Ongoing studies in many species seek to understand the origins, architecture and consequences of phenotypic variation under normal and dysfunctional conditions, with the aim of identifying targets for intervention that can prevent, stabilize or reverse disease. Some suggest that only humans are appropriate for studying these questions and argue that candidate drug targets identified in mouse models are largely unreliable. Here, we review the vast evidence showing that mouse models continue to make fundamental contributions to our understanding of genetic principles, pathogenic mechanisms and therapeutic modalities. We propose a virtuous cycle in which the power of observational studies and natural experiments in humans are closely integrated with the rigour of true experiments in model organisms.
Subscribe to Journal
Get full journal access for 1 year
only $22.08 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Ginsburg, D. Genetics and genomics to the clinic: a long road ahead. Cell 147, 17–19 (2011).
Visscher, P. M. Human complex trait genetics in the 21st century. Genetics 202, 377–379 (2016).
FitzGerald, G. et al. The future of humans as model organisms. Science 361, 552–553 (2018).
Pound, P. & Bracken, M. B. Is animal research sufficiently evidence based to be a cornerstone of biomedical research? BMJ 348, g3387 (2014).
Snow, J. On the Mode of Communication of Cholera (John Churchill, 1855).
US Surgeon General’s Advisory Committee on Smoking and Health. Smoking and Health: Report of the Advisory Committee to the Surgeon General (US Public Health Service & Department of Health, Education and Welfare, 1964).
Smithells, R. W. et al. Possible prevention of neural-tube defects by periconceptional vitamin supplementation. Lancet 1, 339–340 (1980).
Fisher, R. A. The Design of Experiments (Oliver and Boyd, 1935).
Fisher, A. J., Medaglia, J. D. & Jeronimus, B. F. Lack of group-to-individual generalizability is a threat to human subjects research. Proc. Natl Acad. Sci. USA 115, E6106–E6115 (2018). References 9 and 10 underscore the non-ergodic nature of biology, explaining why group-to-individual generalizability is low.
Molenaar, P. C. A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Measurement 2, 201–218 (2004).
Flint, J. & Mackay, T. F. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res. 19, 723–733 (2009). References 11–13, 27 and 278 are reviews illustrating the polygenic and omnigenic nature of traits.
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).
Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J. & Visscher, P. M. Common disease is more complex than implied by the core gene omnigenic model. Cell 173, 1573–1580 (2018).
Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).
McCabe, E. R. B. Modifier genes: moving from pathogenesis to therapy. Mol. Genet. Metab. 122, 1–3 (2017).
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). This is a review illustrating the variable nature of genetics and discussing GxG, GxE and GxA interactions.
Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).
Eichler, E. E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010).
Glazier, A. M., Nadeau, J. H. & Aitman, T. J. Finding genes that underlie complex traits. Science 298, 2345–2349 (2002).
Chakravarti, A., Clark, A. G. & Mootha, V. K. Distilling pathophysiology from complex disease genetics. Cell 155, 21–26 (2013).
Price, A. L., Spencer, C. C. & Donnelly, P. Progress and promise in understanding the genetic basis of common diseases. Proc. Biol. Sci. 282, 20151684 (2015).
Wagner, A. Robustness and Evolvability in Living Systems (Princeton Univ. Press, 2007).
Civelek, M. & Lusis, A. J. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15, 34–48 (2014).
Williams, E. G. & Auwerx, J. The convergence of systems and reductionist approaches in complex trait analysis. Cell 162, 23–32 (2015).
Nadeau, J. H. et al. Pleiotropy, homeostasis, and functional networks based on assays of cardiovascular traits in genetically randomized populations. Genome Res. 13, 2082–2091 (2003).
Shao, H. et al. Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc. Natl Acad. Sci. USA 105, 19910–19914 (2008).
Hui, S. T. et al. The genetic architecture of NAFLD among inbred strains of mice. eLife 4, e05607 (2015).
Parks, B. W. et al. Genetic architecture of insulin resistance in the mouse. Cell Metab. 21, 334–346 (2015).
Chick, J. M. et al. Defining the consequences of genetic variation on a proteome-wide scale. Nature 534, 500–505 (2016). References 30 and 32–34 nicely illustrate the power of large-scale multi-omics analyses (transcript, proteome and metabolome) to map disease candidates in mouse populations.
Wang, X. et al. Joint mouse-human phenome-wide association to test gene function and disease risk. Nat. Commun. 7, 10464 (2016). This is the first report of PheWAS applied to the mouse BXD genetic reference panel.
Williams, E. G. et al. Systems proteomics of liver mitochondria function. Science 352, aad0189 (2016).
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 (2018).
Wu, Y. et al. Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell 158, 1415–1430 (2014).
Houtkooper, R. H. et al. Mitonuclear protein imbalance as a conserved longevity mechanism. Nature 497, 451–457 (2013).
Nadeau, J. H. & Taylor, B. A. Lengths of chromosomal segments conserved since divergence of man and mouse. Proc. Natl Acad. Sci. USA 81, 814–818 (1984).
Wade, C. M. et al. The mosaic structure of variation in the laboratory mouse genome. Nature 420, 574–578 (2002).
Makalowski, W., Zhang, J. & Boguski, M. S. Comparative analysis of 1196 orthologous mouse and human full-length mRNA and protein sequences. Genome Res. 6, 846–857 (1996).
Batzoglou, S., Pachter, L., Mesirov, J. P., Berger, B. & Lander, E. S. Human and mouse gene structure: comparative analysis and application to exon prediction. Genome Res. 10, 950–958 (2000).
Dolinski, K. & Botstein, D. Orthology and functional conservation in eukaryotes. Annu. Rev. Genet. 41, 465–507 (2007).
Sigurdsson, M. I., Jamshidi, N., Steingrimsson, E., Thiele, I. & Palsson, B. O. A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1. BMC Syst. Biol. 4, 140 (2010).
Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nat. Rev. Genetics 11, 855–866 (2010).
Freimer, N. & Sabatti, C. The human phenome project. Nat. Genet. 34, 15–21 (2003). References 43–46 illustrate the worldwide ongoing efforts in various aspects of phenomics.
Jones, A. R., Overly, C. C. & Sunkin, S. M. The Allen Brain Atlas: 5 years and beyond. Nat. Rev. Neurosci. 10, 821–828 (2009).
Fuchs, H. et al. Understanding gene functions and disease mechanisms: phenotyping pipelines in the German Mouse Clinic. Behav. Brain Res. 352, 187–196 (2017).
Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A. & Teichmann, S. A. The Human Cell Atlas: from vision to reality. Nature 550, 451–453 (2017).
Li, H. et al. An integrated systems genetics and omics toolkit to probe gene function. Cell Syst. 6, 90–102 (2018). This study shows the power of the large accumulated data sets in the BXD mouse genetic reference population to connect genotype with phenotypes, using a range of strategies including PheWAS, transcriptome-wide association studies, proteome-wide association studies and mediation analysis.
Ghazalpour, A. et al. Hybrid Mouse Diversity Panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits. Mamm. Genome 23, 680–692 (2012).
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). This is a review describing the HMDP mouse genetic reference panel.
