Skip to main content

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

  • Review Article
  • Published:

Genome-wide association studies in mice

Nature Reviews Genetics volume 13, pages 807–817 (2012)Cite this article

Subjects

Key Points

  • Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice.

  • Until recently, most mouse genetic studies used a standard genetic cross, which has the drawback that the regions implicated in the cross are very large and contain hundreds of genes.

  • Several recently proposed mouse GWAS strategies include those using the Hybrid Mouse Diversity Panel, the Collaborative Cross and heterogeneous and commercially available outbred stocks. Each of these strategies has advantages and disadvantages relative to the others, yet all improve resolution by an order of magnitude over the classic genetic cross.

  • The design of GWASs in mouse strains is fundamentally different from those carried out on humans, creating new challenges and opportunities. The development of modern mouse GWAS strategies is an active research area.

  • Using these strategies, many groups are rapidly identifying regions in the mouse genome that associate with complex traits that are relevant to human disease. This has led to the discovery of additional genes involved in human disease.

  • Mouse GWASs have advantages over human studies in their ability to functionally characterize implicated genes to understand mechanisms. These advantages include the accessibility of relevant tissues and the ability to carry out genetic manipulations, such as knockouts, which are impossible in human studies.

Abstract

Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice. However, the design of GWASs in mouse strains is fundamentally different from the design of human GWASs, creating new challenges and opportunities. This Review gives an overview of the novel study designs for mouse GWASs, which dramatically improve both the statistical power and resolution compared to classical gene-mapping approaches.

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

Access options

Buy this article

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

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

Figure 1: Breeding schemes for mouse genome-wide association study populations.
Figure 2: Overview of mouse GWASs.
Figure 3: Comparison of mouse GWASs for HDL cholesterol.

Similar content being viewed by others

References

  1. Silver, L. M. Mouse Genetics: Concepts and Applications (Oxford Univ. Press, 1995). This book is the classic resource on mouse genetics.

    Google Scholar 

  2. Manolio, T. A., Brooks, L. D. & Collins, F. S. A HapMap harvest of insights into the genetics of common disease. J. Clin. Invest. 118, 1590–1605 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Burke, D. T. et al. Dissection of complex adult traits in a mouse synthetic population. Genome Res. 22, 1549–1557 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Skarnes, W. C. et al. A conditional knockout resource for the genome-wide study of mouse gene function. Nature 474, 337–342 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ringwald, M. et al. The IKMC web portal: a central point of entry to data and resources from the international knockout mouse consortium. Nucleic Acids Res. 39, D849–D855 (2011).

    Article  CAS  PubMed  Google Scholar 

  7. Gunderson, K. L., Steemers, F. J., Lee, G., Mendoza, L. G. & Chee, M. S. A genome-wide scalable SNP genotyping assay using microarray technology. Nature Genet. 37, 549–554 (2005).

    Article  CAS  PubMed  Google Scholar 

  8. Matsuzaki, H. et al. Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays. Nature Methods 1, 109–111 (2004).

    Article  CAS  PubMed  Google Scholar 

  9. Bentley, D. R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Keane, T. M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011). This paper describes an almost complete map of genetic variation in mice.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Yang, H. et al. A customized and versatile high-density genotyping array for the mouse. Nature Methods 6, 663–666 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Frazer, K. A. et al. A sequence-based variation map of 8.27 million SNPs in inbred mouse strains. Nature 448, 1050–1053 (2007).

    Article  CAS  PubMed  Google Scholar 

  13. Yang, H., Bell, T. A., Churchill, G. A. & Pardo-Manuel de Villena, F. On the subspecific origin of the laboratory mouse. Nature Genet. 39, 1100–1107 (2007).

    Article  CAS  PubMed  Google Scholar 

  14. Yang, H. et al. Subspecific origin and haplotype diversity in the laboratory mouse. Nature Genet. 43, 648–655 (2011).

    Article  CAS  PubMed  Google Scholar 

  15. Churchill, G. A. et al. The collaborative cross, a community resource for the genetic analysis of complex traits. Nature Genet. 36, 1133–1137 (2004). This paper describes the motivation and rationale for the development of the Collaborative Cross.

    Article  CAS  PubMed  Google Scholar 

  16. Bennett, B. J. et al. A high-resolution association mapping panel for the dissection of complex traits in mice. Genome Res. 20, 281–290 (2010). This paper describes the HMDP mouse GWAS strategy.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhang, W. et al. Genome-wide association mapping of quantitative traits in outbred mice. G3 2, 167–174 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Aylor, D. L. et al. Genetic analysis of complex traits in the emerging Collaborative Cross. Genome Res. 21, 1213–1222 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Grupe, A. et al. In silico mapping of complex disease-related traits in mice. Science 292, 1915–1918 (2001).

