Abstract
We apply our recently proposed gene regulatory network (GRN) reconstruction framework for genetical genomics data to the StatSeq data. This method uses, in a first step, simple genotype–phenotype and phenotype–phenotype correlation measures to construct an initial GRN. This graph contains a high number of false positive edges that are reduced by (i) identifying eQTLs and by retaining only one candidate edge per eQTL, and (ii) by removing edges reflecting indirect effects by means of TRANSWESD, a transitive reduction approach. We discuss the general performance of our framework on the StatSeq in silico dataset by investigating the sensitivity of the two required threshold parameters and by analyzing the impact of certain network features (size, marker distance, and biological variance) on the reconstruction performance. Using selected examples, we also illustrate prominent sources of reconstruction errors. As expected, best results are obtained with large number of samples and larger marker distances. A less intuitive result is that significant (but not too large) biological variance can increase the reconstruction quality. Furthermore, a somewhat surprising finding was that the best performance (in terms of AUPR) could be found for networks of medium size (1,000 nodes), which we had expected to see for networks of small size (100 nodes).
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References
Bing N, Hoeschele I (2005) Genetical genomic analysis of a yeast segregant population for transcription network inference. Genetics 170:533–542
Flassig RJ, Heise S, Sundmacher K, Klamt S (2013) An effective framework for reconstructing gene regulatory networks from genetical genomics data. Bioinformatics 29(2):246–254
Jansen R, Nap N (2001) Genetical genomics: the added value from segregation. Trends Genet 17:388–391
Jansen R (2003) Studying complex biological systems using multifactorial perturbation. Nat Rev Genet 4:145–151
Keurentjes JJB, Fu J, Terpstra IR et al (2007) Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci. Proc Natl Acad Sci USA 104:1708–1713
Klamt S, Flassig RJ, Sundmacher K (2010) TRANSWESD: inferring cellular networks with transitive reduction. Bioinformatics 26:2160–2168
Li H, Lu L, Manly KF et al (2005) Inferring gene transcriptional modulatory relations: a genetical genomics approach. Hum Mol Genet 14:1119–1125
Liu B, de la Fuente A, Hoeschele I (2008) Gene network inference via structural equation modeling in genetical genomics experiments. Genetics 178:1763–1776
Liu B, Hoeschele I, de la Fuente A (2010) Inferring gene regulatory networks from genetical genomics data. In: Das S, Caragea D, Hsu WH, Welch SM (eds) Computational methodologies in gene regulatory networks. IGI Global, Hershey, pp 79–107
Michaelson JJ, Loguercio S, Beyer A (2009) Detection and interpretation of expression quantitative trait loci (eQTL). Methods 48:265–276
Rockman MV, Kruglyak L (2006) Genetics of global gene expression. Nat Rev Genet 7:862–872
Rockman MV (2008) Reverse engineering the genotype-phenotype map with natural genetic variation. Nature 456:738–744
Zhu J, Wiener MC, Zhang C et al (2007) Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLoS Comput Biol 3:e69
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Heise, S., Flassig, R.J., Klamt, S. (2013). Benchmarking a Simple Yet Effective Approach for Inferring Gene Regulatory Networks from Systems Genetics Data. In: de la Fuente, A. (eds) Gene Network Inference. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45161-4_3
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DOI: https://doi.org/10.1007/978-3-642-45161-4_3
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