Ingalls, A. M., Dickie, M. M. & Snell, G. D. Obese, a new mutation in the house mouse. J. Hered. 41, 317–318 (1950).
Hummel, K. P., Dickie, M. M. & Coleman, D. L. Diabetes, a new mutation in the mouse. Science 153, 1127–1128 (1966).
Coleman, D. L. Effects of parabiosis of obese with diabetes and normal mice. Diabetologia 9, 294–298 (1973).
Zhang, Y. et al. Positional cloning of the mouse obese gene and its human homologue. Nature 372, 425–432 (1994).
Tartaglia, L. A. et al. Identification and expression cloning of a leptin receptor, OB-R. Cell 83, 1263–1271 (1995).
Reis, E. S., Mastellos, D. C., Ricklin, D., Mantovani, A. & Lambris, J. D. Complement in cancer: untangling an intricate relationship. Nat. Rev. Immunol. 18, 5–18 (2018).
Ricklin, D., Mastellos, D. C., Reis, E. S. & Lambris, J. D. The renaissance of complement therapeutics. Nat. Rev. Nephrol. 14, 26–47 (2018).
Hajishengallis, G., Reis, E. S., Mastellos, D. C., Ricklin, D. & Lambris, J. D. Novel mechanisms and functions of complement. Nat. Immunol. 18, 1288–1298 (2017).
Strey, C. W. et al. The proinflammatory mediators C3a and C5a are essential for liver regeneration. J. Exp. Med. 198, 913–923 (2003).
Chung, K. J. et al. A self-sustained loop of inflammation-driven inhibition of beige adipogenesis in obesity. Nat. Immunol. 18, 654–664 (2017).
Rafail, S. et al. Complement deficiency promotes cutaneous wound healing in mice. J. Immunol. 194, 1285–1291 (2015).
Abe, T. et al. Local complement-targeted intervention in periodontitis: proof-of-concept using a C5a receptor (CD88) antagonist. J. Immunol. 189, 5442–5448 (2012).
Segers, F. M. et al. Complement alternative pathway activation in human nonalcoholic steatohepatitis. PLOS ONE 9, e110053 (2014).
Doerner, S. K. et al. High-fat diet-induced complement activation mediates intestinal inflammation and neoplasia, independent of obesity. Mol. Cancer Res. 14, 953–965 (2016).
Bonavita, E. et al. PTX3 is an extrinsic oncosuppressor regulating complement-dependent inflammation in cancer. Cell 160, 700–714 (2015).
Mastellos, D. C., Reis, E. S., Ricklin, D., Smith, R. J. & Lambris, J. D. Complement C3-targeted therapy: replacing long-held assertions with evidence-based discovery. Trends Immunol. 38, 383–394 (2017).
Andreux, P. A., Houtkooper, R. H. & Auwerx, J. Pharmacological approaches to restore mitochondrial function. Nat. Rev. Drug Discov. 12, 465–483 (2013).
Wang, X. & Auwerx, J. Systems phytohormone responses to mitochondrial proteotoxic stress. Mol. Cell 68, 540–551 (2017).
Merkwirth, C. et al. Two conserved histone demethylases regulate mitochondrial stress-induced longevity. Cell 165, 1209–1223 (2016). References 68 and 69 illustrate the conservation of mitochondrial stress signalling, proteostatic networks and longevity pathways through cross-species analysis and apply a combination of genetics and pharmacology.
Moullan, N. et al. Tetracyclines disturb mitochondrial function across eukaryotic models: a call for caution in biomedical research. Cell Rep. 10, 1681–1691 (2015).
Sorrentino, V. et al. Enhancing mitochondrial proteostasis reduces amyloid-beta proteotoxicity. Nature 552, 187–193 (2017).
van de Weijer, T. et al. Evidence for a direct effect of the NAD+precursor acipimox on muscle mitochondrial function in humans. Diabetes 64, 1193–1201 (2015).
Gariani, K. et al. Eliciting the mitochondrial unfolded protein response by nicotinamide adenine dinucleotide repletion reverses fatty liver disease in mice. Hepatology 63, 1190–1204 (2016).
Khan, N. A. et al. Effective treatment of mitochondrial myopathy by nicotinamide riboside, a vitamin B3. EMBO Mol. Med. 6, 721–731 (2014).
Danhauser, K. et al. DHTKD1 mutations cause 2-aminoadipic and 2-oxoadipic aciduria. Am. J. Hum. Genet. 91, 1082–1087 (2012).
Xu, W. Y. et al. A nonsense mutation in DHTKD1 causes Charcot-Marie-Tooth disease type 2 in a large Chinese pedigree. Am. J. Hum. Genet. 91, 1088–1094 (2012).
Houten, S. M. et al. Genetic basis of hyperlysinemia. Orphanet J. Rare Dis. 8, 57 (2013).
Wang, T. J. et al. 2-Aminoadipic acid is a biomarker for diabetes risk. J. Clin. Invest. 123, 4309–4317 (2013).
Mourier, A., Matic, S., Ruzzenente, B., Larsson, N. G. & Milenkovic, D. The respiratory chain supercomplex organization is independent of COX7a2l isoforms. Cell Metab. 20, 1069–1075 (2014).
Lapuente-Brun, E. et al. Supercomplex assembly determines electron flux in the mitochondrial electron transport chain. Science 340, 1567–1570 (2013).
Greggio, C. et al. Enhanced respiratory chain supercomplex formation in response to exercise in human skeletal muscle. Cell Metab. 25, 301–311 (2017).
Day, C. P., Merlino, G. & Van Dyke, T. Preclinical mouse cancer models: a maze of opportunities and challenges. Cell 163, 39–53 (2015).
Bittner, J. J. Some possible effects of nursing on the mammary gland tumor incidence in mice. Science 84, 162 (1936).
Bishop, J. M. The molecular genetics of cancer. Science 235, 305–311 (1987).
Varmus, H. E. Oncogenes and transcriptional control. Science 238, 1337–1339 (1987).
Kleinsmith, L. J. & Pierce, G. B. Jr. Multipotentiality of single emrbyonal carcinoma cells. Cancer Res. 24, 1544–1551 (1964).
Stevens, L. C. Experimental production of testicular teratomas in mice. Proc. Natl Acad. Sci. USA 52, 654–661 (1964).
Heaney, J. D., Lam, M. Y., Michelson, M. V. & Nadeau, J. H. Loss of the transmembrane but not the soluble kit ligand isoform increases testicular germ cell tumor susceptibility in mice. Cancer Res. 68, 5193–5197 (2008). References 87 and 89 describe the conservation inherited cancer susceptibility genes, Kit and Kitl.
Russell, E. S. Hereditary anemias of the mouse: a review for geneticists. Adv. Genet. 20, 357–459 (1979).
Kanetsky, P. A. et al. Common variation in KITLG and at 5q31.3 predisposes to testicular germ cell cancer. Nat. Genet. 41, 811–815 (2009).
Dannenberg, A. J. & Berger, N. A. (eds) Obesity, Inflammation and Cancer (Springer New York, 2013).
Theodoratou, E., Timofeeva, M., Li, X., Meng, X. & Ioannidis, J. P. A. Nature, nurture, and cancer risks: genetic and nutritional contributions to cancer. Annu. Rev. Nutr. 37, 293–320 (2017).
Ben-David, U. et al. Patient-derived xenografts undergo mouse-specific tumor evolution. Nat. Genet. 49, 1567–1575 (2017). This is a landmark paper describing mouse-specific tumour evolution of human cancer xenografts.
Tan, Q. & Zoghbi, H. Y. Mouse models as a tool for discovering new neurological diseases. Neurobiol. Learn. Mem. https://doi.org/10.1016/j.nlm.2018.07.006 (2018). This is a review highlighting the productive cycle of human and mouse work accelerating pioneering work on neurological diseases.
Sztainberg, Y. & Zoghbi, H. Y. Lessons learned from studying syndromic autism spectrum disorders. Nat. Neurosci. 19, 1408–1417 (2016).
Guy, J., Gan, J., Selfridge, J., Cobb, S. & Bird, A. Reversal of neurological defects in a mouse model of Rett syndrome. Science 315, 1143–1147 (2007). References 95–98 show the reversibility of mouse neurodegenerative and affective phenotypes, underscoring the feasibility of such a strategy in humans.
Sztainberg, Y. et al. Reversal of phenotypes in MECP2 duplication mice using genetic rescue or antisense oligonucleotides. Nature 528, 123–126 (2015).
Walsh, J. J. et al. 5-HT release in nucleus accumbens rescues social deficits in mouse autism model. Nature 560, 589–594 (2018).
Heifets, B. D. & Malenka, R. C. MDMA as a probe and treatment for social behaviors. Cell 166, 269–272 (2016).
Grant, J. L. et al. Reversal of paralysis and reduced inflammation from peripheral administration of beta-amyloid in TH1 and TH17 versions of experimental autoimmune encephalomyelitis. Sci. Transl Med. 4, 145ra105 (2012).
Aiba, I. & Noebels, J. L. Spreading depolarization in the brainstem mediates sudden cardiorespiratory arrest in mouse SUDEP models. Sci. Transl Med. 7, 282ra46 (2015).
Olivetti, P. R., Maheshwari, A. & Noebels, J. L. Neonatal estradiol stimulation prevents epilepsy in Arx model of X-linked infantile spasms syndrome. Sci. Transl Med. 6, 220ra12 (2014).
Srivastava, P. K. et al. A systems-level framework for drug discovery identifies Csf1R as an anti-epileptic drug target. Nat. Commun. 9, 3561 (2018). This study provides a nice illustration of the use of systems-level information in a mouse model to identify a drug target.
Collins, A. L. et al. Mild overexpression of MeCP2 causes a progressive neurological disorder in mice. Hum. Mol. Genet. 13, 2679–2689 (2004). References 103 and 104 identify neurobehavioural disease genes that were subsequently validated in humans.
Han, K. et al. SHANK3 overexpression causes manic-like behaviour with unique pharmacogenetic properties. Nature 503, 72–77 (2013).
Henderson, C. et al. Reversal of disease-related pathologies in the fragile X mouse model by selective activation of GABAB receptors with arbaclofen. Sci. Transl Med. 4, 152ra128 (2012).
Berry-Kravis, E. M. et al. Effects of STX209 (arbaclofen) on neurobehavioral function in children and adults with fragile X syndrome: a randomized, controlled, phase 2 trial. Sci. Transl Med. 4, 152ra127 (2012). References 106–108 are mouse studies on fragile X syndrome that led to successful clinical trials in fragile X syndrome and autism.
Berry-Kravis, E. et al. Arbaclofen in fragile X syndrome: results of phase 3 trials. J. Neurodev. Disord. 9, 3 (2017).
Veenstra-VanderWeele, J. et al. Arbaclofen in children and adolescents with autism spectrum disorder: a randomized, controlled, phase 2 trial. Neuropsychopharmacology 42, 1390–1398 (2017).
Berry-Kravis, E. M. et al. Drug development for neurodevelopmental disorders: lessons learned from fragile X syndrome. Nat. Rev. Drug Discov. 17, 280–299 (2018).
Matzuk, M. M. & Lamb, D. J. The biology of infertility: research advances and clinical challenges. Nat. Med. 14, 1197–1213 (2008).
Hunt, P. A. & Hassold, T. J. Sex matters in meiosis. Science 296, 2181–2183 (2002).
Herbert, M., Kalleas, D., Cooney, D., Lamb, M. & Lister, L. Meiosis and maternal aging: insights from aneuploid oocytes and trisomy births. Cold Spring Harb. Perspect. Biol. 7, a017970 (2015).
Eicher, E. M. & Washburn, L. L. Genetic control of primary sex determination in mice. Annu. Rev. Genet. 20, 327–360 (1986).
Yamauchi, Y. et al. Two genes substitute for the mouse Y chromosome for spermatogenesis and reproduction. Science 351, 514–516 (2016).
Lane, M., Robker, R. L. & Robertson, S. A. Parenting from before conception. Science 345, 756–760 (2014).
Pollard, J. W. et al. Apparent role of the macrophage growth factor, CSF-1, in placental development. Nature 330, 484–486 (1987).
Tafuri, A., Alferink, J., Moller, P., Hammerling, G. J. & Arnold, B. T cell awareness of paternal alloantigens during pregnancy. Science 270, 630–633 (1995).
Samstein, R. M., Josefowicz, S. Z., Arvey, A., Treuting, P. M. & Rudensky, A. Y. Extrathymic generation of regulatory T cells in placental mammals mitigates maternal-fetal conflict. Cell 150, 29–38 (2012).
Swaggart, K. A., Pavlicev, M. & Muglia, L. J. Genomics of preterm birth. Cold Spring Harb. Perspect. Med. 5, a023127 (2015).
Zhang, Z., Shao, S. & Meistrich, M. L. Irradiated mouse testes efficiently support spermatogenesis derived from donor germ cells of mice and rats. J. Androl. 27, 365–375 (2006).
de Waal, E. et al. Primary epimutations introduced during intracytoplasmic sperm injection (ICSI) are corrected by germline-specific epigenetic reprogramming. Proc. Natl Acad. Sci. USA 109, 4163–4168 (2012).
Thomson, J. A. et al. Embryonic stem cell lines derived from human blastocysts. Science 282, 1145–1147 (1998).
Martin, G. R. Isolation of a pluripotent cell line from early mouse embryos cultured in medium conditioned by teratocarcinoma stem cells. Proc. Natl Acad. Sci. USA 78, 7634–7638 (1981).
Evans, M. Origin of mouse embryonal carcinoma cells and the possibility of their direct isolation into tissue culture. J. Reprod. Fertil. 62, 625–631 (1981).
Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).
Kim, J. B. et al. Pluripotent stem cells induced from adult neural stem cells by reprogramming with two factors. Nature 454, 646–650 (2008).
Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).
Weeber, F., Ooft, S. N., Dijkstra, K. K. & Voest, E. E. Tumor organoids as a pre-clinical cancer model for drug discovery. Cell Chem. Biol. 24, 1092–1100 (2017).
