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Figure 1.
Distribution of HLA Genetic Burden (HLAGB) Scores
Distribution of HLA Genetic Burden (HLAGB) Scores

The HLAGB scores were plotted for the whole cohort of patients with multiple sclerosis (MS) (ie, relapsing-onset and progressive-onset MS) and the controls using box plots. Heavy horizontal lines in the boxes represent median values and the heights of the boxes show the interquartile range (IQR). The horizontal bars at the top and bottom edges of the whiskers represent the highest values within the 75th percentile values +1.5 × IQR and the lowest values within the 25th percentile values −1.5 × IQR. A Wilcoxon rank sum test P value was determined for each comparison. The patients with MS had a higher HLAGB than the controls, even when data sets were stratified by sex (P = 2.3 × 10−23 for women; P = 5.1 × 10−6 for men). No significant difference was observed for HLAGB scores by sexes in the control group (P = 1.4 × 10−1). Both disease courses had comparable HLAGB scores.

aP = 1.8 × 10−27 whole MS compared with controls.

bP = 7.5 × 10−27 relapsing-onset MS compared with controls.

cP = 6.1 × 10−4 progressive-onset MS compared with controls.

dP = 8.6 × 10−1 progressive-onset MS compared with relapsing-onset MS.

Figure 2.
Effect Size Comparison Between HLA-DRB1 Haplotypes on Subcortical Gray Matter Fraction
Effect Size Comparison Between HLA-DRB1 Haplotypes on Subcortical Gray Matter Fraction

Effect sizes of multiple sclerosis–associated HLA-DRB1 haplotypes on subcortical gray matter fraction. The error bars indicate 95% CI of the estimated effect sizes.

