Skip to main content

Introduction to mammalian genome special issue: the combined role of genetics and environment relevant to human disease outcomes

A vision for greater use of experimental models to evaluate the role of genetic variability in environmental health and toxicology (Festing 2001) was outlined at the same time as the revolution in genome sequencing of humans and other species was taking place (Lander et al. 2001; Venter et al. 2001; Waterston et al. 2002). The Genes, Environment and Health Initiative established by the US Department of Health and Human Services and the National Institutes of Health in 2006 laid the foundation for investigating the interactions between environmental and genetic underpinnings of human disease by funding development of tools for genetic analysis and exposure biology. Several years later, the landmark study that sequenced the genomes of 15 mouse inbred strains (Frazer et al. 2007) opened the opportunity to link genetic variants to disease traits using the mouse, one of the principal experimental models in environmental health sciences and toxicology. Further analyses (Yang et al. 2007) revealed significant identity by descent between the homozygous inbred strains and the dominant contribution of laboratory-derived strains from Mus musculus domesticus. New population-based models have been created based on and aided by this refined understanding of mouse genetics (Churchill et al. 2012; Threadgill and Churchill 2012). Many studies have been published in the decade since that demonstrate the value of population-based testing and the power of genetics as an important dimension of environmental health science and toxicology (Harrill and McAllister 2017; Rusyn et al. 2010).

This Special Issue of Mammalian Genome highlights a variety of topics broadly unified by the interest in the combined role of genetics and environment relevant to human disease outcomes. Population-based studies aiming to understand the relationship between inter-individual variation and environmental stressors are now being conducted in humans (Daly and Day 2012; Kwo and Christiani 2017), rodents (Rusyn et al. 2010), and other model organisms (Anholt and Mackay 2018). Importantly, many recent studies were designed to translate findings in model organisms to human disease susceptibility and draw mechanistic linkages that may help identify individuals who are sensitive to environmental exposures. For example, Jerry et al. (2018) demonstrate how quantitative trait loci (QTL) identified in studies of susceptibility to mammary tumors in estrogen-treated rats, together with surrogate biomarkers in humans, help explain gene by environment interactions and breast cancer risk posed by environmental xenoestrogens and endogenous estrogens. Anholt and Mackay (2018) use the model of D. melanogaster to highlight the opportunities to study environmental exposures and complex traits in a non-mammalian model organism. Despite major differences in genetic architecture and transcriptomes between flies and humans, the authors posit that the genetic underpinnings of complex traits can be represented as simplified gene networks in D. melanogaster on which human orthologues can be superimposed to provide blueprints for subsequent studies on analogous traits in human populations.

Several reviews in this special issue demonstrate the role of inter-individual variability in health and disease associated with infections, lifestyle, and dietary factors. Verhein et al. (2018) review human, rodent, and in vitro association studies that identified biologically plausible gene candidates for susceptibility to pulmonary infections and disease severity, as well as how knowledge of these susceptibility factors can aid in novel strategies to prevent and treat disease that contributes to global morbidity and mortality attributed to respiratory infections. Vellers et al. (2018) used exercise training as a case study of a lifestyle factor that is associated with the individual’s genetic makeup. They describe studies in human and animal models that show a significant contribution of genetic polymorphisms, in both nuclear and mitochondrial genomes, in adaptations to endurance and resistance exercise training. Complex interactions between host genetics and other factors in the adverse health effects of environmental diet-associated exposures, such as arsenic, are reviewed by Chi et al. (2018). The authors posit that nutritional status and the gut microbiome may play an even greater role in the individual susceptibility to arsenic-related diseases than host genetic polymorphisms.

Defining the role that genetics may play in the inter-individual variability and disease susceptibility in the absence of a specific toxicant or exposure was the focus of two studies in this special issue. Shorter et al. (2018) investigated the role of genetics, sex, age, and diet on heart size using a population of DO mice. They found a significant genetic effect on heart weight and identified two mechanistically relevant quantitative trait loci; diet had no significant effect on the heart weight. Balik-Meisner et al. (2018) characterized the extent of genetic diversity in a population of Tropical 5D zebrafish and compared observed population genetic variation across species.

