SpringerProtocols: Abstract: Inferring Molecular Interactions Pathways from eQTL Data

10. Inferring Molecular Interactions Pathways from eQTL Data

By: Imran Rashid⁶, Jason McDermott⁷, Ram Samudrala⁸

Abstract

Full Text

Download PDF (401K)

Analysis of expression quantitative trait loci (eQTL) helps elucidate the connection between genotype, gene expression levels, and phenotype. However, standard statistical genetics can only attribute the changes in expression levels to loci on the genome, not specific genes. Each locus can contain many genes, making it very difficult to discover which gene is controlling the expression levels of other genes. Furthermore, it is even more difficult to find a pathway of molecular interactions responsible for controlling the expression levels. Here we describe a series of techniques for finding explanatory pathways by exploring the graphs of molecular interactions. We show several simple methods can find complete pathways that explain the mechanism of differential expression in eQTL data.

Affiliation(s): (6) Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA
(7) Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA
(8) Department of Microbiology, University of Washington, Seattle, WA, USA

Book Title: Computational Systems Biology

Series: Methods in Molecular Biology | Volume: 541 | Pub. Date: Jul-01-2008 | Page Range: 1-13 | DOI: 10.1007/978-1-59745-243-4_10

Subject: Bioinformatics

Key Words: eQTL - pathway inference - gene regulation - signaling pathways

References

Download References

1.	Brem RB, Storey JD, Whittle J, Kruglyak L. Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 2005, 436:701–03.

2.	Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G, Linsley PS, Mao M, Stoughton RB, Friend SH. Genetics of gene expression surveyed in maize, mouse and man. Nature 2003, 422:297–302.

3.	Tu Z, Wang L, Arbeitman MN, Chen T, Sun F. An integrative approach for causal gene identification and gene regulatory pathway inference. Bioinformatics 2006, 22:e489–96.

4.	Doerge RW, Zeng ZB, Weir BS. Statistical issues in the search for genes affecting quantitative traits in experimental populations. Stat Sci 1997, 123:195–219.

5.	Storey JM, Akey JM, Kruglyak L. Multiple locus linkage analysis of genomewide expression in yeast. Plos Biol 2005, 3:e267.

6.	Chen L, Storey JD. Relaxed significance criteria for linkage analysis. Genetics 2006, 173:2371–81.

7.	Lee S, Pe’er D, Dudley AM, Church GM, Koller D. Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. PNAS 2006, 103:14062–67.

8.	Gldener U, Mnsterktter M, Kastenmller G, Strack N, van Helden J, Lemer C, Richelles J, Wodak SJ, Garca-Martnez J, Prez-Ortn JE, Michael H, Kaps A, Talla E, Dujon B, Andr V, Souciet JL, De Montigny J, Bon E, Gaillardin C, Mewes HW. Cygd: The comprehensive yeast genome database. Nucl Acids Res 2005, 33(Database Issue):D364–68.

9.	Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, Hannett NM, Tagne J, Reynolds DB, Yoo J, Jennings EG, Zeitlinger J, Pokholok M, Kellis DK, Rolfe PA, Takusagawa KT, Lander ES, Gifford DK, Fraenkel E, Young RA. Transcriptional regulatory code of a eukaryotic genome. Nature 2004, 431:99–104.

10.	Ptacek J, Devgan G, Michaud G, Zhu H, Zhu X, Fasolo J, Guo H, Jona G, Breitkreutz A, Sopko R, McCartney RR, Schmidt MC, Rachidi N, Lee S, Mah S, Meng L, Stark MJR, Stern DF, De Virgilio C, Tyers M, Andrews B, Gerstein M, Schweitzer B, Predki PF, Snyder M. Global analysis of protein phosphorylation in yeast. Nature 2005, 438:679–84.

11.	McDermott J, Bumgarner RE, Samudrala R. Functional annotation from predicted protein interaction networks. Bioinformatics 2005, 21:3217–26.

12.	Chandra AK, Raghavan P, Ruzzo WL, Smolensky R. The electrical resistance of a graph captures its commute and cover times. In Proceedings of the Twenty First Annual ACM Symposium on Theory of Computing, 1989, pp. 574–86.

13.	Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer MR, Slade D, Lum PY, Stepaniants SB, Shoemaker DD, Gachotte D, Chakraburtty K, Simon J, Bard M, and Friend SF. Functional discovery via a compendium of expression profiles. Cell 2005, 102:109–26.

14.	Fields S, Herskowitz I. The yeast ste12 product is required for expression of two sets of cell-type-specific genes. Cell 1985, 42:923–30.

15.	Song O, Dolan J, Yuan Y, Fields S. Pheromone-dependent phosphorylation of the yeast ste12 protein correlates with transcriptional activation. Genes Dev 1991, 5:741–50.

16.	Chen Q, Konopka BJ. Regulation of the g-protein-coupled alpha-factor pheromone receptor by phosphorylation. Mol Cell Biol 1996, 161:247–57.

17.	Hartig A, Holly J, Saari G, MacKay VL. Multiple regulation of ste2, a mating type-specific gene of saccharomyces cerevisiae. Mol Cell Biol 1986, 6:2106–14.

18.	Cope MJ, Yang S, Shang C, Drubin DG. Novel protein kinases ark1p and prk1p associate with and regulate the cortical actin cytoskeleton in budding yeast. J Cell Biol 1999, 144:1203–18.

19.	Meier UT. Comparison of the rat nucleolar protein nopp140 with its yeast homolog srp40. Differential phosphorylation in vertebrates and yeast. J Biol Chem 1996, 271:19376–84.

20.	Lu J, Kobayashi E, Brill SJ. Characterization of a high mobility group ½ homolog in yeast. J Biol Chem 1996, 271:33678–85.

21.	Gadal O, Labarre S, Boschiero C, Thuriaux P. Hmo1, an hmg-box protein, belongs to the yeast ribosomal DNA transcription system. EMBO J 2002, 21:5498–507.

22.	Lee SI, Pe’er D, Dudley A, Church G, Koller D. Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. Proc Natl Acad Sci USA 2006, 103:14062–67.

Comments (Loading...)

Post comment/View all comments