Table 1.  
Demographic Characteristics of Patients With MS
Demographic Characteristics of Patients With MS
Table 2.  
Association of HLA Alleles With MSa
Association of HLA Alleles With MSa
Table 3.  
Association Between HLAGB and Phenotypes in Patients With MS
Association Between HLAGB and Phenotypes in Patients With MS
1.
Hauser  SL, Goodin  DS. Multiple Sclerosis and other demyelinating diseases. In: Longo  DL, Fauci  AS, Kasper  DL, Hauser  SL, Jameson  JL, Loscalzo  J, eds.  Harrison’s Principles of Internal Medicine. 18th ed. New York, NY: McGraw Hill; 2012:3395-3409.
2.
Beecham  AH, Patsopoulos  NA, Xifara  DK,  et al; International Multiple Sclerosis Genetics Consortium (IMSGC); Wellcome Trust Case Control Consortium 2 (WTCCC2); International IBD Genetics Consortium (IIBDGC).  Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis.  Nat Genet. 2013;45(11):1353-1360.PubMedGoogle ScholarCrossref
3.
Dyment  DA, Herrera  BM, Cader  MZ,  et al.  Complex interactions among MHC haplotypes in multiple sclerosis: susceptibility and resistance.  Hum Mol Genet. 2005;14(14):2019-2026.PubMedGoogle ScholarCrossref
4.
Barcellos  LF, Sawcer  S, Ramsay  PP,  et al.  Heterogeneity at the HLA-DRB1 locus and risk for multiple sclerosis.  Hum Mol Genet. 2006;15(18):2813-2824.PubMedGoogle ScholarCrossref
5.
Moutsianas  L, Jostins  L, Beecham  AH,  et al; International IBD Genetics Consortium (IIBDGC); International Multiple Sclerosis Genetics Consortium.  Class II HLA interactions modulate genetic risk for multiple sclerosis.  Nat Genet. 2015;47(10):1107-1113.PubMedGoogle ScholarCrossref
6.
Masterman  T, Ligers  A, Olsson  T, Andersson  M, Olerup  O, Hillert  J.  HLA-DR15 is associated with lower age at onset in multiple sclerosis.  Ann Neurol. 2000;48(2):211-219.PubMedGoogle ScholarCrossref
7.
Celius  EG, Harbo  HF, Egeland  T, Vartdal  F, Vandvik  B, Spurkiand  A.  Sex and age at diagnosis are correlated with the HLA-DR2, DQ6 haplotype in multiple sclerosis.  J Neurol Sci. 2000;178(2):132-135.PubMedGoogle ScholarCrossref
8.
Hensiek  AE, Sawcer  SJ, Feakes  R,  et al.  HLA-DR 15 is associated with female sex and younger age at diagnosis in multiple sclerosis.  J Neurol Neurosurg Psychiatry. 2002;72(2):184-187.PubMedGoogle ScholarCrossref
9.
Smestad  C, Brynedal  B, Jonasdottir  G,  et al.  The impact of HLA-A and -DRB1 on age at onset, disease course and severity in Scandinavian multiple sclerosis patients.  Eur J Neurol. 2007;14(8):835-840.PubMedGoogle ScholarCrossref
10.
Okuda  DT, Srinivasan  R, Oksenberg  JR,  et al.  Genotype-phenotype correlations in multiple sclerosis: HLA genes influence disease severity inferred by 1HMR spectroscopy and MRI measures.  Brain. 2009;132(pt 1):250-259.PubMedGoogle Scholar
11.
Fusco  C, Andreone  V, Coppola  G,  et al.  HLA-DRB1*1501 and response to copolymer-1 therapy in relapsing-remitting multiple sclerosis.  Neurology. 2001;57(11):1976-1979.PubMedGoogle ScholarCrossref
12.
Tur  C, Ramagopalan  S, Altmann  DR,  et al.  HLA-DRB1*15 influences the development of brain tissue damage in early PPMS.  Neurology. 2014;83(19):1712-1718.PubMedGoogle ScholarCrossref
13.
Healy  BC, Liguori  M, Tran  D,  et al.  HLA B*44: protective effects in MS susceptibility and MRI outcome measures.  Neurology. 2010;75(7):634-640.PubMedGoogle ScholarCrossref
14.
Schlaeger  R, Papinutto  N, Panara  V,  et al.  Spinal cord gray matter atrophy correlates with multiple sclerosis disability.  Ann Neurol. 2014;76(4):568-580.PubMedGoogle ScholarCrossref
15.
Schlaeger  R, Papinutto  N, Zhu  AH,  et al.  Association between thoracic spinal cord gray matter atrophy and disability in multiple sclerosis.  JAMA Neurol. 2015;72(8):897-904.PubMedGoogle ScholarCrossref
16.
Gourraud  PA, Khankhanian  P, Cereb  N,  et al.  HLA diversity in the 1000 Genomes dataset.  PLoS One. 2014;9(7):e97282.PubMedGoogle ScholarCrossref
17.
Field  J, Browning  SR, Johnson  LJ,  et al; Australia and New Zealand Multiple Sclerosis Genetics Consortium.  A polymorphism in the HLA-DPB1 gene is associated with susceptibility to multiple sclerosis.  PLoS One. 2010;5(10):e13454.PubMedGoogle ScholarCrossref
18.
Friedman  J, Hastie  T, Tibshirani  R.  Regularization paths for generalized linear models via coordinate descent.  J Stat Softw. 2010;33(1):1-22.PubMedGoogle ScholarCrossref
19.
Pharoah  PD, Antoniou  AC, Easton  DF, Ponder  BA.  Polygenes, risk prediction, and targeted prevention of breast cancer.  N Engl J Med. 2008;358(26):2796-2803.PubMedGoogle ScholarCrossref
20.
De Jager  PL, Chibnik  LB, Cui  J,  et al; Steering committee of the BENEFIT study; Steering committee of the BEYOND study; Steering committee of the LTF study; Steering committee of the CCR1 study.  Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score.  Lancet Neurol. 2009;8(12):1111-1119.PubMedGoogle ScholarCrossref
21.
Wei  Z, Wang  K, Qu  HQ,  et al.  From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.  PLoS Genet. 2009;5(10):e1000678.PubMedGoogle ScholarCrossref
22.
Gourraud  PA, McElroy  JP, Caillier  SJ,  et al.  Aggregation of multiple sclerosis genetic risk variants in multiple and single case families.  Ann Neurol. 2011;69(1):65-74.PubMedGoogle ScholarCrossref
23.
Isobe  N, Damotte  V, Lo Re  V,  et al.  Genetic burden in multiple sclerosis families.  Genes Immun. 2013;14(7):434-440.PubMedGoogle ScholarCrossref
24.
Miller  DH, Leary  SM.  Primary-progressive multiple sclerosis.  Lancet Neurol. 2007;6(10):903-912.PubMedGoogle ScholarCrossref
25.
Kearney  H, Miller  DH, Ciccarelli  O.  Spinal cord MRI in multiple sclerosis—diagnostic, prognostic and clinical value.  Nat Rev Neurol. 2015;11(6):327-338.PubMedGoogle ScholarCrossref
26.
Sawcer  S, Hellenthal  G, Pirinen  M,  et al; International Multiple Sclerosis Genetics Consortium; Wellcome Trust Case Control Consortium 2.  Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.  Nature. 2011;476(7359):214-219.PubMedGoogle ScholarCrossref
27.
Harbo  HF, Isobe  N, Berg-Hansen  P,  et al.  Oligoclonal bands and age at onset correlate with genetic risk score in multiple sclerosis.  Mult Scler. 2014;20(6):660-668.PubMedGoogle ScholarCrossref
28.
Hilven  K, Patsopoulos  NA, Dubois  B, Goris  A.  Burden of risk variants correlates with phenotype of multiple sclerosis.  Mult Scler. 2015;21(13):1670-1680.PubMedGoogle ScholarCrossref
29.
Kikuchi  S, Fukazawa  T, Niino  M,  et al.  HLA-related subpopulations of MS in Japanese with and without oligoclonal IgG bands: human leukocyte antigen.  Neurology. 2003;60(4):647-651.PubMedGoogle ScholarCrossref
30.
Hollenbach  JA, Oksenberg  JR.  The immunogenetics of multiple sclerosis: a comprehensive review.  J Autoimmun. 2015;64:13-25.PubMedGoogle ScholarCrossref
31.
Mühlau  M, Buck  D, Förschler  A,  et al.  White-matter lesions drive deep gray-matter atrophy in early multiple sclerosis: support from structural MRI.  Mult Scler. 2013;19(11):1485-1492.PubMedGoogle ScholarCrossref
32.
Vercellino  M, Masera  S, Lorenzatti  M,  et al.  Demyelination, inflammation, and neurodegeneration in multiple sclerosis deep gray matter.  J Neuropathol Exp Neurol. 2009;68(5):489-502.PubMedGoogle ScholarCrossref
33.
Cohen  AB, Neema  M, Arora  A,  et al.  The relationships among MRI-defined spinal cord involvement, brain involvement, and disability in multiple sclerosis.  J Neuroimaging. 2012;22(2):122-128.PubMedGoogle ScholarCrossref
34.
Jovicich  J, Czanner  S, Greve  D,  et al.  Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data.  Neuroimage. 2006;30(2):436-443.PubMedGoogle ScholarCrossref
35.
Thompson  PM, Stein  JL, Medland  SE,  et al; Alzheimer’s Disease Neuroimaging Initiative, EPIGEN Consortium, IMAGEN Consortium, Saguenay Youth Study (SYS) Group.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data.  Brain Imaging Behav. 2014;8(2):153-182.PubMedGoogle Scholar
Original Investigation
July 2016