A collection of research articles in this special issue provides additional mechanistic insights into the combined role of genetics and environment relevant to human disease outcomes. Argos et al. (2018) studied gene–arsenic interactions in humans using genome-wide SNP data, gene expression, and DNA methylation. Using data from a human cohort exposed to various levels of arsenic, they first identified loci that modify the effect of arsenic on gene expression and DNA methylation phenotypes. Then, using this set of loci, they tested SNP–arsenic interactions in relation to skin lesions, a hallmark characteristic of arsenic toxicity. This study not only increased the power of GWAS, but also provided critical mechanistic underpinning to the identified susceptibility loci. The interplay between the status of aryl hydrocarbon receptor (AHR), cytochrome P450 1a2, exposure to polychlorinated biphenyls (PCB), and neurotoxicity was explored using genetically modified mouse models by Klinefelter et al. (2018). The authors were especially interested in the effects of in utero exposures and showed that AHR is a modifier of developmental neurotoxicity of PCB, but not for Parkinson’s disease. Hoffman et al. (2018) used a panel of recombinant inbred rats to further elucidate mechanisms of alcohol toxicity. They used organ-specific gene expression data to identify transcriptional networks that may connect genetic variability and alcohol-associated phenotypes in a tissue-specific manner, an approach that provides mechanistic linkages to gene by environment associations.

Several manuscripts in this special issue explore the mechanisms that may link genetic diversity and effects by focusing on the role of epigenetics. Latchney et al. (2018) reviewed the hypothesized mechanisms of multi- and transgenerational epigenetic inheritance through DNA methylation and post-translational histone modifications and the potential sources of inter-individual variations and the challenges in identifying these variations. The authors used data from studies of endocrine disrupting chemicals in rodents and concluded that it is difficult to translate rodent studies of these effects to humans. An experimental study by Israel et al. (2018) tested a hypothesis that baseline variability in chromatin organization and transcription profiles among various tissues and mouse strains may influence the outcome of exposure to the DNA-damaging chemical 1,3-butadiene. They found that variability in chromatin accessibility across mouse strains only partially explains the variability in gene expression and that variation in the basal states of epigenome and transcriptome may be useful indicators for individuals or tissues susceptible to genotoxic environmental chemicals.

Finally, the utility of population-based studies to environmental health decision-making was considered. Venkatratnam et al. (2018) explored population variability in dose–response relationships in the liver transcriptional response to the known carcinogen trichloroethylene. They used a large population of CC mouse strains to explore both dose- and genetic background-dependent transcriptional responses. While this study demonstrated how mouse population-based studies aid in assessment of inter-individual variability in toxicological endpoints, it showed that genetic mapping of complex gene–exposure–dose relationships is still a major challenge even in large CC populations. Chiu and Rusyn (2018) reviewed a number of published case studies that demonstrate the potential opportunities for improving risk assessment and decision-making using CC and DO mice, as well as populations of human cell lines. These studies were placed into the context of the steps in the traditional risk assessment paradigm—hazard identification, dose response, and mechanistic evaluation. This review also shows how these data can improve confidence in extrapolating from studies in animals or in vitro to human exposures and disease outcomes. Mortensen et al. (2018) propose a complementary approach for how the knowledge of human genetic variability may aid in predicting the extent of inter-individual susceptibility to exposures by identifying key initiating events and adverse outcome pathways.

The collective contribution of the reviews and original research in this Special Issue of Mammalian Genome provides an updated overview of how genetic models are now being used to understand mechanisms of inter-individual variation in response to multiple environmental factors, as well as challenges that remain to be overcome. Continued investigation of gene by environment interactions in diverse animal models and human populations should lead to novel strategies to prevent and treat environmentally driven diseases.

References

  1. Anholt RRH, Mackay TFC (2018) The road less traveled: from genotype to phenotype in flies and humans. Mamm Genome. https://doi.org/10.1007/s00335-017-9722-7

    Google Scholar 

  2. Argos M, Tong L, Roy S, Sabarinathan M, Ahmed A, Islam T, Rakibuz-Zaman M, Sarwar G, Shahriar H, Rahman M, Yunus M, Graziano JH, Jasmine F, Kibriya MG, Zhou X, Ahsan H, Pierce BL (2018) Screening for gene-environment (GxE) interaction using omics data from exposed individuals: an application to gene-arsenic interaction. Mamm Genome. https://doi.org/10.1007/s00335-018-9737-8

