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BMC Syst Biol. 2019 Jan 14;13(1):8. doi: 10.1186/s12918-018-0671-x.

Multi-omics integration reveals molecular networks and regulators of psoriasis.

Author information

1
Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Dr. East, Los Angeles, CA, 90095, USA.
2
Target Sciences, Computational Biology (US) GSK, 1250 South Collegeville Road, Collegeville, PA, 19426, USA.
3
Target Sciences, Computational Biology (US) GSK, 1250 South Collegeville Road, Collegeville, PA, 19426, USA. deepak.k.rajpal@gsk.com.
4
Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Dr. East, Los Angeles, CA, 90095, USA. xyang123@ucla.edu.
5
Institute for Quantitative and Computational Biosciences, University of California , 610 Charles E. Young Dr. East, Los Angeles, CA, 90095, USA. xyang123@ucla.edu.
6
Molecular Biology Institute, University of California, 610 Charles E. Young Dr. East, Los Angeles, CA, 90095, USA. xyang123@ucla.edu.
7
Bioinformatics Interdepartmental Program, University of California, 10 Charles E. Young Dr. East, Los Angeles, CA, 90095, USA. xyang123@ucla.edu.

Abstract

BACKGROUND:

Psoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis.

METHODS:

To achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk.

RESULTS:

This integrative genomics study identified both well-characterized (e.g., the IL17 pathway in both GWAS and EWAS) and novel biological processes (e.g., the branched chain amino acid catabolism process in GWAS and the platelet and coagulation pathway in EWAS) involved in psoriasis. Finally, by utilizing tissue-specific gene regulatory networks, we unraveled the interactions among the psoriasis-associated genes and pathways in a tissue-specific manner and detected potential key regulatory genes in the psoriasis networks.

CONCLUSIONS:

The integration and convergence of multi-omics signals provide deeper and comprehensive insights into the biological mechanisms associated with psoriasis susceptibility.

KEYWORDS:

EWAS; GWAS; Integrative genomics; Psoriasis; Systems biology

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