Association of HLA Genetic Risk Burden With Disease Phenotypes in Multiple Sclerosis

Author Affiliations
  • 1Department of Neurology, School of Medicine, University of California, San Francisco
  • 2Department of Neurology, University Hospital Basel, University of Basel, Basel, Switzerland
  • 3Biological and Medical Informatics, University of California, San Francisco
  • 4Institute of Human Genetics, University of California, San Francisco
  • 5Bioengineering Graduate Group, University of California, San Francisco and Berkeley
  • 6Department of Radiology and Biomedical Imaging, University of California, San Francisco
 

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Neurol. 2016;73(7):795-802. doi:10.1001/jamaneurol.2016.0980
Abstract

Importance  Although multiple HLA alleles associated with multiple sclerosis (MS) risk have been identified, genotype-phenotype studies in the HLA region remain scarce and inconclusive.

Objectives  To investigate whether MS risk-associated HLA alleles also affect disease phenotypes.

Design, Setting, and Participants  A cross-sectional, case-control study comprising 652 patients with MS who had comprehensive phenotypic information and 455 individuals of European origin serving as controls was conducted at a single academic research site. Patients evaluated at the Multiple Sclerosis Center at University of California, San Francisco between July 2004 and September 2005 were invited to participate. Spinal cord imaging in the data set was acquired between July 2013 and March 2014; analysis was performed between December 2014 and December 2015.

Main Outcomes and Measures  Cumulative HLA genetic burden (HLAGB) calculated using the most updated MS-associated HLA alleles vs clinical and magnetic resonance imaging outcomes, including age at onset, disease severity, conversion time from clinically isolated syndrome to clinically definite MS, fractions of cortical and subcortical gray matter and cerebral white matter, brain lesion volume, spinal cord gray and white matter areas, upper cervical cord area, and the ratio of gray matter to the upper cervical cord area. Multivariate modeling was applied separately for each sex data set.

Results  Of the 652 patients with MS, 586 had no missing genetic data and were included in the HLAGB analysis. In these 586 patients (404 women [68.9%]; mean [SD] age at disease onset, 33.6 [9.4] years), HLAGB was higher than in controls (median [IQR], 0.7 [0-1.4] and 0 [−0.3 to 0.5], respectively; P = 1.8 × 10−27). A total of 619 (95.8%) had relapsing-onset MS and 27 (4.2%) had progressive-onset MS. No significant difference was observed between relapsing-onset MS and primary progressive MS. A higher HLAGB was associated with younger age at onset and the atrophy of subcortical gray matter fraction in women with relapsing-onset MS (standard β = −1.20 × 10−1; P = 1.7 × 10−2 and standard β = −1.67 × 10−1; P = 2.3 × 10−4, respectively), which were driven mainly by the HLA-DRB1*15:01 haplotype. In addition, we observed the distinct role of the HLA-A*24:02-B*07:02-DRB1*15:01 haplotype among the other common DRB1*15:01 haplotypes and a nominally protective effect of HLA-B*44:02 to the subcortical gray atrophy (standard β = −1.28 × 10−1; P = 5.1 × 10−3 and standard β = 9.52 × 10−2; P = 3.6 × 10−2, respectively).

Conclusions and Relevance  We confirm and extend previous observations linking HLA MS susceptibility alleles with disease progression and specific clinical and magnetic resonance imaging phenotypic traits.

Introduction

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system and, in countries populated by northern Europeans and their descendants, a leading cause of progressive neurologic dysfunction in young adults.1 A large body of data confirms a multifactorial source, and a decade of large-scale genomic screens identified more than 100 genomic regions in which variants are associated with increased susceptibility.2 The strongest genome-wide susceptibility locus maps to the major histocompatibility complex (MHC) (6p21.3) accounting for approximately 10.5% of the genetic variance underlying the risk for MS. This pivotal association also reflects the complexity of MS risk inheritance by harboring multiple statistically independent allelic and haplotypic effects3,4 including 2 specific interactions involving pairs of class II alleles: DQA1*01:01-DRB1*15:01 and DQB1*03:01-DQB1*03:02.5

Clinical heterogeneity and variance in progression are well-recognized properties of MS. In addition to its influence on risk, HLA alleles, specifically the HLA-DRB1*15:01 allele, have been associated with discrete disease phenotypic traits, such as age at disease onset,6-10 response to immunomodulators,11 and brain magnetic resonance imaging (MRI) outcomes.10,12 For example, carrying HLA-DRB1*15:01 was associated with an increase in the white matter lesion volume and a reduction in normalized brain parenchymal volume.10 Conversely, another study13 showed that HLA-B*44:02, a protective allele for MS susceptibility, correlated with better MRI outcomes in terms of brain parenchymal fraction and T2 hyperintense lesion volume. However, the disease course dichotomy (relapsing-remitting vs primary progressive) appears to be independent of HLA variance.4,6 The observation that HLA haplotypes influence disease progression, whether assessed indirectly through age of onset or through clinical or radiologic findings, suggests that HLA gene products play a singular role in disease pathogenesis, in addition to and distinct from triggering the disease. However, genotype-phenotype studies in MS remain scarce and are typically inconclusive. We assessed, in a well-characterized cross-sectional data set, the association between HLA alleles, independently or collectively, and quantitative clinical and MRI measurements of brain and cervical cord deterioration.

Box Section Ref ID

Key Points

  • Question Do multiple sclerosis risk–associated HLA alleles also affect disease phenotypes?

  • Findings In this cross-sectional observational study comprising 586 well-phenotyped patients with multiple sclerosis, the cumulative HLA genetic risk burden was associated with younger age at onset and the atrophy of subcortical gray matter fraction in women.

