Literature DB >> 27194806

Analysis of Plasminogen Genetic Variants in Multiple Sclerosis Patients.

A Dessa Sadovnick1, Anthony L Traboulsee2, Cecily Q Bernales3, Jay P Ross3, Amanda L Forwell3, Irene M Yee3, Lena Guillot-Noel4, Bertrand Fontaine4, Isabelle Cournu-Rebeix4, Antonio Alcina5, Maria Fedetz6, Guillermo Izquierdo7, Fuencisla Matesanz5, Kelly Hilven8, Bénédicte Dubois9, An Goris8, Ianire Astobiza10, Iraide Alloza11, Alfredo Antigüedad12, Koen Vandenbroeck11, Denis A Akkad13, Orhan Aktas14, Paul Blaschke15, Mathias Buttmann16, Andrew Chan17, Joerg T Epplen13, Lisa-Ann Gerdes18, Antje Kroner19, Christian Kubisch20, Tania Kümpfel18, Peter Lohse21, Peter Rieckmann22, Uwe K Zettl15, Frauke Zipp23, Lars Bertram24, Christina M Lill25, Oscar Fernandez26, Patricia Urbaneja26, Laura Leyva27, Jose Carlos Alvarez-Cermeño28, Rafael Arroyo29, Aroa M Garagorri30, Angel García-Martínez30, Luisa M Villar28, Elena Urcelay30, Sunny Malhotra31, Xavier Montalban31, Manuel Comabella31, Thomas Berger32, Franz Fazekas33, Markus Reindl32, Mascha C Schmied34, Alexander Zimprich34, Carles Vilariño-Güell35.   

Abstract

Multiple sclerosis (MS) is a prevalent neurological disease of complex etiology. Here, we describe the characterization of a multi-incident MS family that nominated a rare missense variant (p.G420D) in plasminogen (PLG) as a putative genetic risk factor for MS. Genotyping of PLG p.G420D (rs139071351) in 2160 MS patients, and 886 controls from Canada, identified 10 additional probands, two sporadic patients and one control with the variant. Segregation in families harboring the rs139071351 variant, identified p.G420D in 26 out of 30 family members diagnosed with MS, 14 unaffected parents, and 12 out of 30 family members not diagnosed with disease. Despite considerably reduced penetrance, linkage analysis supports cosegregation of PLG p.G420D and disease. Genotyping of PLG p.G420D in 14446 patients, and 8797 controls from Canada, France, Spain, Germany, Belgium, and Austria failed to identify significant association with disease (P = 0.117), despite an overall higher prevalence in patients (OR = 1.32; 95% CI = 0.93-1.87). To assess whether additional rare variants have an effect on MS risk, we sequenced PLG in 293 probands, and genotyped all rare variants in cases and controls. This analysis identified nine rare missense variants, and although three of them were exclusively observed in MS patients, segregation does not support pathogenicity. PLG is a plausible biological candidate for MS owing to its involvement in immune system response, blood-brain barrier permeability, and myelin degradation. Moreover, components of its activation cascade have been shown to present increased activity or expression in MS patients compared to controls; further studies are needed to clarify whether PLG is involved in MS susceptibility.
Copyright © 2016 Sadovnick et al.

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Keywords:  association; genetics; linkage; multiple sclerosis; plasminogen

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Year:  2016        PMID: 27194806      PMCID: PMC4938660          DOI: 10.1534/g3.116.030841

Source DB:  PubMed          Journal:  G3 (Bethesda)        ISSN: 2160-1836            Impact factor:   3.154


Multiple sclerosis (MS) is a chronic inflammatory demyelinating and neurodegenerative disease of the central nervous system. A genetic contribution to disease susceptibility has been demonstrated in family and twin studies (Ebers ; Sadovnick 1993; Fagnani ), and the first pathogenic mutation for MS has been recently identified in NR1H3 (Wang ). In addition, a large number genetic risk factors, related primarily to the immune system, have already been identified through association studies (Beecham ; Sawcer ). However, with the exception of HLA-DRB1, all associated variants have a minor effect on overall disease susceptibility. The identification of genetic components of major effect on disease development is paramount for the generation of physiologically relevant cellular and animal models of human disease, and the generation of treatment strategies that address the underlying biological mechanisms responsible for the onset of MS.

