INTEGRATIVE ‘OMIC’ APPROACH TOWARDS
UNDERSTANDING THE NATURE OF HUMAN DISEASES
Peterlin B*, Maver A
*Corresponding Author: Professor Borut Peterlin, M.D., Ph.D., Institute of Medical Genetics, Department
of Gynecology and Obstetrics, University Medical Centre Ljubljana, 3, Šlajmerjeva Street, Ljubljana 1000,
Slovenia; Tel./Fax: +386(0)1-540-1137; E mail: borut.peterlin@guest.arnes.si
page: 45
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RESULTS AND DISCUSSION
Results originating from the positional integratomic
approach represent a prioritized list of
genomic regions, where regions containing the
greatest accumulation of heterogeneous biological
alterations in an investigated disease rank highest
and are characterized by lowest permutation test p
values. As the integrative approach is performed for
regions (bins) across the whole genome, the resulting
genome-wide distribution of results from integration
of data in human disease may be inspected.
Genome-wide distribution of integration results for
MS as an example of a complex autoimmune human
disorder is represented in Figure 2. Here, the
greatest accumulation of signals is observed on
chromosome 6, specifically in the well-known human
leukocyte antigen (HLA) region, suggesting
that data from heterogeneous biological sources of
‘omic’ data indicate the role of this region in MS.
Moreover, other regions have also attained high integration
scores, suggesting importance of non-HLA
regions in MS. Specifically, a region containing an
interleukin-7 receptor gene (IL7R) attained very
high integrative scores, not only on the basis of detections
from genome-wide association studies, but
also on the basis of evidence from expression profiling
studies in blood and brain tissues. Additionally,
the same region has been ranked high due to information
obtained from various bioinformatic sources
of data, such as KEGG (Kyoto Encyclopedia of
Genes and Genomes) pathways and co-expression
information [10,11]. Such a heterogeneous body of
evidence offers information of great relevance to
true biological disease alterations and thus provides
plausible candidate selection for further studies. The positional approach offers great flexibility
and control over parameters on which the final
prioritization of genomic regions is based. Based
on scientific questions, a researcher may be more
interested in a contribution of only selected biological
layers to the final integration score. For this
reason, we have implemented means to allow custom
weighting of different sources of data. For example,
if one is interested in the relation between
genomic variation and differential methylation, one
may attribute those two sources greater weights and
regions where signals from GWAS (genome-wide
association studies), and global methylation studies
aggregate will be obtained. Additional levels of control
may also be obtained by customizing the size of
genomic bins, allowing for detection of interactions
that spread across larger genomic regions.
There has been great interest in deciphering the
genetic factors with medium-to-low effect sizes as
the explanation for the phenomenon of missing heritability
in MS and other complex disorders [12,13].
Here, an integrative approach may assist in promoting
detection of the genomic variant with its actual
role in such complex disorder, and distinguishing
them from spurious noise originating from statistical
noise generated in genome-wide association studies.
As large-scale studies, which attempt to detect
low-effect susceptibility factors in human disease,
have to be performed on large sample sizes, requiring
great resources and effort [14], this approach
may be a mode of comprehensive evidence-based
selection of molecular determinants to investigate
in such downstream validation studies.
With continuing development of high-throughput
technologies, it is expected that the amount of
the resulting data in large databases will continue to
rise. For this reason, novel approaches for interpretation
and understanding will also have to be prepared
to face these challenges. As it is difficult for a
single researcher or research group to have a comprehensive
overview over such a vast information
landscape, new means of presentation and access to
these results will have to be envisaged. A positionbased,
integrative approach not only represents the
means to quick insight into heterogeneous evidence
from several large-scale studies, but is also a basis toward the preparation of an interactive genome
browser-like solutions for fast and easy access to
this body of information.
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