568174

research-article2015

MSJ0010.1177/1352458514568174Multiple Sclerosis JournalK Hilven, NA Patsopoulos et al.

MULTIPLE SCLEROSIS MSJ JOURNAL

Original Research Paper

Burden of risk variants correlates with phenotype of multiple sclerosis Kelly Hilven, Nikolaos A Patsopoulos, Bénédicte Dubois and An Goris

Abstract Background: More than 100 common variants underlying multiple sclerosis (MS) susceptibility have been identified, but their effect on disease phenotype is still largely unknown. Objective: The objective of this paper is to assess whether the cumulative genetic risk score of currently known susceptibility variants affects clinical presentation. Methods: A cumulative genetic risk score was based on four human leukocyte antigen (HLA) and 106 non-HLA risk loci genotyped or imputed in 842 Belgian MS patients and 321 controls. Non-parametric analyses were applied. Results: An increased genetic risk is observed for MS patients, including subsets such as oligoclonal band-negative and primary progressive MS patients, compared to controls. Within the patient group, a stronger association between HLA risk variants and the presence of oligoclonal bands, an increased immunoglobulin G (IgG) index and female gender was apparent. Results suggest an association between a higher accumulation of non-HLA risk variants and increased relapse rate as well as shorter relapse-free intervals after disease onset. Conclusion: MS patients display a significantly increased genetic risk compared to controls, irrespective of disease course or presence of oligoclonal bands. Whereas the cumulative burden of non-HLA risk variants appears to be reflected in the relapses of MS patients, the HLA region influences intrathecal IgG levels. Keywords:  Multiple sclerosis, genetic risk, genetic association, disease course, oligoclonal bands, IgG index, relapse rate Date received: 6 August 2014; revised: 4 December 2014; accepted: 19 December 2014

Introduction Multiple sclerosis (MS) is a common neuroinflammatory disorder1 that mainly affects young adults and causes cognitive and physical impairment. Epidemiological studies have shown the importance of both genetic and environmental factors in the development of the disease. Following the association between the human leukocyte antigen (HLA) region and MS susceptibility discovered in the early 1970s, joint efforts during the last decade have led to enormous progress in the identification of additional genetic risk variants.2–4 Large-scale screens in international study populations, of which the Belgian cohort was a part, have identified four classical HLA and 110 non-HLA susceptibility loci.2–4 These variants point to an important role of the immune system in the pathogenesis of MS, with approximately

one-third of the associated genes having known roles in the immune system and one in five signals overlapping with at least one other autoimmune disease.3 These variants are common in the general population and, with the exception of the HLA region (e.g. HLADRB1*15:01: odds ratio (OR) = 3.1),2 individually exert modest effects (OR = 1.03–1.34).3 Since the identification of single polymorphisms has proven not to be informative to predict disease outcome, several studies have investigated the cumulative effect of combinations of identified risk variants.5–11 Despite results showing it is not applicable for case-control prediction,5,6,11 significant associations with clinical presentation have been suggested.8–10 In this study, we elaborate on these previous efforts by calculating MS risk based on the current extended list of MS risk variants2–4 and investigate the effect on MS phenotype.

Multiple Sclerosis Journal 1­–11 DOI: 10.1177/ 1352458514568174 © The Author(s), 2015. Reprints and permissions: http://www.sagepub.co.uk/ journalsPermissions.nav

Correspondence to: An Goris Laboratory for Neuroimmunology, Department of Neurosciences, Experimental Neurology, KU Leuven – University of Leuven, Herestraat 49 bus 1022, B-3000 Leuven, Belgium. [email protected] Kelly Hilven An Goris Laboratory for Neuroimmunology, Department of Neurosciences, Experimental Neurology, KU Leuven – University of Leuven, Belgium Nikolaos A Patsopoulos Department of Neurology, Brigham & Women’s Hospital, USA/Harvard Medical School, USA/Broad Institute, USA Bénédicte Dubois Laboratory for Neuroimmunology, Department of Neurosciences, Experimental Neurology, KU Leuven – University of Leuven, Belgium/Department of Neurology, University Hospitals Leuven, Belgium

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Multiple Sclerosis Journal  Table 1.  Clinical description of the study cohort. Clinical data

MS patients

N

Controls

N

Male:Female, % BOMS : PPMS : unknown, % AAO (y), median (IQR) MSSS, median (IQR) Relapse rate, median (IQR) Relapse-free interval (y), median (IQR) OCB positive : OCB negative, % OCB count, median (IQR) IgG index, median (IQR)

35.51:64.49 86.34:11.64:2.02 33 (25–41) 6.24 (2.6–8.5) 0.54 (0.21–1.12) 2 (0.83–5.08) 88.01:11.99 11 (5.75–15) 0.88 (0.65–1.32)

842 842 822 666 252 231 559 260 262

47.66:52.34 – – – – – – – –

321 – – – – – – – –

MS: multiple sclerosis; BOMS: bout onset MS; PPMS: primary progressive MS; AAO: age at onset; y: year; IQR: interquartile range; MSSS: MS severity score; OCB: oligoclonal band; IgG index: immunoglobulin G index; N: number of individuals.

Methods Sample recruitment The study cohort consisted of 842 unrelated MS patients and 321 control individuals of Caucasian descent. Patients, diagnosed based on Poser12 or McDonald13 criteria, were recruited from the University Hospitals Leuven and the National Multiple Sclerosis Center Melsbroek. Control individuals were spouses of neurological patients, recruited from the same population. The study was approved by the ethics committee of the University Hospitals Leuven and appropriate informed consent was obtained from all participants. Peripheral blood samples were taken and DNA was extracted according to standard methods. Clinical characteristics (gender, disease course, age at onset, severity, relapse rate, relapse-free interval after disease onset, oligoclonal band (OCB) status and count, immunoglobulin G (IgG) index) of the study cohort were collected and are presented in Table 1. The annualized baseline relapse rate for bout-onset MS (BOMS) patients was calculated by dividing the number of relapses that occurred before the start of any treatment by the time between the first relapse and the initiation of treatment. In order to obtain a representative relapse rate, only patients who were followed for more than three months before treatment were included (median follow-up time was 6.46 years, ranging from 0.26 to 57.14 years). The relapse-free interval for BOMS patients was determined by the time between the first and second relapse that occurred before the start of any treatment. Severity is measured by the Multiple Sclerosis Severity Score (MSSS). Patients were classified as OCB positive when at least two OCBs were present in the cerebrospinal fluid (CSF) that were not seen in the serum, otherwise they were classified as OCB negative. OCB count represents the number of

CSF-specific OCBs. The IgG index represents the ratio of IgG in the CSF compared to serum, corrected for the presence of albumin in both compartments.

Single-nucleotide polymorphism (SNP) genotyping and imputation Cases and controls were genotyped as part of previously published studies.2,3 In short, for all controls and 309 MS patients, 109/110 non-HLA variants were directly genotyped on the Immunochip microarray (Illumina), previously described in detail.3 For 538 MS patients genotyped on the Human660-Quad chip (Illumina) as part of the 2011 genome-wide association study (GWAS),2 53/110 SNPs were directly genotyped and all other risk variants were imputed with the 1000 Genomes Project European phase I (a) panel using BEAGLE14 as part of the replication phase of the Immunochip project.3 Five case samples were genotyped on both platforms and served as an internal control. Variants that were not genotyped or imputed in the GWAS dataset were accordingly replaced by the best proxy (r² > 0.98; Appendix 1) that was also present in the Immunochip dataset, in order to obtain a uniform set of SNPs for MS risk calculation. Based on the post-imputation genotype probabilities, genotypes were assigned if the probability was higher than 0.6, otherwise they were set to missing. SNPs not available for both platforms (rs2150702 in MLANA) or with genotyping call rate below 95% (rs2256814 in SLC2A-4RG, rs4976646 in RGS14, rs716719 (proxy of rs201847125; r² = 1) intergenic between C7orf72 and IKZF1) were excluded, leaving 106 non-HLA risk variants for MS risk calculation (Appendix 1). All variants passed Hardy-Weinberg equilibrium testing and total genotyping rate in all individuals was 99.83% for the remaining non-HLA SNPs.

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K Hilven, NA Patsopoulos et al. HLA genotypes were imputed with HLA*IMP15 as part of the original studies (previously described in detail).2,3 Again, genotypes were assigned when post-imputation genotype probabilities were above 0.6 and set to missing if probabilities were below this threshold. MS risk calculation MS risk calculation was based on four classical HLA loci2 and 106 of the 1103 known non-HLA susceptibility variants (Appendix 1). To calculate the cumulative genetic risk score, corrected odds ratio (ORC) values relative to the population lifetime odds for each genotype were determined as previously described in detail by Sawcer and Wason.16 The ORC was calculated by taking into account the frequencies of the risk allele B (frequency: p) and the alternate allele b (frequency: q = 1 – p). ORC for genotypes BB, Bb, bb is given by ORBB =