    PubMed  Google Scholar 

  3. Balik-Meisner M, Truong L, Scholl EH, Tanguay RL, Reif DM (2018) Population genetic diversity in zebrafish lines. Mamm Genome. https://doi.org/10.1007/s00335-018-9735-x

    PubMed  Google Scholar 

  4. Chi L, Gao B, Tu P, Liu C-W, Xue J, Lai Y, Ru H, Lu K (2018) Individual susceptibility to arsenic-induced diseases: the role of host genetics, nutritional status and the gut microbiome. Mamm Genome. https://doi.org/10.1007/s00335-018-9736-9

    PubMed  Google Scholar 

  5. Chiu WA, Rusyn I (2018) Advancing chemical risk assessment decision-making with population variability data: challenges and opportunities. Mamm Genome. https://doi.org/10.1007/s00335-017-9731-6

    Google Scholar 

  6. Churchill GA, Gatti DM, Munger SC, Svenson KL (2012) The diversity outbred mouse population. Mamm Genome 23:713–718

    Article  PubMed  PubMed Central  Google Scholar 

  7. Daly AK, Day CP (2012) Genetic association studies in drug-induced liver injury. Drug Metab Rev 44:116–126

    CAS  Article  PubMed  Google Scholar 

  8. Festing MF (2001) Experimental approaches to the determination of genetic variability. Toxicol Lett 120:293–300

    CAS  Article  PubMed  Google Scholar 

  9. Frazer KA, Eskin E, Kang HM, Bogue MA, Hinds DA, Beilharz EJ, Gupta RV, Montgomery J, Morenzoni MM, Nilsen GB, Pethiyagoda CL, Stuve LL, Johnson FM, Daly MJ, Wade CM, Cox DR (2007) A sequence-based variation map of 8.27 million SNPs in inbred mouse strains. Nature 448:1050–1053

    CAS  Article  PubMed  Google Scholar 

  10. Harrill AH, McAllister KA (2017) New rodent population models may inform human health risk assessment and identification of genetic susceptibility to environmental exposures. Environ Health Perspect 125:086002

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hoffman PL, Saba LM, Vanderlinden LA, Tabakoff B (2018) Voluntary exposure to a toxin: the genetic influence on ethanol consumption. Mamm Genome. https://doi.org/10.1007/s00335-017-9726-3

  12. Israel JW, Chappell GA, Simon JM, Pott S, Safi A, Lewis L, Cotney P, Boulos HS, Bodnar W, Lieb JD, Crawford GE, Furey TS, Rusyn I (2018) Tissue- and strain-specific effects of a genotoxic carcinogen 1,3-butadiene on chromatin and transcription. Mamm Genome. https://doi.org/10.1007/s00335-018-9739-6

    PubMed  Google Scholar 

  13. Jerry DJ, Shull JD, Dunphy KA, Schneider SS, Hadsell DL, Rijnkels M, Vandenberg LN, Byrne C, Trentham-Dietz A (2018) Genetic variation in sensitivity to estrogens and breast cancer risk. Mamm Genome. https://doi.org/10.1007/s00335-018-9741-z

    PubMed  Google Scholar 

  14. Klinefelter K, Hooven MK, Bates C, Colter BT, Dailey A, Infante SK, Kania-Korwel I, Lehmler HJ, Lopez-Juarez A, Ludwig CP, Curran CP (2018) Genetic differences in the aryl hydrocarbon receptor and CYP1A2 affect sensitivity to developmental polychlorinated biphenyl exposure in mice: relevance to studies of human neurological disorders. Mamm Genome. https://doi.org/10.1007/s00335-017-9728-1

    Google Scholar 

  15. Kwo E, Christiani D (2017) The role of gene-environment interplay in occupational and environmental diseases: current concepts and knowledge gaps. Curr Opin Pulm Med 23:173–176