    Article  CAS  PubMed  Google Scholar 

  22. Pletcher, M. T. et al. Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse. PLoS Biol. 2, e393 (2004).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Cervino, A. C., Darvasi, A., Fallahi, M., Mader, C. C. & Tsinoremas, N. F. An integrated in silico gene mapping strategy in inbred mice. Genetics 175, 321–333 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. McClurg, P. et al. Genomewide association analysis in diverse inbred mice: power and population structure. Genetics 176, 675–683 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bogue, M. A., Grubb, S. C., Maddatu, T. P. & Bult, C. J. Mouse phenome database (MPD). Nucleic Acids Res. 35, D643–D649 (2007).

    Article  CAS  PubMed  Google Scholar 

  26. The Mouse Phenotype Database Integration Consortium. Integration of mouse phenome data resources. Mamm. Genome 18, 157–163 (2007).

  27. Grubb, S. C., Maddatu, T. P., Bult, C. J. & Bogue, M. A. Mouse phenome database. Nucleic Acids Res. 37, D720–D730 (2009).

    Article  CAS  PubMed  Google Scholar 

  28. Beck, J. A. et al. Genealogies of mouse inbred strains. Nature Genet. 24, 23–25 (2000).

    Article  CAS  PubMed  Google Scholar 

  29. Wade, C. M. et al. The mosaic structure of variation in the laboratory mouse genome. Nature. 420, 574–578 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Kirby, A. et al. Fine mapping in 94 inbred mouse strains using a high-density haplotype resource. Genetics 185, 1081–1095 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kang, H. M. et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008). This paper describes the EMMA approach, which is widely applied in mouse GWASs and is used for the correction of population structure in association studies.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Manenti, G. et al. Mouse genome-wide association mapping needs linkage analysis to avoid false-positive loci. PLoS Genet. 5, e1000331 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Payseur, B. A. & Place M. Prospects for association mapping in classical inbred mouse strains. Genetics 175, 1999–2008 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Threadgill, D. W. & Churchill, G. A. Ten years of the collaborative cross. Genetics 190, 291–294 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Collaborative Cross Consortium. The genome architecture of the collaborative cross mouse genetic reference population. Genetics 190, 389–401 (2012). This paper provides a description of genetic architecture of the generated Collaborative Cross strains.

  37. Kelada, S. N. et al. Genetic analysis of hematological parameters in incipient lines of the Collaborative Cross. G3 2, 157–165 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Valdar, W. et al. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nature Genet. 38, 879–887 (2006). This paper provides a description of the heterogeneous stock strategy for mouse GWASs.

    Article  CAS  PubMed  Google Scholar 

  39. Svenson, K. L. et al. High-resolution genetic mapping using the mouse diversity outbred population. Genetics 190, 437–447 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yalcin, B. et al. Commercially available outbred mice for genome-wide association studies. PLoS Genet. 6, e1001085 (2010). This paper describes the commercially available outbred stock mouse GWAS strategy.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genet. 39, 906–913 (2007).

    Article  CAS  PubMed  Google Scholar 

  42. Li, Y., Willer, C. J., Ding, J., Scheet, P. & Abecasis, G. R. Mach: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Wang, J. R. et al. Imputation of single-nucleotide polymorphisms in inbred mice using local phylogeny. Genetics 190, 449–458 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Steinmetz, L. M. et al. Dissecting the architecture of a quantitative trait locus in yeast. Nature 416, 326–330 (2002).

    Article  CAS  PubMed  Google Scholar 

  45. Long, A. D., Mullaney, S. L., Mackay, T. F. & Langley, C. H. Genetic interactions between naturally occurring alleles at quantitative trait loci and mutant alleles at candidate loci affecting bristle number in Drosophila melanogaster. Genetics 144, 1497–1510 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Mackay, T. F. Quantitative trait loci in Drosophila. Nature Rev. Genet. 2, 11–20 (2001).

    Article  CAS  PubMed  Google Scholar 

  47. Nejentsev, S., Walker, N., Riches, D., Egholm, M. & Todd, J. A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Rivas, M. A. et al. Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nature Genet. 43, 1066–1073 (2011).

