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.

Systems genetics approaches to understand complex traits

Key Points

  • Systems genetics is an approach to understand complex traits, including common diseases. It examines intermediate molecular phenotypes, such as transcript, protein or metabolite abundance, to bridge DNA variation with the traits of interest.

  • Systems genetics has been driven by the development of high-throughput technologies, which makes it possible to interrogate molecular phenotypes in populations of humans and of model organisms.

  • Genetic mapping of molecular phenotypes, correlation among the phenotypes, and statistical modelling are used to capture the interactions among these traits. This provides a broad view of information flow from the genetic variant to the trait.

  • Network modelling provides a useful approach in organizing the data into biologically meaningful units and interactions.

  • Systems genetics approaches can be integrated with genome-wide association studies to predict causal genes and their functions; for example, expression quantitative trait loci provide a measure of functional variation.

  • Animal populations have some important advantages for systems genetics studies, such as the availability of relevant tissues and the ability to control the environment of such studies.

Abstract

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Systems genetics strategies.
Figure 2: Collection and analysis of systems genetics data.
Figure 3: Genetics of gene expression and genetic interactions.
Figure 4: Predicting causal genes in GWAS loci.

References

  1. 1

    Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Ayroles, J. F. et al. Systems genetics of complex traits in Drosophila melanogaster. Nature Genet. 41, 299–307 (2009).

    CAS  PubMed  Google Scholar 

  3. 3

    Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics 4th edn (Longman, 1996).

    Google Scholar 

  4. 4

    Huang, W. et al. Epistasis dominates the genetic architecture of Drosophila quantitative traits. Proc. Natl Acad. Sci. USA 109, 15553–15559 (2012).

    CAS  PubMed  Google Scholar 

  5. 5

    Lynch, M. & Walsh, J. B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, 1998).

    Google Scholar 

  6. 6

    Wu, C. et al. Genome-wide association analyses of esophageal squamous cell carcinoma in Chinese identify multiple susceptibility loci and gene–environment interactions. Nature Genet. 44, 1090–1097 (2012).

    CAS  PubMed  Google Scholar 

  7. 7

    Burns, J. in Towards a Theoretical Biology Vol. 3 (ed. Waddington, C. H.) 47–51 (Edinburgh Univ. Press, 1970).

    Google Scholar 

  8. 8

    Waddington, C. H. The Strategy of the Genes 262 (Allen & Unwin, 1957).

    Google Scholar 

  9. 9

    Passador-Gurgel, G., Hsieh, W. P., Hunt, P., Deighton, N. & Gibson, G. Quantitative trait transcripts for nicotine resistance in Drosophila melanogaster. Nature Genet. 39, 264–268 (2007).

    CAS  PubMed  Google Scholar 

  10. 10

    Petretto, E. et al. Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass. Nature Genet. 40, 546–552 (2008).

    CAS  PubMed  Google Scholar 

  11. 11

    Aitman, T. J. et al. Identification of Cd36 (Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nature Genet. 21, 76–83 (1999).

    CAS  PubMed  Google Scholar 

  12. 12

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

    CAS  PubMed  Google Scholar 

  13. 13

    Ehrenreich, I. M. et al. Genetic architecture of highly complex chemical resistance traits across four yeast strains. PLoS Genet. 8, e1002570 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002). This is the first study to carry out a linkage analysis of global gene expression in a cross between a laboratory strain and wild strain of S. cerevisiae , which shows widespread cis and trans regulation of gene expression.

    CAS  PubMed  Google Scholar 

  15. 15

    Brem, R. B. & Kruglyak, L. The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc. Natl Acad. Sci. USA 102, 1572–1577 (2005).

    CAS  PubMed  Google Scholar 

  16. 16

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    van Nas, A. et al. Expression quantitative trait loci: replication, tissue- and sex-specificity in mice. Genetics 185, 1059–1068 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Breitling, R. et al. Genetical genomics: spotlight on QTL hotspots. PLoS Genet. 4, e1000232 (2008).