    PubMed  Google Scholar 

  16. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, Levine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, Stange-Thomann N, Stojanovic N, Subramanian A, Wyman D, Rogers J, Sulston J, Ainscough R, Beck S, Bentley D, Burton J, Clee C, Carter N, Coulson A, Deadman R, Deloukas P, Dunham A, Dunham I, Durbin R, French L, Grafham D, Gregory S, Hubbard T, Humphray S, Hunt A, Jones M, Lloyd C, McMurray A, Matthews L, Mercer S, Milne S, Mullikin JC, Mungall A, Plumb R, Ross M, Shownkeen R, Sims S, Waterston RH, Wilson RK, Hillier LW, McPherson JD, Marra MA, Mardis ER, Fulton LA, Chinwalla AT, Pepin KH, Gish WR, Chissoe SL, Wendl MC, Delehaunty KD, Miner TL, Delehaunty A, Kramer JB, Cook LL, Fulton RS, Johnson DL, Minx PJ, Clifton SW, Hawkins T, Branscomb E, Predki P, Richardson P, Wenning S, Slezak T, Doggett N, Cheng JF, Olsen A, Lucas S, Elkin C, Uberbacher E, Frazier M, Gibbs RA, Muzny DM, Scherer SE, Bouck JB, Sodergren EJ, Worley KC, Rives CM, Gorrell JH, Metzker ML, Naylor SL, Kucherlapati RS, Nelson DL, Weinstock GM, Sakaki Y, Fujiyama A, Hattori M, Yada T, Toyoda A, Itoh T, Kawagoe C, Watanabe H, Totoki Y, Taylor T, Weissenbach J, Heilig R, Saurin W, Artiguenave F, Brottier P, Bruls T, Pelletier E, Robert C, Wincker P, Smith DR, Doucette-Stamm L, Rubenfield M, Weinstock K, Lee HM, Dubois J, Rosenthal A, Platzer M, Nyakatura G, Taudien S, Rump A, Yang H, Yu J, Wang J, Huang G, Gu J, Hood L, Rowen L, Madan A, Qin S, Davis RW, Federspiel NA, Abola AP, Proctor MJ, Myers RM, Schmutz J, Dickson M, Grimwood J, Cox DR, Olson MV, Kaul R, Shimizu N, Kawasaki K, Minoshima S, Evans GA, Athanasiou M, Schultz R, Roe BA, Chen F, Pan H, Ramser J, Lehrach H, Reinhardt R, McCombie WR, de la BM, Dedhia N, Blocker H, Hornischer K, Nordsiek G, Agarwala R, Aravind L, Bailey JA, Bateman A, Batzoglou S, Birney E, Bork P, Brown DG, Burge CB, Cerutti L, Chen HC, Church D, Clamp M, Copley RR, Doerks T, Eddy SR, Eichler EE, Furey TS, Galagan J, Gilbert JG, Harmon C, Hayashizaki Y, Haussler D, Hermjakob H, Hokamp K, Jang W, Johnson LS, Jones TA, Kasif S, Kaspryzk A, Kennedy S, Kent WJ, Kitts P, Koonin EV, Korf I, Kulp D, Lancet D, Lowe TM, McLysaght A, Mikkelsen T, Moran JV, Mulder N, Pollara VJ, Ponting CP, Schuler G, Schultz J, Slater G, Smit AF, Stupka E, Szustakowski J, Thierry-Mieg D, Thierry-Mieg J, Wagner L, Wallis J, Wheeler R, Williams A, Wolf YI, Wolfe KH, Yang SP, Yeh RF, Collins F, Guyer MS, Peterson J, Felsenfeld A, Wetterstrand KA, Patrinos A, Morgan MJ, Szustakowki J, de Jong P, Catanese JJ, Osoegawa K, Shizuya H, Choi S (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921

  17. Latchney SE, Fields AM, Susiarjo M (2018) Linking inter-individual variability to endocrine disruptors: insights for epigenetic inheritance. Mamm Genome. https://doi.org/10.1007/s00335-017-9729-0

    Google Scholar 

  18. Mortensen HM, Chamberlin J, Joubert B, Angrish M, Sipes N, Lee JS, Euling SY (2018) Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment. Mamm Genome. https://doi.org/10.1007/s00335-018-9738-7

    PubMed  Google Scholar 

  19. Rusyn I, Gatti DM, Wiltshire T, Kleeberger SR, Threadgill DW (2010) Toxicogenetics: population-based testing of drug and chemical safety in mouse models. Pharmacogenomics 11:1127–1136

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Shorter JR, Huang W, Beak JY, Hua K, Gatti DM, de Villena FP, Pomp D, Jensen BC (2018) Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size. Mamm Genome. https://doi.org/10.1007/s00335-017-9730-7

