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Dissecting the familial risk of multiple sclerosis.

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EDITORIAL
Dissecting the Familial Risk of
Multiple Sclerosis
W
e are interested in finding the genes for neurological disease for 2 reasons: first to become better
at predicting who is at risk for disease, and second to develop an understanding of the pathogenesis of the disease
and potential treatment response. Although better risk
prediction has been the aim that has captured more
headlines, we should remember that, risk prediction can
never get better than concordance rates in identical twins.
Perhaps the greater goal is to understand these diseases so
that we can intervene effectively.
Multiple sclerosis (MS) is a common and devastating disease that shows familial clustering.1 The association of human leukocyte antigen (HLA) with MS has
been apparent for 40 years,2,3 but until the recent advent
of genome-wide association studies (GWAS), no other
loci had been reliably identified. In the past 3 years,
GWAS have identified many other risk loci, albeit with
much smaller effect sizes than HLA.4 All the loci identified map onto inflammatory pathways and have thus
confirmed that (probably several) immune system-initiated inflammatory responses play an important role in
the disease.
In this issue of Annals of Neurology5 and elsewhere,6,7 analysis is directed at assessing how much of
the familial clustering of the disease has been explained.
Are the familial cases of the disease quantitatively or
qualitatively different from each other? The answers are
clear and interesting. Individuals from multiplex families
have a higher burden of the same risk alleles as their sporadic counterparts. Thus, the differences between the
familial and sporadic diseases are quantitative, not qualitative. A secondary question then is, how well can we use
genetic analysis to predict which individuals will get MS
in these families? The answer to this question is also clear
and important. Genetics alone is a long way from predicting who will get MS. Not only this, but it is also
quite likely that genetic data alone will never be certain
enough to use for prediction in a clinical circumstance.8
It is likely that genetics will always be clinically useful
only in the context of other data on individual subjects,
such as magnetic resonance imaging, cerebrospinal fluid,
or other biomarker findings.9
These developments raise the issue of whether there
are more genetic risk factors still to be identified.
Undoubtedly, there are. Given good design, the power of
GWAS to find loci is simply dependent on the number
of samples included. As the reference list of the paper by
Gourraud and colleagues makes clear, the MS field has
been gathering ever larger groups of samples for analysis,
and still larger studies are in the pipeline. These will lead
to the identification of more risk loci. However, as large
genome sequencing projects get underway, it is likely
that it will be possible to identify further risk alleles at
loci already found by the MS consortia, and thus increase
the proportion of risk that is accounted for. For example,
it has become appreciated that different HLA haplotypes
are each associated with different risks of disease. As further genetic analysis is performed, it will become clear
that, at each of the new loci that have been identified,
there are many haplotypes, each with differing risks associated with them.
Through further genetic and epidemiological analysis, we will undoubtedly gather more information
about the disease pathogenesis and account for more
of the risk of disease. Whether these genetic data alone
will lead to better and earlier diagnosis, improve our
understanding of the variability of the clinical course,
or guide appropriate targeting of interventions is less
clear.
Potential Conflicts of Interest
J.H.: consultancy, Merck Serono, Eisai. A.T.: board
membership, National Hospital Development Foundation,
Patrick Berthoud Charitable Trust; consultancy, Weleda
AG/Society for Clinical Research, Medical Research
Council, MS Society of Great Britain, Merck Serono,
Biogen Idec, DIGNA Biotech, Novartis, Eisai London
Research Laboratories, Teva Pharmaceuticals, Biogen;
grants/grants pending, National Institute for Health
Research, MS Society of Great Britain; honoraria, Sage
Publications, GE Healthcare; royalties, Informa Healthcare, Cambridge University Press, Taylor & Francis;
educational presentations, Serono Symposia, Sanofi-Aventis; travel expenses, MS International Federation, National
MS Society (USA), Biogenidec, PACTRIMS; free subC 2011 American Neurological Association
V
11
ANNALS
of Neurology
scriptions, Lancet Neurology, Current Medical Literature Multiple Sclerosis.
3.
Naito S, Namerow N, Mickey MR, Terasaki PI. Multiple sclerosis:
association with HL-A3. Tissue Antigens 1972;2:1–4.
4.
De Jager PL, Jia X, Wang J, et al. Meta-analysis of genome scans
and replication identify CD6, IRF8 and TNFRSF1A as new multiple
sclerosis susceptibility loci. Nat Genet 2009;41:776–782.
5.
Gourraud PA, McElroy PP, Caillier SJ, et al. Aggregation of multiple sclerosis genetic risk variants in multiple and single case families. Ann Neurol 2010;69:65–74.
6.
D’Netto MJ, Ward H, Morrison KM, et al. Risk alleles for multiple
sclerosis in multiplex families. Neurology 2009;72:1984–1988.
7.
De Jager PL, Chibnik LB, Cui J, et al. Integration of genetic risk
factors into a clinical algorithm for multiple sclerosis susceptibility:
a weighted genetic risk score. Lancet Neurol 2009;8:1111–1119.
8.
Sawcer S, Ban M, Wason J, Dudbridge F. What role for genetics
in the prediction of multiple sclerosis? Ann Neurol 2010;67:3–10.
9.
Ramagopalan SV, Dobson R, Meier UC, Giovannoni G. Multiple
sclerosis: risk factors, prodromes, and potential causal pathways.
Lancet Neurol 2010;9:727–739.
John Hardy, PhD1
Alan J. Thompson, MD2
1
Rita Lilla Weston Laboratory, Department of Molecular Neuroscience, and
Department of Brain Repair and Rehabilitation, University College
London Institute of Neurology, London, United Kingdom
2
References
1.
2.
12
Stüve O, Oksenberg J. Multiple sclerosis overview. In: Pagon RA,
Bird TC, Dolan CR, Stephens K, eds. GeneReviews. Seattle, WA:
University of Washington, 2006 [updated May 11, 2010]. Available
at: http://www.ncbi.nlm.nih.gov/books/NBK1316/
Bertrams J, Kuwert E. HL-A antigen frequencies in multiple sclerosis. Significant increase of HL-A3, HL-A10 and W5, and decrease
of HL-A12. Eur Neurol 1972;7:74–78.
DOI: 10.1002/ana.22353
Volume 69, No. 1
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