    PubMed  PubMed Central  Google Scholar 

  20. 20

    Orozco, L. D. et al. Unraveling inflammatory responses using systems genetics and gene–environment interactions in macrophages. Cell 151, 658–670 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Schaub, M. A., Boyle, A. P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 1748–1759 (2012). This study investigates the overlap between the disease-associated SNPs that were identified in GWASs and multiple types of ENCODE data; it shows that up to 80% of the disease-associated variants lie in functional regions of the genome.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Civelek, M. et al. Genetic regulation of human adipose microRNA expression and its consequences for metabolic traits. Hum. Mol. Genet. 22, 3023–3037 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Kumar, V. et al. Human disease-associated genetic variation impacts large intergenic non-coding RNA expression. PLoS Genet. 9, e1003201 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

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

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Babak, T. et al. Genetic validation of whole-transcriptome sequencing for mapping expression affected by cis-regulatory variation. BMC Genomics 11, 473 (2010).

    PubMed  PubMed Central  Google Scholar 

  28. 28

    Almlof, J. C. et al. Powerful identification of cis-regulatory SNPs in human primary monocytes using allele-specific gene expression. PLoS ONE 7, e52260 (2012).

    PubMed  PubMed Central  Google Scholar 

  29. 29

    Lagarrigue, S. et al. Analysis of allele specific expression in mouse liver by RNA-seq: a comparison with cis-eQTL identified using genetic linkage. Genetics 195, 1157–1166 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Bell, J. T. et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 12, R10 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Gaffney, D. J. et al. Dissecting the regulatory architecture of gene expression QTLs. Genome Biol. 13, R7 (2012). This study combines eQTL results from LCLs and regulatory information from the ENCODE project to annotate the putative function of variants that affect gene expression.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Neph, S. et al. Circuitry and dynamics of human transcription factor regulatory networks. Cell 150, 1274–1286 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Heinz, S. et al. Effect of natural genetic variation on enhancer selection and function. Nature http://dx.doi.org/10.1038/nature12615 (2013). This study compares the binding of lineage-determining and specific transcription factors in primary macrophages of two different strains of mice.

  35. 35

    Kasowski, M. et al. Extensive variation in chromatin states across humans. Science 342, 750–752 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Kilpinen, H. et al. Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science 342, 744–747 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    McVicker, G. et al. Identification of genetic variants that affect histone modifications in human cells. Science 342, 747–749 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Foss, E. J. et al. Genetic basis of proteome variation in yeast. Nature Genet. 39, 1369–1375 (2007).

    CAS  PubMed  Google Scholar 

  39. 39

    Holdt, L. M. et al. Quantitative trait loci mapping of the mouse plasma proteome (pQTL). Genetics 193, 601–608 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Lourdusamy, A. et al. Identification of cis-regulatory variation influencing protein abundance levels in human plasma. Hum. Mol. Genet. 21, 3719–3726 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Melzer, D. et al. A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet. 4, e1000072 (2008).

    PubMed  PubMed Central  Google Scholar 

  42. 42

    Wu, L. et al. Variation and genetic control of protein abundance in humans. Nature 499, 79–82 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Krishna, R. G. & Wold, F. Post-translational modification of proteins. Adv. Enzymol. Relat. Areas Mol. Biol. 67, 265–298 (1993).

    CAS  PubMed  Google Scholar 

  44. 44

    Patti, G. J., Yanes, O. & Siuzdak, G. Innovation: Metabolomics: the apogee of the omics trilogy. Nature Rev. Mol. Cell Biol. 13, 263–269 (2012).

    CAS  Google Scholar 

  45. 45

    Liu, S. et al. A diurnal serum lipid integrates hepatic lipogenesis and peripheral fatty acid use. Nature 502, 550–554 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Gieger, C. et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 4, e1000282 (2008).

    PubMed  PubMed Central  Google Scholar 

  47. 47

    Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nature Genet. 44, 269–276 (2012).

    CAS  PubMed  Google Scholar 

  48. 48

    Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011). This study profiles >250 metabolites that represent >60 biochemical pathways in ~3,000 people. It shows that many GWAS loci are associated with serum metabolite levels and that the effect sizes for metabolites are much larger than those for clinical traits.

    CAS  PubMed  Google Scholar 

  49. 49

    Flint, J. & Mackay, T. F. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res. 19, 723–733 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Jarvis, J. P. & Cheverud, J. M. Mapping the epistatic network underlying murine reproductive fatpad variation. Genetics 187, 597–610 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Shao, H. et al. Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc. Natl Acad. Sci. USA 105, 19910–19914 (2008).

