/) was
used to assign genotypes. The following alleles detected were
classified as shared epitope positive: DRB1*0101, 0102, 0104,
0105, 0401, 0404, 0405, 0408, 0409, 1001, 1402, and 1406.
Substantial previous research has documented association of
HLA–DRB1 alleles encoding the shared epitope sequence
with RA (3,31).
Statistical analysis. Linkage analysis for the 2 quantitative traits, RF titer and anti-CCP titer, was performed
using the Multipoint Engine for Rapid Likelihood Inference
(Merlin) statistical package (32) (http://www.sph.umich.edu/
csg/abecasis/Merlin/). We chose to use the nonparametric
quantitative trait linkage statistic, which implements a
score test as proposed by Whittemore and Halpern (33). The
procedure also allows for variable marker information following the approach suggested by Kong and Cox (34). Because our
sample was selected to include individuals with RA, reference
to the population mean was not an effective approach for
CRISWELL ET AL
analysis. We therefore used the “deviates” option, in which the
population mean is fixed at zero, in order to allow for the
sampling scheme used (i.e., families selected based on RA
diagnosis). Prior to analysis we subtracted a “mean” value of
11 from all RF titers because all 20 normal subjects assayed
had a recorded value ⬍11. Similarly, we subtracted 4.6 from
anti-CCP titers, since this was the mean for the normal subjects we assayed. To obtain asymptotic P values we converted
all positive scores to a chi-square deviate with a 1:1 mixture
of 0 df and 1 df, by multiplying them by 4.6 and then identifying that part of the resulting chi-square distribution exceeding the observed logarithm of odds (LOD) score. Allele
frequencies were calculated by counting the number of alleles
genotyped in each subject. As reported by Boehnke, this
approach provides a consistent estimate of the allele frequencies that is adequate given the large number of independent
families studied (35).
Because variance components procedures can yield
excess false-positive findings in the presence of non-normal
trait distributions, we also performed simulation studies to
assess the empirical P values. For these simulation studies, we
maintained the same pedigree structure and phenotypes as
were observed. We then simulated marker data according to
the marker allele frequencies, availability of samples from
individuals, and distances among markers, using Allegro version 1.2c (36). For chromosome 6, which showed the most
significant results, we performed 10,000 replicate simulations
and then used the same Merlin setup described above to obtain
empirical P values. We then compared the resultant empirical
(i.e., experimental) P values with asymptotic P values calculated from a mixture of chi-square distributions (i.e., derived
from asymptotic theory). We also studied the impact that
transforming the data would have on our analyses. However,
we found that standard approaches such as Box-Cox or probit
transformation did not reduce the skewness or kurtosis because of the right (for anti-CCP) or left (for RF) truncation of
values associated with the measurements.
Several covariates were associated with 1 or both of
the quantitative traits. Therefore, we repeated the linkage
analysis for each trait after adjusting for the associated covariates. The following covariates, determined at the time of
anti-CCP and RF testing, were considered for these analyses:
sex, ethnicity, age at study entry, disease duration (time since
RA diagnosis), exposure to tobacco smoke, and HLA–DRB1
Table 1.
Clinical and demographic features of the 1,002 subjects*
% Caucasian
% female
Age at RA diagnosis, mean ⫾ SD years
Disease duration, mean ⫾ SD years
% with erosions
% HLA–DRB1 shared epitope positive
% current smoker†
IgM-RF titer, mean ⫾ SD
Anti-CCP titer, mean ⫾ SD
92
77
41 ⫾ 13
14 ⫾ 11
92
84
18
262 ⫾ 542
109 ⫾ 78
* Subjects were affected siblings from 491 multiplex rheumatoid
arthritis (RA) families.
† Smoking status defined at study entry, which corresponds to the time
of blood sample collection for anti–cyclic citrullinated peptide (antiCCP) and rheumatoid factor (RF) titer determination.
