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Cell Syst. 2018 Jan 24;6(1):90-102.e4. doi: 10.1016/j.cels.2017.10.016. Epub 2017 Nov 30.

An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function.

Author information

1
Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.
2
Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.
3
Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.
4
Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften, Humboldt-Universität zu Berlin, D-10115 Berlin, Germany.
5
Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne 1010, Switzerland.
6
Department of Pharmaceutical Sciences, University of Colorado, Aurora, CO 80045, USA.
7
University Medical Center Utrecht, 3584CT Utrecht, the Netherlands; Department of Genetics, Genomics and Informatics, University of Tennessee, Memphis, TN 38163, USA.
8
Department of Genetics, Genomics and Informatics, University of Tennessee, Memphis, TN 38163, USA.
9
Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland. Electronic address: admin.auwerx@epfl.ch.

Abstract

Identifying genetic and environmental factors that impact complex traits and common diseases is a high biomedical priority. Here, we developed, validated, and implemented a series of multi-layered systems approaches, including (expression-based) phenome-wide association, transcriptome-/proteome-wide association, and (reverse-) mediation analysis, in an open-access web server (systems-genetics.org) to expedite the systems dissection of gene function. We applied these approaches to multi-omics datasets from the BXD mouse genetic reference population, and identified and validated associations between genes and clinical and molecular phenotypes, including previously unreported links between Rpl26 and body weight, and Cpt1a and lipid metabolism. Furthermore, through mediation and reverse-mediation analysis we established regulatory relations between genes, such as the co-regulation of BCKDHA and BCKDHB protein levels, and identified targets of transcription factors E2F6, ZFP277, and ZKSCAN1. Our multifaceted toolkit enabled the identification of gene-gene and gene-phenotype links that are robust and that translate well across populations and species, and can be universally applied to any populations with multi-omics datasets.

KEYWORDS:

BXD; PheWAS; QTL; TWAS; ePheWAS; genetic reference population; mediation analysis; systems genetics

PMID:
29199021
DOI:
10.1016/j.cels.2017.10.016
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