Eppig, J. J. Analysis of mouse oogenesis in vitro. Oocyte isolation and the utilization of exogenous energy sources by growing oocytes. J. Exp. Zool. 198, 375–382 (1976).
Brinster, R. L. & Biggers, J. D. In-vitro fertilization of mouse ova within the explanted fallopian tube. J. Reprod. Fertil. 10, 277–279 (1965).
Whittingham, D. G. Fertilization of mouse eggs in vitro. Nature 220, 592–593 (1968).
Sztein, J. M., Farley, J. S. & Mobraaten, L. E. In vitro fertilization with cryopreserved inbred mouse sperm. Biol. Reprod. 63, 1774–1780 (2000).
Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014). References 133–136 are a set of studies in which the importance of the host genome on the microbiome was established first in the mouse and subsequently validated in humans.
Goodrich, J. K., Davenport, E. R., Clark, A. G. & Ley, R. E. The relationship between the human genome and microbiome comes into view. Annu. Rev. Genet. 51, 413–433 (2017).
Benson, A. K. et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc. Natl Acad. Sci. USA 107, 18933–18938 (2010).
McKnite, A. M. et al. Murine gut microbiota is defined by host genetics and modulates variation of metabolic traits. PLOS ONE 7, e39191 (2012).
Surana, N. K. & Kasper, D. L. Deciphering the tete-a-tete between the microbiota and the immune system. J. Clin. Invest. 124, 4197–4203 (2014).
Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).
Kau, A. L., Ahern, P. P., Griffin, N. W., Goodman, A. L. & Gordon, J. I. Human nutrition, the gut microbiome and the immune system. Nature 474, 327–336 (2011).
Barratt, M. J., Lebrilla, C., Shapiro, H. Y. & Gordon, J. I. The gut microbiota, food science, and human nutrition: a timely marriage. Cell Host Microbe 22, 134–141 (2017).
Kim, K. S. et al. Dietary antigens limit mucosal immunity by inducing regulatory T cells in the small intestine. Science 351, 858–863 (2016).
Blanton, L. V. et al. Gut bacteria that prevent growth impairments transmitted by microbiota from malnourished children. Science 351, aad3311 (2016).
Schwarzer, M. et al. Lactobacillus plantarum strain maintains growth of infant mice during chronic undernutrition. Science 351, 854–857 (2016).
Sears, C. L. & Garrett, W. S. Microbes, microbiota, and colon cancer. Cell Host Microbe 15, 317–328 (2014). References 144–148 are a set of mouse studies establishing the major impact of the microbiome on many aspects of physiology.
Garrett, W. S. Cancer and the microbiota. Science 348, 80–86 (2015).
Ritze, Y. et al. Lactobacillus rhamnosus GG protects against non-alcoholic fatty liver disease in mice. PLOS ONE 9, e80169 (2014).
Vogt, N. M. et al. Gut microbiome alterations in Alzheimer’s disease. Sci. Rep. 7, 13537 (2017).
Harach, T. et al. Reduction of Abeta amyloid pathology in APPPS1 transgenic mice in the absence of gut microbiota. Sci. Rep. 7, 41802 (2017).
Viaud, S. et al. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 342, 971–976 (2013).
Iida, N. et al. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342, 967–970 (2013).
Vetizou, M. et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350, 1079–1084 (2015).
Wang, Z. et al. Non-lethal inhibition of gut microbial trimethylamine production for the treatment of atherosclerosis. Cell 163, 1585–1595 (2015). This study shows, in the mouse, the importance of gut trimethylamine production on atherosclerosis — a finding of great importance for human disease.
Reardon, S. A mouse’s house may ruin studies: environmental factors lie behind many irreproducible rodent experiments. Nature 530, 264–265 (2016). References 153–155 illustrate the importance of artificial housing conditions on the mouse microbiome and physiology.
Beura, L. K. et al. Normalizing the environment recapitulates adult human immune traits in laboratory mice. Nature 532, 512–516 (2016).
Abolins, S. et al. The comparative immunology of wild and laboratory mice, Mus musculus domesticus. Nat. Commun. 8, 14811 (2017).
Bowman, M., Leiter, E. & Atkinson, M. Autoimmune diabetes in NOD mice: a genetic program interruptible by environmental manipulation. Immunol. Today 15, 115–120 (1994).
Mahler, M. & Leiter, E. H. Genetic and environmental context determines the course of colitis developing in IL-10-deficient mice. Inflamm. Bowel Dis. 8, 347–355 (2002).
Ussar, S. et al. Interactions between gut microbiota, host genetics and diet modulate the predisposition to obesity and metabolic syndrome. Cell Metab. 22, 516–530 (2015).
Collins, F. S. & Tabak, L. A. Policy: NIH plans to enhance reproducibility. Nature 505, 612–613 (2014).
Jasny, B. R., Chin, G., Chong, L. & Vignieri, S. Again, and again, and again. Science 334, 1225 (2011).
Kleinert, M. et al. Animal models of obesity and diabetes mellitus. Nat. Rev. Endocrinol. 14, 140–162 (2018).
Champy, M. F. et al. Mouse functional genomics requires standardization of mouse handling and housing conditions. Mamm. Genome 15, 768–783 (2004).
Seok, J. et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc. Natl Acad. Sci. USA 110, 3507–3512 (2013).
Takao, K. & Miyakawa, T. Genomic responses in mouse models greatly mimic human inflammatory diseases. Proc. Natl Acad. Sci. USA 112, 1167–1172 (2015).
Liu, D. J. et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat. Genet. 49, 1758–1766 (2017).
von Scheidt, M. et al. Applications and limitations of mouse models for understanding human atherosclerosis. Cell Metab. 25, 248–261 (2017).
Kebede, M. A. & Attie, A. D. Insights into obesity and diabetes at the intersection of mouse and human genetics. Trends Endocrinol. Metab. 25, 493–501 (2014).
Rozman, J. et al. Identification of genetic elements in metabolism by high-throughput mouse phenotyping. Nat. Commun. 9, 288 (2018). This study identifies 400 new candidate metabolic genes through analysis of knockout mouse models.
Stoll, M. & Jacob, H. J. Genetic rat models of hypertension: relationship to human hypertension. Curr. Hypertens. Rep. 3, 157–164 (2001).
Jha, P. et al. Genetic regulation of plasma lipid species and their association with metabolic phenotypes. Cell Syst. 6, 709–721 (2018).
Jha, P. et al. Systems analyses reveal physiological roles and genetic regulators of liver lipid species. Cell Syst. 6, 722–733 (2018).
Barriga, E. H., Trainor, P. A., Bronner, M. & Mayor, R. Animal models for studying neural crest development: is the mouse different? Development 142, 1555–1560 (2015).
Copp, A. J. et al. Spina bifida. Nat. Rev. Dis. Primers 1, 15007 (2015).
Lerman, I. et al. Genetic variability in forced and voluntary endurance exercise performance in seven inbred mouse strains. J. Appl. Physiol. 92, 2245–2255 (2002).
Ho, M. et al. Disruption of muscle membrane and phenotype divergence in two novel mouse models of dysferlin deficiency. Hum. Mol. Genet. 13, 1999–2010 (2004).
