Ankylosing spondylitis (AS) is a chronic inflammatory disease with complex genetic traits. Multiple sequence variations have been associated with AS, but explained only a proportion of heritability. The studies herein aimed to explore potential associations between genomic copy number (CN) variation (CNV) and AS in Han Chinese. Five AS patients were examined with the high-density comparative genomic hybridization microarrays in the first screen test for AS-associated CNVs. A total of 533 AS patients and 792 unrelated controls were examined in confirmation studies with the AccuCopy assays. A significant association was observed between the CNV of HLA-DQA1 and that of AS. Compared with controls, AS patients showed an aberrant CN, and a significantly increased number of patients had more than two copies of HLA-DQA1. Therefore, the CNV of HLA-DQA1 may have an important role in susceptibility to AS in the Han Chinese population.
Ankylosing spondylitis (AS) is an immune-mediated complex disease with inflammation of the spine and extra-spinal sites such as the peripheral joints, entheses and eyes. Although its etiopathogenesis is not fully understood, genetic predisposition is strongly associated with AS. A spectrum of sequence variants including HLA-B27, ERAP1, RUNX3, IL23R, IL12B, PTGER4, CARD9, STAT3, TNFRSF1A, LTBR, TBKBP1, TRADD, ANTXR2 and IL1R21, 2, 3 have been associated with heritability of AS. The genetic contribution of whole risk alleles to AS based on genome-wide association studies was estimated as 25.39%, the majority of which comes from HLA-B27.1, 2
Recently, CNV in the human genome is increasingly recognized to also have important roles in trait heritability. CNV is another class of genetic variation that alters the quantity of the gene rather than the DNA sequence. Common CNVs include deletion, duplication and insertion that occur in ∼15% of the human genome.4 Specific CNVs may explain a proportion of the disease risk in addition to sequence variations, which has been reported in a number of human complex diseases, such as systemic lupus erythematosus,5 type 1 diabetes,6 human immunodeficiency virus acquisition and progression7 and Alzheimer’s disease.8
Like many other human complex diseases, sequence variations of genetic risk may explain only a proportion of trait heritability to AS. The aim of the studies herein was to explore specific CNVs that potentially contribute to genetic susceptibility to AS.
Characteristics of study subjects
The median and interquartile range (IQR) of age of all AS cases and controls were 32 years (IQR=16) and 60.5 years (IQR=17), respectively. In the first and second cohorts, they were 33 (IQR=18) and 32 (IQR=16) in cases, and 64 (IQR=11) and 58 (IQR=17) in controls, respectively.
Male to female ratios were 3.6/1 (77.3 vs 19.5%) in cases and 0.81/1 (44.7 vs 55.2%) in controls. In the first and second cohorts, there were 137/43 and 243/61 male/female cases, and 126/99 and 228/338 male/female controls, respectively.
The HLA-B27 allele was observed in 90.6% of AS cases. There is no significant association between HLA-B27 positivity and age, gender or CNVs of the tested genomic region.
Analysis of CNV and AS
The CNV studies using comparative genomic hybridization (CGH) microarrays showed aberrant copy number (CN) at 6p21.32 in the majority of AS patients. In particular, aberrant CN of a 42-kb fragment in the region containing HLA-DRB5 was seen in four of five AS patients, and that of a 12-kb fragment in the region containing HLA-DQA1 was seen in three of five AS patients. The CGH results for the CNV of the DRB5 and DQA1 regions are shown in Figure 1.
Specific probes were designed for validation assays with the AccuCopy technology. Two cohorts were examined in validation assays. In the first cohort (N=430: 205 cases, 225 controls) CN distributions of HLA-DQA1 alleles among the AS patients and controls were significantly different by χ2 test for trend proportions with P-values of 0.0033, but no significant association for HLA-DRB5 was observed (P=0.78). In particular, higher CN (⩾2) of the HLA-DQA1 allele were seen more in AS patients than in the controls (AS: CN<2=4.39%, CN=2=88.8% and CN>2=6.83% vs control: CN<2=12.9%, CN=2=84.4% and CN>2=2.67%) (Table 1). In the second cohort (N=895: 328 cases and 567 controls), a similar trend in proportions of the CN of HLA-DQA1 was observed (Table 1). In a combined analysis of both cohorts, χ2 test for trend proportions showed a significant difference between cases and controls (P=0.00016) (Table 1). A low CN (<2) of the HLA-DQA1 appeared less in AS (P=0.0016, odds ratio (OR)=0.42, 95% confidence interval (CI)=0.24–0.72). In contrast, a high CN (>2) may be a potential risk for AS (P=0.047, OR=1.64, 95% CI=1.0–2.72).
Such differences are displayed by the histograms, in which the distribution of CN of the HLA-DQA1 allele in AS patients is switched to the right side (CN>2) of that of controls, while the HLA-DRB5 shows an overlapped distribution of CN in cases and controls (Figure 2).
In considering the heterogeneity of gender in the study population, we further tested the associations between the CNV and AS using a mixed model for logistic regression analysis and a linear mixed model with gender as covariates. The results showed that AS association with the CN of the HLA-DQA1 allele became stronger (logistic regression analysis: P=6.99 × 10−8, OR=8.72, 95% CI=3.97–19.17; linear mixed model: P<10−8, β=1.5, 95% CI=1.3–1.74). The association between AS and the CN of the HLA-DRB5 allele remained insignificant. In addition, we performed analysis on male and female subgroups for the CN of HLA-DQA1’s association with AS. The results showed that in male and female subgroups the achieved P values were 4.75 × 10−5 (OR=6.55, 95% CI=2.65–16.19) and 1.88 × 10−4 (OR=19.77, 95% CI=4.13–94.6), respectively.
To examine the independency of the association of the HLA-DQA1 from HLA-B27, we performed an analysis of HLA-B27-negative cases and controls on the available data. The high CN of the HLA-DQA1 (>2) allele was observed in 5 of 28 B27-negative AS patients (17.9%) and in 1 of 43 B27-negative controls (2.3%) (P=0.022, OR=9.13, 95% CI=1.0–82.9).
In studies of genetic predisposition to AS, sequence variations have been extensively investigated, and from which multiple genetic susceptibility loci for AS have been identified.9, 10, 11 However, sequence variations may not fully cover the genetic contribution to AS. CNV is another significant source of human genetic variation and the attributed cause for disease and population diversity. Recent studies have indicated that CNVs of the HLA region are abundant in human genome.12, 13 Our studies demonstrated that aberrant CN of the HLA-DQA1 allele is strongly associated with AS, which provided first evidence that CNV may also have important roles in the heritability of AS.
HLA-DQA1 is a HLA class II gene encoding an alpha chain of the HLA-DQ molecule, along with a beta chain (HLA-DQB) to form a heterodimer anchored in the membrane of antigen-presenting cells. Like other HLA class II molecules, HLA-DQ plays a central role in immune response to foreign antigens by presenting specific antigenic peptides to T cells. Genetic variations including sequence and CN of HLA genes contribute to enhance the recognition repertoire of the immune system, as well as to a wide range of disease susceptibility. Specific alleles and gene dosage of the HLA-DQA1 have been associated with celiac disease14, 15, 16 and type 1 diabetes.16, 17, 18, 19 Although HLA class I region, especially HLA-B27, has been clearly defined as the major genetic factor to AS, the CNV of the HLA-DQA1 identified herein suggested that the HLA class II gene may also have important roles in disease pathogenesis.
Although the results are appealing and the statistical P-values greatly exceed random expectation, there are still several limitations presented in the studies. Only five patients were examined in the genome-wide CNV screen with CGH arrays, which would potentially miss other important genomic regions that have an association with AS, as well as might result in false positive results in the discovery phase. A typical example is that deletion of the HLA-DRB5 allele was observed more in AS patients in the CNV screen with CGH arrays. However, it was not confirmed in the validation studies, which suggests that interpretation based on a small number of cases screened by the CGH arrays should be cautious. The findings observed in the studies are limited to AS patients of the Chinese Han population. Further confirmation studies, especially in other ethnic populations, are necessary. In addition, biological functions of the CNV of the HLA-DQA1 have not been studied. Whether it impacts on presenting specific antigenic peptides to T cells, and induces or accelerates an immune response would be interesting to know.
Nonetheless, this is the first report based on studies of CNVs in AS. The results suggest that the CN of the HLA-DQA1 allele is strongly associated with susceptibility to AS in the Chinese Han population. Further replication and functional studies of this finding will be necessary.
Materials and methods
A total of 533 AS patients and 792 unrelated controls of Chinese Han were examined in the studies. AS patients and controls were enrolled from Shanghai and Taizhou cities in China. The first 205 patients were enrolled from 2010 to 2011, and were studied earlier as the first cohort. The remaining 328 patients were enrolled from 2012 to 2013, and were examined later as the second cohort. All patients met the modified New York diagnostic criteria for AS.20 Controls were selected on the basis of a history of immune-mediated diseases. All participants signed informed consent. The studies were approved by the Ethics Committees of both Fudan University and the University of Texas Medical School at Houston.
