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Systems genetics applications in metabolism research

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Abstract

The common forms of metabolic diseases are highly complex, involving hundreds of genes, environmental and lifestyle factors, age-related changes, sex differences and gut–microbiome interactions. Systems genetics is a population-based approach to address this complexity. In contrast to commonly used ‘reductionist’ approaches, such as gain or loss of function, that examine one element at a time, systems genetics uses high-throughput ‘omics’ technologies to quantitatively assess the many molecular differences among individuals in a population and then to relate these to physiologic functions or disease states. Unlike genome-wide association studies, systems genetics seeks to go beyond the identification of disease-causing genes to understand higher-order interactions at the molecular level. The purpose of this review is to introduce the systems genetics applications in the areas of metabolic and cardiovascular disease. Here, we explain how large clinical and omics-level data and databases from both human and animal populations are available to help researchers place genes in the context of pathways and networks and formulate hypotheses that can then be experimentally examined. We provide lists of such databases and examples of the integration of reductionist and systems genetics data. Among the important applications emerging is the development of improved nutritional and pharmacological strategies to address the rise of metabolic diseases.

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Fig. 1: Integration across biologic scales assayed in three different rodent reference populations.
Fig. 2: Analysis of tissue-specific regulation and tissue–tissue cross-talk by using systems genetics.
Fig. 3: Application of Mergeomics to identify key regulators of liver and mitochondrial functions.
Fig. 4: Examples of genetic interactions involved in metabolic traits.

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  • 05 December 2019

    In the version of the article originally published, the heading ‘Which tissue is likely to mediate the effects of genetic variation on disease susceptibility?’ was incorrectly displayed as a second-level heading but should have been a third-level heading. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We are grateful to our colleagues, particularly M. Mehrabian, B. Pasaniuc, H. Allayee, C. Pan and K. Chella Krishnan for useful discussions, and to R. Chen for help in manuscript preparation. This work was supported by NIH grants HL28481, GM115318, HL144651, DK117850, HL147883 (A.J.L.), HL138193 (M.S.) and DK104363 (X.Y.).

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Seldin, M., Yang, X. & Lusis, A.J. Systems genetics applications in metabolism research. Nat Metab 1, 1038–1050 (2019). https://doi.org/10.1038/s42255-019-0132-x

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