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.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).
Ayroles, J. F. et al. Systems genetics of complex traits in Drosophila melanogaster. Nature Genet. 41, 299–307 (2009).
Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics 4th edn (Longman, 1996).
Huang, W. et al. Epistasis dominates the genetic architecture of Drosophila quantitative traits. Proc. Natl Acad. Sci. USA 109, 15553–15559 (2012).
Lynch, M. & Walsh, J. B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, 1998).
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).
Burns, J. in Towards a Theoretical Biology Vol. 3 (ed. Waddington, C. H.) 47–51 (Edinburgh Univ. Press, 1970).
Waddington, C. H. The Strategy of the Genes 262 (Allen & Unwin, 1957).
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).
Petretto, E. et al. Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass. Nature Genet. 40, 546–552 (2008).
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).
Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nature Genet. 37, 710–717 (2005).
Ehrenreich, I. M. et al. Genetic architecture of highly complex chemical resistance traits across four yeast strains. PLoS Genet. 8, e1002570 (2012).
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.
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).
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).
Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).
van Nas, A. et al. Expression quantitative trait loci: replication, tissue- and sex-specificity in mice. Genetics 185, 1059–1068 (2010).
Breitling, R. et al. Genetical genomics: spotlight on QTL hotspots. PLoS Genet. 4, e1000232 (2008).
Orozco, L. D. et al. Unraveling inflammatory responses using systems genetics and gene–environment interactions in macrophages. Cell 151, 658–670 (2012).
Romanoski, C. E. et al. Systems genetics analysis of gene-by-environment interactions in human cells. Am. J. Hum. Genet. 86, 399–410 (2010).
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.
Civelek, M. et al. Genetic regulation of human adipose microRNA expression and its consequences for metabolic traits. Hum. Mol. Genet. 22, 3023–3037 (2013).
Kumar, V. et al. Human disease-associated genetic variation impacts large intergenic non-coding RNA expression. PLoS Genet. 9, e1003201 (2013).
Ghazalpour, A. et al. Comparative analysis of proteome and transcriptome variation in mouse. PLoS Genet. 7, e1001393 (2011).
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).
Babak, T. et al. Genetic validation of whole-transcriptome sequencing for mapping expression affected by cis-regulatory variation. BMC Genomics 11, 473 (2010).
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).
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).
Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482, 390–394 (2012).
Bell, J. T. et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 12, R10 (2011).
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.
Neph, S. et al. Circuitry and dynamics of human transcription factor regulatory networks. Cell 150, 1274–1286 (2012).
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.
Kasowski, M. et al. Extensive variation in chromatin states across humans. Science 342, 750–752 (2013).
Kilpinen, H. et al. Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science 342, 744–747 (2013).
McVicker, G. et al. Identification of genetic variants that affect histone modifications in human cells. Science 342, 747–749 (2013).
Foss, E. J. et al. Genetic basis of proteome variation in yeast. Nature Genet. 39, 1369–1375 (2007).
Holdt, L. M. et al. Quantitative trait loci mapping of the mouse plasma proteome (pQTL). Genetics 193, 601–608 (2013).
Lourdusamy, A. et al. Identification of cis-regulatory variation influencing protein abundance levels in human plasma. Hum. Mol. Genet. 21, 3719–3726 (2012).
Melzer, D. et al. A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet. 4, e1000072 (2008).
Wu, L. et al. Variation and genetic control of protein abundance in humans. Nature 499, 79–82 (2013).
Krishna, R. G. & Wold, F. Post-translational modification of proteins. Adv. Enzymol. Relat. Areas Mol. Biol. 67, 265–298 (1993).
Patti, G. J., Yanes, O. & Siuzdak, G. Innovation: Metabolomics: the apogee of the omics trilogy. Nature Rev. Mol. Cell Biol. 13, 263–269 (2012).
Liu, S. et al. A diurnal serum lipid integrates hepatic lipogenesis and peripheral fatty acid use. Nature 502, 550–554 (2013).
Gieger, C. et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 4, e1000282 (2008).
Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nature Genet. 44, 269–276 (2012).
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.
Flint, J. & Mackay, T. F. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res. 19, 723–733 (2009).
Jarvis, J. P. & Cheverud, J. M. Mapping the epistatic network underlying murine reproductive fatpad variation. Genetics 187, 597–610 (2011).
Shao, H. et al. Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc. Natl Acad. Sci. USA 105, 19910–19914 (2008).
Naya, F. J. et al. Mitochondrial deficiency and cardiac sudden death in mice lacking the MEF2A transcription factor. Nature Med. 8, 1303–1309 (2002).
Weiss, J. N. et al. “Good enough solutions” and the genetics of complex diseases. Circ. Res. 111, 493–504 (2012).
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.
Prabhu, S. & Pe'er, I. Ultrafast genome-wide scan for SNP–SNP interactions in common complex disease. Genome Res. 22, 2230–2240 (2012).
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).
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%.
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%.
Smith, E. N. & Kruglyak, L. Gene–environment interaction in yeast gene expression. PLoS Biol. 6, e83 (2008).
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).
Fu, J. et al. System-wide molecular evidence for phenotypic buffering in Arabidopsis. Nature Genet. 41, 166–167 (2009).
Zhu, J. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature Genet. 40, 854–861 (2008).
Pearl, J. Causality (Cambridge Univ. Press, 2009).
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).
Shipley, B. Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations, and Causal Inference (Cambridge Univ. Press, 2002).
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.
Huan, T. et al. A systems biology framework identifies molecular underpinnings of coronary heart disease. Arterioscler Thromb. Vasc. Biol. (2013).
Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013).
Heinig, M. et al. A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Nature 467, 460–464 (2010).
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).
Neto, E. C. et al. Modeling causality for pairs of phenotypes in system genetics. Genetics 193, 1003–1013 (2013).
Blair, R. H., Kliebenstein, D. J. & Churchill, G. A. What can causal networks tell us about metabolic pathways? PLoS Comput. Biol. 8, e1002458 (2012).
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).
Chaibub Neto, E., Ferrara, C. T., Attie, A. D. & Yandell, B. S. Inferring causal phenotype networks from segregating populations. Genetics 179, 1089–1100 (2008).
Li, R. et al. Structural model analysis of multiple quantitative traits. PLoS Genet. 2, e114 (2006).
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).
Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).
Lehner, B. Genotype to phenotype: lessons from model organisms for human genetics. Nature Rev. Genet. 14, 168–178 (2013).
Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).
Dixon, S. J., Costanzo, M., Baryshnikova, A., Andrews, B. & Boone, C. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).
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).
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.
Sreekumar, A. et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 457, 910–914 (2009).
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.
Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nature Med. 17, 448–453 (2011).
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).
Koeth, R. A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nature Med. 19, 576–585 (2013).
Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).
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).
Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).
Kandoth, C. et al. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).
Quigley, D. & Balmain, A. Systems genetics analysis of cancer susceptibility: from mouse models to humans. Nature Rev. Genet. 10, 651–657 (2009).
Fendler, B. & Atwal, G. Systematic deciphering of cancer genome networks. Yale J. Biol. Med. 85, 339–345 (2012).
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).
Yang, X. et al. Identification and validation of genes affecting aortic lesions in mice. J. Clin. Invest. 120, 2414–2422 (2010).
Hubner, N. et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nature Genet. 37, 243–253 (2005).
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.
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.
Keller, M. P. & Attie, A. D. Physiological insights gained from gene expression analysis in obesity and diabetes. Annu. Rev. Nutr. 30, 341–364 (2010).
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).
Yang, X. et al. Validation of candidate causal genes for obesity that affect shared metabolic pathways and networks. Nature Genet. 41, 415–423 (2009).
Calabrese, G. et al. Systems genetic analysis of osteoblast-lineage cells. PLoS Genet. 8, e1003150 (2012).
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).
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).
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).
Davis, R. C. et al. Genome-wide association mapping of blood cell traits in mice. Mamm. Genome 24, 105–118 (2013).
Baud, A. et al. Combined sequence-based and genetic mapping analysis of complex traits in outbred rats. Nature Genet. 45, 767–775 (2013).
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).
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).
Erler, J. T. & Linding, R. Network medicine strikes a blow against breast cancer. Cell 149, 731–733 (2012).
Lee, M. J. et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149, 780–794 (2012).
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).
Medina, M. W. et al. RHOA is a modulator of the cholesterol-lowering effects of statin. PLoS Genet. 8, e1003058 (2012).
Mangravite, L. M. et al. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy. Nature 502, 377–380 (2013).
Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nature Rev. Genet. 11, 855–866 (2010).
GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nature Genet. 45, 580–585 (2013).
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).
Arnold, A. P. & Lusis, A. J. Understanding the sexome: measuring and reporting sex differences in gene systems. Endocrinology 153, 2551–2555 (2012).
van Nas, A. et al. Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks. Endocrinology 150, 1235–1249 (2009).
Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nature Genet. 42, 579–589 (2010).
Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
Civelek, M. & Lusis, A. J. Conducting the metabolic syndrome orchestra. Nature Genet. 43, 506–508 (2011).
Schadt, E. E. Molecular networks as sensors and drivers of common human diseases. Nature 461, 218–223 (2009).
Zhu, J. et al. Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation. PLoS Biol. 10, e1001301 (2012).
Atwell, S. et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631 (2010).
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).
Rockman, M. V., Skrovanek, S. S. & Kruglyak, L. Selection at linked sites shapes heritable phenotypic variation in C. elegans. Science 330, 372–376 (2010).
Jumbo-Lucioni, P. et al. Systems genetics analysis of body weight and energy metabolism traits in Drosophila melanogaster. BMC Genomics 11, 297 (2010).
King, E. G. et al. Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource. Genome Res. 22, 1558–1566 (2012).
Philip, V. M. et al. Genetic analysis in the Collaborative Cross breeding population. Genome Res. 21, 1223–1238 (2011).
Churchill, G. A., Gatti, D. M., Munger, S. C. & Svenson, K. L. The Diversity Outbred mouse population. Mamm. Genome 23, 713–718 (2012).
Aitman, T. J. et al. Progress and prospects in rat genetics: a community view. Nature Genet. 40, 516–522 (2008).
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).
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).
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).
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
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Related links
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
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
Published:
Issue Date:
DOI: https://doi.org/10.1038/nrg3575
This article is cited by
-
From Mendel to multi-omics: shifting paradigms
European Journal of Human Genetics (2024)
-
Mitochondrial disorders leading to Alzheimer’s disease—perspectives of diagnosis and treatment
GeroScience (2024)
-
Integration of omics analyses into GMO risk assessment in Europe: a case study from soybean field trials
Environmental Sciences Europe (2023)
-
X-ray cross-complementing family: the bridge linking DNA damage repair and cancer
Journal of Translational Medicine (2023)
-
Genetic risk assessment based on association and prediction studies
Scientific Reports (2023)