Genome-wide CN variation (CNV) analysis
Five AS patients were examined by using Agilent SurePrint G3 Human CGH Microarrays (1 × 1M) following the manufacturer’s protocol. Commercial genomic DNA (Promega, Madison, WI, USA) was used as the internal control. Briefly, genomic DNA of each subject was treated with restriction digestion, and then was labeled with ULS-Cy5 (for patients) and ULS-Cy3 (for sex-matched controls). The labeled products of one patient and one control were mixed and hybridized to the array for 40 h at 60 °C. Then, the array was washed and scanned on an Agilent Microarray Scanner. The data were extracted by Agilent Feature Extraction 10.7.3.1 and analyzed by Agilent Workbench 7.0. ADM-2 was used as statistical algorithm with a P-value threshold of 0.05. CN gains or losses of at least five consecutive oligomers on the array were selected for further analysis. The genome-wide common CNVs in human populations were adopted from the public data via the Database of Genomic Variants (http://projects.tcag.ca/variation/)21 and our private Chinese CNV data.22 Genes present in these common aberrations regions (referred to as CNV regions) were identified using the human genome browser at UCSC.23
AccuCopy technology for CNV validation
Two sets of primers were designed to examine the CNVs of two regions (HLA-DRB5 and HLA-DQA1) at 6p21.32 selected from the results of the CGH arrays. The primer sequences for these two regions are: 5′-IndexTermGAGCGGGTGCGGTTCCTGC-3′/5′-IndexTermACAGCGACGTGGGGGAGTAC-3′ and 5′-IndexTermGGTCACAGTGTTTTCCAAGTCTCCC-3′/5′-IndexTermTATGACTGCAAGGTGGAGCACTG-3′. For the HLA-DRB5 and HLA-DQA1 regions, the probes cover human chromosomes (genome assembly of UCSC hg19) chr6: 32489824–32489901 and chr6: 32609759–32609998, respectively.
A total of 205 AS cases and 225 controls were examined as the first cohort, and then 328 cases and 567 controls were examined as the second cohort for validation of specific CNVs identified from the array-based CGH studies with the AccuCopyTM assay following the manufacturer’s protocol.24 Briefly, the genomic DNA of each subject was mixed with fluorescence-labeled specific primers, PCR Master mix and a competitive DNA with known CN for a multiple competitive real-time PCR reaction. The PCR products were diluted, and were then loaded on an ABI 3730XL sequencer for quantification analysis. Raw data were analyzed by Gene Mapper 4.0. HG19 was used for the genome build for the genomic coordinates. The peak ratio between sample DNA and the corresponding competitive DNA (S/C) was calculated and then normalized to the median of four preset two-copy reference genes, respectively. Two normalized S/C ratios were further normalized to the median value in all samples for each reference gene and then averaged. The CN of each target fragment was determined by the average S/C ratio times two. Cases and controls were examined and read at the same time to minimize non-random errors.
HLA-B sequence typing
HLA-B genotyping was performed using the sequence-based typing method using SeCore Kits (Life Technologies, Grand Island, NY, USA). Briefly, allele-specific PCRs were performed using primers supplied in the SeCore kits, and were then followed by sequencing exon 2 and 3 of the HLA-B gene. The HLA sequence-based typing uTYPE 6.0 program (Life Technologies) was used in sequencing analysis and assigning HLA-B alleles.
Median and interquartile range (IQR) were used to describe the distribution of the epidemiological variables in the studies. The distributions of CNs between patients and controls after CN assignment according to the pre-defined threshold were compared using χ2 test for trend in proportions with R. Logistic regression models were constructed to determine the OR and 95% CI after adjusting for gender using SPSS (version 17.0, SPSS Inc., 2008). Thresholds for deletions and duplications were empirically set at <1.75 and >2.35, respectively, in the above CNV validation assays according to the manufacturer’s instruction.24 All samples were tested in duplicates.
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These studies were supported by research grants from the National Basic Research Program (2012CB944600), International S&T Cooperation Program of China (2013DFA30870), the Science and Technology Committee of Shanghai Municipality (11410701800, 11DJ1400102), Ministry of Science and Technology (2011BAI09B00), Ministry of Health (201002007) and the US NIH NIAID UO1, 1U01AI09090.
The authors declare no conflict of interest.
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