    Google Scholar 

  21. Threadgill DW, Churchill GA (2012) Ten years of the collaborative cross. Genetics 190:291–294

    Article  PubMed  PubMed Central  Google Scholar 

  22. Vellers HL, Kleeberger SR, Lightfoot JT (2018) Inter-individual variation in adaptations to endurance and resistance exercise training: genetic approaches towards understanding a complex phenotype. Mamm Genome. https://doi.org/10.1007/s00335-017-9732-5

    Google Scholar 

  23. Venkatratnam A, House JS, Konganti K, McKenney C, Threadgill DW, Chiu WA, Aylor DL, Wright FA, Rusyn I (2018) Population-based dose-response analysis of liver transcriptional response to trichloroethylene in mouse. Mamm Genome. https://doi.org/10.1007/s00335-018-9734-y

    PubMed  Google Scholar 

  24. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S, Clark AG, Nadeau J, McKusick VA, Zinder N, Levine AJ, Roberts RJ, Simon M, Slayman C, Hunkapiller M, Bolanos R, Delcher A, Dew I, Fasulo D, Flanigan M, Florea L, Halpern A, Hannenhalli S, Kravitz S, Levy S, Mobarry C, Reinert K, Remington K, Abu-Threideh J, Beasley E, Biddick K, Bonazzi V, Brandon R, Cargill M, Chandramouliswaran I, Charlab R, Chaturvedi K, Deng Z, Di FV, Dunn P, Eilbeck K, Evangelista C, Gabrielian AE, Gan W, Ge W, Gong F, Gu Z, Guan P, Heiman TJ, Higgins ME, Ji RR, Ke Z, Ketchum KA, Lai Z, Lei Y, Li Z, Li J, Liang Y, Lin X, Lu F, Merkulov GV, Milshina N, Moore HM, Naik AK, Narayan VA, Neelam B, Nusskern D, Rusch DB, Salzberg S, Shao W, Shue B, Sun J, Wang Z, Wang A, Wang X, Wang J, Wei M, Wides R, Xiao C, Yan C, Yao A, Ye J, Zhan M, Zhang W, Zhang H, Zhao Q, Zheng L, Zhong F, Zhong W, Zhu S, Zhao S, Gilbert D, Baumhueter S, Spier G, Carter C, Cravchik A, Woodage T, Ali F, An H, Awe A, Baldwin D, Baden H, Barnstead M, Barrow I, Beeson K, Busam D, Carver A, Center A, Cheng ML, Curry L, Danaher S, Davenport L, Desilets R, Dietz S, Dodson K, Doup L, Ferriera S, Garg N, Gluecksmann A, Hart B, Haynes J, Haynes C, Heiner C, Hladun S, Hostin D, Houck J, Howland T, Ibegwam C, Johnson J, Kalush F, Kline L, Koduru S, Love A, Mann F, May D, McCawley S, McIntosh T, McMullen I, Moy M, Moy L, Murphy B, Nelson K, Pfannkoch C, Pratts E, Puri V, Qureshi H, Reardon M, Rodriguez R, Rogers YH, Romblad D, Ruhfel B, Scott R, Sitter C, Smallwood M, Stewart E, Strong R, Suh E, Thomas R, Tint NN, Tse S, Vech C, Wang G, Wetter J, Williams S, Williams M, Windsor S, Winn-Deen E, Wolfe K, Zaveri J, Zaveri K, Abril JF, Guigo R, Campbell MJ, Sjolander KV, Karlak B, Kejariwal A, Mi H, Lazareva B, Hatton T, Narechania A, Diemer K, Muruganujan A, Guo N, Sato S, Bafna V, Istrail S, Lippert R, Schwartz R, Walenz B, Yooseph S, Allen D, Basu A, Baxendale J, Blick L, Caminha M, Carnes-Stine J, Caulk P, Chiang YH, Coyne M, Dahlke C, Mays A, Dombroski M, Donnelly M, Ely D, Esparham S, Fosler C, Gire H, Glanowski S, Glasser K, Glodek A, Gorokhov M, Graham K, Gropman B, Harris M, Heil J, Henderson S, Hoover J, Jennings D, Jordan C, Jordan J, Kasha J, Kagan L, Kraft C, Levitsky A, Lewis M, Liu X, Lopez J, Ma D, Majoros W, McDaniel J, Murphy S, Newman M, Nguyen T, Nguyen N, Nodell M (2001) The sequence of the human genome. Science 291:1304–1351

