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.

  • Article
  • Published:

Genome-wide association study of behavioral, physiological and gene expression traits in outbred CFW mice

Abstract

Although mice are the most widely used mammalian model organism, genetic studies have suffered from limited mapping resolution due to extensive linkage disequilibrium (LD) that is characteristic of crosses among inbred strains. Carworth Farms White (CFW) mice are a commercially available outbred mouse population that exhibit rapid LD decay in comparison to other available mouse populations. We performed a genome-wide association study (GWAS) of behavioral, physiological and gene expression phenotypes using 1,200 male CFW mice. We used genotyping by sequencing (GBS) to obtain genotypes at 92,734 SNPs. We also measured gene expression using RNA sequencing in three brain regions. Our study identified numerous behavioral, physiological and expression quantitative trait loci (QTLs). We integrated the behavioral QTL and eQTL results to implicate specific genes, including Azi2 in sensitivity to methamphetamine and Zmynd11 in anxiety-like behavior. The combination of CFW mice, GBS and RNA sequencing constitutes a powerful approach to GWAS in mice.

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

Access options

Buy this article

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

Figure 1: Components of the study.
Figure 2: Genetic characteristics of the CFW mouse population.
Figure 3: QTLs for physiological and behavioral traits.
Figure 4: Overview of eQTL mapping.

Similar content being viewed by others

References

  1. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Manolio, T.A. Bringing genome-wide association findings into clinical use. Nat. Rev. Genet. 14, 549–558 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  4. Albert, F.W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Mott, R. & Flint, J. Dissecting quantitative traits in mice. Annu. Rev. Genomics Hum. Genet. 14, 421–439 (2013).

    Article  CAS  PubMed  Google Scholar 

  6. 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 

  7. Lynch, C.J. The so-called Swiss mouse. Lab. Anim. Care 19, 214–220 (1969).

    CAS  PubMed  Google Scholar 

  8. Rice, M.C. & O'Brien, S.J. Genetic variance of laboratory outbred Swiss mice. Nature 283, 157–161 (1980).

    Article  CAS  PubMed  Google Scholar 

  9. Yalcin, B. et al. Commercially available outbred mice for genome-wide association studies. PLoS Genet. 6, e1001085 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Chia, R., Achilli, F., Festing, M.F.W. & Fisher, E.M.C. The origins and uses of mouse outbred stocks. Nat. Genet. 37, 1181–1186 (2005).

    Article  CAS  PubMed  Google Scholar 

  11. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gatti, D.M. et al. Quantitative trait locus mapping methods for diversity outbred mice. G3 (Bethesda) 4, 1623–1633 (2014).

    Article  Google Scholar 

  13. Morgan, A.P. et al. The Mouse Universal Genotyping Array: from substrains to subspecies. G3 (Bethesda) 6, 263–279 (2015).

    Article  CAS  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Elshire, R.J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6, e19379 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Pritchard, J.K. & Przeworski, M. Linkage disequilibrium in humans: models and data. Am. J. Hum. Genet. 69, 1–14 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Laurie, C.C. et al. Linkage disequilibrium in wild mice. PLoS Genet. 3, e144 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chesler, E.J. Out of the bottleneck: the Diversity Outcross and Collaborative Cross mouse populations in behavioral genetics research. Mamm. Genome 25, 3–11 (2014).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  20. Collaborative Cross Consortium. The genome architecture of the Collaborative Cross mouse genetic reference population. Genetics 190, 389–401 (2012).

  21. Lee, S.H. et al. Estimation of SNP heritability from dense genotype data. Am. J. Hum. Genet. 93, 1151–1155 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wray, N.R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Cheng, R. & Palmer, A.A. A simulation study of permutation, bootstrap, and gene dropping for assessing statistical significance in the case of unequal relatedness. Genetics 193, 1015–1018 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Churchill, G.A. & Doerge, R.W. Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Mendis, S.H.S., Meachem, S.J., Sarraj, M.A. & Loveland, K.L. Activin A balances Sertoli and germ cell proliferation in the fetal mouse testis. Biol. Reprod. 84, 379–391 (2011).