  • Meaning In multiple sclerosis, HLA susceptibility alleles are linked to specific clinical and magnetic resonance imaging phenotypic traits.

Methods
Study Participants

This study comprised 652 patients with MS and 455 healthy individuals of European ancestry serving as controls evaluated at the University of California, San Francisco, Multiple Sclerosis Center. The inclusion and exclusion criteria were previously described.14 To assess disease progression, we used the Multiple Sclerosis Severity Score (range, 0 and 10, with 10 indicating the greatest severity), as a measure of clinical progression and an ordinal decile score that indicates how a patient’s Expanded Disability Status Scale score ranks in comparison with other patients with the same duration of disease. A cross-sectional data set was used in this study except for analysis of the conversion timing from clinically isolated syndrome to clinically definite MS, which used a longitudinally recorded data set. The Committee on Human Research at the University of California, San Francisco, approved the study protocol. Written informed consent was obtained from all participants. Participants did not receive financial compensation.

Image Acquisition

Brain MRI scans were acquired on the same 3-T scanner (Signa 3T; GE Healthcare), and pulse sequences and acquisition protocol were consistent among patients. Both T1- and T2-weighted images were used to determine MS lesion borders using semiautomated lesion segmentation software (Amira, version 5.4; Mercury Computer Systems). Lesion masks were created for each time point. The lesion-masked T1-weighted images were used to segment brain structures, such as cortical and subcortical gray matter volumes and cerebral white matter volumes, for volumetric analyses (FreeSurfer, http://surfer.nmr.mgh.harvard.edu/). Freesurfer volumes were corrected for misclassification of lesions as gray matter and reclassified to the white matter volume. Each volume was converted to its fraction of the estimated total intracranial volume. The MS lesion masks were also used to determine the T2 lesion volume. Three outliers of brain MRI factors were removed from further analysis. To assess the consistency of the findings at the baseline brain MRI, we also included the brain MRI data that were collected 1 year after the baseline visits.

For the cervical cord MRI data, the upper cervical cord area and spinal cord gray matter area were assessed for 130 patients at the intervertebral disk level of C2/C3 as described previously.14,15 The white matter area was obtained by subtracting the spinal cord gray matter area from the upper cervical cord area, and the ratio of spinal cord gray matter to the upper cervical cord area was also included in the analysis.

HLA Typing and Single-Nucleotide Polymorphism Genotyping

High-resolution HLA allele typing for HLA-A, HLA-B, HLA-DRB1, and HLA-DQB1 loci was conducted for 610 patients with MS and 455 controls of European origin by sequence-based typing technique.16 For rs9277565, a single-nucleotide polymorphism (SNP) tagging HLA-DPB1*03:01,5,17 samples were genotyped using predesigned TaqMan SNP genotyping assays, performed in 384-well plates (ABI 7900HT Sequence Detection System; Thermo Fisher Scientific) using SDS, version 2.3, software. All of the reported MS-associated HLA alleles and SNPs passed Hardy-Weinberg equilibrium tests at P > .01 in our control data set.

Computation of HLA Genetic Burden

Using the most updated list of established MS-associated HLA alleles,5 we calculated HLA genetic burden (HLAGB) for each study participant as the sum of the burden of each MS-associated HLA allele. Each allele burden was calculated as the allele dose multiplied by the reported allele effect size. The scores were compared between MS cases and controls using the Wilcoxon rank-sum test. Patients were also stratified into high or low HLAGB categories based on whether the scores were above or below the median value, respectively.

Statistical Analysis

To compare demographic features between groups, a t test was used for normally distributed variables and Wilcoxon rank-sum test was applied for the other numeric variables; the χ2 test or Fisher exact test was used for categorical factors. Sex predominance of HLA-DRB1*15:01 in MS was analyzed with a χ2 test. An additive model of logistic regression was applied to assess the association of the HLA alleles and SNP with MS. The association of genetic components against quantitative phenotypic markers of MS was assessed with a linear regression model. When the objective variables were not normally distributed, they were appropriately transformed. Correlation between the MRI data and genetic variables in each sex data set was investigated with disease duration and age at examination as candidate covariates. A stepwise covariate selection by minimizing the Akaike information criterion was conducted for each model to find the optimal covariate subsets, with the genetic variables always maintained within the model. Nominal significance in a 1-tailed test (P < .05) was applied as a threshold in the association test of the established HLA alleles for MS risk in our data set; otherwise, the thresholds for statistical significance were P < 2.50 × 10−2 for clinical outcomes and P < 1.25 × 10−2 for brain and cervical cord MRI outcomes. For the phenotypic variables that had nominally significant associations with HLAGB, HLA alleles with a frequency higher than 3% in our controls were explored to observe the effect of each HLA allele composing the HLAGB. To estimate the HLAGB contribution to MS heritability, Nagelkerke R2 values were obtained using the R package of fmsb. To overcome the possible issue of overfitting derived from a stepwise approach, we also applied a multiresponse lasso model that performs coefficient shrinkage and variable selection through penalized regression with the R package of glmnet. The optimal level of regularization was fit using cross validation and the 1 SE rule.18

To assess the effect of MS-associated haplotypes on disease phenotypes, haplotype estimation was conducted using the R package of haplo.stats. For the 5 HLA-DRB1*15:01 haplotypes with a frequency greater than 1.0% in the data set, the dose of each haplotype was calculated with consideration of each posterior probability. The Cochrane heterogeneity Q test was performed to test the effect size differences among HLA-DRB1*15:01 haplotypes on MS susceptibility and phenotypes using the R package of rmeta. Statistical analysis was conducted in JMP Pro, version 11.0.0 (http://www.jmp.com), and R, version 3.2.1 (http://www.r-project.org/).

