Emerging importance of big data in neuroimmunological research
With rapid advances in information and communica-
tion technology, enormous amount of data can be
produced and utilized in research and medicine. For
example, from just a small amount of saliva, single-
nucleotide polymorphisms across the genome can be
genotyped using extracted DNA and microarray chip,
and even whole genome sequencing can be carried
out. However, to understand the meaning of this
obtained information, the data should be properly
processed and annotated. Thus, multiple technolo-
gies and tools are required to take advantage of big
data. It is expected that big data can help us to over-
view the ﬁelds of our interest from a wider point of
view, and grasp subtle but fundamental trends in
research using, for example, genetic variants dataset
(single-nucleotide polymorphisms or sequencing),
gene expression datasets and medical records.
The current issue of Clinical and Experimental Neu-
roimmunology focuses on “big data in neuroimmunol-
ogy” and contains three review articles.
Ogawa and Okada reviewed statistical genetics and
its application to neuroimmunology in this issue. The
manuscript consists of two parts.
First, the authors
introduced past genetic studies carried out in neu-
roimmunological diseases, such as multiple sclerosis
(MS) and myasthenia gravis. They next introduced
updated methods for human leukocyte antigen impu-
tation including a Japanese population-speciﬁc panel
that they had developed and useful tools to link the
obtained genetic ﬁndings with potential future treat-
ment by repurposing drugs that were already devel-
oped for other diseases. They provide us important
insights on the future direction of where genetic stud-
ies in MS or other neuroimmunological diseases
should go with the latest study methodologies.
Another example of big data sources for medical
research is electronic medical records (EMR). Origi-
nally, clinical data had been documented on paper-
based medical records, and the data of interest that
clinical researchers wanted to investigate had to be
manually extracted and registered on a database. By
the transition to EMR, the data of interest can be easily
obtained by text mining techniques. Damotte and
Gourraud reviewed EMR in this issue, and gave us an
overview on how EMR is efﬁcient for research in the
ﬁeld of neuroimmunological diseases, especially in MS
research, and what the current limitations are.
In the era of increasingly emphasizing big data,
how should we maximize the voluminous data in
research on neuroimmunological diseases and what
should be the ideal strategy for carrying out research?
At the end of a focused review in this issue, I brieﬂy
reviewed MS genetics, introducing the rationale for
genetic contributions to MS susceptibility and sever-
ity, and emphasized the importance of active collabo-
ration with researchers from different professions in
carrying out research using large datasets.
ally, some tools that can be useful for genetic
research are also summarized.
In this issue, the three review articles introduced
above emphasize the importance of maximizing the
big data, such as genetic datasets and EMR, to reveal
new disease-associated genetic loci or links between
genetic variants and speciﬁc disease phenotypes that
have been difﬁcult to identify because of the lack of
statistical power in traditional datasets. In any
aspects, a collaborative work as a team or a consor-
tium would be a key to enforce the challenging, but
promising, studies with big data.
This work was supported by grants from JSPS
KAKENHI (grant number 17H06938) and Japan
Multiple Sclerosis Society.
Conflict of interest
Dr Isobe has received a grant and salary from Tan-
abe Mitsubishi Pharma, Bayer Yakuhin and Japan
Blood Products Organization.
1. Ogawa K, Okada Y. Statistical genetics and its application
to neuroimmunology. Clin Exp Neuroimmunol. 2018; 9:7–
2. Damotte V, Gourraud PA. Electronic medical records in
multiple sclerosis research. Clin Exp Neuroimmunol. 2018;
3. Isobe N. Genetics in multiple sclerosis: updates in the era of
big data. Clin Exp Neuroimmunol. 2018; 9:19–24.
Department of Neurological Therapeutics, Graduate School
of Medical Sciences, Kyushu University, Fukuoka, Japan
© 2018 Japanese Society for Neuroimmunology
Clinical and Experimental Neuroimmunology 9 (2018) 3
Clinical & Experimental