    Article  CAS  PubMed  Google Scholar 

  26. Mithraprabhu, S. et al. Activin bioactivity affects germ cell differentiation in the postnatal mouse testis in vivo. Biol. Reprod. 82, 980–990 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tomaszewski, J., Joseph, A., Archambeault, D. & Yao, H.H.-C. Essential roles of inhibin βA in mouse epididymal coiling. Proc. Natl. Acad. Sci. USA 104, 11322–11327 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lee, S.-J. Quadrupling muscle mass in mice by targeting TGF-β signaling pathways. PLoS One 2, e789 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lee, S.-J. et al. Regulation of muscle mass by follistatin and activins. Mol. Endocrinol. 24, 1998–2008 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lionikas, A. et al. Resolving candidate genes of mouse skeletal muscle QTL via RNA-Seq and expression network analyses. BMC Genomics 13, 592 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Sala, D. et al. Autophagy-regulating TP53INP2 mediates muscle wasting and is repressed in diabetes. J. Clin. Invest. 124, 1914–1927 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Estrada, K. et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44, 491–501 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zheng, H.-F. et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526, 112–117 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Coury, F. et al. SLC4A2-mediated Cl/HCO3 exchange activity is essential for calpain-dependent regulation of the actin cytoskeleton in osteoclasts. Proc. Natl. Acad. Sci. USA 110, 2163–2168 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Meyers, S.N. et al. A deletion mutation in bovine SLC4A2 is associated with osteopetrosis in Red Angus cattle. BMC Genomics 11, 337 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Sillence, D.O., Senn, A. & Danks, D.M. Genetic heterogeneity in osteogenesis imperfecta. J. Med. Genet. 16, 101–116 (1979).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Sykes, B., Wordsworth, P., Ogilvie, D., Anderson, J. & Jones, N. Osteogenesis imperfecta is linked to both type I collagen structural genes. Lancet 2, 69–72 (1986).

    Article  CAS  PubMed  Google Scholar 

  38. Long, J.-R. et al. Association between COL1A1 gene polymorphisms and bone size in Caucasians. Eur. J. Hum. Genet. 12, 383–388 (2004).

    Article  CAS  PubMed  Google Scholar 

  39. Flutre, T., Wen, X., Pritchard, J. & Stephens, M. A statistical framework for joint eQTL analysis in multiple tissues. PLoS Genet. 9, e1003486 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Serre, D. et al. Differential allelic expression in the human genome: a robust approach to identify genetic and epigenetic cis-acting mechanisms regulating gene expression. PLoS Genet. 4, e1000006 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Pickrell, J.K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Coe, B.P. et al. Refining analyses of copy number variation identifies specific genes associated with developmental delay. Nat. Genet. 46, 1063–1071 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Cheng, R. et al. Genome-wide association studies and the problem of relatedness among advanced intercross lines and other highly recombinant populations. Genetics 185, 1033–1044 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Samocha, K.E., Lim, J.E., Cheng, R., Sokoloff, G. & Palmer, A.A. Fine mapping of QTL for prepulse inhibition in LG/J and SM/J mice using F2 and advanced intercross lines. Genes Brain Behav. 9, 759–767 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Parker, C.C. et al. Fine-mapping alleles for body weight in LG/J × SM/J F and F34 advanced intercross lines. Mamm. Genome 22, 563–571 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Parker, C.C. et al. High-resolution genetic mapping of complex traits from a combined analysis of F2 and advanced intercross mice. Genetics 198, 103–116 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Talbot, C.J. et al. High-resolution mapping of quantitative trait loci in outbred mice. Nat. Genet. 21, 305–308 (1999).