Results
Demographic Features

Demographic features of the individuals enrolled in this study are reported in Table 1. A total of 619 patients (95.8%) had relapsing-onset MS and 27 (4.2%) had progressive-onset MS. There were no significant differences between women and men in age at disease onset as well as age and disease duration at examination. However, a higher proportion of progressive disease was observed in men (7.2%) vs in women (2.9%) (P = 2.1 × 10−2). Cumulative risk statistics are often used to quantify the collective effects of genome-wide disease susceptibility variants in single scores19-23 and, more recently, to represent the effect of the MHC locus.5 Of the 652 patients with MS, 586 had no missing genetic data (relapsing-onset, 563; progressive-onset, 23) and were included in the HLAGB analysis. In the present data set, there were trends for both higher HLAGB scores and a greater load of HLA-DRB1*15:01 alleles in women compared with men, suggesting that sex may be a confounder when assessing genetic effects on MS phenotypes. Differences in distribution of the HLA-DRB1*15 genotypes between men and women with MS have been previously observed and reported.7,8 Thus, to determine the effect of genetic factors including HLA-DRB1*15:01 in different sexes, we regressed the phenotypic variables separately for men and women.

Distribution of HLA Genetic Burden

The HLAGB scores were, as expected, higher in patients with MS (median [interquartile range (IQR)], 0.7 [0-1.4]) than in controls (median [IQR], 0 [−0.3 to 0.5]) (P = 1.8 × 10−27) (Figure 1), explaining 14.7% of MS risk in our data. Of 10 HLA alleles included in the HLAGB statistics, all but HLA-DRB1*08:01 and HLA-DQB1*03:02 had the same direction of association as recently reported5 and 4 were nominally associated with risk in a 1-tailed test (Table 2). When we compared the median (IQR) HLAGB scores between patients with relapsing-onset and progressive-onset MS, there was no statistically significant difference, which was in line with what has been reported before (0.7 [0-1.4] vs 0.6 [0.2-1.3], respectively; P = 8.6 × 10−1),22,23 although we acknowledge the limited sample size of the progressive-onset group. Nevertheless, to avoid additional confounding effects of disease progression,24,25 all further analyses focused on patients with the better-powered relapsing-onset course.

Effects of HLA on Clinical Factors

High HLAGB scores were associated with earlier age at onset in women (standard β = −1.20 × 10−1; P = 1.7 × 10−2) but not in men (standard β = 3.67 × 10−3; P = 9.6 × 10−1) (Table 3). When women were divided into 2 groups (binary: high vs low HLAGB), the contrast was more prominent (standard β = −1.32 × 10−1; P = 8.9 × 10−3). When the effect of each HLAGB-contributing allele on age at onset was assessed in women, HLA-DRB1*15:01 was the main contributor (with each copy of HLA-DRB1*15:01 associated with reduced age at onset by 1.78 years; P = 1.7 × 10−2), although the association did not remain after multiple test corrections (eTable 1 in the Supplement). There was no correlation between HLAGB and Multiple Sclerosis Severity Score. When survival analysis was conducted for the conversion timing from clinically isolated syndrome to clinically definite MS, the women with a high HLAGB converted to clinically definite MS faster than those with low HLAGB (P = 5.0 × 10−2) (eFigure 1 in the Supplement). The contrast was more significant in women with extreme HLAGB scores (in the top and bottom 20 percentiles) (P = 2.7 × 10−2). After removing HLA-DRB1*15:01 from the HLAGB, no statistically significant effect remained on the clinically isolated syndrome–relapsing-remitting conversion. Furthermore, we observed a modest HLA-DRB1*15:01 dose effect on a faster conversion (P = 5.5 × 10−2).

Effects of HLA on MRI Measurements

High HLAGB was nominally associated with reductions of subcortical gray matter fraction and cerebral white matter fraction in women (standard β = −1.67 × 10−1; P = 2.3 × 10−4 and standard β = −1.01 × 10−1; P = 3.5 × 10−2, respectively) (Table 3), with only the former remaining significant after correction for multiple comparisons; HLA-DRB1*15:01 was the most statistically significant contributor to this association (standard β = −1.35 × 10−1; P = 3.0 × 10−3) (eTable 1 in the Supplement). Concordant results were confirmed for MRI variables measured 1 year after the baseline visits (eTable 2 in the Supplement). The HLA-B*44:02 allele (frequencies of 4.4% in women with MS and 6.9% in women serving as controls) was also nominally linked to the conservation of the subcortical gray matter fraction (standard β = 9.52 × 10−2; P = 3.6 × 10−2) (eTable 1 in the Supplement), but only HLA-DRB1*15:01 correlated with the decrease of cerebral white matter fraction with near nominal significance (standard β = −9.05 × 10−2; P = 5.7 × 10−2). However, HLA-A*02:01, the most common protective allele for MS (carrier frequencies in women of 33.7% in the MS group and 43.8% in the control group), did not show any effect on brain MRI variables.

In the cervical cord MRI, although HLAGB had no correlation with each anatomical area measurement in both sexes, the ratio of gray matter area to the upper cervical cord area was lower in men with a higher HLAGB (standard β = −3.28 × 10−1; P = 2.8 × 10−2) (Table 3), which was derived primarily from the facilitating effect of HLA-DRB1*15:01 with nominal significance (standard β = −3.01 × 10−1; P = 4.5 × 10−2) (eTable 1 in the Supplement).

Finally, to overcome the possible issue of overfitting derived from the present stepwise covariate selection, we also assessed the association between HLAGB and MRI variables using a multiresponse lasso model. Although the impact of HLAGB was smaller than the effect of aging and disease duration, we were able to confirm the HLAGB influences on disease progression in women that were reflected in lesion load increase and brain atrophy (eFigure 2 and eTable 3 in the Supplement).