Materials and Methods

Participants

A total of 2160 MS patients and 886 unrelated healthy controls from Canada, which includes 1857 multi-incident families, collected through the Canadian Collaborative Project on the Genetic Susceptibility to Multiple Sclerosis (CCPGSMS), were included in this study (Sadovnick ). Five independent European cohorts consisting of 2391 MS patients and 672 healthy controls from France, 4288 patients and 4018 controls from Spain, 3733 patients and 2722 controls from Germany, 1006 patients and 504 controls from Belgium, and 925 patients from Austria, were used for replication. All patients were diagnosed with MS according to published criteria (Poser ; McDonald ; Polman ), and the demographics for each cohort are presented in Table 1. The ethical review board at each institution approved the study, and all participants provided written informed consent.
Table 1

Logistic regression analysis for PLG p.G420D (rs139071351) and risk of MS

GroupGender M(%)Age (mean ± SD)Age at onset (mean ± SD)Genotypes (GA/GG)P-ValueOR (95% CI)
CanadaControls51.067.1 ± 9.81/8800.04610.19 (1.04–267.89)
MS patients26.946.7 ± 11.731.0 ± 9.712/2091
FranceControls39.139.3 ± 13.14/6680.0492.69 (1.00–9.37)
MS patients30.049.1 ± 11.430.5 ± 9.732/2359
SpainControls40.542.8 ± 12.834/39840.4751.20 (0.73–1.96)
MS patients34.844.5 ± 11.530.9 ± 9.842/4246
GermanyControls40.341.3 ± 16.811/27110.4761.31 (0.63–2.84)
MS patients29.240.5 ± 11.330.8 ± 10.321/3712
BelgiumControls47.256.2 ± 14.75/4990.7470.81 (0.23–3.04)
MS patients34.048.3 ± 13.133.3 ± 10.96/1000
AustriaMS patients29.849.2 ± 12.128.7 ± 9.17/918NANA
CombinedControls41.844.3 ± 15.955/87420.1171.32 (0.93–1.87)
MS patients31.045.1 ± 12.130.9 ± 9.9120/14326

M, male; OR, odds ratio; CI, confidence interval; NA, not applicable.

M, male; OR, odds ratio; CI, confidence interval; NA, not applicable.

Exome sequencing

We performed exome sequencing in three patients diagnosed with MS (pedigree A; II-1, II-4, and III-1) from a multi-incident family (Figure 1). Exonic regions were enriched using an Ion AmpliSeq exome kit (57.7 Mb), and sequenced in an Ion Proton sequencer (Life Technologies, Carlsbad, CA) with a minimum average coverage of 50 reads per base, and an average read length of 150 bases. The Ion Torrent Server v4 was used to map reads to NCBI Build 37.1 reference genome using the Torrent Mapping Alignment Program (TMAP), and to identify variants differing from the reference. Sequences with a mapping Phred quality score under 20, fewer than five reads, or over 95% strand bias were excluded from further analysis.
Figure 1

Simplified pedigrees for families presenting the PLG p.G420D variant. Males are represented by squares and females by circles, the proband is indicated with an arrow head. Patients diagnosed with MS have black filled symbols, and carriers of unknown clinical phenotype have gray filled symbols. Heterozygote carriers (M) and wild-type (wt) genotypes are indicated. An asterisk indicates an inferred carrier. Pedigree A was used for exome analysis, and, with the exception of pedigree E, which is of Asian descent, all families are of Caucasian ancestry.

Simplified pedigrees for families presenting the PLG p.G420D variant. Males are represented by squares and females by circles, the proband is indicated with an arrow head. Patients diagnosed with MS have black filled symbols, and carriers of unknown clinical phenotype have gray filled symbols. Heterozygote carriers (M) and wild-type (wt) genotypes are indicated. An asterisk indicates an inferred carrier. Pedigree A was used for exome analysis, and, with the exception of pedigree E, which is of Asian descent, all families are of Caucasian ancestry.

Sequencing, genotyping, and statistical analysis

Sanger sequencing was used to genotype amplicons containing exome variants of interest, and all 19 coding exons, and exon–intron boundaries, of plasminogen (PLG, NM_000301.3) by polymerase chain reaction (PCR) as previously described (Sadovnick ). Nine tagging SNPs (tSNPs) spanning a 61 kb region encompassing the PLG locus were selected based on HapMap data (version 3, release 27) using Haploview software (Barrett ). Selected tSNPs captured over 92% of the polymorphic variation in the region [minor allele frequency (MAF) > 5%, and r2 > 0.8] in Caucasian population standards. Genotyping of variants was performed using a combination of TaqMan probes and Sequenom MassArray iPLEX as previously described (Traboulsee ; Nishioka ). Genotyping success rate was over 99.4% for all variants, and without deviation from Hardy-Weinberg equilibrium expectation (p-value > 0.005). Statistical association was determined using logistic regression analysis adjusted for age and gender, in addition, the combined cohort analysis was adjusted for site. Genotypes were dichotomized as presence vs. absence of the minor allele (dominant model). The combined dataset was obtained by pooling samples from all populations. Segregation was quantified using nonparametric and parametric linkage analysis. Nonparametric linkage analysis was performed using SimWalk2 software (version 2.91), and NPL-All statistic (Sobel ). Two-point parametric logarithm of odds (LOD) scores were obtained with MLINK, assuming a dominant model, with a fully penetrant disease, and without phenocopies (Ott 1989). All MS patients were treated as affected, noncarrier individuals as healthy, and unaffected mutation carriers were treated as having an unknown disease status. The deleterious allele was defined with a 0.0001 frequency, and the marker-allele frequency was determined empirically from genotyped individuals.

Haplotype analysis

Microsatellite markers spanning the PLG locus between D6S1633 and D6S297 were chosen to define the disease-carrying haplotype (Supplemental Material, Table S1). All family members from those families identified with the PLG p.G420D mutation were genotyped. One primer for each pair was labeled with a fluorescent tag, and PCR reactions were performed under standard conditions. PCR products were run on an ABI 3730xl (Life Technologies, Carlsbad, CA), and analyzed using GeneMapper 4.0. Marker sizes were normalized to those reported in the CEPH database and manually phased within each family.

Data availability

The authors state that all data necessary for confirming the conclusions presented in the article are represented fully within the article.

Results

To identify genes and variants of major effect on MS susceptibility, we applied exome sequencing analysis to a multi-incident family consisting of 12 individuals over three generations, with DNA available for nine family members, including six diagnosed with MS (Figure 1A). Exome analysis of II-1, II-4, and III-1, identified 47479, 46545, and 46580 variants, respectively. Of those, 25 missense variants with a MAF below 1% from public and proprietary databases of variants were identified in all three individuals (Table S2). Segregation in additional family members identified 10 variants shared among at least five of the six family members diagnosed with MS for whom DNA was available, and no more than one of the two unaffected blood relatives. Three of these variants were subsequently excluded as they were identified at a frequency over 1% in 366 ethnically matched controls (Table S2). The seven remaining variants were genotyped in a multi-ethnic cohort consisting of 2160 MS patients and 886 unrelated healthy controls from Canada. Three variants [TGFBI, p.V608L (ss1467426521); SPINK13, p.C72R (ss1467426567); OR1E1, p.D96Y (ss1467426912)] appear to be private as they were not observed in any of the other samples genotyped in this study, and have not been described in public databases of variants (Abecasis ; Exome Aggregation Consortium ). ARHGAP10, p.T518K (rs375188932), with a reported MAF of 5 × 10−5 in the ExAC database, was also not observed in any additional samples. Segregation of these four variants within the exome sequenced family is provided in Figure S1. Of the remainder, SPATA18 p.P286L (rs150116592) was identified in two MS patients, UNC45B p.R776Q (rs34242925) was identified in one patient and one control, and PLG p.G420D (rs139071351) in 12 MS patients and one control. Segregation for variants identified in SPATA18 and UNC45B did not support cosegregation with disease in additional families, and were excluded from further analysis (Figure S1). Segregation of PLG p.G420D identified the variant in 26 out of 30 family members diagnosed with MS (87%), 14 parents of MS patients (including eight obligate carriers) not known to suffer from MS, and 12 out of 30 family members not diagnosed with disease (Figure 1, B–M). To quantifiably assess segregation, we performed nonparametric and parametric linkage analysis for PLG p.G420D. The more conservative nonparametric score resulted in a LOD score of 1.29, whereas parametric linkage analysis resulted in a maximum LOD score of 5.48 (θ = 0.05), despite a penetrance estimate of 50%. Additional support for a role in disease susceptibility is provided by the level of conservation for the glycine residue in mammals, indicating the importance of this amino acid for protein function (Figure 2). Haplotype analysis of PLG p.G420D carriers between D6S1633 and D6S297 did not identify a shared haplotype among families (Table S1), thus suggesting that PLG p.G420D is a mutational hotspot that has independently arisen in each family rather than being inherited from a common ancestor.
Figure 2

PLG variants and cross-species conservation. Protein orthologs were aligned via ClustalO. Amino acid positions for PLG variants are highlighted in black. Protein orthologs with amino acid positions differing from those of the human sequence are indicated in gray. RefSeq accession numbers: Homo sapiens NP_000292.1, Macaca mulatta NP_001036540.1, Mus musculus NP_032903.3, Rattus norvegicus NP_445943.1, Canis lupus familiaris NP_001273889.1, Sus scrofa NP_001038055.1, Bos taurus NP_776376.1, Myotis davidii ELK34830.1, Tarsius syrichta XP_008066085.1, Gallus gallus XP_419618.2, and Danio rerio AAH59801.1.

