Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Systems genetics applications in metabolism research

Nature Metabolism volume 1, pages 1038–1050 (2019)Cite this article

Subjects

This article has been updated

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

Change history

  • 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.

References

  1. Civelek, M. & Lusis, A. J. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15, 34–48 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Karczewski, K. J. & Snyder, M. P. Integrative omics for health and disease. Nat. Rev. Genet. 19, 299–310 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Li, H. et al. An integrated systems genetics and omics toolkit to probe gene function. Cell Syst. 6, 90–102.e104 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Riordan, J. D. & Nadeau, J. H. From peas to disease: modifier genes, network resilience, and the genetics of health. Am. J. Hum. Genet. 101, 177–191 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sittig, L. J. et al. Genetic background limits generalizability of genotype-phenotype relationships. Neuron 91, 1253–1259 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Stoeger, T., Gerlach, M., Morimoto, R. I. & Nunes Amaral, L. A. Large-scale investigation of the reasons why potentially important genes are ignored. PLoS Biol. 16, e2006643 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017). The GTEx project’s characterization of variations in gene expression levels across individuals and 44 tissues of the human body.

    Article  PubMed  Google Scholar 

  9. Heinz, S. et al. Effect of natural genetic variation on enhancer selection and function. Nature 503, 487–492 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lusis, A. J. et al. The Hybrid Mouse Diversity Panel: a resource for systems genetics analyses of metabolic and cardiovascular traits. J. Lipid Res. 57, 925–942 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Andreux, P. A. et al. Systems genetics of metabolism: the use of the BXD murine reference panel for multiscalar integration of traits. Cell 150, 1287–1299 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Williams, E. G. et al. Systems proteomics of liver mitochondria function. Science 352, aad0189 (2016). Detailed phenotypic, molecular and genetic analyses of BXD animals fed normal or high-fat diets, uncovering new regulatory pathways of hepatic mitochondrial function and clinical outcomes.

    Article  PubMed  CAS  Google Scholar 

  13. Threadgill, D. W., Miller, D. R., Churchill, G. A. & de Villena, F. P. The collaborative cross: a recombinant inbred mouse population for the systems genetic era. ILAR J. 52, 24–31 (2011).

    Article  CAS  PubMed  Google Scholar 

  14. Bogue, M. A., Churchill, G. A. & Chesler, E. J. Collaborative Cross and Diversity Outbred data resources in the Mouse Phenome Database. Mamm. Genome 26, 511–520 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Nicod, J. et al. Genome-wide association of multiple complex traits in outbred mice by ultra-low-coverage sequencing. Nat. Genet. 48, 912–918 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Parker, C. C. et al. Genome-wide association study of behavioral, physiological and gene expression traits in outbred CFW mice. Nat. Genet. 48, 919–926 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Gonzales, N. M. & Palmer, A. A. Fine-mapping QTLs in advanced intercross lines and other outbred populations. Mamm. Genome 25, 271–292 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Holl, K. et al. Heterogeneous stock rats: a model to study the genetics of despair-like behavior in adolescence. Genes Brain Behav. 17, 139–148 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Buchner, D. A. & Nadeau, J. H. Contrasting genetic architectures in different mouse reference populations used for studying complex traits. Genome Res. 25, 775–791 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Laakso, M. et al. The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases. J. Lipid Res. 58, 481–493 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Moayyeri, A., Hammond, C. J., Hart, D. J. & Spector, T. D. The UK Adult Twin Registry (TwinsUK Resource). Twin Res. Hum. Genet. 16, 144–149 (2013).