Haplotype Analysis for the Effect of HLA-DRB1*15:01

To complement the information attained using global burden statistics, account for linkage disequilibrium, and further assess the effect of HLA on disease progression, the common HLA-DRB1*15:01+ extended haplotypes were tested separately. All 5 haplotypes conferred risk for MS with nominal significance (eTable 4 in the Supplement) and no evidence of heterogeneity (P > 1.4 × 10−1). However, only the HLA-A*24:02-HLA-B*07:02-HLA-DRB1*15:01 haplotype was significantly associated with the shrinkage of subcortical gray matter fraction (standard β = −1.28 × 10−1; P = 5.1 × 10−3) (Figure 2 and eTable 5 in the Supplement). Surprisingly, the HLA-A*X-HLA-B*X-HLA-DRB1*15:01 haplotype, which included close to half of the HLA-DRB1*15:01 positive data set, did not have any correlation with the phenotype. This finding suggests that genetic elements other than HLA-DRB1 influence disease trajectory.

Discussion

Several studies addressed the role of HLA allelic isoforms on the MS phenotype, the most replicated being the association between HLA-DRB1*15:01 and earlier disease onset,6,8,10,26,27 followed by the predominance of female patients7,28 and the positivity of oligoclonal bands.28,29 The HLA alleles and SNPs included for analysis in the present study were originated from the most recent study of the International Multiple Sclerosis Genetics Consortium5 in which HLA alleles were statistically imputed from a large, high-density SNP data set.2 In our sequence-based data set, 2 of 10 HLAGB-composing genetic variants (HLA-DQB1*03:02 and HLA-DRB1*08:01) had an opposite direction of action from what was reported. This discordant observation can be owing to sample size differences, the relatively mild effect sizes of these variants, and, especially for HLA-DRB1*08:01, the limited SNP imputation accuracy in combination with its rare frequency.30

To perform this study, it was necessary to consider 2 distinct influences: the differential effect of MS susceptibility alleles according to sex and the effect of sex on MS phenotypes. To avoid overlooking the distinct genetic load between sexes and, given that the sex difference might not be able to be corrected in a multivariate linear regression model, we decided to analyze each sex separately. Not unexpectedly, the observed associations were mainly derived from the large group of women, except for the lower proportion of gray matter in the cervical cord area in men. The analysis combining data sets on both sexes did not strengthen the statistical significance of our findings or increase the power to detect additional correlations (eTable 6 in the Supplement), which confirms the existence of sex-specific genotype-phenotype interactions.

We report that (1) the cumulative HLAGB was higher in patients than in controls and explains approximately 15% of MS risk, which is in line with previous studies,2 (2) there was no HLAGB difference between relapsing-onset and primary progressive MS cases, (3) a higher HLAGB was associated with a younger age of onset, and (4) higher HLAGB drove the shrinkage of subcortical gray matter fraction and cortical white matter fraction in women and the ratio of cervical cord gray matter area to upper cervical cord area in men, all of which were mediated primarily by HLA-DRB1*15:01. Furthermore, the disease resistance HLA class I allele HLA-B*44:02 inhibited the decrease of subcortical gray matter fraction, although it did not pass multiple testing corrections. The protective role of HLA-B*44 on brain MRI variables, such as brain parenchymal fraction and T2 lesion volume, has been previously reported.13 Although the variables were not the same in that study and ours and the resolution of HLA typing differed, given the similarity of the studies’ sample sizes and the obtained P values, we could confirm, with a comparable strength of evidence, the protective role of HLA-B*44:02 on disease phenotype as well as disease susceptibility, at least in women. However, the HLA-A*02 disease-protective allele had no detectable influence on the phenotype in this data set. We also conducted a haplotype analysis anchored by 3 gene loci (HLA-A, HLA-B, and HLA-DRB1), which enabled us to assess the possible effect of other genes that locate in the extended haplotype and whether alleles in MHC class I and class II work together on disease susceptibility or phenotypes. Although we cannot avoid the effect of small sample size with each haplotype, the results suggest that a specific combination of HLA alleles or non-HLA variants on the HLA-A*24:02-B*07:02-DRB1*15:01 haplotype contributes to disease phenotypes.

In our study, the subcortical gray matter fraction was the only MS outcome that remained significantly associated with HLAGB after multiple corrections. Noteworthy similar results were obtained with MRI variables measured 1 year after the baseline visits. The strong link of subcortical gray matter fraction with HLAGB can be owing to its sensitivity to genetic susceptibility, its accuracy to reflect disease activity, or the robustness in measurement. Deep gray matter atrophy was previously reported31 to be correlated with cerebral white matter lesion volume, suggesting a causal link through axonal transection with subsequent degeneration along axonal projections. In addition, demyelination32 and iron deposition were suggested to be possible reasons for the shrinkage of the subcortical gray area. Furthermore, as previously reported,33 the link with genotypes was observed only for the brain MRI outcome, suggesting independent pathogenesis for the brain and spinal cord.

Conclusions

We observed a robust link between HLA and phenotypic traits in patients with MS, confirming that genetic susceptibility variants contribute to disease outcome. The contribution of MHC genetic variants to risk as well as to the phenotypes were in the same directions. Likewise, HLA-DRB1*15:01 is the main—but not sole—genetic driver of risk and progression of MS. Considering the conflicting reports of association between HLA and clinical traits, such as Multiple Sclerosis Severity Score, MRI variables represent more precise and objective markers to reflect disease outcome affected by genetic susceptibility variants. With the increasing availability of robust statistical approaches to minimize scanner- and sequence-related differences and harmonize multisite MRI volumetrics,34,35 the results also highlight the strong necessity to conduct genotype-phenotype studies within the framework of well-organized, large, multicenter collaborations.

