Teamwork: Improved EQTL mapping using combinations of machine learning methods

PLoS ONE

Volumn 7, Issue 7, 2012, Pages

  Ackermann, Marit ^a   Clément Ziza, Mathieu ^a   Michaelson, Jacob J ^a,c   Beyer, Andreas ^a,b  

^a DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^b CENTER FOR REGENERATIVE THERAPIES DRESDEN   (Germany)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; AREA UNDER THE CURVE; ARTICLE; DIALOGUE ON REVERSE ENGINEERING ASSESSMENTS AND METHODS; ELASTIC NET COEFFICIENT; GENETIC ANALYSIS; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR; MACHINE LEARNING; METHODOLOGY; QUANTITATIVE TRAIT LOCUS MAPPING; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHROMOSOME MAPPING; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; GENOTYPE; QUANTITATIVE TRAIT LOCI; ROC CURVE;

EID: 84864197093     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0040916     Document Type: Article

References (37)

1. Kempermann G, (2006) Natural variation and genetic covariance in adult hippocampal neurogenesis. Proceedings of the National Academy of Sciences 103: 780-785 doi:10.1073/pnas.0510291103.
2. Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, et al. (2006) Heritability and Tissue Specificity of Expression Quantitative Trait Loci. PLoS Genetics 2: e172 doi:10.1371/journal.pgen.0020172.
3. Veyrieras J-B, Kudaravalli S, Kim SY, Dermitzakis ET, Gilad Y, et al. (2008) High-Resolution Mapping of Expression-QTLs Yields Insight into Human Gene Regulation. PLoS Genetics 4: e1000214 doi:10.1371/journal.pgen.1000214.
4. Huang G-J, Shifman S, Valdar W, Johannesson M, Yalcin B, et al. (2009) High resolution mapping of expression QTLs in heterogeneous stock mice in multiple tissues. Genome Research 19: 1133-1140 doi:10.1101/gr.088120.108.
5. Michaelson JJ, Loguercio S, Beyer A, (2009) Detection and interpretation of expression quantitative trait loci (eQTL). Methods 48: 265-276 doi:10.1016/j.ymeth.2009.03.004.
6. Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M, (2009) Mapping complex disease traits with global gene expression. Nat Rev Genet 10: 184-194 doi:10.1038/nrg2537.
7. Kendziorski CM, Chen M, Yuan M, Lan H, Attie AD, (2006) Statistical Methods for Expression Quantitative Trait Loci (eQTL) Mapping. Biometrics 62: 19-27 doi:10.1111/j.1541-0420.2005.00437.x.
8. Michaelson JJ, Alberts R, Schughart K, Beyer A, (2010) Data-driven assessment of eQTL mapping methods. BMC genomics 11: 502.
9. Broman KW, Speed TP, (2002) A model selection approach for the identification of quantitative trait loci in experimental crosses. J Royal Statistical Soc B 64: 641-656 doi:10.1111/1467-9868.00354.
10. Efron B, Hastie T, Johnstone I, Tibshirani R, (2004) Least angle regression. The Annals of Statistics 32: 407-499 doi:10.1214/009053604000000067.
11. Breiman L, (2001) Random Forests. Machine Learning. Vol. 45: 5-32.
12. Marbach D, Mattiussi C, Floreano D, (2009) Combining Multiple Results of a Reverse-Engineering Algorithm: Application to the DREAM Five-Gene Network Challenge. Annals of the New York Academy of Sciences 1158: 102-113 doi:10.1111/j.1749-6632.2008.03945.x.
13. Hastie T, Tibshirani R, Friedman J, (2009) The elements of statistical learning: data mining, inference, and prediction: Trevor Hastie, Robert Tibshirani, Jerome Friedman. New York: Springer. p.
14. Tibshirani R, (1996) Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society 58: 267-288.
15. Zou H, Hastie T, (2005) Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67: 301-320 doi:10.1111/j.1467-9868.2005.00503.x.
16. R Development Core Team (2009) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. p. Available http://www.R-project.org.
17. Liaw A, Wiener M, (2002) Classification and Regression by randomForest. R News 2: 18-22.
18. Stolovitzky G, Prill RJ, Califano A, (2009) Lessons from the DREAM2 Challenges. Ann N Y Acad Sci 1158: 159-195 doi:10.1111/j.1749-6632.2009.04497.x.
19. Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, et al. (2010) Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE 5: e9202 doi:10.1371/journal.pone.0009202.
20. Provost F, Fawcett T, (1997) Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining. AAAI Press pp. 43-48.
21. Goadrich M, Oliphant L, Shavlik J, (2004) Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction. In: Camacho R, King R, Srinivasan A, editors. editors. Inductive Logic Programming. Berlin, Heidelberg: Springer Berlin Heidelberg, Vol. 3194. 98-115. Available:http://www.springerlink.com/index/10.1007/978-3-540-30109-7_11. Accessed 7 November 2011.
22. Obuchowski NA, (2005) ROC Analysis. AJR 184: 364-372.
23. Hanley JA, McNeil BJ, (1982) The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve. Radiology 143: 29-36.
24. Yilmaz E, Aslam JA, (2006) Estimating average precision with incomplete and imperfect judgments ACM Press. p.102. Available:http://portal.acm.org/citation.cfm?doid=1183614.1183633. Accessed 25 April 2012.
25. Vignes M, Vandel J, Allouche D, Ramadan-Alban N, Cierco-Ayrolles C, et al. (2011) Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis. PLoS ONE 6: e29165 doi:10.1371/journal.pone.0029165.
26. Pinna A, Soranzo N, Hoeschele I, de la Fuente A, (2011) Simulating systems genetics data with SysGenSIM. Bioinformatics 27: 2459-2462 doi:10.1093/bioinformatics/btr407.
27. Hastie T, Tibshirani R, Friedman J, (2009) Ensemble Learning. The Elements of Statistical Learning. New York, NY: Springer New York. 1-20. Available:http://www.springerlink.com/index/10.1007/b94608_16. Accessed 25 April 2012.
28. Dietterich TG (n.d.) (2011) Ensemble Methods in Machine Learning. Multiple Classifier Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, Vol. 1857. 1-15. Accessed Available:http://www.springerlink.com/index/10.1007/3-540-45014-9_1.
29. Norel R, Rice JJ, Stolovitzky G, (2011) The self-assessment trap: can we all be better than average? Molecular Systems Biology 7. Available:http://www.nature.com/doifinder/10.1038/msb.2011.70. Accessed 7 November 2011.
30. Davis J, Goadrich M, (2006) The relationship between Precision-Recall and ROC curves ACM Press. 233-240. Available:http://portal.acm.org/citation.cfm?doid=1143844.1143874. Accessed 7 November 2011.
31. Drummond C, Holte RC, (2000) Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria. Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann pp. 239-246.
32. Cordell HJ, (2002) Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Human molecular genetics 11: 2463.
33. Phillips PC, (2008) Epistasis - the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 9: 855-867 doi:10.1038/nrg2452.
34. Loguercio S, Overall RW, Michaelson JJ, Wiltshire T, Pletcher MT, et al. (2010) Integrative Analysis of Low- and High-Resolution eQTL. PLoS ONE 5: e13920 doi:10.1371/journal.pone.0013920.
35. Suthram S, Beyer A, Karp RM, Eldar Y, Ideker T, (2008) eQED: an efficient method for interpreting eQTL associations using protein networks. Mol Syst Biol 4: 162 doi:10.1038/msb.2008.4.
36. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, et al. (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40: 854-861 doi:10.1038/ng.167.
37. Lee S-I, Dudley AM, Drubin D, Silver PA, Krogan NJ, et al. (2009) Learning a prior on regulatory potential from eQTL data. PLoS Genet 5: e1000358 doi:10.1371/journal.pgen.1000358.