RA QUANTITATIVE TRAIT LINKAGE ANALYSIS
61
The mean levels of the traits were reset to the mean among
affected subjects prior to analysis. We assessed our regression
analyses for potential effects from outliers by evaluating the
Cook’s D measure.
To further evaluate the impact of specific covariates in
the linkage analysis, we performed stratified analyses. The
stratification variables that we studied included sex, disease
duration, exposure to tobacco smoke, and presence of the
shared epitope. In the first analysis, we treated the families as
units without further modification except when affected individuals within a family were discordant for an exposure, in
which case we created pairs of discordant relatives. To assess
significance, we compared the evidence of linkage after dividing the families into strata with the evidence of linkage that we
obtained using the same family structures but without dividing
the families into strata. After multiplying the difference in
LOD scores for the 2 analyses by 4.6, the scores so obtained
approximately followed a chi-square distribution with 2 df.
Thus, we obtained P values for this test by comparison with
a chi-square distribution. However, because the family sizes
for the stratified analyses varied for this analysis, the mean LOD
scores per family are not meaningful. For descriptive purposes,
we therefore divided the families into all possible pairs of affected
relatives and then presented the mean LOD scores per family
among the strata. Finally, we compared linkage results for the 2
quantitative traits with nonparametric linkage results for RA
defined qualitatively among the same group of families, using the
nonparametric linkage statistic in Merlin.
RESULTS
Figure 1. Distribution of IgM rheumatoid factor (RF) and anti–cyclic
citrullinated peptide (anti-CCP) autoantibody titers in 1,002 rheumatoid arthritis patients from 491 multiplex (sibpair) families.
shared epitope positivity. Linkage analysis incorporating the
significant covariates for each trait was performed using
covariate-adjusted trait residuals in Merlin (deviates option).
Table 1 summarizes demographic and clinical
characteristics of the 1,002 affected individuals. The
majority of subjects were Caucasian, and 77% were
female. The mean age at RA diagnosis was relatively
young, and most patients had well-established disease
at study entry. The majority of affected individuals
had evidence of erosions, as expected based on our
Table 2. Genomic regions with LOD scores of ⬎1.0 based on quantitative linkage analysis of anti-CCP
and IgM-RF titers among 491 multiplex RA families*
Anti-CCP
Chromosome (cM)
1
1
5
5
6
8
9
10
16
16
18
(136.9)
(254.6)†
(22.9)†
(105.3)
(47.7)
(8.3)
(44.3)†
(75.6)†
(43.9)
(100.4)
(80.4)
IgM-RF
Best marker
LOD
P
LOD
P
D1S1631
D1S235
D5S817
D5S1462
D6S1629
D8S277
D9S1121
D10S1221
D16S403
D16S516
D18S858
1.37
–
–
1.23
14.02
1.68
1.41
1.1
1.97
1.98
1.71
0.006
–
–
0.009
9.99 ⫻ 10⫺16
0.003
0.005
0.012
0.0013
0.0013
0.003
2.18
1.06
1.21
1.47
12.09
1.3
–
–
1.42
1.19
1.34
0.0008
0.014
0.009
0.005
8.82 ⫻ 10⫺14
0.007
–
–
0.005
0.01
0.007
* See Table 1 for other definitions.
† Genomic regions associated with (i.e., logarithm of odds [LOD] score ⬎1.0) 1 but not both of the
quantitative traits.
62
eligibility criteria. Eighty-four percent of subjects were
HLA–DRB1 shared epitope positive. Table 1 also
shows the mean ⫾ SD values for the 2 quantitative
traits, IgM-RF and anti-CCP autoantibody titers. The
distribution of RF and anti-CCP titers among the
study population revealed striking variation and a nonnormal distribution of trait values. The median trait
values were 83 for IgM-RF and 113 for anti-CCP titer.
The skewness and kurtosis coefficients were 4.97 and
33.03, respectively, for RF titer and 0.07 and ⫺1.31,
respectively, for anti-CCP, reflecting the fact that some
individuals had very high RF titers (positive skewing
and kurtosis). The distributions of IgM-RF and antiCCP titers are shown in Figure 1. We also assessed
skewness and kurtosis of the residuals following adjustments and checked for evidence of influence. For
anti-CCP titer, no Cook’s D observations were near a
critical value of 1.0 and the largest value was 0.01,
providing evidence against excess influence from outliers in the regression analyses. For RF titer, the largest
Cook’s D value was 0.11, suggesting some potential
excess influence.
We used the ACT program (http://www.epigenetic.
org/Linkage/act.html) (37) to estimate the heritability
of these 2 quantitative traits. The results of these estimates showed 67% heritability for anti-CCP titer and
14% for RF titer. Although it is important to keep in
mind that the ascertainment scheme may have influenced these results (i.e., all individuals examined had
RA), the relatively higher heritability estimate for antiCCP versus RF titer suggests that anti-CCP titer is
more strongly influenced by genetic factors, whereas
RF titer might be more strongly influenced by environmental or other factors. After adjusting for covariates,
the heritability of anti-CCP titer converged to 1.0, while
the heritability estimate for RF titer was 37%.
Table 2 summarizes the genomic regions with
the strongest evidence of linkage to each of the quantitative traits (i.e., LOD ⬎1.0), including the name and
position of the best marker in each linkage region. These
results do not include adjustment for covariates. For
each of the quantitative traits, evidence of linkage, with
LOD scores ⬎1.0, was found in 9 regions. The marker
that showed the strongest evidence of linkage was the
same for both traits, i.e., D6S1629 in the major histocompatibility complex on chromosome 6p (anti-CCP
LOD 14.02, P ⬍ 10⫺15; RF LOD 12.09, P ⬍ 10⫺13).
Six other regions with LOD scores of ⬎1.0 overlapped
between the 2 traits, on chromosomes 1 (D1S1631), 5
(D5S1462), 8 (D8S277), 16 (D16S403, D16S516), and
18 (D18S858). Evidence of linkage to anti-CCP but not
CRISWELL ET AL
Figure 2. Plot of the empirical and asymptotic ⫺log10 P values
associated with various logarithm of odds scores for each of the
quantitative traits. See Figure 1 for definitions.
RF titer was found in 2 regions (chromosomes 9
[D9S1121] and 10 [D10S1221]), and evidence of linkage
to RF but not anti-CCP titer was found in 2 regions
(chromosomes 5p [D5S817] and 1q [D1S235]).
Simulation studies of the linkage results for
chromosome 6p were consistent with very strong evidence of linkage and also indicated that the asymptotic
P values conformed well to the empirical P values
(Figure 2), except that for anti-CCP the asymptotic
RA QUANTITATIVE TRAIT LINKAGE ANALYSIS
63
Figure 3. Results of linkage analysis for 2 quantitative traits, IgM-RF titer (Œ) and anti-CCP
titer (■), and for rheumatoid arthritis defined qualitatively (}). See Figure 1 for definitions.
significance level for P values ⬍1 ⫻ 10⫺5 appeared to
be underestimated. We therefore report asymptotic P
values.
Comparison of unadjusted linkage results for
both quantitative traits with results of nonparametric
linkage analysis of RA defined qualitatively (i.e., the
presence of RA) revealed generally similar regions with
evidence of linkage. However, the strength of evidence
of linkage varied across the 3 traits, as illustrated in
Figure 3, which displays the results of linkage analysis
for all 3 traits, for the 8 chromosomes with LOD scores
⬎1.0 for at least 1 of the quantitative traits. For example,
the evidence of linkage on chromosome 1 was strongest
for RF titer, whereas the linkage evidence on chromosome 18 was strongest for anti-CCP titer, and the linkage
evidence on chromosome 16 was strongest for RA
defined qualitatively.
Several covariates were associated with the quantitative traits examined. Specifically, male sex was significantly associated with both quantitative traits (P ⬍
0.0001 for anti-CCP titer and P ⫽ 0.020 for RF titer),
whereas disease duration and exposure to tobacco
64
CRISWELL ET AL
Table 3. Differences in LOD score in linkage analyses of IgM-RF
and anti-CCP titers after adjustment for covariates*
Change in LOD
Chromosome (cM)
3
5
5
16
1
6
(22.33)
(22.88)
(105.29)
(43.89)
(136.88)
(47.71)
Marker
IgM-RF titer
Anti-CCP titer
D3S1304
D5S817
D5S1642
D16S403
D1S1631
D6S1629
1.90
⫺1.19
⫺1.29
⫺1.38
⫺2.15
⫺9.36
–
–
–
–
–
⫺5.38
* Covariates for RF titer analysis were sex, disease duration, and
exposure to tobacco smoke. Covariates for anti-CCP titer analysis were
sex and HLA–DRB1 shared epitope positivity. LOD ⫽ logarithm of
odds (see Table 1 for other definitions).
smoke (at the time of autoantibody testing) were significantly associated with RF titer (P ⫽ 0.021 for disease
duration and P ⫽ 0.0007 for tobacco exposure) but not
anti-CCP titer. The HLA–DRB1 shared epitope was
significantly associated with anti-CCP titer (P ⬍ 0.0001)
but not RF titer.
Based on these results, we repeated the linkage
analysis for anti-CCP titer after adjusting for sex and
HLA–DRB1 shared epitope positivity (see Patients and
Methods for definition of shared epitope alleles). Similarly, we repeated the linkage analysis for RF titer after
adjusting for sex, disease duration, and exposure to
tobacco smoke. Results of linkage analyses that substantially changed the LOD scores for either RF or anti-CCP
are presented in Table 3 and in Figure 4, which shows
results obtained before and after adjustments for covariates. Covariate adjustments influenced the results of
linkage analysis for both traits, but particularly RF titer.
For example, adjustment of RF titer for sex, disease
duration, and exposure to tobacco smoke revealed 1
region with a 1.90 increase in the LOD score (on
chromosome 3 [D3S1304]) compared with the unadjusted analysis. Five regions demonstrated a decrease of
⬎1.0 in the LOD score (range 1.19–9.36 decrease) in the
covariate-adjusted linkage analyses. Most striking was
the 9.36 decrease in LOD score for chromosome 6
(marker D6S1629). Of note, this analysis did not include
HLA–DRB1 shared epitope positivity as a covariate,
Figure 4. Results of linkage analysis for anti-CCP titer on chromosome 6 (top left), for RF titer on chromosome 6 (bottom left), and for RF titer
on chromosomes 1, 3, 5, and 16 before adjustment (■) and after adjustment for covariates (Œ). Covariates for RF titer analysis were sex, disease
duration, and exposure to tobacco smoke. Covariates for anti-CCP titer analysis were sex and HLA–DRB1 shared epitope positivity. See Figure 1
for definitions.
RA QUANTITATIVE TRAIT LINKAGE ANALYSIS
65
since it was not significantly associated with RF titer in
the crude analyses.
Adjustment of anti-CCP titer for sex and DRB1
shared epitope positivity revealed 1 region with a 5.38
decrease in LOD score, on chromosome 6 (from 14.02 to
8.64 at marker D6S1629). It was of interest that the peak
LOD score on chromosome 6 was high (8.64) even after
adjusting for DRB1 shared epitope positivity and sex,
suggesting substantial additional genetic contribution to
anti-CCP titer in this region. Further, the decrease in
LOD score was greater with adjustment for all shared
epitope–positive alleles compared with adjustment for
only the subgroup of DR4⫹ shared epitope alleles
(decrease of 4.09, from 14.02 to 9.93), providing evidence of the importance of this group of alleles (versus
individual DRB1 alleles) in relation to anti-CCP titers.
Overall, the changes in linkage results after adjustment
for specific covariates suggest possible interaction between a gene or genes in these regions and the specific
covariates analyzed, or that covariate effects explain part
of the linkage signal.
In order to evaluate further the impact of specific
covariates on the linkage results for these quantitative
traits, we performed stratified analyses, defined by specific covariates, on the results shown in Table 3 and
Figure 4. Table 4 shows results for chromosome 6, from
analyses of sex strata that were formed by organizing the
data into pairs of individuals and then treating these as
independent pairs. Similarly, smoking status strata and
duration of disease strata (using the median value as cut
point) in relation to the RF titer were analyzed for
chromosomes 1, 3, 5, 6, and 16, and HLA–DRB1 shared
epitope strata in relation to anti-CCP titer were analyzed
for chromosome 6.
Results of these stratified analyses indicated that
sex had a striking impact on the linkage results for both
quantitative traits on chromosome 6. Table 4 shows the
mean LOD score per sibpair by sex stratum, in the
anti-CCP and RF titer linkage analyses. The sex of the
sibling pair had a dramatic impact on the strength of the
linkage signal on chromosome 6p. For example, the
per-pair LOD score associated with anti-CCP was 0.0235
for female/female pairs, 0.0558 for female/male pairs,
and 0.1058 for male/male pairs. There was also significant evidence that the sex of the pairs influences the
evidence of linkage (P ⫽ 0.0007 for anti-CCP titer and
P ⫽ 0.0012 for RF titer). Significance was assessed by
summing the LOD scores across strata and then comparing the summed LOD score with that obtained from
pooling the data. The finding suggests a sex–HLA region
interaction and may explain why adjustment of the
quantitative trait levels for the covariate sex resulted in
a substantial decrease in the observed LOD scores.
Analyses in which we did not divide the families into
sibling pairs showed similar results (P ⫽ 0.014 for
anti-CCP, P ⫽ 0.027 for RF).
On chromosome 16, we detected a significant
interaction with sex prior to division of the families into
pairs (P ⫽ 0.0043 for RF) and an effect that was not
significant after division into pairs (P ⫽ 0.080 for RF).
Results of stratifications according to the smoking status
and disease duration of the sibpair members did not
reveal significant heterogeneity for the analysis of RF
titer in relation to these chromosomes (1, 3, 5, 6, and 16),
suggesting that the covariate sex was of primary importance in the covariate-adjusted analyses.
Finally, results of stratification according to
shared epitope strata for the analysis of anti-CCP titer in
relation to chromosome 6 revealed striking differences
(P ⬍ 10⫺7), as expected based on the well-documented
importance of the HLA region to anti-CCP autoantibody production in RA. Further, although shared
epitope positivity was not significantly associated with
RF titer in univariate analyses, results of stratified
linkage analysis for RF titer in relation to chromosome
Table 4. Results of stratified linkage analyses of anti-CCP and IgM-RF titers on chromosome 6 according to sex strata*
Anti-CCP titer
IgM-RF titer
Stratum
No. of pairs
analyzed
Peak LOD
score†
Mean LOD score
per sibpair
No. of pairs
analyzed
Peak LOD
score†
Mean LOD score
per sibpair
Female/female
Female/male
Male/male
Pooled
340
293
45
678
7.99
16.36
4.76
25.95
0.0235
0.0558
0.1058
0.0383
340
295
45
680
7.25
15.83
3.71
23.88
0.0213
0.0537
0.0824
0.0355
* See Table 1 for other definitions.
† The location of the peak logarithm of odds (LOD) score for each stratum was at 44.7 cM (marker tumor necrosis factor a
microsatellite).
66
CRISWELL ET AL
6 revealed a significant effect of the HLA–DRB1 shared
epitope (P ⬍ 10⫺5).
DISCUSSION
Significant progress has been made during the
past 5 years in the identification of genomic regions
likely to contribute to RA etiology, primarily through
genome-wide linkage analyses. However, in view of the
clinical heterogeneity of the disorder, as well as evidence
that genetic differences explain at least part of this
variation (1), it is likely that analysis of more homogeneous subgroups will be required to fully unravel the
genetics of this complex disorder, as has been the case
for a number of other diseases, such as asthma and
diabetes (14,15,38).
Our goal for this study was to begin to dissect the
complexity of RA through linkage analysis of 2 quantitative RA-related traits, specifically serum levels of 2
autoantibodies. The availability of a large and wellcharacterized collection of RA families recruited by the
NARAC, in conjunction with recent developments in
methods of quantitative trait linkage analysis, has been
essential to our ability to perform the present study.
Comparison of linkage results for the 2 quantitative traits examined with results for RA defined qualitatively revealed similarity in regions where evidence of
linkage was found. However, there was variation in the
strength of evidence of linkage to these 3 phenotypes,
which presumably reflects the presence of certain genes
or genomic regions that primarily influence the production of 1 or both autoantibodies, and other genes or
regions that influence other RA disease processes.
The similarity of linkage results for the 2 quantitative traits (i.e., anti-CCP and RF titers) suggests the
existence of genomic regions that influence titers of both
autoantibodies. This phenomenon is known as pleiotropy. Although we sought to formally evaluate the
evidence of pleiotropy in the genomic regions exhibiting
the most striking similarities in linkage results for the 2
quantitative traits examined, we were unable to successfully perform these analyses given the characteristics of
our sample (i.e., the selection of RA patients resulted in
a sample of individuals with high levels of both traits)
and limitations of the currently available software programs. Further, despite the similarity of linkage findings
for RF and anti-CCP titers, the lower heritability and
high skewness of RF titers indicate that results of
analysis of RF titers are less reliable than results of
analysis of anti-CCP titers. Nonetheless, given the potential value of such analyses (for example, as a means of
identifying chromosomal regions that control autoantibody production in RA and possibly other autoimmune
diseases), this is an important area of future methodologic research.
It is interesting to note that the recently discovered autoimmunity gene PTPN22 is located within the
first peak on chromosome 1. A missense single-nucleotide polymorphism (SNP) in this gene is strongly associated with RF-positive RA and other autoimmune
diseases characterized by prominent autoantibody production (39–41). Our results indicated that the linkage
evidence in this region was strongest for RF titer.
However, the missense SNP alone does not explain the
linkage signal in this region, since the proportion of
variance of anti-CCP explained by PTPN22 R620W
genotypes was 0.02% for anti-CCP titer and 0.2% for RF
titer; thus, there are likely to be other genetic variants in
this region that also influence autoantibody production.
It is also of note that we failed to detect a strong
linkage signal on chromosome 1p in the region of the
PADI4 gene, which is known to influence citrullination
and has been shown to be a risk factor for RA in Asian
populations (42). One might have anticipated that antiCCP levels would show linkage in this region, and our
results suggest that variants of PADI4 that may influence
anti-CCP levels are uncommon in our study population.
Clearly, additional work will be needed to confirm our findings and to narrow linkage regions. Future
studies might involve higher-resolution genome screens
using dense panels of SNPs and/or genome screens in
other multiplex RA family cohorts. We have recently
completed a genome screen, using SNP markers, on
families in the NARAC cohort (10), which will provide a
valuable resource for refining the genomic regions that
most strongly influence these important RA-related
phenotypes. This work has important implications regarding our ability to identify genetic loci relevant to
specific etiologic processes and hence to elucidate distinct underlying mechanisms of disease.
We also sought to investigate genetic heterogeneity through incorporation of specific demographic, genetic, and environmental covariates in the linkage analyses. The association of specific covariates with these
quantitative traits, and their influence on the results of
linkage analysis, provide evidence of the genetic heterogeneity of the disorder, which should be pursued more
completely in future studies. These findings were the
most novel results of the present study and warrant
careful consideration in future genetic and epidemiologic studies of RA. Of particular interest was the
substantial impact of sex on the evidence of linkage to
RA QUANTITATIVE TRAIT LINKAGE ANALYSIS
RF titer in the HLA region on chromosome 6. Other
chromosomal regions also exhibited substantial changes
in linkage results for RF titer after adjustment for
covariates (see Table 3), suggesting that this trait may be
importantly influenced by nongenetic factors, perhaps as
a result of gene–environment interaction.
