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How replicable are mRNA expression QTL?

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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.

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References

  • Belknap JK (1998) Effects of within strain sample size on QTL detection and mapping using recombinant inbrede mouse strains. Behav Genet 28: 29–38

    Article  CAS  Google Scholar 

  • Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57: 289–300

    Google Scholar 

  • Benjamini Y, Yekutieli D (2005) Quantitative trait loci analysis using the false discovery rate. Genetics 171(2): 783–790

    Article  CAS  Google Scholar 

  • Brem RB, Yvert G, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296(5568): 752–755

    Article  CAS  Google Scholar 

  • 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): 485–486

    Article  CAS  Google Scholar 

  • 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): 233–242

    Article  CAS  Google Scholar 

  • Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138(3): 963–971

    Article  CAS  Google Scholar 

  • 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): 289–301

    Article  CAS  Google Scholar 

  • 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): 29–39

    Google Scholar 

  • Doss S, Schadt EE, Drake TA, Lusis AJ (2005) Cis-acting expression quantitative trait loci in mice. Genome Res 15(5): 681–691

    Article  CAS  Google Scholar 

  • Jansen RC, Nap JP (2001) Genetical genomics: the added value from segregation. Trends Genet 17: 388–391

    Article  CAS  Google Scholar 

  • Klose J, Nock C, Herrmann M, Stuhler K, Marcus K, et al. (2002) Genetic analysis of the mouse brain proteome. Nat Genet 30(4): 385–393

    Article  CAS  Google Scholar 

  • Lander E, Kruglyak L (1995) Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11(3): 241–247

    Article  CAS  Google Scholar 

  • Li J, Jiang T, Mao JH, Balmain A, Peterson L, et al. (2005) Genomic segmental polymorphisms in inbred mouse strains. Nat Genet 36(9): 952–954

    Article  Google Scholar 

  • 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): 7

    Article  Google Scholar 

  • Rosenthal D (1994) Parametric measures of effect size. In The Handbook of Research Synthesis, Cooper H, Hedges LV (eds.) (New York: Russell Sage), pp 232–244

    Google Scholar 

  • 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): 297–302

    Article  CAS  Google Scholar 

  • Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100(16): 9440–9445

    Article  CAS  Google Scholar 

  • Wang J, Williams RW, Manly KF (2003) WebQTL: web-based complex trait analysis. Neuroinformatics 1(4): 299–308

    Article  Google Scholar 

  • 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): 637–647

    Article  CAS  Google Scholar 

  • 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): 1377–1419

    Article  CAS  Google Scholar 

  • 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: 16

    Article  Google Scholar 

  • Zhang L, Miles MF, Aldape KD (2003) A model of molecular interactions on short oligonucleotide microarrays. Nat Biotechnol 21(7): 818–821

    Article  CAS  Google Scholar 

Download references

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.

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Correspondence to Lu Lu.

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Peirce, J.L., Li, H., Wang, J. et al. How replicable are mRNA expression QTL?. Mamm Genome 17, 643–656 (2006). https://doi.org/10.1007/s00335-005-0187-8

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