    Article  CAS  PubMed  Google Scholar 

  48. Demarest, K., Koyner, J., McCaughran, J. Jr., Cipp, L. & Hitzemann, R. Further characterization and high-resolution mapping of quantitative trait loci for ethanol-induced locomotor activity. Behav. Genet. 31, 79–91 (2001).

    Article  CAS  PubMed  Google Scholar 

  49. Valdar, W. et al. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat. Genet. 38, 879–887 (2006).

    Article  CAS  PubMed  Google Scholar 

  50. Ghazalpour, A. et al. High-resolution mapping of gene expression using association in an outbred mouse stock. PLoS Genet. 4, e1000149 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Sittig, L.J., Carbonetto, P., Engel, K.A., Krauss, K.S. & Palmer, A.A. Integration of genome-wide association and extant brain expression QTL identifies candidate genes influencing prepulse inhibition in inbred F1 mice. Genes Brain Behav. 15, 260–270 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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 

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

    Article  CAS  PubMed  Google Scholar 

  55. Eberle, M.A., Rieder, M.J., Kruglyak, L. & Nickerson, D.A. Allele frequency matching between SNPs reveals an excess of linkage disequilibrium in genic regions of the human genome. PLoS Genet. 2, e142 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Mangin, B. et al. Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity 108, 285–291 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. CONVERGE Consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015).

  59. Le, S.Q. & Durbin, R. SNP detection and genotyping from low-coverage sequencing data on multiple diploid samples. Genome Res. 21, 952–960 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Li, Y., Sidore, C., Kang, H.M., Boehnke, M. & Abecasis, G.R. Low-coverage sequencing: implications for design of complex trait association studies. Genome Res. 21, 940–951 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Parker, C.C., Sokoloff, G., Cheng, R. & Palmer, A.A. Genome-wide association for fear conditioning in an advanced intercross mouse line. Behav. Genet. 42, 437–448 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Parker, C.C., Cheng, R., Sokoloff, G. & Palmer, A.A. Genome-wide association for methamphetamine sensitivity in an advanced intercross mouse line. Genes Brain Behav. 11, 52–61 (2012).

    Article  CAS  PubMed  Google Scholar 

  65. Smemo, S. et al. Obesity-associated variants within FTO form long-range functional connections with IRX3. Nature 507, 371–375 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Schadt, E.E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003).

    Article  CAS  PubMed  Google Scholar 

  67. Chesler, E.J., Lu, L., Wang, J., Williams, R.W. & Manly, K.F. WebQTL: rapid exploratory analysis of gene expression and genetic networks for brain and behavior. Nat. Neurosci. 7, 485–486 (2004).

    Article  CAS  PubMed  Google Scholar 

  68. Chesler, E.J. et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat. Genet. 37, 233–242 (2005).

    Article  CAS  PubMed  Google Scholar 

  69. Bystrykh, L. et al. Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'. Nat. Genet. 37, 225–232 (2005).

    Article  CAS  PubMed  Google Scholar 

  70. Palmer, A.A. et al. Gene expression differences in mice divergently selected for methamphetamine sensitivity. Mamm. Genome 16, 291–305 (2005).

    Article  CAS  PubMed  Google Scholar 

  71. Huang, G.-J. et al. High resolution mapping of expression QTLs in heterogeneous stock mice in multiple tissues. Genome Res. 19, 1133–1140 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. de Klerk, E. & 't Hoen, P.A.C. Alternative mRNA transcription, processing, and translation: insights from RNA sequencing. Trends Genet. 31, 128–139 (2015).

    Article  CAS  PubMed  Google Scholar 

  75. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  PubMed  Google Scholar 

  76. Mane, S.P. et al. Transcriptome sequencing of the Microarray Quality Control (MAQC) RNA reference samples using next generation sequencing. BMC Genomics 10, 264 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  78. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Walter, N.A. et al. High throughput sequencing in mice: a platform comparison identifies a preponderance of cryptic SNPs. BMC Genomics 10, 379 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  82. Sander, J.D. & Joung, J.K. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32, 347–355 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Majewski, J. & Pastinen, T. The study of eQTL variations by RNA-seq: from SNPs to phenotypes. Trends Genet. 27, 72–79 (2011).

    Article  CAS  PubMed  Google Scholar 

  85. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Roberts, A., Pimentel, H., Trapnell, C. & Pachter, L. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics 27, 2325–2329 (2011).

    Article  CAS  PubMed  Google Scholar 

  88. Grabowski, P.P., Morris, G.P., Casler, M.D. & Borevitz, J.O. Population genomic variation reveals roles of history, adaptation and ploidy in switchgrass. Mol. Ecol. 23, 4059–4073 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Van der Auwera, G.A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 11, 11.10.1–11.10.33 (2013).

    Google Scholar 

  91. Keane, T.M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Pickrell, J.K. & Pritchard, J.K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Hayes, B.J., Visscher, P.M. & Goddard, M.E. Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. (Camb.) 91, 47–60 (2009).

    Article  CAS  Google Scholar 

  95. Listgarten, J. et al. Improved linear mixed models for genome-wide association studies. Nat. Methods 9, 525–526 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Abney, M. Permutation testing in the presence of polygenic variation. Genet. Epidemiol. 39, 249–258 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Speed, D., Hemani, G., Johnson, M.R. & Balding, D.J. Improved heritability estimation from genome-wide SNPs. Am. J. Hum. Genet. 91, 1011–1021 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors wish to acknowledge technical assistance from: D. Godfrey, S. Lionikaite, V. Lionikaite, A.S. Lionikiene and J. Zekos as well as technical and intellectual input from M. Abney, J. Borevitz, K. Broman, N. Cai, R. Cheng, N. Cox, R. Davies, J. Flint, L. Goodstadt, P. Grabowski, B. Harr, E. Leffler, R. Mott, J. Nicod, J. Novembre, A. Price, M. Stephens, D. Weeks and X. Zhou. This project was funded by NIH R01GM097737 and P50DA037844 (A.A.P.), NIH T32DA07255 (C.C.P.), NIH T32GM07197 (N.M.G.), NIH R01AR056280 (D.A.B.), NIH R01AR060234 (C.L.A.-B.), the Fellowship from the Human Frontiers Science Program (P.C.) and the Howard Hughes Medical Institute (J.K.P.).

Author information

Authors and Affiliations

Authors

Contributions

A.A.P. conceived the study. C.C.P. and A.A.P. supervised the project. S.G. and P.C. designed and implemented the statistical and bioinformatics analyses with contributions from C.C.P., J.K.P. and A.A.P. N.M.G. designed and executed the RNA-seq and GBS protocols with assistance from E.A. and J.D. C.C.P. performed the behavioral phenotyping with assistance from E.L. and Y.J.P. A.L. performed the muscle and bone phenotyping with input from D.A.B. C.L.A.-B. performed the BMD phenotyping. C.C.P., S.G., P.C. and A.A.P. wrote the manuscript, with input from all co-authors.

Corresponding author

Correspondence to Abraham A Palmer.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Note and Supplementary Figures 1–29. (PDF 8502 kb)

Supplementary Table 1

Summary of phenotypes. (XLSX 15 kb)

Supplementary Table 2

GWAS results. (XLSX 17 kb)

Supplementary Table 3

The number of expression QTLs (eQTLs) found in the three brain tissues assayed. (XLSX 10 kb)

Supplementary Table 4

Discordance rates when comparing GBS and MegaMUGA genotypes using 24 subjects genotyped using both platforms. (XLSX 10 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parker, C., Gopalakrishnan, S., Carbonetto, P. et al. Genome-wide association study of behavioral, physiological and gene expression traits in outbred CFW mice. Nat Genet 48, 919–926 (2016). https://doi.org/10.1038/ng.3609

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3609

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing