Mammalian Genome

, Volume 20, Issue 7, pp 437–446 | Cite as

Replication and narrowing of gene expression quantitative trait loci using inbred mice

  • Daniel M. Gatti
  • Alison H. Harrill
  • Fred A. Wright
  • David W. Threadgill
  • Ivan Rusyn


Gene expression quantitative trait locus (eQTL) mapping has become a powerful tool in systems biology. While many authors have made important discoveries using this approach, one persistent challenge in eQTL studies is the selection of loci and genes that should receive further biological investigation. In this study we compared eQTL generated from gene expression profiling in the livers of two panels of mouse strains: 41 BXD recombinant inbred and 36 Mouse Diversity Panel (MDP) strains. Cis-eQTL, loci in which the transcript and its maximum QTL are colocated, have been shown to be more reproducible than trans-eQTL, which are not colocated with the transcript. We observed that between 9.9 and 12.1% of cis-eQTL and between 2.0 and 12.6% of trans-eQTL replicated between the two panels depending on the degree of statistical stringency. Notably, a significant eQTL hotspot on distal chromosome 12 observed in the BXD panel was reproduced in the MDP. Furthermore, the shorter linkage disequilibrium in the MDP strains allowed us to considerably narrow the locus and limit the number of candidate genes to a cluster of Serpin genes, which code for extracellular proteases. We conclude that this strategy has some utility in increasing confidence and resolution in eQTL mapping studies; however, due to the high false-positive rate in the MDP, eQTL mapping in inbred strains is best carried out in combination with an eQTL linkage study.


Inbred Strain Recombinant Inbred Single Nucleotide Polymorphism Data Serpina1 Gene eQTL Mapping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank several anonymous reviewers for excellent suggestions and criticism which strengthened the final manuscript. Financial support for these studies was provided in part by the United States Environmental Protection Agency grants RD833825 and F08D20579. However, the research described in this article was not subjected to the Agency’s peer review and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. DMG was also supported by the UNC Environmental Sciences & Engineering Interdisciplinary Fellowship.

Supplementary material

335_2009_9199_MOESM1_ESM.tif (11.5 mb)
Supplemental Figure 1 eQTL trans-band size under permutation. Each bar represents the proportion of 100 permutations in which an eQTL trans-band of size n occurred at least once. The largest trans-band that occurred by chance contained 17 transcripts. The Chr 12 trans-band contains 19 transcripts and thus is unlikely to have occurred by chance. Supplementary material 1 (TIFF 11786 kb)
335_2009_9199_MOESM2_ESM.xls (52 kb)
Supplementary material 2 (XLS 52 kb)