    CAS  PubMed  Google Scholar 

  52. 52

    Naya, F. J. et al. Mitochondrial deficiency and cardiac sudden death in mice lacking the MEF2A transcription factor. Nature Med. 8, 1303–1309 (2002).

    CAS  PubMed  Google Scholar 

  53. 53

    Weiss, J. N. et al. “Good enough solutions” and the genetics of complex diseases. Circ. Res. 111, 493–504 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012). This paper discusses that the estimates of missing heritability may be misleading owing to the assumptions of no epistasis when calculating heritability from population data.

    CAS  PubMed  Google Scholar 

  55. 55

    Prabhu, S. & Pe'er, I. Ultrafast genome-wide scan for SNP–SNP interactions in common complex disease. Genome Res. 22, 2230–2240 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Hill, W. G., Goddard, M. E. & Visscher, P. M. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4, e1000008 (2008).

    PubMed  PubMed Central  Google Scholar 

  57. 57

    Bloom, J. S., Ehrenreich, I. M., Loo, W. T., Lite, T. L. & Kruglyak, L. Finding the sources of missing heritability in a yeast cross. Nature 494, 234–237 (2013). This paper uses a cross in yeast to identify the additive and epistatic contributions to heritability of 46 different traits and shows that contribution of gene–gene interactions varies among traits, from near zero to ~50%.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    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). This study uses the Hybrid Mouse Diversity Panel to identify the genetic loci that regulate body fat gain and gut microbiota composition in response to a high fat diet. It shows that the estimated heritability of body fat changes can be as high as 85%.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Smith, E. N. & Kruglyak, L. Gene–environment interaction in yeast gene expression. PLoS Biol. 6, e83 (2008).

    PubMed  PubMed Central  Google Scholar 

  60. 60

    Smirnov, D. A., Morley, M., Shin, E., Spielman, R. S. & Cheung, V. G. Genetic analysis of radiation-induced changes in human gene expression. Nature 459, 587–591 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Fu, J. et al. System-wide molecular evidence for phenotypic buffering in Arabidopsis. Nature Genet. 41, 166–167 (2009).

    CAS  PubMed  Google Scholar 

  62. 62

    Zhu, J. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genet. 40, 854–861 (2008).

    CAS  PubMed  Google Scholar 

  63. 63

    Pearl, J. Causality (Cambridge Univ. Press, 2009).

    Google Scholar 

  64. 64

    Schwartz, S. M., Schwartz, H. T., Horvath, S., Schadt, E. & Lee, S. I. A systematic approach to multifactorial cardiovascular disease: causal analysis. Arterioscler Thromb. Vasc. Biol. 32, 2821–2835 (2013).

    Google Scholar 

  65. 65

    Shipley, B. Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations, and Causal Inference (Cambridge Univ. Press, 2002).

    Google Scholar 

  66. 66

    Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nature Methods 9, 796–804 (2012). This study compares >30 methods that aim to reconstruct regulatory networks from high-throughput data and concludes that a consensus network that is constructed by integrating the predictions of different methods has the best performance to infer regulatory interactions.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67

    Huan, T. et al. A systems biology framework identifies molecular underpinnings of coronary heart disease. Arterioscler Thromb. Vasc. Biol. (2013).

  68. 68

    Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Heinig, M. et al. A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Nature 467, 460–464 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    Hageman, R. S., Leduc, M. S., Korstanje, R., Paigen, B. & Churchill, G. A. A Bayesian framework for inference of the genotype–phenotype map for segregating populations. Genetics 187, 1163–1170 (2011).

    PubMed  PubMed Central  Google Scholar 

  71. 71

    Neto, E. C. et al. Modeling causality for pairs of phenotypes in system genetics. Genetics 193, 1003–1013 (2013).

    PubMed  PubMed Central  Google Scholar 

  72. 72

    Blair, R. H., Kliebenstein, D. J. & Churchill, G. A. What can causal networks tell us about metabolic pathways? PLoS Comput. Biol. 8, e1002458 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    Li, Y., Tesson, B. M., Churchill, G. A. & Jansen, R. C. Critical reasoning on causal inference in genome-wide linkage and association studies. Trends Genet. 26, 493–498 (2010).

    PubMed  PubMed Central  Google Scholar 

  74. 74

    Chaibub Neto, E., Ferrara, C. T., Attie, A. D. & Yandell, B. S. Inferring causal phenotype networks from segregating populations. Genetics 179, 1089–1100 (2008).

    PubMed  PubMed Central  Google Scholar 

  75. 75

    Li, R. et al. Structural model analysis of multiple quantitative traits. PLoS Genet. 2, e114 (2006).

    PubMed  PubMed Central  Google Scholar 

  76. 76

    Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77

    Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78

    Lehner, B. Genotype to phenotype: lessons from model organisms for human genetics. Nature Rev. Genet. 14, 168–178 (2013).

    CAS  PubMed  Google Scholar 

  79. 79

    Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

    Dixon, S. J., Costanzo, M., Baryshnikova, A., Andrews, B. & Boone, C. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).

    CAS  PubMed  Google Scholar 

  81. 81

    Choy, E. et al. Genetic analysis of human traits in vitro: drug response and gene expression in lymphoblastoid cell lines. PLoS Genet. 4, e1000287 (2008).

    PubMed  PubMed Central  Google Scholar 

  82. 82

    Small, K. S. et al. Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nature Genet. 43, 561–564 (2011). This study identifies KLF14 as the causal gene in a GWAS locus that is associated with both diabetes and lipoprotein levels and dissects its role as a master regulator of gene expression in human fat tissues.

    CAS  PubMed  Google Scholar 

  83. 83

    Sreekumar, A. et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457, 910–914 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84

    Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011). Using an unbiased metabolomics approach this study reports the identification of a serum metabolite that is derived from dietary choline produced by the gut microbiota as a novel risk factor for cardiovascular disease.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85

    Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nature Med. 17, 448–453 (2011).

    PubMed  Google Scholar 

  86. 86

    Craciun, S. & Balskus, E. P. Microbial conversion of choline to trimethylamine requires a glycyl radical enzyme. Proc. Natl Acad. Sci. USA 109, 21307–21312 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87

    Koeth, R. A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nature Med. 19, 576–585 (2013).

    CAS  PubMed  Google Scholar 

  88. 88

    Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89

    Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).

    CAS  Google Scholar 

  91. 91

    Kandoth, C. et al. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).

    PubMed  PubMed Central  Google Scholar 

  92. 92

    Quigley, D. & Balmain, A. Systems genetics analysis of cancer susceptibility: from mouse models to humans. Nature Rev. Genet. 10, 651–657 (2009).

    CAS  PubMed  Google Scholar 

  93. 93

    Fendler, B. & Atwal, G. Systematic deciphering of cancer genome networks. Yale J. Biol. Med. 85, 339–345 (2012).

    PubMed  PubMed Central  Google Scholar 

  94. 94

    Wang, S. S. et al. Identification of pathways for atherosclerosis in mice: integration of quantitative trait locus analysis and global gene expression data. Circ. Res. 101, e11–e30 (2007).

    CAS  PubMed  Google Scholar 

  95. 95

    Yang, X. et al. Identification and validation of genes affecting aortic lesions in mice. J. Clin. Invest. 120, 2414–2422 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96

    Hubner, N. et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nature Genet. 37, 243–253 (2005).

    CAS  PubMed  Google Scholar 

  97. 97

    McDermott-Roe, C. et al. Endonuclease G is a novel determinant of cardiac hypertrophy and mitochondrial function. Nature 478, 114–118 (2011). References 10 and 97 use various systems genetics approaches to identify both endonuclease G and osteoglycin as causal genes in loci that underlie left ventricular heart mass in rats.

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98

    Hodgin, J. B. et al. Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli. Diabetes 62, 299–308 (2012). This study shows the conservation of glomerular gene expression networks of humans and of different mouse models of diabetic nephropathy.

    PubMed  PubMed Central  Google Scholar 

  99. 99

    Keller, M. P. & Attie, A. D. Physiological insights gained from gene expression analysis in obesity and diabetes. Annu. Rev. Nutr. 30, 341–364 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100

    Wang, S. et al. Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity. PLoS Genet. 2, e15 (2006).

    PubMed  PubMed Central  Google Scholar 

  101. 101

    Yang, X. et al. Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nature Genet. 41, 415–423 (2009).

    CAS  PubMed  Google Scholar 

  102. 102

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

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104

    Park, C. C. et al. Gene networks associated with conditional fear in mice identified using a systems genetics approach. BMC Syst. Biol. 5, 43 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105

    Langley, S. R. et al. Systems-level approaches reveal conservation of trans-regulated genes in the rat and genetic determinants of blood pressure in humans. Cardiovasc. Res. 97, 653–665 (2013).

    CAS  PubMed  Google Scholar 

  106. 106

    Davis, R. C. et al. Genome-wide association mapping of blood cell traits in mice. Mamm. Genome 24, 105–118 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107

    Baud, A. et al. Combined sequence-based and genetic mapping analysis of complex traits in outbred rats. Nature Genet. 45, 767–775 (2013).

    CAS  PubMed  Google Scholar 

  108. 108

    van Nas, A. et al. The systems genetics resource (SGR): a web application to mine global data for complex disease traits. Front. Genet. 4, 84 (2013).

    PubMed  PubMed Central  Google Scholar 

  109. 109

    Schadt, E. E., Friend, S. H. & Shaywitz, D. A. A network view of disease and compound screening. Nature Rev. Drug Discov. 8, 286–295 (2009).

    CAS  Google Scholar 

  110. 110

    Erler, J. T. & Linding, R. Network medicine strikes a blow against breast cancer. Cell 149, 731–733 (2012).

    CAS  PubMed  Google Scholar 

  111. 111

    Lee, M. J. et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149, 780–794 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112

    Min, J. L. et al. The use of genome-wide eQTL associations in lymphoblastoid cell lines to identify novel genetic pathways involved in complex traits. PLoS ONE 6, e22070 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113

    Medina, M. W. et al. RHOA is a modulator of the cholesterol-lowering effects of statin. PLoS Genet. 8, e1003058 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114

    Mangravite, L. M. et al. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy. Nature 502, 377–380 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115

    Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nature Rev. Genet. 11, 855–866 (2010).

    CAS  PubMed  Google Scholar 

  116. 116

    GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nature Genet. 45, 580–585 (2013).

  117. 117

    Pai, A. A. et al. The contribution of RNA decay quantitative trait loci to inter-individual variation in steady-state gene expression levels. PLoS Genet. 8, e1003000 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118

    Arnold, A. P. & Lusis, A. J. Understanding the sexome: measuring and reporting sex differences in gene systems. Endocrinology 153, 2551–2555 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119

    van Nas, A. et al. Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks. Endocrinology 150, 1235–1249 (2009).

    CAS  PubMed  Google Scholar 

  120. 120

    Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nature Genet. 42, 579–589 (2010).

    CAS  PubMed  Google Scholar 

  121. 121

    Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122

    Civelek, M. & Lusis, A. J. Conducting the metabolic syndrome orchestra. Nature Genet. 43, 506–508 (2011).

    CAS  PubMed  Google Scholar 

  123. 123

    Schadt, E. E. Molecular networks as sensors and drivers of common human diseases. Nature 461, 218–223 (2009).

    CAS  PubMed  Google Scholar 

  124. 124

    Zhu, J. et al. Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol. 10, e1001301 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125

    Atwell, S. et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126

    Gaertner, B. E., Parmenter, M. D., Rockman, M. V., Kruglyak, L. & Phillips, P. C. More than the sum of its parts: a complex epistatic network underlies natural variation in thermal preference behavior in Caenorhabditis elegans. Genetics 192, 1533–1542 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 127

    Rockman, M. V., Skrovanek, S. S. & Kruglyak, L. Selection at linked sites shapes heritable phenotypic variation in C. elegans. Science 330, 372–376 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. 128

    Jumbo-Lucioni, P. et al. Systems genetics analysis of body weight and energy metabolism traits in Drosophila melanogaster. BMC Genomics 11, 297 (2010).

    PubMed  PubMed Central  Google Scholar 

  129. 129

    King, E. G. et al. Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource. Genome Res. 22, 1558–1566 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 130

    Philip, V. M. et al. Genetic analysis in the Collaborative Cross breeding population. Genome Res. 21, 1223–1238 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. 131

    Churchill, G. A., Gatti, D. M., Munger, S. C. & Svenson, K. L. The Diversity Outbred mouse population. Mamm. Genome 23, 713–718 (2012).

    PubMed  PubMed Central  Google Scholar 

  132. 132

    Aitman, T. J. et al. Progress and prospects in rat genetics: a community view. Nature Genet. 40, 516–522 (2008).

    CAS  PubMed  Google Scholar 

  133. 133

    Printz, M. P., Jirout, M., Jaworski, R., Alemayehu, A. & Kren, V. Genetic models in applied physiology. HXB/BXH rat recombinant inbred strain platform: a newly enhanced tool for cardiovascular, behavioral, and developmental genetics and genomics. J. Appl. Physiol. 94, 2510–2522 (2003).

    CAS  PubMed  Google Scholar 

  134. 134

    Simonis, M. et al. Genetic basis of transcriptome differences between the founder strains of the rat HXB/BXH recombinant inbred panel. Genome Biol. 13, R31 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. 135

    Stancakova, A. et al. Hyperglycemia and a common variant of GCKR are associated with the levels of eight amino acids in 9,369 Finnish men. Diabetes 61, 1895–1902 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank R. Chen for assistance in the preparation of this paper. M.C. is supported by Ruth L. Kirschstein National Research Service Award T32HL69766; A.J.L. is supported by the US National Institutes of Health grants HL30568, HL28481, HL094322, HL110667 and DP3D094311, and Transatlantic Networks of Excellence Award from Foundation Leducq. They are also grateful to the detailed and critical reviewers.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Aldons J. Lusis.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Glossary

Systems genetics

A global analysis of the molecular factors that underlie variability in physiological or clinical phenotypes across individuals in a population. It considers not only the underlying genetic variation but also intermediate phenotypes such as gene expression, protein levels and metabolite levels, in addition to gene-by-gene and gene-by-environment interactions.

Natural populations

Human populations, or animal populations in wild environments, that are experiencing normal selective pressures. By contrast, laboratory animal populations, such as inbred strains, can show natural genetic variation, but they have been subjected to nonrandom breeding and artificial selection.

Natural genetic variation

Genetic variation that is present in all populations as a result of mutations that occur in the germline; the frequencies of such mutations in populations are affected by selection and by random drift. This is in contrast with experimental variation that is introduced by techniques such as gene targeting and chemical mutagenesis.

Principal components

Dominant patterns in multivariate data, as extracted by the principal component analysis data reduction method.

Modules

In the context of network modelling, groups of components that are tightly connected or correlated across a set of conditions, perturbations or genetic backgrounds.

Inbred strains

Strains in which a set of naturally occurring genetic variations have been fixed by many generations of inbreeding.

Biological scales

Various levels in the flow of information from DNA to proteins to metabolites to cell structures to cell interactions.

Chromatin immunoprecipitation followed by sequencing

(ChIP–seq). A method that is used to analyse protein–DNA interactions by combining chromatin immunoprecipitation with next-generation sequencing to identify binding sites of DNA-associated proteins.

Epistasis

A statistical interaction between two or more genetic loci, such that their effects are non-additive.

Missing heritability

The phenomenon whereby the fraction of the heritability of a trait that is explained by a genome-wide association study is modest.

Haplotypes

Combinations of alleles at genetic loci that are inherited together.

Recombinant inbred strains

A set of inbred strains that is generally produced by crossing two parental inbred strains and then inbreeding random intercross progeny; they provide a permanent resource for examining the segregation of traits that differ between the parental strains.

Congenic strains

Strains in which a small region of the genome from one strain has been placed, by repeated crossing, onto the genetic background of a second strain.

Linkage disequilibrium blocks

Regions of high correlation across genetic markers, which results from their linkage in cis on a chromosome and thus infrequent recombination during meiosis. LD blocks are often demarcated by recombination hot spots.

CEPH cell lines

A large set of lymphoblastoid cell lines from European pedigrees that serves as a reference collection for studies of allele frequencies, linkage mapping and the genetics of gene expression.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Civelek, M., Lusis, A. Systems genetics approaches to understand complex traits. Nat Rev Genet 15, 34–48 (2014). https://doi.org/10.1038/nrg3575

Download citation

Further reading

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