Burden of Genetic Risk Variants in Multiple Sclerosis Families in the Netherlands

Overview

Journal Mult Scler J Exp Transl Clin

Date 2017 Jun 14

PMID 28607725

Citations 5

Authors

Julia Y Mescheriakova

Linda Broer

Simin Wahedi

Andre G Uitterlinden

Cornelia M Van Duijn

Rogier Q Hintzen

Affiliations

Soon will be listed here.

Abstract

Background: Approximately 20% of multiple sclerosis patients have a family history of multiple sclerosis. Studies of multiple sclerosis aggregation in families are inconclusive.

Objective: To investigate the genetic burden based on currently discovered genetic variants for multiple sclerosis risk in patients from Dutch multiple sclerosis multiplex families versus sporadic multiple sclerosis cases, and to study its influence on clinical phenotype and disease prediction.

Methods: Our study population consisted of 283 sporadic multiple sclerosis cases, 169 probands from multiplex families and 2028 controls. A weighted genetic risk score based on 102 non-human leukocyte antigen loci and was calculated.

Results: The weighted genetic risk score based on all loci was significantly higher in familial than in sporadic cases. The contributed significantly to the difference in genetic burden between the groups. A high weighted genetic risk score was significantly associated with a low age of disease onset in all multiple sclerosis patients, but not in the familial cases separately. The genetic risk score was significantly but modestly better in discriminating familial versus sporadic multiple sclerosis from controls.

Conclusion: Familial multiple sclerosis patients are more loaded with the common genetic variants than sporadic cases. The difference is mainly driven by . The predictive capacity of genetic loci is poor and unlikely to be useful in clinical settings.

Citing Articles

Contribution of common risk variants to multiple sclerosis in Orkney and Shetland.

Barnes C, Hayward C, Porteous D, Campbell H, Joshi P, Wilson J Eur J Hum Genet. 2021; 29(11):1701-1709.

PMID: 34088990 PMC: 8560837. DOI: 10.1038/s41431-021-00914-w.

EBNA-1 titer gradient in families with multiple sclerosis indicates a genetic contribution.

Mescheriakova J, van Nierop G, van der Eijk A, Kreft K, Hintzen R Neurol Neuroimmunol Neuroinflamm. 2020; 7(6).

PMID: 32796079 PMC: 7428359. DOI: 10.1212/NXI.0000000000000872.

Distinct HLA class I and II genotypes and haplotypes are associated with multiple sclerosis in Bahrain.

Al-Nashmi M, Taha S, Salem A, Alsharoqi I, Bakhiet M Biomed Rep. 2018; 9(6):531-539.

PMID: 30546882 PMC: 6256251. DOI: 10.3892/br.2018.1164.

Inferring disease risk genes from sequencing data in multiplex pedigrees through sharing of rare variants.

Bureau A, Begum F, Taub M, Hetmanski J, Parker M, Albacha-Hejazi H Genet Epidemiol. 2018; 43(1):37-49.

PMID: 30246882 PMC: 6330140. DOI: 10.1002/gepi.22155.

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

Mescheriakova J, Verkerk A, Amin N, Uitterlinden A, Van Duijn C, Hintzen R Mult Scler. 2018; 25(7):909-917.

PMID: 29873607 PMC: 6545620. DOI: 10.1177/1352458518777202.

References

Sawcer S, Hellenthal G, Pirinen M, Spencer C, Patsopoulos N, Moutsianas L . Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature. 2011; 476(7359):214-9. PMC: 3182531. DOI: 10.1038/nature10251. View

Patsopoulos N, Barcellos L, Hintzen R, Schaefer C, Van Duijn C, Noble J . Fine-mapping the genetic association of the major histocompatibility complex in multiple sclerosis: HLA and non-HLA effects. PLoS Genet. 2013; 9(11):e1003926. PMC: 3836799. DOI: 10.1371/journal.pgen.1003926. View

Harbo H, Isobe N, Berg-Hansen P, Bos S, Caillier S, Gustavsen M . Oligoclonal bands and age at onset correlate with genetic risk score in multiple sclerosis. Mult Scler. 2013; 20(6):660-8. PMC: 4066985. DOI: 10.1177/1352458513506503. View