Chang, B. et al. Retinal degeneration mutants in the mouse. Vision Res. 42, 517–525 (2002).
Johnson, K. R., Erway, L. C., Cook, S. A., Willott, J. F. & Zheng, Q. Y. A major gene affecting age-related hearing loss in C57BL/6J mice. Hear. Res. 114, 83–92 (1997).
Doran, A. G. et al. Deep genome sequencing and variation analysis of 13 inbred mouse strains defines candidate phenotypic alleles, private variation and homozygous truncating mutations. Genome Biol. 17, 167 (2016).
Srivastava, A. et al. Genomes of the Mouse Collaborative Cross. Genetics 206, 537–556 (2017).
Taft, R. A., Davisson, M. & Wiles, M. V. Know thy mouse. Trends Genet. 22, 649–653 (2006).
Zurita, E. et al. Genetic polymorphisms among C57BL/6 mouse inbred strains. Transgenic Res. 20, 481–489 (2011).
Kraev, A. Parallel universes of Black Six biology. Biol. Direct 9, 18 (2014).
Threadgill, D. W., Yee, D., Matin, A., Nadeau, J. H. & Magnuson, T. Genealogy of the 129 inbred strains: 129/SvJ is a contaminated inbred strain. Mamm. Genome 8, 390–393 (1997).
Brown, S. D., Chambon, P. & de Angelis, M. H. EMPReSS: standardized phenotype screens for functional annotation of the mouse genome. Nat. Genet. 37, 1155 (2005).
Daugherty, A. et al. Recommendation on design, execution, and reporting of animal atherosclerosis studies: a scientific statement from the American Heart Association. Arteriorscler. Thromb. Vasc. Biol. 37, e131–e157 (2017).
Ayala, J. E. et al. Standard operating procedures for describing and performing metabolic tests of glucose homeostasis in mice. Dis. Model. Mech. 3, 525–534 (2010).
Daugherty, A. et al. Recommendation on design, execution, and reporting of animal atherosclerosis studies: a scientific statement from the American Heart Association. Circ. Res. 121, e53–e79 (2017).
Hughes, M. E. et al. Guidelines for genome-scale analysis of biological rhythms. J. Biol. Rhythms 32, 380–393 (2017).
Sham, P. C. & Purcell, S. M. Statistical power and significance testing in large-scale genetic studies. Nat. Rev. Genet. 15, 335 (2014). References 189–192 are a set of papers discussing statistical considerations and reporting related to replicability.
Loken, E. & Gelman, A. Measurement error and the replication crisis. Science 355, 584–585 (2017).
Patil, P., Peng, R. D. & Leek, J. T. What should researchers expect when they replicate studies? A statistical view of replicability in psychological science. Perspect. Psychol. Sci. 11, 539–544 (2016).
Wasserstein, R. L. & Lazar, N. A. The ASA’s statement on p-values: context, process, and purpose. Am. Stat. 70, 129–133 (2016).
Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. Osteoarthritis Cartilage 20, 256–260 (2012).
Burrage, L. C. et al. Genetic resistance to diet-induced obesity in chromosome substitution strains of mice. Mamm. Genome 21, 115–129 (2010).
Lin, C. et al. Body composition QTLs identified in intercross populations are reproducible in consomic mouse strains. PLOS ONE 10, e0141494 (2015).
Leek, J. T. & Peng, R. D. Reproducible research can still be wrong: adopting a prevention approach. Proc. Natl Acad. Sci. USA 112, 1645–1646 (2015). References 196 and 197 discuss differences between replicability and generality.
Leek, J. T. & Peng, R. D. What is the question? Science 347, 1314–1315 (2015).
Garner, J. P., Gaskill, B. N., Weber, E. M., Ahloy-Dallaire, J. & Pritchett-Corning, K. R. Introducing therioepistemology: the study of how knowledge is gained from animal research. Lab Anim. 46, 103–113 (2017).
Schadt, E. E., Buchanan, S., Brennand, K. J. & Merchant, K. M. Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders. Front. Pharmacol. 5, 252 (2014).
Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015). References 200 and 201 report on the human genetic evidence to support drug indications.
Floris, M., Olla, S., Schlessinger, D. & Cucca, F. Genetic-driven druggable target identification and validation. Trends Genet. 34, 558–570 (2018).
Zambrowicz, B. P. & Sands, A. T. Knockouts model the 100 best-selling drugs—will they model the next 100? Nat. Rev. Drug Discov. 2, 38–51 (2003). This is a landmark review discussing the value of mouse knockout models for drug discovery.
Elmer, G. I., Pieper, J. O., Hamilton, L. R. & Wise, R. A. Qualitative differences between C57BL/6J and DBA/2J mice in morphine potentiation of brain stimulation reward and intravenous self-administration. Psychopharmacology 208, 309–321 (2010). References 203–205 report some striking phenotypic differences between mouse strains based on genetic variation, illustrating why testing compounds in mice with a single genetic background can be misleading.
Gatti, D. M., Weber, S. N., Goodwin, N. C., Lammert, F. & Churchill, G. A. Genetic background influences susceptibility to chemotherapy-induced hematotoxicity. Pharmacogenomics J. 18, 319–330 (2017).
Harrill, A. H. et al. Mouse population-guided resequencing reveals that variants in CD44 contribute to acetaminophen-induced liver injury in humans. Genome Res. 19, 1507–1515 (2009).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Ledford, H. Drug bests cystic-fibrosis mutation. Nature 482, 145 (2012).
Oprea, T. I. et al. Unexplored therapeutic opportunities in the human genome. Nat. Rev. Drug Discov. 17, 317–332 (2018).
Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).
Friddle, C. J. et al. High-throughput mouse knockouts provide a functional analysis of the genome. Cold Spring Harb. Symp. Quant. Biol. 68, 311–315 (2003).
Nadeau, J. H. et al. Sequence interpretation. Functional annotation of mouse genome sequences. Science 291, 1251–1255 (2001).
Meehan, T. F. et al. Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium. Nat. Genet. 49, 1231–1238 (2017). This study reports the phenotypes of more than 3,000 mouse knockout models as a showcase for the IMPC.
Haendel, M. A. et al. Disease insights through cross-species phenotype comparisons. Mamm. Genome 26, 548–555 (2015). References 213–214 discuss large initiatives to link human diseases with phenotypes from model organisms.
McMurry, J. A. et al. Navigating the phenotype frontier: the monarch initiative. Genetics 203, 1491–1495 (2016).
Mangravite, L. M. et al. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy. Nature 502, 377–380 (2013).
Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).
Scheer, N. et al. Defining human pathways of drug metabolism in vivo through the development of a multiple humanized mouse model. Drug Metab. Dispos. 43, 1679–1690 (2015). References 217 and 227 describe humanized mouse models and chimeric mice with value to study human drug metabolism.
Zhang, J. et al. Clinical exposure boost predictions by integrating cytochrome P450 3A4-humanized mouse studies with PBPK modeling. J. Pharm. Sci. 105, 1398–1404 (2016).
Salphati, L. et al. Evaluation of organic anion transporting polypeptide 1B1 and 1B3 humanized mice as a translational model to study the pharmacokinetics of statins. Drug Metab. Dispos. 42, 1301–1313 (2014).
