Abstract
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.
Similar content being viewed by others
References
Alberts R, Terpstra P, Li Y, Breitling R, Nap JP et al (2007) Sequence polymorphisms cause many false cis eQTL. PLoS ONE 2:e622
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 2:351–356
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 80:515–522
Breitling R, Li Y, Tesson BM, Fu J, Wu C et al (2008) Genetical genomics: spotlight on QTL hotspots. PLoS Genet 4:e1000232
Brem RB, Yvert G, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296:752–755
Burgess-Herbert SL, Cox A, Tsaih SW, Paigen B (2008) Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci. Genetics 180:2227–2235
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 37:225–232
Chesler EJ, Rodriguez-Saz SL, Mogil JS (2001) In silico mapping of mouse quantitative trait loci. Science 294:2423–2423
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 37:233–242
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 36:1133–1137
de Koning DJ, Haley CS (2005) Genetical genomics in humans and model organisms. Trends Genet 21:377–381
Dipetrillo K, Wang X, Stylianou IM, Paigen B (2005) Bioinformatics toolbox for narrowing rodent quantitative trait loci. Trends Genet 21:683–692
Doss S, Schadt EE, Drake TA, Lusis AJ (2005) Cis-acting expression quantitative trait loci in mice. Genome Res 15:681–691
Farrall M (2004) Quantitative genetic variation: a post-modern view. Hum Mol Genet 13 Spec No 1:R1-R7
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 46:548–557
Gatti DM, Shabalin AA, Lam TC, Wright FA, Rusyn I et al (2009) FastMap: fast eQTL mapping in homozygous populations. Bioinformatics 25:482–489
Gilad Y, Rifkin SA, Pritchard JK (2008) Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet 24:408–415
Kliebenstein D (2008) Quantitative Genomics: analyzing intraspecific variation using global gene expression polymorphisms or eQTL. Annu Rev Plant Biol 60:93–114
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 169:1699–1709
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 17:1193–1204
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:e1000331
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 176:675–683
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 75:1094–1105
Paigen K, Eppig JT (2000) A mouse phenome project. Mamm Genome 11:715–717
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 330:209–220
Payseur BA, Place M (2007) Prospects for association mapping in classical inbred mouse strains. Genetics 175:1999–2008
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 5:7
Peirce JL, Li H, Wang J, Manly KF, Hitzemann RJ et al (2006) How replicable are mRNA expression QTL? Mamm Genome 17:643–656
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:e1036
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):S157
Perez-Enciso M, Quevedo JR, Bahamonde A (2007) Genetical genomics: use all data. BMC Genomics 8:69
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–i407
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 18:473–481
Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N et al (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297–302
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 8:22
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:e395
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 12:6005–6011
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 19:199–208
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 10:335–348
Threadgill DW, Hunter KW, Williams RW (2002) Genetic dissection of complex and quantitative traits: from fantasy to reality via a community effort. Mamm Genome 13:175–178
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 155:1369–1378
Wang J, Williams RW, Manly KF (2003) WebQTL: web-based complex trait analysis. Neuroinformatics 1:299–308
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 175:1441–1450
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:e1000070
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 150–161
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
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)
Rights and permissions
About this article
Cite this article
Gatti, D.M., Harrill, A.H., Wright, F.A. et al. Replication and narrowing of gene expression quantitative trait loci using inbred mice. Mamm Genome 20, 437–446 (2009). https://doi.org/10.1007/s00335-009-9199-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00335-009-9199-0