  1. Alberts R, Terpstra P, Li Y, Breitling R, Nap JP et al (2007) Sequence polymorphisms cause many false cis eQTL. PLoS ONE 2:e622PubMedCrossRefGoogle Scholar
  2. Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A et al (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2:351–356PubMedCrossRefGoogle Scholar
  3. Barbour KW, Wei F, Brannan C, Flotte TR, Baumann H et al (2002) The murine alpha(1)-proteinase inhibitor gene family: polymorphism, chromosomal location, and structure. Genomics 80:515–522PubMedCrossRefGoogle Scholar
  4. Breitling R, Li Y, Tesson BM, Fu J, Wu C et al (2008) Genetical genomics: spotlight on QTL hotspots. PLoS Genet 4:e1000232PubMedCrossRefGoogle Scholar
  5. Brem RB, Yvert G, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296:752–755PubMedCrossRefGoogle Scholar
  6. Burgess-Herbert SL, Cox A, Tsaih SW, Paigen B (2008) Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci. Genetics 180:2227–2235PubMedCrossRefGoogle Scholar
  7. Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT et al (2005) Uncovering regulatory pathways that affect hematopoietic stem cell function using ‘genetical genomics’. Nat Genet 37:225–232PubMedCrossRefGoogle Scholar
  8. Chesler EJ, Rodriguez-Saz SL, Mogil JS (2001) In silico mapping of mouse quantitative trait loci. Science 294:2423–2423PubMedCrossRefGoogle Scholar
  9. Chesler EJ, Lu L, Shou S, Qu Y, Gu J et al (2005) Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat Genet 37:233–242PubMedCrossRefGoogle Scholar
  10. Churchill GA, Airey DC, Allayee H, Angel JM, Attie AD et al (2004) The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat Genet 36:1133–1137PubMedCrossRefGoogle Scholar
  11. de Koning DJ, Haley CS (2005) Genetical genomics in humans and model organisms. Trends Genet 21:377–381PubMedCrossRefGoogle Scholar
  12. Dipetrillo K, Wang X, Stylianou IM, Paigen B (2005) Bioinformatics toolbox for narrowing rodent quantitative trait loci. Trends Genet 21:683–692PubMedCrossRefGoogle Scholar
  13. Doss S, Schadt EE, Drake TA, Lusis AJ (2005) Cis-acting expression quantitative trait loci in mice. Genome Res 15:681–691PubMedCrossRefGoogle Scholar
  14. Farrall M (2004) Quantitative genetic variation: a post-modern view. Hum Mol Genet 13 Spec No 1:R1-R7Google Scholar
  15. Gatti D, Maki A, Chesler EJ, Kirova R, Kosyk O et al (2007) Genome-level analysis of genetic regulation of liver gene expression networks. Hepatology 46:548–557PubMedCrossRefGoogle Scholar
  16. Gatti DM, Shabalin AA, Lam TC, Wright FA, Rusyn I et al (2009) FastMap: fast eQTL mapping in homozygous populations. Bioinformatics 25:482–489PubMedCrossRefGoogle Scholar
  17. Gilad Y, Rifkin SA, Pritchard JK (2008) Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet 24:408–415PubMedCrossRefGoogle Scholar
  18. Kliebenstein D (2008) Quantitative Genomics: analyzing intraspecific variation using global gene expression polymorphisms or eQTL. Annu Rev Plant Biol 60:93–114CrossRefGoogle Scholar
  19. Li R, Lyons MA, Wittenburg H, Paigen B, Churchill GA (2005) Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping. Genetics 169:1699–1709PubMedCrossRefGoogle Scholar
  20. Malmanger B, Lawler M, Coulombe S, Murray R, Cooper S et al (2006) Further studies on using multiple-cross mapping (MCM) to map quantitative trait loci. Mamm Genome 17:1193–1204PubMedCrossRefGoogle Scholar
  21. Manenti G, Galvan A, Pettinicchio A, Trincucci G, Spada E et al (2009) Mouse genome-wide association mapping needs linkage analysis to avoid false-positive loci. PLoS Genet 5:e1000331PubMedCrossRefGoogle Scholar
  22. McClurg P, Janes J, Wu C, Delano DL, Walker JR et al (2007) Genomewide association analysis in diverse inbred mice: power and population structure. Genetics 176:675–683PubMedCrossRefGoogle Scholar
  23. Monks SA, Leonardson A, Zhu H, Cundiff P, Pietrusiak P et al (2004) Genetic inheritance of gene expression in human cell lines. Am J Hum Genet 75:1094–1105PubMedCrossRefGoogle Scholar
  24. Paigen K, Eppig JT (2000) A mouse phenome project. Mamm Genome 11:715–717PubMedCrossRefGoogle Scholar
  25. Papoutsi M, Dudas J, Becker J, Tripodi M, Opitz L et al (2007) Gene regulation by homeobox transcription factor Prox1 in murine hepatoblasts. Cell Tissue Res 330:209–220PubMedCrossRefGoogle Scholar
  26. Payseur BA, Place M (2007) Prospects for association mapping in classical inbred mouse strains. Genetics 175:1999–2008PubMedCrossRefGoogle Scholar
  27. Peirce JL, Lu L, Gu J, Silver LM, Williams RW (2004) A new set of BXD recombinant inbred lines from advanced intercross populations in mice. BMC Genet 5:7PubMedCrossRefGoogle Scholar
  28. Peirce JL, Li H, Wang J, Manly KF, Hitzemann RJ et al (2006) How replicable are mRNA expression QTL? Mamm Genome 17:643–656PubMedCrossRefGoogle Scholar
  29. Peirce JL, Broman KW, Lu L, Williams RW (2007) A simple method for combining genetic mapping data from multiple crosses and experimental designs. PLoS ONE 2:e1036PubMedCrossRefGoogle Scholar
  30. Peng J, Wang P, Tang H (2007) Controlling for false positive findings of trans-hubs in expression quantitative trait loci mapping. BMC Proc 1(Suppl 1):S157PubMedCrossRefGoogle Scholar
  31. Perez-Enciso M, Quevedo JR, Bahamonde A (2007) Genetical genomics: use all data. BMC Genomics 8:69PubMedCrossRefGoogle Scholar
  32. Roberts A, McMillan L, Wang W, Parker J, Rusyn I et al (2007a) Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows. Bioinformatics 23:i401–i407PubMedCrossRefGoogle Scholar
  33. Roberts A, Pardo-Manuel de Villena F, Wang W, McMillan L, Threadgill DW (2007b) The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics. Mamm Genome 18:473–481PubMedCrossRefGoogle Scholar
  34. Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N et al (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297–302PubMedCrossRefGoogle Scholar
  35. Shi C, Uzarowska A, Ouzunova M, Landbeck M, Wenzel G et al (2007) Identification of candidate genes associated with cell wall digestibility and eQTL (expression quantitative trait loci) analysis in a Flint × Flint maize recombinant inbred line population. BMC Genomics 8:22PubMedCrossRefGoogle Scholar
  36. Shifman S, Bell JT, Copley RR, Taylor MS, Williams RW et al (2006) A high-resolution single nucleotide polymorphism genetic map of the mouse genome. PLoS Biol 4:e395PubMedCrossRefGoogle Scholar
  37. Shimoda M, Takahashi M, Yoshimoto T, Kono T, Ikai I et al (2006) A homeobox protein, prox1, is involved in the differentiation, proliferation, and prognosis in hepatocellular carcinoma. Clin Cancer Res 12:6005–6011PubMedCrossRefGoogle Scholar
  38. Szatkiewicz JP, Beane GL, Ding Y, Hutchins L, Pardo-Manuel de Villena F et al (2008) An imputed genotype resource for the laboratory mouse. Mamm Genome 19:199–208PubMedCrossRefGoogle Scholar
  39. Taylor BA, Wnek C, Kotlus BS, Roemer N, MacTaggart T et al (1999) Genotyping new BXD recombinant inbred mouse strains and comparison of BXD and consensus maps. Mamm Genome 10:335–348PubMedCrossRefGoogle Scholar
  40. Threadgill DW, Hunter KW, Williams RW (2002) Genetic dissection of complex and quantitative traits: from fantasy to reality via a community effort. Mamm Genome 13:175–178PubMedCrossRefGoogle Scholar
  41. Walling GA, Visscher PM, Andersson L, Rothschild MF, Wang L et al (2000) Combined analyses of data from quantitative trait loci mapping studies. Chromosome 4 effects on porcine growth and fatness. Genetics 155:1369–1378PubMedGoogle Scholar
  42. Wang J, Williams RW, Manly KF (2003) WebQTL: web-based complex trait analysis. Neuroinformatics 1:299–308PubMedCrossRefGoogle Scholar
  43. West MA, Kim K, Kliebenstein DJ, van Leeuwen H, Michelmore RW et al (2007) Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics 175:1441–1450PubMedCrossRefGoogle Scholar
  44. Wu C, Delano DL, Mitro N, Su SV, Janes J et al (2008) Gene set enrichment in eQTL data identifies novel annotations and pathway regulators. PLoS Genet 4:e1000070PubMedCrossRefGoogle Scholar
  45. Zhang Q, McMillan L, Pardo-Manuel de Villena F, Threadgill DW, Wang W (2009) Inferring genome-wide mosaic structure. Proceedings of the 14th Pacific Symposium on Biocomputing (PSB). Singapore: World Scientific Publishing, vol 14, pp 150–161Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Daniel M. Gatti
    • 1
  • Alison H. Harrill
    • 1
  • Fred A. Wright
    • 2
  • David W. Threadgill
    • 3
  • Ivan Rusyn
    • 1
  1. 1.Department of Environmental Sciences & EngineeringUniversity of North CarolinaChapel HillUSA
  2. 2.Department of BiostatisticsUniversity of North CarolinaChapel HillUSA
  3. 3.Department of GeneticsUniversity of North CarolinaChapel HillUSA

Personalised recommendations