BACKGROUND: The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis. RESULTS: Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods. CONCLUSIONS: Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.
BACKGROUND: The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis. RESULTS: Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods. CONCLUSIONS: Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.
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Authors: Eric E Schadt; John Lamb; Xia Yang; Jun Zhu; Steve Edwards; Debraj Guhathakurta; Solveig K Sieberts; Stephanie Monks; Marc Reitman; Chunsheng Zhang; Pek Yee Lum; Amy Leonardson; Rolf Thieringer; Joseph M Metzger; Liming Yang; John Castle; Haoyuan Zhu; Shera F Kash; Thomas A Drake; Alan Sachs; Aldons J Lusis Journal: Nat Genet Date: 2005-06-19 Impact factor: 38.330
Authors: Leonid Bystrykh; Ellen Weersing; Bert Dontje; Sue Sutton; Mathew T Pletcher; Tim Wiltshire; Andrew I Su; Edo Vellenga; Jintao Wang; Kenneth F Manly; Lu Lu; Elissa J Chesler; Rudi Alberts; Ritsert C Jansen; Robert W Williams; Michael P Cooke; Gerald de Haan Journal: Nat Genet Date: 2005-02-13 Impact factor: 38.330
Authors: Salvatore Loguercio; Rupert W Overall; Jacob J Michaelson; Tim Wiltshire; Mathew T Pletcher; Brooke H Miller; John R Walker; Gerd Kempermann; Andrew I Su; Andreas Beyer Journal: PLoS One Date: 2010-11-10 Impact factor: 3.240
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Authors: Paola Picotti; Mathieu Clément-Ziza; Henry Lam; David S Campbell; Alexander Schmidt; Eric W Deutsch; Hannes Röst; Zhi Sun; Oliver Rinner; Lukas Reiter; Qin Shen; Jacob J Michaelson; Andreas Frei; Simon Alberti; Ulrike Kusebauch; Bernd Wollscheid; Robert L Moritz; Andreas Beyer; Ruedi Aebersold Journal: Nature Date: 2013-01-20 Impact factor: 49.962