Anderson C, Pettersson F, Clarke G, Cardon L, Morris A, Zondervan K . Data quality control in genetic case-control association studies. Nat Protoc. 2010; 5(9):1564-73. PMC: 3025522. DOI: 10.1038/nprot.2010.116. View

Xia Z, White C, Owen E, Von Korff A, Clarkson S, McCabe C . Genes and Environment in Multiple Sclerosis project: A platform to investigate multiple sclerosis risk. Ann Neurol. 2015; 79(2):178-89. PMC: 4778957. DOI: 10.1002/ana.24560. View

Compston A, Coles A . Multiple sclerosis. Lancet. 2008; 372(9648):1502-17. DOI: 10.1016/S0140-6736(08)61620-7. View

Weinshenker B, Bulman D, Carriere W, Baskerville J, Ebers G . A comparison of sporadic and familial multiple sclerosis. Neurology. 1990; 40(9):1354-8. DOI: 10.1212/wnl.40.9.1354. View

DNetto M, Ward H, Morrison K, Ramagopalan S, Dyment D, DeLuca G . Risk alleles for multiple sclerosis in multiplex families. Neurology. 2009; 72(23):1984-8. DOI: 10.1212/WNL.0b013e3181a92c25. View

De Jager P, Chibnik L, Cui J, Reischl J, Lehr S, Simon K . Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score. Lancet Neurol. 2009; 8(12):1111-9. PMC: 3099419. DOI: 10.1016/S1474-4422(09)70275-3. View

10.

McDonald W, Compston A, Edan G, Goodkin D, Hartung H, Lublin F . Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol. 2001; 50(1):121-7. DOI: 10.1002/ana.1032. View

11.

Esposito F, Guaschino C, Sorosina M, Clarelli F, Ferre L, Mascia E . Impact of MS genetic loci on familial aggregation, clinical phenotype, and disease prediction. Neurol Neuroimmunol Neuroinflamm. 2015; 2(4):e129. PMC: 4503410. DOI: 10.1212/NXI.0000000000000129. View

12.

Jersild C, FOG T, Hansen G, Thomsen M, Svejgaard A, DuPont B . Histocompatibility determinants in multiple sclerosis, with special reference to clinical course. Lancet. 1973; 2(7840):1221-5. DOI: 10.1016/s0140-6736(73)90970-7. View

13.

Miller S, Dykes D, Polesky H . A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988; 16(3):1215. PMC: 334765. DOI: 10.1093/nar/16.3.1215. View

14.

Wilson P, DAgostino R, Levy D, Belanger A, Silbershatz H, Kannel W . Prediction of coronary heart disease using risk factor categories. Circulation. 1998; 97(18):1837-47. DOI: 10.1161/01.cir.97.18.1837. View

15.

Hofman A, Van Duijn C, Franco O, Ikram M, Janssen H, Klaver C . The Rotterdam Study: 2012 objectives and design update. Eur J Epidemiol. 2011; 26(8):657-86. PMC: 3168750. DOI: 10.1007/s10654-011-9610-5. View

16.

Jafari N, Broer L, Van Duijn C, Janssens A, Hintzen R . Perspectives on the use of multiple sclerosis risk genes for prediction. PLoS One. 2011; 6(12):e26493. PMC: 3229479. DOI: 10.1371/journal.pone.0026493. View

17.

Isobe N, Damotte V, Lo Re V, Ban M, Pappas D, Guillot-Noel L . Genetic burden in multiple sclerosis families. Genes Immun. 2013; 14(7):434-440. PMC: 4102601. DOI: 10.1038/gene.2013.37. View

18.

OGorman C, Lin R, Stankovich J, Broadley S . Modelling genetic susceptibility to multiple sclerosis with family data. Neuroepidemiology. 2012; 40(1):1-12. DOI: 10.1159/000341902. View

19.

Kundu S, Aulchenko Y, Van Duijn C, Janssens A . PredictABEL: an R package for the assessment of risk prediction models. Eur J Epidemiol. 2011; 26(4):261-4. PMC: 3088798. DOI: 10.1007/s10654-011-9567-4. View

20.

Hilven K, Patsopoulos N, Dubois B, Goris A . Burden of risk variants correlates with phenotype of multiple sclerosis. Mult Scler. 2015; 21(13):1670-80. DOI: 10.1177/1352458514568174. View