1 OR OR 2 = ; ORBb ; ORbb = D D D

where D = OR²p²+OR×2pq+q² and represents the OR between the odds of disease in the population as a whole and the odds of disease in the lowest risk genotype.16 OR and risk allele frequency (RAF) were obtained from the 2011 GWAS2 (HLA loci) or Immunochip study3 (non-HLA loci). RAF were based on the frequencies observed in the United Kingdom (UK) control cohort,2,3 since this was the largest and most closely related control population available. OR point estimates were obtained from these previous studies2,3 and were determined based on the entire study population. The cumulative MS risk score was calculated per sample by summing up the natural logarithm of the corrected OR values for each genotype for each of the four HLA and 106 non-HLA risk loci. To assess the effect of the HLA region, MS risk was calculated based on all risk variants combined or the HLA and non-HLA variants separately. 4

Cumulative MSrisk score =

∑ln(OR

C(HLA) ) n

n =1





106

+

∑ ln(OR

C(non-HLA) ) n

n =1

In the rare event that an individual’s genotype for a SNP was missing, the score for that SNP was set to zero. Next, the exponent of this summed score was multiplied by the risk of disease in all individuals

prior to genotyping, i.e. the population prevalence (0.001). This absolute risk was used to investigate the association with MS phenotype. Statistical analysis Statistical analysis was performed using R statistical software (version 2.15.2). Correlations between MS risk and phenotype were assessed by using the nonparametric Wilcoxon rank-sum test, Kruskal-Wallis chi-squared or Spearman’s rank correlation coefficient (linear regression). For the relapse-free interval after disease onset, additionally an extreme of outcome analysis using the log-rank test was performed, allowing the inclusion of censored untreated patients without a second relapse before the end of the follow-up period. P values are represented as pAll when non-HLA and HLA risk variants were combined and pnon-HLA or pHLA when MS risk was based solely on the non-HLA or HLA component, respectively. The significance threshold, corrected for multiple testing (45 tests), was set at 0.0011. Results The distribution of MS risk was significantly higher in MS cases compared to controls (Figure 1(a) and (b)), either when MS risk calculation was based on all risk variants combined or when non-HLA and HLA risk variants were regarded separately (p < 10−15) (Table 2). When including all risk variants, 3.68% of cases showed an absolute risk above 1%, compared to only 0.31% of controls. Next, we classified patients according to disease course and observed no difference in MS risk in patients with BOMS compared to primary progressive MS (PPMS) (Table 2; Figure 1(c) and 1(d)). Both groups of patients still presented with significantly higher genetic risk scores compared to the control group based on all risk variants (BOMS versus controls: pAll < 10−15; PPMS versus controls: pAll = 5.45×10−15) as well as non-HLA (BOMS versus controls: pnon-HLA < 10−15; PPMS versus controls: pnon-HLA = 4×10−9) or HLA risk variants alone (BOMS versus controls: pHLA < 10−15; PPMS versus controls: pHLA = 3.097×10−7). The presence of OCBs in the CSF of MS patients showed, compared to absence of OCBs, a trend for association with a higher MS risk score based on all known risk variants (pAll = 0.0050) (Figure 2(a); Table 2). This appears due in particular to the HLA region (pHLA = 0.012). Despite OCB-negative patients having a lower genetic risk compared to OCB-positive

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Multiple Sclerosis Journal 

Figure 1.  Distribution of MS risk according to disease course. (a) and (b) Histograms representing MS risk distribution in patients (gray) and control individuals (white) when (a) including all or (b) only non-HLA loci in MS risk calculation. MS patients have higher cumulative MS risk scores compared to controls. The population prevalence of disease (0.001) is indicated by the dotted vertical line. (c) and (d) Boxplots representing the MS risk in patients, classified according to disease course, and controls based on (c) all risk variants or (d) non-HLA alone. Non-parametric statistical testing confirmed that MS risk in bout onset and primary progressive MS patients did not differ, but both groups showed significantly higher MS risk scores compared to controls. P values from the Wilcoxon rank-sum test are reported. MS risk is plotted on a logarithmic axis. MS: multiple sclerosis; HLA: human leukocyte antigen; BOMS: bout-onset MS; PPMS: primary progressive MS; N: number of individuals.

patients, both groups still presented with a significantly heightened genetic burden compared to controls, either overall (OCB+ versus controls: pAll < 10−15; OCB– versus controls: pAll = 2.77×10−5), for non-HLA (OCB+ versus controls: pnon-HLA

Burden of risk variants correlates with phenotype of multiple sclerosis.

More than 100 common variants underlying multiple sclerosis (MS) susceptibility have been identified, but their effect on disease phenotype is still l...
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