Fattinger, K. et al. The endothelin antagonist bosentan inhibits the canalicular bile salt export pump: a potential mechanism for hepatic adverse reactions. Clin. Pharmacol. Ther. 69, 223–231 (2001).
Jacobson-Kram, D., Sistare, F. D. & Jacobs, A. C. Use of transgenic mice in carcinogenicity hazard assessment. Toxicol. Pathol. 32 (Suppl. 1), 49–52 (2004).
Peltz, G. Can ‘humanized’ mice improve drug development in the 21st century? Trends Pharmacol. Sci. 34, 255–260 (2013).
Suemizu, H. et al. Establishment of a humanized model of liver using NOD/Shi-scid IL2Rgnull mice. Biochem. Biophys. Res. Commun. 377, 248–252 (2008).
Azuma, H. et al. Robust expansion of human hepatocytes in Fah−/−/Rag2−/−/Il2rg−/− mice. Nat. Biotechnol. 25, 903–910 (2007).
Tateno, C. et al. Near completely humanized liver in mice shows human-type metabolic responses to drugs. Am. J. Pathol. 165, 901–912 (2004).
Rhim, J. A., Sandgren, E. P., Degen, J. L., Palmiter, R. D. & Brinster, R. L. Replacement of diseased mouse liver by hepatic cell transplantation. Science 263, 1149–1152 (1994).
Scheer, N. & Wilson, I. D. A comparison between genetically humanized and chimeric liver humanized mouse models for studies in drug metabolism and toxicity. Drug Discov. Today 21, 250–263 (2016).
Wilson, C. E. et al. The pharmacokinetics and metabolism of diclofenac in chimeric humanized and murinized FRG mice. Arch. Toxicol. 92, 1953–1967 (2018).
Hu, Y., Wu, M., Nishimura, T., Zheng, M. & Peltz, G. Human pharmacogenetic analysis in chimeric mice with ‘humanized livers’. Pharmacogenet. Genomics 23, 78–83 (2013).
Legrand, N. et al. Humanized mice for modeling human infectious disease: challenges, progress, and outlook. Cell Host Microbe 6, 5–9 (2009).
Long, C. et al. Prevention of muscular dystrophy in mice by CRISPR/Cas9-mediated editing of germline DNA. Science 345, 1184–1188 (2014). References 231 and 232 nicely illustrate that CRISPR–Cas9-mediated gene editing can repair inherited genetic defects in vivo in the mouse.
Gao, X. et al. Treatment of autosomal dominant hearing loss by in vivo delivery of genome editing agents. Nature 553, 217–221 (2018).
Zhu, W. et al. Precision editing of the gut microbiota ameliorates colitis. Nature 553, 208–211 (2018).
Liao, H. K. et al. In vivo target gene activation via CRISPR/Cas9-mediated trans-epigenetic modulation. Cell 171, 1495–1507 (2017).
Nadeau, J. H. & Topol, E. J. The genetics of health. Nat. Genet. 38, 1095–1098 (2006).
Greenfield, A. et al. Assisted reproductive technologies to prevent human mitochondrial disease transmission. Nat. Biotechnol. 35, 1059–1068 (2017).
Eyre-Walker, A. Mitochondrial replacement therapy: are mito-nuclear interactions likely to be a problem? Genetics 205, 1365–1372 (2017).
Leroi, A. M. The Lagoon: How Aristotle Invented Science (Penguin Publishing Group, 2015).
Riordan, J. R. et al. Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA. Science 245, 1066–1073 (1989).
MacDonald, M. E. et al. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 72, 971–983 (1993).
Amir, R. E. et al. Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat. Genet. 23, 185–188 (1999).
Peters, T., Ausmeier, K. & Ruther, U. Cloning of Fatso (Fto), a novel gene deleted by the Fused toes (Ft) mouse mutation. Mamm. Genome 10, 983–986 (1999).
Flanagan, J. G., Chan, D. C. & Leder, P. Transmembrane form of the kit ligand growth factor is determined by alternative splicing and is missing in the Sld mutant. Cell 64, 1025–1035 (1991).
Koutnikova, H. et al. Identification of the UBP1 locus as a critical blood pressure determinant using a combination of mouse and human genetics. PLOS Genet. 5, e1000591 (2009).
Frayling, T. M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894 (2007).
Winston, F. & Koshland, D. Back to the future: mutant hunts are still the way to go. Genetics 203, 1007–1010 (2016).
Brown, S. D. & Nolan, P. M. Mouse mutagenesis-systematic studies of mammalian gene function. Hum. Mol. Genet. 7, 1627–1633 (1998).
Hrabe de Angelis, M. H. et al. Genome-wide, large-scale production of mutant mice by ENU mutagenesis. Nat. Genet. 25, 444–447 (2000).
Balling, R. ENU mutagenesis: analyzing gene function in mice. Annu. Rev. Genomics Hum. Genet. 2, 463–492 (2001).
Beutler, B. Finding new components of the mammalian immune system. Rambam Maimonides Med. J. 7, e0018 (2016).
Gordon, J. W., Scangos, G. A., Plotkin, D. J., Barbosa, J. A. & Ruddle, F. H. Genetic transformation of mouse embryos by microinjection of purified DNA. Proc. Natl Acad. Sci. USA 77, 7380–7384 (1980).
Brinster, R. L. et al. Somatic expression of herpes thymidine kinase in mice following injection of a fusion gene into eggs. Cell 27, 223–231 (1981).
Costantini, F. & Lacy, E. Introduction of a rabbit beta-globin gene into the mouse germ line. Nature 294, 92–94 (1981).
Wagner, E. F., Stewart, T. A. & Mintz, B. The human beta-globin gene and a functional viral thymidine kinase gene in developing mice. Proc. Natl Acad. Sci. USA 78, 5016–5020 (1981).
Smithies, O., Gregg, R. G., Boggs, S. S., Koralewski, M. A. & Kucherlapati, R. S. Insertion of DNA sequences into the human chromosomal beta-globin locus by homologous recombination. Nature 317, 230–234 (1985).
Capecchi, M. R. The new mouse genetics: altering the genome by gene targeting. Trends Genet. 5, 70–76 (1989).
Johnson, M. P., Drugan, A., Miller, O. J. & Evans, M. I. Genetic correction of hereditary disease. Fetal Ther. 4 (Suppl. 1), 28–39 (1989).
Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).
Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
Austin, C. P. et al. The knockout mouse project. Nat. Genet. 36, 921–924 (2004).
Koutnikova, H. et al. Compensation by the muscle limits the metabolic consequences of lipodystrophy in PPARγ hypomorphic mice. Proc. Natl Acad. Sci. USA 100, 14457–14462 (2003).
Shultz, L. D., Brehm, M. A., Garcia-Martinez, J. V. & Greiner, D. L. Humanized mice for immune system investigation: progress, promise and challenges. Nat. Rev. Immunol. 12, 786–798 (2012).
Holash, J. et al. VEGF-Trap: a VEGF blocker with potent antitumor effects. Proc. Natl Acad. Sci. USA 99, 11393–11398 (2002).
Rongvaux, A. et al. Human thrombopoietin knockin mice efficiently support human hematopoiesis in vivo. Proc. Natl Acad. Sci. USA 108, 2378–2383 (2011).
Vaughan, A. M. et al. Plasmodium falciparum genetic crosses in a humanized mouse model. Nat. Methods 12, 631–633 (2015).
Martin, F. H. et al. Primary structure and functional expression of rat and human stem cell factor DNAs. Cell 63, 203–211 (1990).
Willinger, T. et al. Human IL-3/GM-CSF knock-in mice support human alveolar macrophage development and human immune responses in the lung. Proc. Natl Acad. Sci. USA 108, 2390–2395 (2011).
Macdonald, L. E. et al. Precise and in situ genetic humanization of 6Mb of mouse immunoglobulin genes. Proc. Natl Acad. Sci. USA 111, 5147–5152 (2014).
Fernandez-Salguero, P. et al. Immune system impairment and hepatic fibrosis in mice lacking the dioxin-binding Ah receptor. Science 268, 722–726 (1995).
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).
Youngren, K. K. et al. The Ter mutation in the dead end gene causes germ cell loss and testicular germ cell tumours. Nature 435, 360–364 (2005). References 272 and 278 illustrate that modifier genes can have a robust influence on phenotypic outcomes.
Heaney, J. D., Michelson, M. V., Youngren, K. K., Lam, M. Y. & Nadeau, J. H. Deletion of eIF2beta suppresses testicular cancer incidence and causes recessive lethality in agouti-yellow mice. Hum. Mol. Genet. 18, 1395–1404 (2009).
Huszar, D. et al. Targeted disruption of the melanocortin-4 receptor results in obesity in mice. Cell 88, 131–141 (1997).
Enerback, S. et al. Mice lacking mitochondrial uncoupling protein are cold-sensitive but not obese. Nature 387, 90–94 (1997).
Carlborg, O. & Haley, C. S. Epistasis: too often neglected in complex trait studies? Nat. Rev. Genet. 5, 618–625 (2004).
Mackay, T. F. Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nat. Rev. Genet. 15, 22–33 (2014).
Yazbek, S. N. et al. Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis. Genome Res. 21, 1065–1073 (2011).
Spiezio, S. H., Takada, T., Shiroishi, T. & Nadeau, J. H. Genetic divergence and the genetic architecture of complex traits in chromosome substitution strains of mice. BMC Genet. 13, 38 (2012).
Nadeau, J. H. Modifier genes in mice and humans. Nat. Rev. Genet. 2, 165–174 (2001).
Chen, R. et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat. Biotechnol. 34, 531–538 (2016). References 281 and 282 are two landmark papers illustrating that many humans with LOF mutations are resilient to developing disease.
Saleheen, D. et al. Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature 544, 235–239 (2017).
Hartman, J. L.t., Garvik, B. & Hartwell, L. Principles for the buffering of genetic variation. Science 291, 1001–1004 (2001).
Wagner, A. Robustness against mutations in genetic networks of yeast. Nat. Genet. 24, 355–361 (2000).
Ober, C. & Vercelli, D. Gene-environment interactions in human disease: nuisance or opportunity? Trends Genet. 27, 107–115 (2011).
Hobbs, C. A., Cleves, M. A., Karim, M. A., Zhao, W. & MacLeod, S. L. Maternal folate-related gene environment interactions and congenital heart defects. Obstet. Gynecol. 116, 316–322 (2010).
Murray, J. C. Gene/environment causes of cleft lip and/or palate. Clin. Genet. 61, 248–256 (2002).
Surwit, R. S., Kuhn, C. M., Cochrane, C., McCubbin, J. A. & Feinglos, M. N. Diet-induced type II diabetes in C57BL/6J mice. Diabetes 37, 1163–1167 (1988).
Hagopian, W. A. et al. TEDDY — the environmental determinants of diabetes in the young: an observational clinical trial. Ann. NY Acad. Sci. 1079, 320–326 (2006).
Chan, Y. Y. et al. The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit. Nat. Biotechnol. 35, 354–362 (2017).
Zohn, I. E. Mouse as a model for multifactorial inheritance of neural tube defects. Birth Defects Res. C 96, 193–205 (2012).
Greene, N. D. & Copp, A. J. Inositol prevents folate-resistant neural tube defects in the mouse. Nat. Med. 3, 60–66 (1997).
Greene, N. D., Leung, K. Y. & Copp, A. J. Inositol, neural tube closure and the prevention of neural tube defects. Birth Defects Res. 109, 68–80 (2017).
Stevens, L. C. & Varnum, D. S. The development of teratomas from parthenogenetically activated ovarian mouse eggs. Dev. Biol. 37, 369–380 (1974).
Mann, J. R. & Lovell-Badge, R. H. Inviability of parthenogenones is determined by pronuclei, not egg cytoplasm. Nature 310, 66–67 (1984).
McGrath, J. & Solter, D. Completion of mouse embryogenesis requires both the maternal and paternal genomes. Cell 37, 179–183 (1984).
Kong, A. et al. Parental origin of sequence variants associated with complex diseases. Nature 462, 868–874 (2009).
Mott, R. et al. The architecture of parent-of-origin effects in mice. Cell 156, 332–342 (2014).
Lyon, M. F. Gene action in the X-chromosome of the mouse (Mus musculus L.). Nature 190, 372–373 (1961).
Alves, I., Houle, A. A., Hussin, J. G. & Awadalla, P. The impact of recombination on human mutation load and disease. Phil. Trans. R. Soc. B 372, 20160465 (2017).
Hollick, J. B. Paramutation and related phenomena in diverse species. Nat. Rev. Genet. 18, 5–23 (2017).
Nadeau, J. H. Do gametes woo? Evidence for their nonrandom union at fertilization. Genetics 207, 369–387 (2017).
Jablonka, E. & Lamb, M. J. Epigenetic Inheritance and Evolution (Oxford Univ. Press, 1999).
Bygren, L. O. Intergenerational health responses to adverse and enriched environments. Annu. Rev. Public Health 34, 49–60 (2013).
Heard, E. & Martienssen, R. A. Transgenerational epigenetic inheritance: myths and mechanisms. Cell 157, 95–109 (2014).
Vickers, M. H. Developmental programming and transgenerational transmission of obesity. Ann. Nutr. Metab. 64 (Suppl. 1), 26–34 (2014).
Schaefer, S. & Nadeau, J. H. The genetics of epigenetic inheritance: modes, molecules and mechanisms. Q. Rev. Biol. 90, 381–415 (2015).
Chakrabortee, S. et al. Intrinsically disordered proteins drive emergence and inheritance of biological traits. Cell 167, 369–381 (2016).
Yazbek, S. N., Spiezio, S. H., Nadeau, J. H. & Buchner, D. A. Ancestral paternal genotype controls body weight and food intake for multiple generations. Hum. Mol. Genet. 19, 4134–4144 (2010).
Williams, E. G. et al. An evolutionarily conserved role for the aryl hydrocarbon receptor in the regulation of movement. PLOS Genet. 10, e1004673 (2014).
Buchner, D. A. et al. The juxtaparanodal proteins CNTNAP2 and TAG1 regulate diet-induced obesity. Mamm. Genome 23, 431–442 (2012).
Vuillaume, M. L. et al. New candidate loci identified by array-CGH in a cohort of 100 children presenting with syndromic obesity. Am. J. Med. Genet. 164A, 1965–1975 (2014).
Schultz, J. M. et al. Modification of human hearing loss by plasma-membrane calcium pump PMCA2. N. Engl. J. Med. 352, 1557–1564 (2005).
Lu, H. C. et al. Disruption of the ATXN1-CIC complex causes a spectrum of neurobehavioral phenotypes in mice and humans. Nat. Genet. 49, 527–536 (2017).
Lim, J. et al. A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell 125, 801–814 (2006).
Pendergrass, S. A. et al. The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery. Genet. Epidemiol. 35, 410–422 (2011). References 316 and 317 are the first descriptions of the application of human PheWAS.
Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).
Denny, J. C., Bastarache, L. & Roden, D. M. Phenome-wide association studies as a tool to advance precision medicine. Annu. Rev. Genomics Hum. Genet. 17, 353–373 (2016).
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).
Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17, 129–145 (2016).
Gagneur, J. et al. Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype. PLOS Genet. 9, e1003803 (2013). References 321 and 322 illustrate the potential but also the difficulty with transcriptome-wide and proteome-wide association studies.
Mancuso, N. et al. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet. 100, 473–487 (2017).
Okada, H., Ebhardt, H. A., Vonesch, S. C., Aebersold, R. & Hafen, E. Proteome-wide association studies identify biochemical modules associated with a wing-size phenotype in Drosophila melanogaster. Nat. Commun. 7, 12649 (2016).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
The authors thank R. Balling, B. Beutler, M. B. Sleiman, S. Brown, M. Bucan, M. Buttini, T. Chavakis, A. Economides, M. A. Handel, P. Jha, N. Katsanis, B. Knowles, G. Kollias, J. Lambris, E. Leiter, H. Li, K. Lloyd, J. Noebels, S. Robertson, D. Solter, P. Treuting, E. Williams and H. Zoghbi for suggesting papers that highlight important discoveries in mouse models or helping with figure suggestions. The authors also thank J. Riordan for contributing a draft paragraph about the merits of mouse models and J. Wecker for critically reading a draft of the manuscript.
J.A. is a scientific adviser to Astellas, Amazentis and TES pharma.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- Complex traits
Biological features that depend on multiple genes and environmental conditions and often involve interactions among genes, environment and age.
- Model organisms
Organisms that are amenable to experimental studies and for which rich genetic, genomic, phenotyping and analytical resources have been developed.
- Gene–gene interactions
(GxG, also known as epistasis). The signal for GxGs is non-additivity where their combined action is more (or less) than the simple summation of their separate phenotypic effects.
- Gene–age interactions
(GxA). In many cases, phenotypes depend not only on genes and environment but also on stage of life.
- Gene–environment interactions
(GxE). The signal for GxEs is non-additivity where their combined action is more (or less) than the simple summation of their separate phenotypic effects.
- Genetic modifiers
Genetic variants in one gene (the modifier gene) that affect the phenotype associated with another gene (the target gene). In many cases, the target is the phenotype associated with a naturally occurring or an engineered single-gene variant. In other cases, the phenotype target is a multigenic trait where the action of one or more underlying genes depends on the action of a modifier gene. Modifier genes are examples of gene–gene interactions.
The phenotypic variation that arises independent of the genetic variants in study subjects. Epigenetic effects can result from chemical modifications (methylation) of nucleic acids (DNA or RNA) or of associated proteins (histone modifications). RNAs and proteins can also induce and transmit epigenetic information. Such changes arise during development to control differentiation as cells transition from totipotency to specialization. In addition to changes within generations, epigenetic changes can be inherited to affect phenotypes in later generations.
The collection of commensal microorganisms (including bacteria, fungi and species) that live in or on organisms. The relation between host and their microbiome is usually symbiotic — their survival and functionality are interdependent.
The cellular mitochondrial content.
A primary goal of genetic research is establishing the molecular mechanisms and systems properties that connect genotype and phenotype.
- Personalized medicine
Can also be referred to as precision medicine. A medical practice where health care and disease treatments are based on the individual’s genetic constitution.
- Forward genetics
An approach that begins with a phenotype of interest and searches for its genetic basis.
- Reverse genetics
An approach that seeks to learn about gene function by examining phenotypic consequences of spontaneous, engineered and naturally occurring genetic variants.
- Quantitative trait loci
(QTLs). The genetic variants that control phenotypic variation. These sometimes have independent genetic effects and other times depend on gene–gene, gene–environment and gene–age effects.
- Mendelian traits
When a single gene contributes to phenotypic variation.
- Polygenic traits
When two or more genes contribute to phenotypic variation.
- Systems biology
A biological approach that studies higher-order organismal forms and functions that emerge from multilayered molecular and physical features and their interactions.
- Systems genetics
A field that seeks to reveal the relations between genotype and phenotype and thereby account for both high-order and emergent organismal properties by integrated studies at the molecular, cellular and physiological levels.
- Comparative models
Analyses of genetic and phenotypic variation in humans and in at least one model organism. Reliable results depend on a clear understanding of the biological similarities and differences between the species being compared.
- Genetic reference populations
These are genetically defined strains that can be scored for phenotypes of interest. Usually, they support genome-wide surveys to test for genotype–phenotype relations. Examples for mice include inbred strains, recombinant inbred strains and chromosome substitution strains.
- Genome-wide association studies
(GWAS). Tests for statistical evidence for preferential co-occurrence of genetic and phenotypic variants across populations and environments. Positive results argue that the tested genetic marker accounts for a statistically significant portion of the total phenotypic variance and that genetic variants near the marker contribute functionally to phenotypic variation in the trait of interest.
(LOF). LOF variants reduce functionality or loss of phenotypes. These terms apply to specific variants and phenotypes; a given variant could result in the gain of one phenotype and in the loss of another.
- Gene mapping
A mapping approach that encompasses two kinds of activities. The first involves using linkage analysis in segregating crosses, in families and in natural populations to establish the location of genes in the genome. Availability of largely complete genome sequences has mostly supplanted this activity. The second involves determining the genomic location of genetic variants that control phenotypic variants (quantitative trait loci), that is, forward genetics.
- Genetic engineering
An approach that involves intentional changes in DNA sequences to test its effects on traits of interest. Often, engineering involves embryonic stem cells, induced pluripotent stem cells and early embryos so that phenotypic consequences can be assessed in intact organisms or in related cells and tissues derived from engineered pluripotent cells.
An approach that involves tests to determine whether similar results would be obtained with the same study designs, materials, reagents, analytical methods and protocols.
A principle that involves tests to determine whether similar results are found in different populations and environments and with different study designs and assay protocols.
About this article
Cite this article
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). https://doi.org/10.1038/s41573-018-0009-9
Nicotine & Tobacco Research (2020)
Trends in Genetics (2020)
Taar1 gene variants have a causal role in methamphetamine intake and response and interact with Oprm1
Neuroscience & Biobehavioral Reviews (2019)