PLG variants and cross-species conservation. Protein orthologs were aligned via ClustalO. Amino acid positions for PLG variants are highlighted in black. Protein orthologs with amino acid positions differing from those of the human sequence are indicated in gray. RefSeq accession numbers: Homo sapiens NP_000292.1, Macaca mulatta NP_001036540.1, Mus musculus NP_032903.3, Rattus norvegicus NP_445943.1, Canis lupus familiaris NP_001273889.1, Sus scrofa NP_001038055.1, Bos taurus NP_776376.1, Myotis davidii ELK34830.1, Tarsius syrichta XP_008066085.1, Gallus gallus XP_419618.2, and Danio rerio AAH59801.1. Clinical details were available for 17 PLG p.G420D carriers, five males and 12 females (Table S3). The disease course observed in these carriers was predominantly consistent with relapsing-remitting MS, or secondary progressive MS, with only two patients presenting primary progressive MS. On average, the age at onset of disease was 35.1 years (SD ± 9.1), with a disease duration of 19.9 years (SD ± 10.4). Disease severity was overall relatively moderate, with an average expanded disability status scale (EDSS) score of 3.92 (SD ± 2.9) and a median of 2.75. Association analysis of PLG p.G420D was performed in Caucasian samples from Canada already genotyped for the identification of additional PLG p.G420D families. This subset consists of 2103 MS patients and 881 controls, and resulted in a marginally significant association with disease risk (P = 0.046), and an odds ratio (OR) of 10.19 (Table 1). In order to validate this association we genotyped PLG p.G420D in five independent cohorts from Europe consisting of 12343 MS patients and 7916 healthy controls. Logistic regression analysis corrected for age and gender identified a similarly marginal association with disease in the French cohort (P = 0.049; OR = 2.69), whereas no association was observed for any additional cohort (Table 1). Although the combined dataset did not result in a significant association with disease risk (P = 0.117), with the exception of Belgium which is the smallest set, all cohorts resulted in OR greater than 1, indicating a higher prevalence of PLG p.G420D in MS patients than controls. To assess whether common variants in PLG lead to an increased susceptibility to develop MS, we identified nine tSNPs spanning the entire PLG loci, and genotyped them in 2103 MS patients and 881 controls from Canada (Table S4). Association analysis failed to identify a significant association between any of the tSNPs and susceptibility to MS (P > 0.05). Since common variants in PLG do not appear to have an effect on MS disease risk, we assessed for the presence of additional rare PLG substitutions in MS patients. To this end, we sequenced all PLG-coding exons in 293 familial probands from Canada, which identified 11 silent and 11 missense variants (Supplementary Table S5). Of those, nine missense variants with a MAF below 1% in at least two of three publicly available databases (1000G, ExAC, or ESP) were genotyped in cases and controls from Canada (Abecasis ; Exome Aggregation Consortium ; Exome Sequencing Project 2014). This analysis identified six variants (p.K38E, p.R89K, p.R261H, p.R490Q, p.A494V, and p.R523W) at similar frequencies in MS patients and controls; whereas p.T200A (rs149145958), p.T500M (rs140970354) and p.A507V (rs372603134) were identified only in eight, two and one MS patient, respectively (Table 2). Despite all three variants being predicted likely damaging to protein function with a phred-scaled CADD score of 29.3, 14.4, and 18.9 for p.T200A, p.T500M, and p.A507V, respectively (Kircher ), and two of them being evolutionarily conserved (Figure 2), segregation and parametric linkage analysis, which resulted in negative LOD scores, does not support a role for these variants in disease pathogenicity (Figure S2).
Table 2

Case-control frequency for rare missense PLG variants identified in MS patients

dbSNP IDaChromosome and PositionNucleotide ChangeProtein ChangeMinor Allele Frequency
ExACbControls (n)MS (n)
rs730159656:161127501A/Gp.K38E0.0030.006 (10)0.007 (28)
rs1430796296:161128812G/Ap.R89K0.0070.010 (16)0.010 (44)
rs1491459586:161135876A/Gp.T200A0.00100.002 (8)
rs42521876:161137790G/Ap.R261H0.0030.007 (12)0.005 (24)
rs1405377246:161152807G/Ap.R490Q0.0010.002 (3)0.002 (9)
rs42521286:161152819C/Ap.A494V0.0080.005 (8)0.005 (20)
rs1409703546:161152837C/Tp.T500M0.000200.0005 (2)
rs3726031346:161152858C/Tp.A507V0.000100.0002 (1)
rs42521296:161152905C/Tp.R523W0.0070.012 (19)0.013 (56)

dbSNP Build 138.

The Exome Aggregation Consortium (ExAC) database.

dbSNP Build 138. The Exome Aggregation Consortium (ExAC) database.

Discussion

Exome sequencing analysis in a multi-incident family suffering from MS has nominated PLG p.G420D as a putative new risk factor for MS. Although four private missense variants cannot be conclusively excluded as a potential cause of disease in this kindred, and copy number changes were not evaluated, the identification of PLG p.G420D in 12 additional MS patients, and one control from Canada, suggests a role for PLG in MS susceptibility. Genotyping of additional family members from multi-incident families with PLG p.G420D resulted in positive cosegregation of the variant and disease, albeit with 50% reduced penetrance (Figure 1). Additional support for pathogenicity was sought from a large case-control cohort of MS patients from Europe, and, although most populations present a higher prevalence of PLG p.G420D in MS patients than controls, a nominally significant difference was observed only in the French cohort (Table 1). A possible Acadian origin of PLG p.G420D was considered due to the marginal associations in the French and Canadian population; however, the wide geographical distribution of variant carriers from Canada, and the lack of a shared ancestral haplotype (Table S1), do not support this hypothesis. Association analysis for PLG p.G420D in the entire cohort resulted in a nonsignificant p-value of 0.117, and an OR of 1.32. Despite the overall lack of association observed, it is possible that carriers of the PLG p.G420D variant have an increased risk of developing MS, as suggested by the OR and initially observed familial segregation pattern. In contrast, common PLG tagging variants genotyped in this study were clearly not associated with MS risk in the Canadian population (Table S4). This data corroborates previously described genome wide association studies that did not nominate common variants in PLG as a risk factor for MS (Beecham ; Sawcer ). Sequencing of PLG in MS patients from Canada led to the identification of nine rare missense variants (Table 2). Six of these were subsequently identified at a similar frequency in MS patients and controls, suggesting they are not likely to have an effect on MS risk. Interestingly one of these variants (p.K38E, rs73015965) has been described as the cause of PLG deficiency type I when identified in homozygous or compound heterozygous form (Tefs ). Similarly, p.R523W (rs4252129) has been associated with decreased plasma PLG levels (Ma ). Severe PLG deficiency type I has been causally linked to ligneous conjunctivitis, a rare chronic inflammatory disease of mainly mucous membranes. Although there is no indication that heterozygous carriers are at an increased risk of developing disease (Tefs ), PLG dysregulation could lead to an increased susceptibility to inflammatory and autoimmune diseases. In our study, three additional variants (p.T200A, p.T500M, and p.A507V) not known to cause hypoplasminogenemia, were observed exclusively in MS patients. Although the allelic frequencies and segregation for rare missense PLG variants do not initially support a role in disease susceptibility, genotyping in additional MS patients is warranted to fully define these preliminary findings. PLG p.T200A seems of particular interest, as it was identified in eight MS patients and no controls (Table 2), it is evolutionary conserved (Figure 2), and a threonine to proline substitution at the same position has been identified in a patient with severe type I PLG deficiency (Tefs ). PLG is a plausible biological candidate for MS susceptibility as it is involved in the inflammatory response, blood-brain barrier (BBB) permeability, neuronal viability, and myelin degradation (Syrovets ; Yao and Tsirka 2011; Chen and Strickland 1997; Cuzner and Opdenakker 1999). PLG has been shown to play a role in the immune response, with plasmin deficiency, the active form of PLG, resulting in a compromised inflammatory response in mouse brain (Hultman ). Microglia and astrocytes are the primary mediators of inflammation in the central nervous system, and fibrin has been shown to activate their immune response by stimulating the production of inflammatory mediators, including proinflammatory cytokines and reactive oxygen species, as well as act as a chemoattractant for immune cells (Syrovets ; Hultman ). Genetic variants in PLG may also have an effect on brain inflammation by altering the BBB permeability. Plasmin alters BBB permeability by inducing morphological changes in brain astrocytes and endothelial cells through the reorganization of the actin cytoskeleton and the redistribution of tight junction proteins (Niego and Medcalf 2014; Yao and Tsirka 2011). In addition to its effects on the inflammatory response and BBB permeability, plasmin has also been shown to affect neuronal viability, including sprouting, plasticity, and extracellular matrix-related neuronal death (Chen and Strickland 1997; Nakagami ; Wu ). Plasmin activates highly active matrix metalloproteinases (MMPs) which are recognized as key proteases in the demyelination process. Synthetic inhibitors of MMPs have been found to ameliorate clinical symptoms and pathological signs in experimental autoimmune encephalomyelitis (EAE) animal models (Cuzner and Opdenakker 1999); minocycline, which has several immunomodulating activities including the inhibition of MMP-9, has been used successfully in clinical trials as an add-on therapy for MS patients (Metz ). Despite the existence of extended families with a high incidence of MS (Fagnani ; Sadovnick 1993), only one rare pathogenic mutations has been reported (Wang ). In this study, the implementation of exome sequencing analysis in a multi-incident MS family nominated PLG p.G420D as a potential susceptibility risk for MS. Additional support was provided by 10 additional multi-incident MS families in which the variant segregates with disease, albeit with reduced penetrance. Disappointingly, genotyping of PLG p.G420D in a large European case-control cohort failed to identify a significant association with MS, thus not supporting a role for PLG p.G420D in disease. Despite this lack of association, dysregulation of the PLG/plasmin activation cascade is a plausible pathomechanism of MS, which, in conjunction with the positive segregation of PLG p.G420D in families (Figure 1), the overall higher incidence of PLG p.G420D carriers in European MS patients (Table 1), and the identification of additional rare PLG substitutions in MS patients not observed in controls (Table 2), warrants further genetic and functional characterization of PLG in order to elucidate its potential role on MS susceptibility and pathogenesis.
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Journal:  Proc Natl Acad Sci U S A       Date:  1989-06       Impact factor: 11.205