    Article  CAS  PubMed  Google Scholar 

  49. Yalcin, B. et al. Genetic dissection of a behavioral quantitative trait locus shows that Rgs2 modulates anxiety in mice. Nature Genet. 36, 1197–1202 (2004).

    Article  CAS  PubMed  Google Scholar 

  50. Su, Z. et al. Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene. J. Lipid. Res. 50, 2083–2094 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  52. Mackay, T. F. Complementing complexity. Nature Genet. 36, 1145–1147 (2004).

    Article  CAS  PubMed  Google Scholar 

  53. Wilkie, A. O. Bad bones, absent smell, selfish testes: the pleiotropic consequences of human FGF receptor mutations. Cytokine Growth Factor Rev. 16, 187–203 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. Flint, J. & Mott, R. Applying mouse complex-trait resources to behavioural genetics. Nature 456, 724–727 (2008).

    Article  CAS  PubMed  Google Scholar 

  55. Wakeland, E., Morel, L., Achey, K., Yui, M. & Longmate, J. Speed congenics: a classic technique in the fast lane (relatively speaking). Immunol. Today 18, 472–477 (1997).

    Article  CAS  PubMed  Google Scholar 

  56. Markel, P. et al. Theoretical and empirical issues for marker-assisted breeding of congenic mouse strains. Nature Genet. 17, 280–284 (1997).

    Article  CAS  PubMed  Google Scholar 

  57. Shao, H. et al. Analyzing complex traits with congenic strains. Mamm. Genome 21, 276–286 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Davis, R. C. et al. A genome-wide set of congenic mouse strains derived from CAST/ei on a C57BL/6 background. Genomics 90, 306–313 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Moreau, Y. & Tranchevent, L. C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nature Rev. Genet. 13, 523–536 (2012).

    Article  CAS  PubMed  Google Scholar 

  62. Cooper, G. M. & Shendure, J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nature Rev. Genet. 12, 628–640 (2011).

    Article  CAS  PubMed  Google Scholar 

  63. Verdugo, R. A., Farber, C. R., Warden, C. H. & Medrano, J. F. Serious limitations of the QTL/microarray approach for QTL gene discovery. BMC Biol. 8, 96 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature Genet. 42, 937–948 (2010).

    Article  CAS  PubMed  Google Scholar 

  65. Lango, Allen H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

    Article  CAS  Google Scholar 

  66. Manolio, T. A. & Collins, F. S. The HapMap and genome-wide association studies in diagnosis and therapy. Annu. Rev. Med. 60, 443–456 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Chan, Y. F. et al. Parallel selection mapping using artificially selected mice reveals body weight control loci. Curr. Biol. 22, 794–800 (2012).

    Article  CAS  PubMed  Google Scholar 

  68. Phifer-Rixey, M. et al. Adaptive evolution and effective population size in wild house mice. Mol. Biol. Evol. 3 Apr 2012 (doi:10.1093/molbev/mss105).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Bennett, B. J. et al. High-resolution association mapping of atherosclerosis loci in mice. Arterioscler. Thromb. Vasc. Biol. 32, 1790–1798 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Bhasin, J. M. et al. Sex specific gene regulation and expression QTLs in mouse macrophages from a strain intercross. PLoS ONE 3, e1435 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Smith, J. D. et al. Transcriptome profile of macrophages from atherosclerosis-sensitive and atherosclerosis-resistant mice. Mamm. Genome 17, 220–229 (2006).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Myers, S., Freeman, C., Auton, A., Donnelly, P. & McVean, G. A common sequence motif associated with recombination hot spots and genome instability in humans. Nature Genet. 40, 1124–1129 (2008).

    Article  CAS  PubMed  Google Scholar 

  74. Myers, S. et al. Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic recombination. Science 327, 876–879 (2010).

    Article  CAS  PubMed  Google Scholar 

  75. Cordell, H. J. Detecting gene-gene interactions that underlie human diseases. Nature Rev. Genet. 10, 392–404 (2009).

    Article  CAS  PubMed  Google Scholar 

  76. Thomas, D. Gene-environment-wide association studies: emerging approaches. Nature Rev. Genet. 11, 259–272 (2010).

    Article  CAS  PubMed  Google Scholar 

  77. Cirulli, E. T. & Goldstein, D. B. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nature Rev. Genet. 11, 415–425 (2010).

    Article  CAS  PubMed  Google Scholar 

  78. Conrad, D. F. et al. Origins and functional impact of copy number variation in the human genome. Nature 464, 704–712 (2010).

    Article  CAS  PubMed  Google Scholar 

  79. Parker, C. C. & Palmer, A. A. Dark matter: are mice the solution to missing heritability? Front. Genet. 2, 32 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Kang, H. M. et al. Variance component model to account for sample structure in genome-wide association studies. Nature Genet. 42, 348–354 (2010).

    Article  CAS  PubMed  Google Scholar 

  81. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  82. Valdar, W., Holmes, C. C., Mott, R. & Flint, J. Mapping in structured populations by resample model averaging. Genetics 182, 1263–1277 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Anunciado, R. V. et al. Quantitative trait loci for body weight in the intercross between SM/J and A/J mice. Exp. Anim. 50, 319–324 (2001).

    Article  CAS  PubMed  Google Scholar 

  84. Hunter, K. W. Mouse models of cancer: does the strain matter? Nature Rev. Cancer 12, 144–149 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  86. Warden, C. H., Hedrick, C. C., Qiao, J. H., Castellani, L. W. & Lusis, A. J. Atherosclerosis in transgenic mice overexpressing apolipoprotein A-II. Science 261, 469–472 (1993).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

J.F. is supported by the Wellcome Trust. E.E. is supported by US National Science Foundation grants 0513612, 0731455, 0729049, 0916676 and 1065276, and US National Institutes of Health grants K25-HL080079, U01-DA024417, P01-HL30568 and PO1-HL28481. This research was supported in part by the University of California, Los Angeles, subcontract of contract N01-ES-45530 from the National Toxicology Program and the National Institute of Environmental Helath Sciences to Perlegen Sciences.

Author information

Authors and Affiliations

  1. The Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK

    Jonathan Flint

  2. Department of Computer Science, Department of Human Genetics, University of California, Los Angeles, 3532-J Boelter Hall, Los Angeles, 90095–91596, California, USA

    Eleazar Eskin

Authors
  1. Jonathan Flint
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eleazar Eskin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eleazar Eskin.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Related links

Related links

FURTHER INFORMATION

Jonathan Flint's homepage

Eleazar Eskin's homepage

GeneNetwork

The Jackson Laboratory

MGI — Mouse Genome Informatics

Mouse Genome Variation Identifier

Mouse Phenome Database

Nature Reviews Genetics Series on Genome-wide association studies

Nature Reviews Genetics Series on Study designs

UCLA Mouse Genetics and Genomics Software and Tools

Glossary

Inbred strains

Mouse strains that have been sibling-mated for at least 20 generations to the point that both alleles at each locus are expected to be identical.

Linkage analysis

A statistical method for identifying a region of the genome that is implicated in a trait by observing which region is inherited from the parental strain carrying the trait in offspring that carry the trait.

Quantitative trait loci

(QTLs). Regions of the genome that are implicated in a quantitative trait.

Recombinant inbred strains

Inbred strains that are generated by sibling-mating the offspring of a genetic cross until the progenies are inbred.

Collaborative Cross project

A large panel of inbred mouse strains that are currently being developed through a community effort. The strains are derived from an eight-way cross using a set of founder strains that include three wild-derived strains.

Population structure

Differences in levels of genetic similarity between individuals in the study population. Population structure can cause spurious associations in genetic studies.

Imputation

A statistical procedure to predict the values of genetic variation which was not collected using observed genetic variation and genetic reference data sets.

Heritability

A measure of the genetic component of phenotypic variance of a trait.

Linkage disequilibrium decay

The decrease in the correlation between genetic variants as the distance between the variants increases.

Private variants

Genetic variants that are confined to single individuals, families or populations.

Multiple testing

A statistical problem that arises from carrying out many (in the order of thousands) hypothesis tests together. The significance threshold must be appropriately corrected to avoid false positives: for example, by using the Bonferroni correction.

F1 strains

Mouse strains that are generated by breeding two inbred strains together. An F1 mouse has one chromosome from each of the parental strains.

Co-isogenic wild-type strain

A strain that differs from the wild-type strain at only a single locus through a mutation occurring in the wild-type strain.

Congenic strains

Strains that are produced by a breeding strategy in which recombinants between two inbred strains are backcrossed to produce a strain that carries a single genomic segment from one strain on the genetic background of the other.

Additive

In the context of a genetic effect, the linear relationship between the replacement of an allele and its effect on the phenotype.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Flint, J., Eskin, E. Genome-wide association studies in mice. Nat Rev Genet 13, 807–817 (2012). https://doi.org/10.1038/nrg3335

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg3335

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research