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Article Information

Corresponding Author: Roland G. Henry, PhD, Department of Neurology, School of Medicine, University of California, 675 Nelson Rising Ln, Room 216, San Francisco, CA 94158 (roland.henry@ucsf.edu).

Accepted for Publication: March 9, 2016.

Published Online: May 31, 2016. doi:10.1001/jamaneurol.2016.0980.

Author Contributions: Drs Oksenberg and Henry contributed equally to the work. Drs Isobe and Henry had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Hauser, Oksenberg, Henry.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Isobe, Oksenberg, Henry.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Isobe, Gourraud, Lizée, Himmelstein, Hollenbach, Henry.

Obtained funding: Hauser, Oksenberg, Henry.

Administrative, technical, or material support: Keshavan, Zhu, Datta, Schlaeger, Caillier, Santaniello.

Study supervision: Hauser, Oksenberg, Henry.

Conflict of Interest Disclosures: Dr Cree has received compensation for consulting from AbbVie, Biogen, EMD Serono, MedImmune, Novartis, Genzyme and Teva. Dr Hauser currently serves on the scientific advisory board of Symbiotix, Annexon, Bionure, Neurona, and Molecular Stethoscope. No other disclosures were reported.

Funding/Support: This study was supported by grants RO1NS26799 (Drs Hauser and Oksenberg) and U19NS095774 (Dr Oksenberg) from the National Institutes of Health and PA02122 (Dr Baranzini) from a progressive MS alliance. Dr Isobe is a research fellow supported by the Japan Society for the Promotion of Science.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: Hourieh Mousavi, BSc, and Rosa Guerrero (University of California, San Francisco) assisted with sample processing and management. There was no financial compensation other than their salary. We thank the patients with multiple sclerosis and healthy controls who participated in this study.