8.  Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Authors:  Stephen Sawcer; Garrett Hellenthal; Matti Pirinen; Chris C A Spencer; Nikolaos A Patsopoulos; Loukas Moutsianas; Alexander Dilthey; Zhan Su; Colin Freeman; Sarah E Hunt; Sarah Edkins; Emma Gray; David R Booth; Simon C Potter; An Goris; Gavin Band; Annette Bang Oturai; Amy Strange; Janna Saarela; Céline Bellenguez; Bertrand Fontaine; Matthew Gillman; Bernhard Hemmer; Rhian Gwilliam; Frauke Zipp; Alagurevathi Jayakumar; Roland Martin; Stephen Leslie; Stanley Hawkins; Eleni Giannoulatou; Sandra D'alfonso; Hannah Blackburn; Filippo Martinelli Boneschi; Jennifer Liddle; Hanne F Harbo; Marc L Perez; Anne Spurkland; Matthew J Waller; Marcin P Mycko; Michelle Ricketts; Manuel Comabella; Naomi Hammond; Ingrid Kockum; Owen T McCann; Maria Ban; Pamela Whittaker; Anu Kemppinen; Paul Weston; Clive Hawkins; Sara Widaa; John Zajicek; Serge Dronov; Neil Robertson; Suzannah J Bumpstead; Lisa F Barcellos; Rathi Ravindrarajah; Roby Abraham; Lars Alfredsson; Kristin Ardlie; Cristin Aubin; Amie Baker; Katharine Baker; Sergio E Baranzini; Laura Bergamaschi; Roberto Bergamaschi; Allan Bernstein; Achim Berthele; Mike Boggild; Jonathan P Bradfield; David Brassat; Simon A Broadley; Dorothea Buck; Helmut Butzkueven; Ruggero Capra; William M Carroll; Paola Cavalla; Elisabeth G Celius; Sabine Cepok; Rosetta Chiavacci; Françoise Clerget-Darpoux; Katleen Clysters; Giancarlo Comi; Mark Cossburn; Isabelle Cournu-Rebeix; Mathew B Cox; Wendy Cozen; Bruce A C Cree; Anne H Cross; Daniele Cusi; Mark J Daly; Emma Davis; Paul I W de Bakker; Marc Debouverie; Marie Beatrice D'hooghe; Katherine Dixon; Rita Dobosi; Bénédicte Dubois; David Ellinghaus; Irina Elovaara; Federica Esposito; Claire Fontenille; Simon Foote; Andre Franke; Daniela Galimberti; Angelo Ghezzi; Joseph Glessner; Refujia Gomez; Olivier Gout; Colin Graham; Struan F A Grant; Franca Rosa Guerini; Hakon Hakonarson; Per Hall; Anders Hamsten; Hans-Peter Hartung; Rob N Heard; Simon Heath; Jeremy Hobart; Muna Hoshi; Carmen Infante-Duarte; Gillian Ingram; Wendy Ingram; Talat Islam; Maja Jagodic; Michael Kabesch; Allan G Kermode; Trevor J Kilpatrick; Cecilia Kim; Norman Klopp; Keijo Koivisto; Malin Larsson; Mark Lathrop; Jeannette S Lechner-Scott; Maurizio A Leone; Virpi Leppä; Ulrika Liljedahl; Izaura Lima Bomfim; Robin R Lincoln; Jenny Link; Jianjun Liu; Aslaug R Lorentzen; Sara Lupoli; Fabio Macciardi; Thomas Mack; Mark Marriott; Vittorio Martinelli; Deborah Mason; Jacob L McCauley; Frank Mentch; Inger-Lise Mero; Tania Mihalova; Xavier Montalban; John Mottershead; Kjell-Morten Myhr; Paola Naldi; William Ollier; Alison Page; Aarno Palotie; Jean Pelletier; Laura Piccio; Trevor Pickersgill; Fredrik Piehl; Susan Pobywajlo; Hong L Quach; Patricia P Ramsay; Mauri Reunanen; Richard Reynolds; John D Rioux; Mariaemma Rodegher; Sabine Roesner; Justin P Rubio; Ina-Maria Rückert; Marco Salvetti; Erika Salvi; Adam Santaniello; Catherine A Schaefer; Stefan Schreiber; Christian Schulze; Rodney J Scott; Finn Sellebjerg; Krzysztof W Selmaj; David Sexton; Ling Shen; Brigid Simms-Acuna; Sheila Skidmore; Patrick M A Sleiman; Cathrine Smestad; Per Soelberg Sørensen; Helle Bach Søndergaard; Jim Stankovich; Richard C Strange; Anna-Maija Sulonen; Emilie Sundqvist; Ann-Christine Syvänen; Francesca Taddeo; Bruce Taylor; Jenefer M Blackwell; Pentti Tienari; Elvira Bramon; Ayman Tourbah; Matthew A Brown; Ewa Tronczynska; Juan P Casas; Niall Tubridy; Aiden Corvin; Jane Vickery; Janusz Jankowski; Pablo Villoslada; Hugh S Markus; Kai Wang; Christopher G Mathew; James Wason; Colin N A Palmer; H-Erich Wichmann; Robert Plomin; Ernest Willoughby; Anna Rautanen; Juliane Winkelmann; Michael Wittig; Richard C Trembath; Jacqueline Yaouanq; Ananth C Viswanathan; Haitao Zhang; Nicholas W Wood; Rebecca Zuvich; Panos Deloukas; Cordelia Langford; Audrey Duncanson; Jorge R Oksenberg; Margaret A Pericak-Vance; Jonathan L Haines; Tomas Olsson; Jan Hillert; Adrian J Ivinson; Philip L De Jager; Leena Peltonen; Graeme J Stewart; David A Hafler; Stephen L Hauser; Gil McVean; Peter Donnelly; Alastair Compston
Journal:  Nature       Date:  2011-08-10       Impact factor: 49.962

9.  Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis.

Authors:  W I McDonald; A Compston; G Edan; D Goodkin; H P Hartung; F D Lublin; H F McFarland; D W Paty; C H Polman; S C Reingold; M Sandberg-Wollheim; W Sibley; A Thompson; S van den Noort; B Y Weinshenker; J S Wolinsky
Journal:  Ann Neurol       Date:  2001-07       Impact factor: 10.422

10.  An integrated map of genetic variation from 1,092 human genomes.

Authors:  Goncalo R Abecasis; Adam Auton; Lisa D Brooks; Mark A DePristo; Richard M Durbin; Robert E Handsaker; Hyun Min Kang; Gabor T Marth; Gil A McVean
Journal:  Nature       Date:  2012-11-01       Impact factor: 49.962

View more
  5 in total

1.  Common genetic etiology between "multiple sclerosis-like" single-gene disorders and familial multiple sclerosis.

Authors:  Anthony L Traboulsee; A Dessa Sadovnick; Mary Encarnacion; Cecily Q Bernales; Irene M Yee; Maria G Criscuoli; Carles Vilariño-Güell
Journal:  Hum Genet       Date:  2017-03-23       Impact factor: 4.132

2.  A Genome-wide Association Study Identifies Risk Alleles in Plasminogen and P4HA2 Associated with Giant Cell Arteritis.

Authors:  F David Carmona; Augusto Vaglio; Sarah L Mackie; José Hernández-Rodríguez; Paul A Monach; Santos Castañeda; Roser Solans; Inmaculada C Morado; Javier Narváez; Marc Ramentol-Sintas; Colin T Pease; Bhaskar Dasgupta; Richard Watts; Nader Khalidi; Carol A Langford; Steven Ytterberg; Luigi Boiardi; Lorenzo Beretta; Marcello Govoni; Giacomo Emmi; Francesco Bonatti; Marco A Cimmino; Torsten Witte; Thomas Neumann; Julia Holle; Verena Schönau; Laurent Sailler; Thomas Papo; Julien Haroche; Alfred Mahr; Luc Mouthon; Øyvind Molberg; Andreas P Diamantopoulos; Alexandre Voskuyl; Elisabeth Brouwer; Thomas Daikeler; Christoph T Berger; Eamonn S Molloy; Lorraine O'Neill; Daniel Blockmans; Benedicte A Lie; Paul Mclaren; Timothy J Vyse; Cisca Wijmenga; Yannick Allanore; Bobby P C Koeleman; Jennifer H Barrett; María C Cid; Carlo Salvarani; Peter A Merkel; Ann W Morgan; Miguel A González-Gay; Javier Martín
Journal:  Am J Hum Genet       Date:  2016-12-29       Impact factor: 11.025

3.  Linkage analysis and whole exome sequencing identify a novel candidate gene in a Dutch multiple sclerosis family.

Authors:  Julia Y Mescheriakova; Annemieke Jmh Verkerk; Najaf Amin; André G Uitterlinden; Cornelia M van Duijn; Rogier Q Hintzen
Journal:  Mult Scler       Date:  2018-06-06       Impact factor: 6.312

4.  Exome sequencing in multiple sclerosis families identifies 12 candidate genes and nominates biological pathways for the genesis of disease.

Authors:  Carles Vilariño-Güell; Alexander Zimprich; Filippo Martinelli-Boneschi; Bruno Herculano; Zhe Wang; Fuencisla Matesanz; Elena Urcelay; Koen Vandenbroeck; Laura Leyva; Denis Gris; Charbel Massaad; Jacqueline A Quandt; Anthony L Traboulsee; Mary Encarnacion; Cecily Q Bernales; Jordan Follett; Irene M Yee; Maria G Criscuoli; Angela Deutschländer; Eva M Reinthaler; Tobias Zrzavy; Elisabetta Mascia; Andrea Zauli; Federica Esposito; Antonio Alcina; Guillermo Izquierdo; Laura Espino-Paisán; Jorge Mena; Alfredo Antigüedad; Patricia Urbaneja-Romero; Jesús Ortega-Pinazo; Weihong Song; A Dessa Sadovnick
Journal:  PLoS Genet       Date:  2019-06-06       Impact factor: 5.917

5.  Multi-omic studies on missense PLG variants in families with otitis media.

Authors:  Tori C Bootpetch; Lena Hafrén; Christina L Elling; Erin E Baschal; Ani W Manichaikul; Harold S Pine; Wasyl Szeremeta; Melissa A Scholes; Stephen P Cass; Eric D Larson; Kenny H Chan; Rafaqat Ishaq; Jeremy D Prager; Rehan S Shaikh; Samuel P Gubbels; Ayesha Yousaf; Todd M Wine; Michael J Bamshad; Patricia J Yoon; Herman A Jenkins; Deborah A Nickerson; Sven-Olrik Streubel; Norman R Friedman; Daniel N Frank; Elisabet Einarsdottir; Juha Kere; Saima Riazuddin; Kathleen A Daly; Suzanne M Leal; Allen F Ryan; Petri S Mattila; Zubair M Ahmed; Michele M Sale; Tasnee Chonmaitree; Regie Lyn P Santos-Cortez
Journal:  Sci Rep       Date:  2020-09-14       Impact factor: 4.379

  5 in total

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