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GENETIC RISK SCORE MAY IDENTIFY ANKYLOSING SPONDYLITIS EARLIER
EMBARGO LIFTS: Saturday, Oct. 20, 4:30 p.m. (CT)
Media Contact: Monica McDonald
Newsroom – (312) 949-8822
Office – (404) 633-3777 ext. 332 [email protected]
GENETIC RISK SCORE MAY IDENTIFY ANKYLOSING SPONDYLITIS EARLIER
Newswise — CHICAGO – Genetic risk score may be a clinically useful way to help identify ankylosing spondylitis far earlier, and at a lower cost, than current testing methods, according to new research findings presented this week at the 2018 ACR/ARHP Annual Meeting (Abstract #836).
Ankylosing spondylitis (AS) is a common cause of chronic back pain, and may also involve the skin, intestines and eyes. AS typically starts in the teens and 20s, and strikes males two to three times more often than females. It runs strongly in families, and most of the risk of developing the disease is genetic. Unfortunately, early diagnosis is a challenge.
“The impetus of this study was to develop a highly accurate method to assist in early AS diagnosis, or to identify if a subject is at high risk for developing AS,” said Zhixiu Li, PhD, Research Fellow, Queensland University of Technology Institute of Health and Biomedical Innovation, and the study’s co-author.
Li notes that the typical delay between the onset of AS symptoms and a diagnosis is eight to 10 years, and during this delay, patients often receive inappropriate treatment. Although evidence has shown that early diagnosis and intervention lead to better outcomes for AS patients, it remains a challenging and imperfect process even with tools like magnetic resonance imaging (MRI).
“In early disease, MRI imaging is the current gold standard for diagnosis, but it is very expensive, and many patients with early changes on an MRI scan don’t go on to get AS. Thus, we think genetic profiling may even be informative, particularly in early disease, but also potentially prior to onset of symptoms.”
Genetic risk score (GRS) is a new method that uses test results for up to thousands of genetic variants to better calculate the genetic susceptibility people have for a particular disease. Could GRS have enough discriminatory capacity and accuracy to predict AS risk earlier? A new study by researchers based at the Queensland University of Technology in Australia tested two GRS models to measure its potential usefulness as an early diagnostic tool in AS.
The researchers developed two polygenic GRS models for AS. The first was based on European-descent samples, and included 7,742 AS patients and 14,542 controls. The second was based on East Asian-descent samples, and included 6,001 AS patients and 4,943 controls. Each profile involved thousands of genetic variants. The researchers confirmed the performance of the risk score in European and East Asian populations, and in Turkish and Iranian cohorts. They tested discriminatory capacity by receiver operating characteristic analysis, reporting as “area under the curve” (AUC). If the AUC is 0.5 the test is no better “than a coin toss,” said Dr. Li. If it discriminates perfectly between those at risk and those not, the AUC is 1. Generally, tests with AUC less than 0.7-0.8 are thought to be clinically useful.
In the European GRS model, the AUC was 0.92, with 83 percent sensitivity and 92 percent specificity, compared with 0.87 using a test for HLA-B27 alone. The test of HLA-B27 status is a standard test for AS. In the East Asian GRS model, the AUC was 0.95, with 91 percent sensitivity and 95 percent specificity. Both GRS models showed moderate discriminatory capacity in the other ethnic groups (AUC 0.85). These figures compare with the AUC for MRI scans for patients with AS of 0.9, and costs far more than a genetic test. To put this into context, the predictive value of LDL cholesterol for myocardial infarction has been reported as 0.82.
The study’s results indicated that in AS, genetic risk score could be clinically useful as an early diagnostic tool.
“Our findings show that GRS has high discriminatory capacity and could be of clinical utility in early diagnosis at lower cost than MRI,” said Dr. Li. “In addition, AS GRS could also be applied to identify individuals with high risk of developing AS before major symptoms appear. It could also be applied to patients with arthritic symptoms, but without a clear diagnosis. We think we can improve the GRS further, and are working on optimizing it using different statistical approaches. We also are looking at how GRS performs when combined with other clinical features or test results to further improve the power of detecting AS patients. Even without these improvements, this test performs significantly better than the current genetic test used for AS, typing HLA-B27, yet it has a very similar cost. Therefore, I think it should be applied in the clinic now.”
This research was supported by supported by the NIH’s National Institute of Arthritis and Musculoskeletal and Skin Diseases.