    Article  PubMed  Google Scholar 

  22. Hedman, A. K. et al. Epigenetic patterns in blood associated with lipid traits predict incident coronary heart disease events and are enriched for results from genome-wide association studies. Circ. Cardiovasc Genet 10, e001487 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Huan, T. et al. Integrative network analysis reveals molecular mechanisms of blood pressure regulation. Mol. Syst. Biol. 11, 799 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Huan, T. et al. Dissecting the roles of microRNAs in coronary heart disease via integrative genomic analyses. Arterioscler. Thromb. Vasc. Biol. 35, 1011–1021 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Talukdar, H. A. et al. Cross-tissue regulatory gene networks in coronary artery disease. Cell Syst. 2, 196–208 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Keller, M. P. et al. Genetic drivers of pancreatic islet function. Genetics 209, 335–356 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Romanoski, C. E. et al. Network for activation of human endothelial cells by oxidized phospholipids: a critical role of heme oxygenase 1. Circ. Res. 109, e27–e41 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Romanoski, C. E. et al. Systems genetics analysis of gene-by-environment interactions in human cells. Am. J. Hum. Genet. 86, 399–410 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang, X. et al. Interrogation of the atherosclerosis-associated SORT1 (Sortilin 1) locus with primary human hepatocytes, induced pluripotent stem cell-hepatocytes, and locus-humanized mice. Arterioscler. Thromb. Vasc. Biol. 38, 76–82 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Chick, J. M. et al. Defining the consequences of genetic variation on a proteome-wide scale. Nature 534, 500–505 (2016). A detailed integration of transcriptomic and proteomic data in DO mice.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ghazalpour, A. et al. Comparative analysis of proteome and transcriptome variation in mouse. PLoS Genet. 7, e1001393 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. Parker, B. L. et al. An integrative systems genetic analysis of mammalian lipid metabolism. Nature 567, 187–193 (2019). Combined proteomic and lipidomic analyses of HMDP livers paired with experimental validation, identifying novel mechanisms of hepatic lipid regulation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Jha, P. et al. Systems analyses reveal physiological roles and genetic regulators of liver lipid species. Cell Syst. 6, 722–733.e726 (2018). An analysis of hepatic and plasma lipidomes in BXD RI strains, identifying new regulatory mechanisms and providing insight into human disease.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jha, P. et al. Genetic regulation of plasma lipid species and their association with metabolic phenotypes. Cell Syst. 6, 709–721.e706 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Romanov, N. et al. Disentangling genetic and environmental effects on the proteotypes of individuals. Cell 177, 1308–1318.e1310 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zeevi, D. et al. Structural variation in the gut microbiome associates with host health. Nature 568, 43–48 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710–717 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Voight, B. F. et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet 380, 572–580 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A. & Kim, D. Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 16, 85–97 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Sun, Y. V. & Hu, Y. J. Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv. Genet. 93, 147–190 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Argelaguet, R. et al. Multi-Omics Factor Analysis: a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Arneson, D., Bhattacharya, A., Shu, L., Mäkinen, V. P. & Yang, X. Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration. BMC Genomics 17, 722 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Shu, L. et al. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC Genomics 17, 874 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fryett, J. J., Inshaw, J., Morris, A. P. & Cordell, H. J. Comparison of methods for transcriptome imputation through application to two common complex diseases. Eur. J. Hum. Genet. 26, 1658–1667 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Albert, F. W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).

    Article  CAS  PubMed  Google Scholar 

  49. Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018). Survey of human type 2 diabetes GWAS SNPs and integration with open chromatic marks, highlighting pancreatic islet mechanisms as potential key drivers of disease.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kessler, T. et al. Functional characterization of the GUCY1A3 coronary artery disease risk locus. Circulation 136, 476–489 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bennett, B. J. et al. A high-resolution association mapping panel for the dissection of complex traits in mice. Genome Res. 20, 281–290 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hui, S. T. et al. The genetic architecture of NAFLD among inbred strains of mice. eLife 4, e05607 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Kojima, Y. et al. CD47-blocking antibodies restore phagocytosis and prevent atherosclerosis. Nature 536, 86–90 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Iotchkova, V. et al. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps. Nat. Genet. 48, 1303–1312 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Rajbhandari, P. et al. IL-10 signaling remodels adipose chromatin architecture to limit thermogenesis and energy expenditure. Cell 172, 218–233.e217 (2018).

    Article  CAS  PubMed  Google Scholar 

  57. Buscher, K. et al. Natural variation of macrophage activation as disease-relevant phenotype predictive of inflammation and cancer survival. Nat. Commun. 8, 16041 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Seldin, M. M. et al. A strategy for discovery of endocrine interactions with application to whole-body metabolism. Cell Metab. 27, 1138–1155.e1136 (2018). A systems genetics application for the discovery of novel endocrine factors on the basis of correlation structure of expression data across tissues.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Thomou, T. et al. Adipose-derived circulating miRNAs regulate gene expression in other tissues. Nature 542, 450–455 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Huang, J. K. et al. Systematic evaluation of molecular networks for discovery of disease genes. Cell Syst. 6, 484–495.e485 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008).

    Article  CAS  PubMed  Google Scholar 

  65. Keller, M. P. et al. A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res. 18, 706–716 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Song, W. M. & Zhang, B. Multiscale embedded gene co-expression network analysis. PLoS Comput. Biol. 11, e1004574 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Calabrese, G. et al. Systems genetic analysis of osteoblast-lineage cells. PLoS Genet. 8, e1003150 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Calabrese, G. M. et al. Integrating GWAS and co-expression network data identifies bone mineral density genes SPTBN1 and MARK3 and an osteoblast functional module. Cell Syst. 4, 46–59.e44 (2017). A beautiful example of the application of network modelling of systems genetics data to identify novel genes and pathways underlying the complex trait of BMD.

    Article  CAS  PubMed  Google Scholar 

  69. Farber, C. R. et al. Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis. PLoS Genet. 7, e1002038 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Mesner, L. D. et al. Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density. J. Clin. Invest. 124, 2736–2749 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Shu, L. et al. Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States. PLoS Genet. 13, e1007040 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Chella Krishnan, K. et al. Integration of multi-omics data from mouse diversity panel highlights mitochondrial dysfunction in non-alcoholic fatty liver disease. Cell Syst. 6, 103–115.e107 (2018). Application of Mergeomics to pinpoint mitochondrial function as a key contributor to hepatic triglyceride accumulation.

    Article  CAS  PubMed  Google Scholar 

  73. von Scheidt, M. et al. Applications and limitations of mouse models for understanding human atherosclerosis. Cell Metab. 25, 248–261 (2017).