  25. Verhein KC, Vellers HL, Kleeberger SR (2018) Inter-individual variation in health and disease associated with pulmonary infectious agents. Mamm Genome. https://doi.org/10.1007/s00335-018-9733-z

    PubMed  Google Scholar 

  26. Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, Antonarakis SE, Attwood J, Baertsch R, Bailey J, Barlow K, Beck S, Berry E, Birren B, Bloom T, Bork P, Botcherby M, Bray N, Brent MR, Brown DG, Brown SD, Bult C, Burton J, Butler J, Campbell RD, Carninci P, Cawley S, Chiaromonte F, Chinwalla AT, Church DM, Clamp M, Clee C, Collins FS, Cook LL, Copley RR, Coulson A, Couronne O, Cuff J, Curwen V, Cutts T, Daly M, David R, Davies J, Delehaunty KD, Deri J, Dermitzakis ET, Dewey C, Dickens NJ, Diekhans M, Dodge S, Dubchak I, Dunn DM, Eddy SR, Elnitski L, Emes RD, Eswara P, Eyras E, Felsenfeld A, Fewell GA, Flicek P, Foley K, Frankel WN, Fulton LA, Fulton RS, Furey TS, Gage D, Gibbs RA, Glusman G, Gnerre S, Goldman N, Goodstadt L, Grafham D, Graves TA, Green ED, Gregory S, Guigo R, Guyer M, Hardison RC, Haussler D, Hayashizaki Y, Hillier LW, Hinrichs A, Hlavina W, Holzer T, Hsu F, Hua A, Hubbard T, Hunt A, Jackson I, Jaffe DB, Johnson LS, Jones M, Jones TA, Joy A, Kamal M, Karlsson EK, Karolchik D, Kasprzyk A, Kawai J, Keibler E, Kells C, Kent WJ, Kirby A, Kolbe DL, Korf I, Kucherlapati RS, Kulbokas EJ, Kulp D, Landers T, Leger JP, Leonard S, Letunic I, Levine R, Li J, Li M, Lloyd C, Lucas S, Ma B, Maglott DR, Mardis ER, Matthews L, Mauceli E, Mayer JH, McCarthy M, McCombie WR, McLaren S, McLay K, McPherson JD, Meldrim J, Meredith B, Mesirov JP, Miller W, Miner TL, Mongin E, Montgomery KT, Morgan M, Mott R, Mullikin JC, Muzny DM, Nash WE, Nelson JO, Nhan MN, Nicol R, Ning Z, Nusbaum C, O’Connor MJ, Okazaki Y, Oliver K, Overton-Larty E, Pachter L, Parra G, Pepin KH, Peterson J, Pevzner P, Plumb R, Pohl CS, Poliakov A, Ponce TC, Ponting CP, Potter S, Quail M, Reymond A, Roe BA, Roskin KM, Rubin EM, Rust AG, Santos R, Sapojnikov V, Schultz B, Schultz J, Schwartz MS, Schwartz S, Scott C, Seaman S, Searle S, Sharpe T, Sheridan A, Shownkeen R, Sims S, Singer JB, Slater G, Smit A, Smith DR, Spencer B, Stabenau A, Stange-Thomann N, Sugnet C, Suyama M, Tesler G, Thompson J, Torrents D, Trevaskis E, Tromp J, Ucla C, Ureta-Vidal A, Vinson JP, Von Niederhausern AC, Wade CM, Wall M, Weber RJ, Weiss RB, Wendl MC, West AP, Wetterstrand K, Wheeler R, Whelan S, Wierzbowski J, Willey D, Williams S, Wilson RK, Winter E, Worley KC, Wyman D, Yang S, Yang SP, Zdobnov EM, Zody MC, Lander ES (2002) Initial sequencing and comparative analysis of the mouse genome. Nature 420:520–562

  27. Yang H, Bell TA, Churchill GA, Pardo-Manuel de Villena F (2007) On the subspecific origin of the laboratory mouse. Nat Genet 39:1100–1107

    CAS  Article  PubMed  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ivan Rusyn.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rusyn, I., Kleeberger, S.R., McAllister, K.A. et al. Introduction to mammalian genome special issue: the combined role of genetics and environment relevant to human disease outcomes. Mamm Genome 29, 1–4 (2018). https://doi.org/10.1007/s00335-018-9740-0

Download citation