About the ACR/ARHP Annual Meeting
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ABSTRACT NUMBER: 836
Genetic Risk Score Prediction in Ankylosing Spondylitis
Zhixiu Li1, Erika De Guzman1, Jessica Harris1, Nurullah Akkoc2, Mahdi Mahmoudi3, Maxime Breban4, Chung-Tei Chou5, Michael Weisman6, Lianne S. Gensler7, Michael Ward8, Mohammad H. Rahbar9, Laura A. Diekman10, Tae-Hwan Kim11, Paul Leo1, John D. Reveille10, Paul Wordsworth12, Matthew Brown1 and Huji Xu13, 1Translational Genomics Group, Institute of Health and Biomedical Innovation, Queensland University of Technology, Translational Research Institute, Brisbane, Australia, Brisbane, Australia, 2Rheumatology, İzmir, Turkey, İzmir, Turkey, 3Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran, Tehran, Iran (Islamic Republic of), 4Hôpital Ambroise Paré, Assistance Publique-Hôpitaux de Paris, Boulogne, France, Boulogne, France, 5Division of Allergy-Immunology-Rheumatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, Taipei, Taiwan, 6Cedars-Sinai Medical Center, Los Angeles, CA, USA, Los Angeles, CA, 7University of California San Francisco, San Francisco, CA, 8National Institutes of Health, Bethesda, MD, USA, Bethesda, MD, 9Biostatistics/Epidemiology/Research Design (BERD) Core | Center for Clinical and Translational Sciences, McGovern Medical School at the University of Texas Health Science Center at Houston, USA, Houston, TX, 10Rheumatology, McGovern Medical School at the University of Texas Health Science Center at Houston, USA, Houston, TX, 11Rheumatology, The Hospital for Rheumatic Diseases, Hanyang University, Seoul, Korea, Seoul, Korea, Republic of (South), 12Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, Oxford, United Kingdom, 13School of Clinical Medicine, Tsinghua University, Beijing, China, Beijing, China
Background/Purpose: The diagnosis of ankylosing spondylitis (AS) is delayed by on average 8–11 years after the onset of symptoms, and there is increasing evidence that early intervention in the disease can lead to better outcomes for patients. Early diagnosis of AS is challenging and the performance is still imperfect, even with magnetic resonance imaging (MRI). Whilst genetic risk scores (GRS) utilising only genomewide-significant associated SNPs capture only a small proportion of total heritability and consequently have modest discriminatory capacity, polygenic risk scores involving hundreds to many thousand SNPs capture a higher proportion of overall disease heritability and have greater discriminatory capacity and accuracy in disease-risk prediction.
Methods: In this study, we developed two polygenic GRS, one for AS based on European-descent samples (7,742 AS patients and 14,542 controls), and a second based on East Asian-descent samples (6,001 AS patients and 4,943 controls). AS was defined according to the Modified New York Criteria and all the samples were genotyped using Illumina CoreExome microarrays involving ~270,000 SNPs after quality control. The European-GRS involved 3,947 SNPs and East Asian-GRS 8,659 SNPs. Validation was performed using 10-fold cross-validation in the originator population, and also tested in Turkish (873 patients and 961 controls) and Iranian case-control cohorts (430 AS patients and 761 controls). Genetic risk scores were calculated using the adaptive MultiBLUP algorithm (Speed and Balding, Genome Res, 2014). Discriminatory capacity was tested by receiver operating characteristic analysis, and reported as area under the curve (AUC), with sensitivity and specificity reported at the best Matthews correlation coefficient.
Results: For the European GRS the AUC was 0.92 (83% sensitivity, 92% specificity), compared with 0.87 by using imputed HLA-B27 status alone (P=2.4×10-10 vs GRS). In the East Asian cohort, the AUC is 0.95 (91% sensitivity, 95% specificity). The risk score in AS patients is significantly higher than the score in controls in both cohort (p<2.2×10-15). Both models showed moderate discriminant capacity in other ethnics groups (European model AUC 0.85, 0.85 and 0.78 in the Turkish, Iranian and East Asian cohorts respectively; East Asian model AUC 0.84, 0.87 and 0.88 in the Turkish cohort, Iranian and European cohorts respectively).
Conclusion: Our results indicate that in AS GRS has high discriminatory capacity and could be of clinical utility in early diagnosis. Given that the AUC for MRI imaging is currently thought to be 0.90, this data suggests that genetic test scoring performs at least as well as the current gold standard imaging procedure. Given the cost of a SNP microarray is <$US50, this test is potentially far cheaper than MRI and less expensive than traditional HLA-B27 testing.
Disclosure: Z. Li, None; E. D. Guzman, None; J. Harris, None; N. Akkoc, None; M. Mahmoudi, None; M. Breban, None; C. T. Chou, None; M. Weisman, Lilly, Novartis, UCB, 2, 5; L. S. Gensler, None; M. Ward, None; M. H. Rahbar, None; L. A. Diekman, None; T. H. Kim, None; P. Leo, None; J. D. Reveille, None; P. Wordsworth, None; M. Brown, None; H. Xu, None.
To cite this abstract in AMA style:
Li Z, Guzman ED, Harris J, Akkoc N, Mahmoudi M, Breban M, Chou CT, Weisman M, Gensler LS, Ward M, Rahbar MH, Diekman LA, Kim TH, Leo P, Reveille JD, Wordsworth P, Brown M, Xu H. Genetic Risk Score Prediction in Ankylosing Spondylitis [abstract]. Arthritis Rheumatol. 2018; 70 (suppl 10). https://acrabstracts.org/abstract/genetic-risk-score-prediction-in-ankylosing-spondylitis/. Accessed September 21, 2018.
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