    Article  CAS  Google Scholar 

  74. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Sackton, T. B. & Hartl, D. L. Genotypic context and epistasis in individuals and populations. Cell 166, 279–287 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Hemani, G. et al. Detection and replication of epistasis influencing transcription in humans. Nature 508, 249–253 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Lenz, T. L. et al. Widespread non-additive and interaction effects within HLA loci modulate the risk of autoimmune diseases. Nat. Genet. 47, 1085–1090 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Parks, B. W. et al. Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab. 17, 141–152 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Org, E. et al. Genetic and environmental control of host-gut microbiota interactions. Genome Res. 25, 1558–1569 (2015). Analysis of the genetics of gut microbiota composition in HMDP mice, demonstrating high heritability and GxE interactions.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Karp, N. A. et al. Prevalence of sexual dimorphism in mammalian phenotypic traits. Nat. Commun. 8, 15475 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Ober, C., Loisel, D. A. & Gilad, Y. Sex-specific genetic architecture of human disease. Nat. Rev. Genet. 9, 911–922 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Arnold, A. P., van Nas, A. & Lusis, A. J. Systems biology asks new questions about sex differences. Trends Endocrinol. Metab. 20, 471–476 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Norheim, F. et al. Gene-by-sex interactions in mitochondrial functions and cardio-metabolic traits. Cell Metab. 29, 932–949.e4 (2019). Demonstration of the importance of adipose-tissue respiration in the mediation of GxSex interactions in cardio-metabolic traits.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    Article  CAS  PubMed  Google Scholar 

  86. Subramanian, A. et al. A next generation connectivity map: l1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e1417 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Liu, J., Lee, J., Salazar Hernandez, M. A., Mazitschek, R. & Ozcan, U. Treatment of obesity with celastrol. Cell 161, 999–1011 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Lin, L. Y. et al. Systems genetics approach to biomarker discovery: Gpnmb and heart failure in mice and humans. G3 (Bethesda) 8, 3499–3506 (2018).

    Article  CAS  Google Scholar 

  89. Pirie, E. et al. Mouse genome-wide association studies and systems genetics uncover the genetic architecture associated with hepatic pharmacokinetic and pharmacodynamic properties of a constrained ethyl antisense oligonucleotide targeting Malat1. PLoS Genet. 14, e1007732 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. FitzGerald, G. et al. The future of humans as model organisms. Science 361, 552–553 (2018).

    Article  CAS  PubMed  Google Scholar 

  91. Attie, A. D., Churchill, G. A. & Nadeau, J. H. How mice are indispensable for understanding obesity and diabetes genetics. Curr. Opin. Endocrinol. Diabetes Obes. 24, 83–91 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Nadeau, J. H. & Auwerx, J. The virtuous cycle of human genetics and mouse models in drug discovery. Nat. Rev. Drug Discov. 18, 255–272 (2019).

    Article  CAS  PubMed  Google Scholar 

  93. Parks, B. W. et al. Genetic architecture of insulin resistance in the mouse. Cell Metab. 21, 334–347 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018).

    Article  CAS  PubMed  Google Scholar 

  95. Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Chappell, L., Russell, A. J. C. & Voet, T. Single-cell (multi)omics technologies. Annu. Rev. Genomics Hum. Genet. 19, 15–41 (2018).

    Article  CAS  PubMed  Google Scholar 

  99. Macaulay, I. C., Ponting, C. P. & Voet, T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 33, 155–168 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Mezger, A. et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nat. Commun. 9, 3647 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  101. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. van der Wijst, M. G. P. et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50, 493–497 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    Article  CAS  PubMed  Google Scholar 

  104. Kasahara, K. et al. Interactions between Roseburia intestinalis and diet modulate atherogenesis in a murine model. Nat. Microbiol. 3, 1461–1471 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Hoffman, N. J. et al. Global phosphoproteomic analysis of human skeletal muscle reveals a network of exercise-regulated kinases and AMPK substrates. Cell Metab. 22, 922–935 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Liao, C. Y., Rikke, B. A., Johnson, T. E., Diaz, V. & Nelson, J. F. Genetic variation in the murine lifespan response to dietary restriction: from life extension to life shortening. Aging Cell 9, 92–95 (2010).

    Article  CAS  PubMed  Google Scholar 

  108. Houtkooper, R. H. et al. Mitonuclear protein imbalance as a conserved longevity mechanism. Nature 497, 451–457 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Chen, R. et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat. Biotechnol. 34, 531–538 (2016).

    Article  CAS  PubMed  Google Scholar 

Download references

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.).

Author information

Author notes

  1. Marcus Seldin

    Present address: Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, CA, USA

Authors and Affiliations

  1. Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, USA

    Marcus Seldin & Aldons J. Lusis

  2. Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    Marcus Seldin & Aldons J. Lusis

  3. Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    Marcus Seldin & Aldons J. Lusis

  4. Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA

    Xia Yang

Authors
  1. Marcus Seldin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xia Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aldons J. Lusis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aldons J. Lusis.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: Pooja Jha.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-019-0132-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing