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A systems biology approach to genetic studies of complex diseases
Open accessAbstract
Revealing mechanisms underlying complex diseases poses great challenges to biologists. The traditional linkage and linkage disequilibrium analysis that have been successful in the identification of genes responsible for Mendelian traits, however, have not led to similar success in discovering genes influencing the development of complex diseases. Emerging functional genomic and proteomic (‘omic’) resources and technologies provide great opportunities to develop new methods for systematic identification of genes underlying complex diseases. In this report, we propose a systems biology approach, which integrates omic data, to find genes responsible for complex diseases. This approach consists of five steps: (1) generate a set of candidate genes using gene–gene interaction data sets; (2) reconstruct a genetic network with the set of candidate genes from gene expression data; (3) identify differentially regulated genes between normal and abnormal samples in the network; (4) validate regulatory relationship between the genes in the network by perturbing the network using RNAi and monitoring the response using RT‐PCR; and (5) genotype the differentially regulated genes and test their association with the diseases by direct association studies. To prove the concept in principle, the proposed approach is applied to genetic studies of the autoimmune disease scleroderma or systemic sclerosis.
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Most major changes in physiological states have multigenic or multifactorial origins, and most diseases are therefore to be seen as systems or network diseases.
“It is not news that most major changes in physiological states have multigenic or multifactorial origins (e.g. [2092,2093]). This means, as an inevitable consequence, that we need to recognize that their observation requires a systems approach, and that most diseases are therefore in fact to be seen as systems or network diseases [631,2094-2102].”
From Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases (2009) · cites this paper as [2093]
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Metadata
- Authors
- Xiong, M., Feghali-Bostwick, C. A., Arnett, F. C., & Zhou, X.
- Affiliations
- The University of Texas Health Science Center at Houston (US)
- Corresponding author
- Zhou, Xiaodong
- Venue
- FEBS Letters
- Publisher
- Wiley
- Published
- 2005-09-13
- Volume
- 579
- Issue
- 24
- Pages
- 5325-5332
- ISSN
- 0014-5793 (ISSN-L)
- 1873-3468
- OpenAlex
- W2045586512
- Cited by
- 34
- References
- 25
- Citations by year
- 2023: 1 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 3 · 2012: 1
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- FEBS Letters (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Arthritis Foundation
- Scleroderma Foundation
- Cooley's Anemia Foundation
- Deutsches Krebsforschungszentrum
- University of Texas Health Science Center at Houston
- National Institutes of Health (5r01ar050840-07, 5r03ar050517-02)
- National Institute of Environmental Health Sciences (ES-09912, R01ES009912)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (AR-050517-01A2, AR 050840, IP50-AR-44888)
- National Center for Research Resources (M01RR002558)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Rheumatology › Systemic Sclerosis and Related Diseases
- Keywords
- complex diseases, systems biology, gene-gene interaction, gene expression data, differentially regulated genes, genetic association studies, candidate gene identification, systemic sclerosis, autoimmune disease, multi-omics integration
- MeSH terms
- Genomics, RNA Interference, Proteomics, Systems Biology
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