Balkan Journal of Medical Genetics

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

INTRODUCTION

The development of microarray technology in the last decade and the upsurge of next-generation sequencing in the last few years has provided an abundance of data originating from various biological levels, most prominently from genomic and transcriptomic levels [1,2]. Such novel approaches have contributed greatly towards our understanding of physiological cellular processes, as well as molecular changes that occur in human disease. The high-dimensional nature of data originating from these studies has also opened an array of new theoretical and statistical challenges that had to be faced in order to attain acceptable reproducibility and consistency of scientific results [3]. In particular, a large number of hypotheses tested in a single experiment produced a substantial amount of statistical noise, causing large numbers of false-positive detections and undue omission of many true-positive results. Although statistical methods have been developed to address these issues, difficulties in in-creasing specificity and sensitivity of highly parallel approaches still exist, with the greatest notoriety in the field of human diseases belonging to a group of common, complex disorders. In an attempt to alleviate these drawbacks, we developed a method that harnesses the biological relations between data originating from studies investigating human disease on various biological levels. An example of such an approach may be illustrated by the fact that genomic alterations associated with human disease, i.e., multiple sclerosis (MS), are usually investigated and interpreted separately from transcriptomic alterations occurring in MS. The biological relation between these two layers may thus be utilized to favor prioritization of genes that were detected on both layers, therefore reducing noisy results and facilitating detection of true biological data. We expect that with the inclusion of increasing the number of biological layers and increasing the number of studies in the database used for integration, the comprehensiveness and biological validity of prioritized genes would increase progressively.

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