References
1.
Hauser  SL, Goodin  DS. Multiple Sclerosis and other demyelinating diseases. In: Longo  DL, Fauci  AS, Kasper  DL, Hauser  SL, Jameson  JL, Loscalzo  J, eds.  Harrison’s Principles of Internal Medicine. 18th ed. New York, NY: McGraw Hill; 2012:3395-3409.
2.
Beecham  AH, Patsopoulos  NA, Xifara  DK,  et al; International Multiple Sclerosis Genetics Consortium (IMSGC); Wellcome Trust Case Control Consortium 2 (WTCCC2); International IBD Genetics Consortium (IIBDGC).  Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis.  Nat Genet. 2013;45(11):1353-1360.PubMedGoogle ScholarCrossref
3.
Dyment  DA, Herrera  BM, Cader  MZ,  et al.  Complex interactions among MHC haplotypes in multiple sclerosis: susceptibility and resistance.  Hum Mol Genet. 2005;14(14):2019-2026.PubMedGoogle ScholarCrossref
4.
Barcellos  LF, Sawcer  S, Ramsay  PP,  et al.  Heterogeneity at the HLA-DRB1 locus and risk for multiple sclerosis.  Hum Mol Genet. 2006;15(18):2813-2824.PubMedGoogle ScholarCrossref
5.
Moutsianas  L, Jostins  L, Beecham  AH,  et al; International IBD Genetics Consortium (IIBDGC); International Multiple Sclerosis Genetics Consortium.  Class II HLA interactions modulate genetic risk for multiple sclerosis.  Nat Genet. 2015;47(10):1107-1113.PubMedGoogle ScholarCrossref
6.
Masterman  T, Ligers  A, Olsson  T, Andersson  M, Olerup  O, Hillert  J.  HLA-DR15 is associated with lower age at onset in multiple sclerosis.  Ann Neurol. 2000;48(2):211-219.PubMedGoogle ScholarCrossref
7.
Celius  EG, Harbo  HF, Egeland  T, Vartdal  F, Vandvik  B, Spurkiand  A.  Sex and age at diagnosis are correlated with the HLA-DR2, DQ6 haplotype in multiple sclerosis.  J Neurol Sci. 2000;178(2):132-135.PubMedGoogle ScholarCrossref
8.
Hensiek  AE, Sawcer  SJ, Feakes  R,  et al.  HLA-DR 15 is associated with female sex and younger age at diagnosis in multiple sclerosis.  J Neurol Neurosurg Psychiatry. 2002;72(2):184-187.PubMedGoogle ScholarCrossref
9.
Smestad  C, Brynedal  B, Jonasdottir  G,  et al.  The impact of HLA-A and -DRB1 on age at onset, disease course and severity in Scandinavian multiple sclerosis patients.  Eur J Neurol. 2007;14(8):835-840.PubMedGoogle ScholarCrossref
10.
Okuda  DT, Srinivasan  R, Oksenberg  JR,  et al.  Genotype-phenotype correlations in multiple sclerosis: HLA genes influence disease severity inferred by 1HMR spectroscopy and MRI measures.  Brain. 2009;132(pt 1):250-259.PubMedGoogle Scholar
11.
Fusco  C, Andreone  V, Coppola  G,  et al.  HLA-DRB1*1501 and response to copolymer-1 therapy in relapsing-remitting multiple sclerosis.  Neurology. 2001;57(11):1976-1979.PubMedGoogle ScholarCrossref
12.
Tur  C, Ramagopalan  S, Altmann  DR,  et al.  HLA-DRB1*15 influences the development of brain tissue damage in early PPMS.  Neurology. 2014;83(19):1712-1718.PubMedGoogle ScholarCrossref
13.
Healy  BC, Liguori  M, Tran  D,  et al.  HLA B*44: protective effects in MS susceptibility and MRI outcome measures.  Neurology. 2010;75(7):634-640.PubMedGoogle ScholarCrossref
14.
Schlaeger  R, Papinutto  N, Panara  V,  et al.  Spinal cord gray matter atrophy correlates with multiple sclerosis disability.  Ann Neurol. 2014;76(4):568-580.PubMedGoogle ScholarCrossref
15.
Schlaeger  R, Papinutto  N, Zhu  AH,  et al.  Association between thoracic spinal cord gray matter atrophy and disability in multiple sclerosis.  JAMA Neurol. 2015;72(8):897-904.PubMedGoogle ScholarCrossref
16.
Gourraud  PA, Khankhanian  P, Cereb  N,  et al.  HLA diversity in the 1000 Genomes dataset.  PLoS One. 2014;9(7):e97282.PubMedGoogle ScholarCrossref
17.
Field  J, Browning  SR, Johnson  LJ,  et al; Australia and New Zealand Multiple Sclerosis Genetics Consortium.  A polymorphism in the HLA-DPB1 gene is associated with susceptibility to multiple sclerosis.  PLoS One. 2010;5(10):e13454.PubMedGoogle ScholarCrossref
18.
Friedman  J, Hastie  T, Tibshirani  R.  Regularization paths for generalized linear models via coordinate descent.  J Stat Softw. 2010;33(1):1-22.PubMedGoogle ScholarCrossref
19.
Pharoah  PD, Antoniou  AC, Easton  DF, Ponder  BA.  Polygenes, risk prediction, and targeted prevention of breast cancer.  N Engl J Med. 2008;358(26):2796-2803.PubMedGoogle ScholarCrossref
20.
De Jager  PL, Chibnik  LB, Cui  J,  et al; Steering committee of the BENEFIT study; Steering committee of the BEYOND study; Steering committee of the LTF study; Steering committee of the CCR1 study.  Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score.  Lancet Neurol. 2009;8(12):1111-1119.PubMedGoogle ScholarCrossref
21.
Wei  Z, Wang  K, Qu  HQ,  et al.  From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.  PLoS Genet. 2009;5(10):e1000678.PubMedGoogle ScholarCrossref
22.
Gourraud  PA, McElroy  JP, Caillier  SJ,  et al.  Aggregation of multiple sclerosis genetic risk variants in multiple and single case families.  Ann Neurol. 2011;69(1):65-74.PubMedGoogle ScholarCrossref
23.
Isobe  N, Damotte  V, Lo Re  V,  et al.  Genetic burden in multiple sclerosis families.  Genes Immun. 2013;14(7):434-440.PubMedGoogle ScholarCrossref
24.
Miller  DH, Leary  SM.  Primary-progressive multiple sclerosis.  Lancet Neurol. 2007;6(10):903-912.PubMedGoogle ScholarCrossref
25.
Kearney  H, Miller  DH, Ciccarelli  O.  Spinal cord MRI in multiple sclerosis—diagnostic, prognostic and clinical value.  Nat Rev Neurol. 2015;11(6):327-338.PubMedGoogle ScholarCrossref
26.
Sawcer  S, Hellenthal  G, Pirinen  M,  et al; International Multiple Sclerosis Genetics Consortium; Wellcome Trust Case Control Consortium 2.  Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.  Nature. 2011;476(7359):214-219.PubMedGoogle ScholarCrossref
27.
Harbo  HF, Isobe  N, Berg-Hansen  P,  et al.  Oligoclonal bands and age at onset correlate with genetic risk score in multiple sclerosis.  Mult Scler. 2014;20(6):660-668.PubMedGoogle ScholarCrossref
28.
Hilven  K, Patsopoulos  NA, Dubois  B, Goris  A.  Burden of risk variants correlates with phenotype of multiple sclerosis.  Mult Scler. 2015;21(13):1670-1680.PubMedGoogle ScholarCrossref
29.
Kikuchi  S, Fukazawa  T, Niino  M,  et al.  HLA-related subpopulations of MS in Japanese with and without oligoclonal IgG bands: human leukocyte antigen.  Neurology. 2003;60(4):647-651.PubMedGoogle ScholarCrossref
30.
Hollenbach  JA, Oksenberg  JR.  The immunogenetics of multiple sclerosis: a comprehensive review.  J Autoimmun. 2015;64:13-25.PubMedGoogle ScholarCrossref
31.
Mühlau  M, Buck  D, Förschler  A,  et al.  White-matter lesions drive deep gray-matter atrophy in early multiple sclerosis: support from structural MRI.  Mult Scler. 2013;19(11):1485-1492.PubMedGoogle ScholarCrossref
32.
Vercellino  M, Masera  S, Lorenzatti  M,  et al.  Demyelination, inflammation, and neurodegeneration in multiple sclerosis deep gray matter.  J Neuropathol Exp Neurol. 2009;68(5):489-502.PubMedGoogle ScholarCrossref
33.
Cohen  AB, Neema  M, Arora  A,  et al.  The relationships among MRI-defined spinal cord involvement, brain involvement, and disability in multiple sclerosis.  J Neuroimaging. 2012;22(2):122-128.PubMedGoogle ScholarCrossref
34.
Jovicich  J, Czanner  S, Greve  D,  et al.  Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data.  Neuroimage. 2006;30(2):436-443.PubMedGoogle ScholarCrossref
35.
Thompson  PM, Stein  JL, Medland  SE,  et al; Alzheimer’s Disease Neuroimaging Initiative, EPIGEN Consortium, IMAGEN Consortium, Saguenay Youth Study (SYS) Group.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data.  Brain Imaging Behav. 2014;8(2):153-182.PubMedGoogle Scholar
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