Prior research has also provided evidence of an
important role of male sex and environmental factors in
RF autoantibody production (43–45). The differences in
LOD score according to the sex of the sibpair members
observed for certain chromosomes, such as the HLA
region on chromosome 6p, are particularly striking and
suggest interaction between sex and genes within these
genomic regions. Also of interest was the residual evidence of linkage in the HLA region to anti-CCP titer
after adjustment for the HLA–DRB1 shared epitope,
which is considered to be the primary RA genetic
association in this region. Further, adjustment for
DR4⫹ shared epitope alleles had a smaller impact on
the linkage results compared with adjustment for the
entire group of shared epitope alleles, supporting the
notion that this entire group of alleles is important, at
least with regard to anti-CCP titers.
As mentioned above, the development of statistical methods for linkage analysis of quantitative traits is
an area of active investigation in the field of statistical
genetics. One of the challenges faced in the current
analysis relates to the sampling scheme for families.
Specifically, the selection of families with multiple affected siblings introduced additional complexity into our
analysis, since most methods have been developed based
on the assumption that samples will be obtained from
the general population. Due to this design feature we
investigated a number of different statistical tests and
performed simulation studies to evaluate significance
values for specific methods. Although we chose to use a
specific method that is robust with regard to this design
feature, it may also have influenced our ability to fully
investigate covariate effects and will require additional
methodologic work in future studies. The selection of
multicase families and other design features of the
NARAC collection also influenced the characteristics of
the study population, resulting in a group of patients
with younger age at RA onset and more severe disease
compared with RA patients in the general population.
Further, the majority of enrolled families were of Caucasian ancestry. Thus, it will be important in future studies
to evaluate the relevance of potential quantitative trait
loci in RA patients with different ethnic or clinical
characteristics, such as later-onset sporadic disease.
In summary, we have performed genome-wide
67
linkage analysis of 2 RA-related quantitative traits in
order to dissect the genetic contribution to this complex
disease. Given the clinical heterogeneity of RA, careful
analysis of quantitative traits may facilitate the identification of genes that contribute to this disease and shed
light on distinct underlying mechanisms. To the extent
that such pathways are associated with distinct outcomes
and responses to treatment, this information will guide
the development of more specific diagnostic and therapeutic tools. These results also highlight the potential
importance of specific covariates and may be useful in
identifying regions containing genes that interact with
sex or with specific environmental factors to influence
the risk of RA or specific disease manifestations.
ACKNOWLEDGMENTS
The authors wish to thank participants in the NARAC
family collection and the NARAC investigative team. We are
also grateful to the following individuals for their assistance
with genotyping for this project: Michael F. Seldin, Russell
Shigeta, Susan Dowbak, Xiangli Xiao, Joanita Monteiro, and
Dong Chen. Naila Ahmad and Kirsten Pfeiffer also provided
valuable assistance with this project. Gonzalo Abecasis provided considerable guidance in the use of Merlin.
AUTHOR CONTRIBUTIONS
Dr. Criswell had full access to all of the data in the study and
takes responsibility for the integrity of the data and the accuracy of the
data analysis.
Study design. Drs. Criswell, Wener, Gregersen, and Amos.
Acquisition of data. Drs. Criswell, Jawaheer, Wener, and Gregersen.
Analysis and interpretation of data. Dr. Criswell, Ms Chen, Mr. Lum,
and Drs. Wener, Gregersen, and Amos.
Manuscript preparation. Dr. Criswell, Ms Chen, and Drs. Jawaheer
and Amos.
Statistical analysis. Dr. Criswell, Ms Chen, Mr. Gu, and Dr. Amos.
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