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Mammalian Genome

, Volume 17, Issue 6, pp 643–656 | Cite as

How replicable are mRNA expression QTL?

  • Jeremy L. Peirce
  • Hongqiang Li
  • Jintao Wang
  • Kenneth F. Manly
  • Robert J. Hitzemann
  • John K. Belknap
  • Glenn D. Rosen
  • Shirlean Goodwin
  • Thomas R. Sutter
  • Robert W. Williams
  • Lu Lu
Article

Abstract

Applying quantitative trait analysis methods to genome-wide microarray-derived mRNA expression phenotypes in segregating populations is a valuable tool in the attempt to link high-level traits to their molecular causes. The massive multiple-testing issues involved in analyzing these data make the correct level of confidence to place in mRNA abundance quantitative trait loci (QTL) a difficult problem. We use a unique resource to directly test mRNA abundance QTL replicability in mice: paired recombinant inbred (RI) and F2 data sets derived from C57BL/6J (B6) and DBA/2J (D2) inbred strains and phenotyped using the same Affymetrix arrays. We have one forebrain and one striatum data set pair. We describe QTL replication at varying stringencies in these data. For instance, 78% of mRNA expression QTL (eQTL) with genome-wide adjusted p ≤ 0.0001 in RI data replicate at a genome-wide adjusted p < 0.05 or better. Replicated QTL are disproportionately putatively cis-acting, and approximately 75% have higher apparent expression levels associated with B6 genotypes, which may be partly due to probe set generation using B6 sequence. Finally, we note that while trans-acting QTL do not replicate well between data sets in general, at least one cluster of trans-acting QTL on distal Chr 1 is notably preserved between data sets.

Keywords

Quantitative Trait Locus Recombinant Inbred Recombinant Inbred Strain Collaborative Cross Quantitative Trait Locus Peak 
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.

Notes

Acknowledgments

RWW received support from The Informatics Center for Mouse Neurogenetics; P20-MH62009 from NIMH, NIDA, and NSF; and INIA grants U01AA13499 and U24AA135B from NIAAA for generation of the BXD RI brain data set and development of analysis tools. LL received support from INIA grant U01AA01442501 from NIAAA. JLP received salary support from U01AA01442501 from NIAAA to LL and P20-MH62009 from NIMH, NIDA, and NSF to RWW. Thanks to Arthur Centeno for computer support, Fred Korz for AWK scripting advice, and Pamela Franklin for administrative assistance. RJH received support from NIAAA grants AA11034 and AA11384, and grant MH51372, for generating data from B6D2 F2 data in brain and striatum. TS received support from NIAAA INIA grant U01AA13515 and the W. Harry Feinstone Center for Genome Research for array phenotyping of the brain R1 data set. The B6D2 F2 whole-brain and striatal data were also supported by AA06243, AA10760, and two Merit Review programs from the Department of Veterans Affairs to JKB and RH. GR received support from P20-MH62009 for generating BXD RI striatum data. Thanks to Christopher Pung and Stephanie Chin for technical assistance and the BIDMC Genomics Core for array processing.

References

  1. Belknap JK (1998) Effects of within strain sample size on QTL detection and mapping using recombinant inbrede mouse strains. Behav Genet 28: 29–38PubMedCrossRefGoogle Scholar
  2. Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57: 289–300Google Scholar
  3. Benjamini Y, Yekutieli D (2005) Quantitative trait loci analysis using the false discovery rate. Genetics 171(2): 783–790PubMedCrossRefGoogle Scholar
  4. Brem RB, Yvert G, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296(5568): 752–755PubMedCrossRefGoogle Scholar
  5. Chesler EJ, Lu L, Wang J, Williams RW, Manly KF (2004) WebQTL: rapid exploratory analysis of gene expression and genetic networks for brain and behavior. Nat Neurosci 7(5): 485–486PubMedCrossRefGoogle Scholar
  6. 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(3): 233–242PubMedCrossRefGoogle Scholar
  7. Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138(3): 963–971PubMedGoogle Scholar
  8. Damerval C, Maurice A, Josse JM, de Vienne D (1994) Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. Genetics 137(1): 289–301PubMedGoogle Scholar
  9. de Vienne D, Maurice A, Josse JM, Leonardi A, Damerval C (1994) Mapping factors controlling genetic expression. Cell Mol Biol (Noisy-le-grand) 40(1): 29–39Google Scholar
  10. Doss S, Schadt EE, Drake TA, Lusis AJ (2005) Cis-acting expression quantitative trait loci in mice. Genome Res 15(5): 681–691PubMedCrossRefGoogle Scholar
  11. Jansen RC, Nap JP (2001) Genetical genomics: the added value from segregation. Trends Genet 17: 388–391PubMedCrossRefGoogle Scholar
  12. Klose J, Nock C, Herrmann M, Stuhler K, Marcus K, et al. (2002) Genetic analysis of the mouse brain proteome. Nat Genet 30(4): 385–393PubMedCrossRefGoogle Scholar
  13. Lander E, Kruglyak L (1995) Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11(3): 241–247PubMedCrossRefGoogle Scholar
  14. Li J, Jiang T, Mao JH, Balmain A, Peterson L, et al. (2005) Genomic segmental polymorphisms in inbred mouse strains. Nat Genet 36(9): 952–954CrossRefGoogle Scholar
  15. 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(1): 7PubMedCrossRefGoogle Scholar
  16. Rosenthal D (1994) Parametric measures of effect size. In The Handbook of Research Synthesis, Cooper H, Hedges LV (eds.) (New York: Russell Sage), pp 232–244Google Scholar
  17. 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(6929): 297–302PubMedCrossRefGoogle Scholar
  18. Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100(16): 9440–9445PubMedCrossRefGoogle Scholar
  19. Wang J, Williams RW, Manly KF (2003) WebQTL: web-based complex trait analysis. Neuroinformatics 1(4): 299–308PubMedCrossRefGoogle Scholar
  20. Williams RW, Bennett B, Lu L, Gu J, De Fries JC, et al. (2004) Genetic structure of the LXS panel of recombinant inbred mouse strains: a powerful resource for complex trait analysis. Mamm Genome 15(8): 637–647PubMedCrossRefGoogle Scholar
  21. Wray GA, Hahn MW, Abouheif E, Balhoff JP, Pizer M, et al. (2003) The evolution of transcriptional regulation in eukaryotes. Mol Biol Evol 20(9): 1377–1419PubMedCrossRefGoogle Scholar
  22. Zhang B, Schmoyer D, Kirov S, Snoddy J (2004) GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics 5: 16PubMedCrossRefGoogle Scholar
  23. Zhang L, Miles MF, Aldape KD (2003) A model of molecular interactions on short oligonucleotide microarrays. Nat Biotechnol 21(7): 818–821PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Jeremy L. Peirce
    • 1
    • 2
  • Hongqiang Li
    • 1
    • 2
  • Jintao Wang
    • 3
  • Kenneth F. Manly
    • 2
    • 3
    • 4
  • Robert J. Hitzemann
    • 5
  • John K. Belknap
    • 5
  • Glenn D. Rosen
    • 6
  • Shirlean Goodwin
    • 7
  • Thomas R. Sutter
    • 7
  • Robert W. Williams
    • 1
    • 2
  • Lu Lu
    • 1
    • 2
    • 8
  1. 1.Center for Neuroscience, Department of Anatomy and NeurobiologyUniversity of Tennessee Health Science CenterMemphisUSA
  2. 2.Center for Genomics and Bioinformatics, Department of Anatomy and NeurobiologyUniversity of Tennessee Health Science CenterMemphisUSA
  3. 3.Department of Anatomy and NeurobiologyUniversity of Tennessee Health Science CenterMemphisUSA
  4. 4.Department of Pathology and Laboratory MedicineUniversity of Tennessee Health Science CenterMemphisUSA
  5. 5.Portland Alcohol Research Center, Department of Behavioral NeuroscienceOregon Health & Science UniversityPortlandUSA
  6. 6.Department of NeurologyBeth Israel Deaconess Medical CenterBostonUSA
  7. 7.W. Harry Feinstone Center for Genomic ResearchUniversity of MemphisMemphisUSA
  8. 8.Key Laboratory of Nerve Regeneration in Jiangsu ProvinceNantong UniversityNantongChina

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