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Aging Cell. 2015 Dec;14(6):933-44. doi: 10.1111/acel.12386. Epub 2015 Aug 30.

Integration of 'omics' data in aging research: from biomarkers to systems biology.

Author information

1
Department of Twins Research and Genetic Epidemiology, Kings College London, London, United Kingdom.
2
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.

Abstract

Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

KEYWORDS:

data integration; graphical models; high-throughput data; omics; systems biology

PMID:
26331998
PMCID:
PMC4693464
DOI:
10.1111/acel.12386
[Indexed for MEDLINE]
Free PMC Article

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