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dc.contributor.authorLudl, Adriaan-Alexander
dc.contributor.authorMichoel, Tom Luk Robert
dc.date.accessioned2021-07-01T13:01:44Z
dc.date.available2021-07-01T13:01:44Z
dc.date.created2021-01-29T15:39:24Z
dc.date.issued2021
dc.identifier.issn1742-206X
dc.identifier.urihttps://hdl.handle.net/11250/2762853
dc.descriptionUnder embargo until: 2021-12-17en_US
dc.description.abstractCausal gene networks model the flow of information within a cell. Reconstructing causal networks from omics data is challenging because correlation does not imply causation. When genomics and transcriptomics data from a segregating population are combined, genomic variants can be used to orient the direction of causality between gene expression traits. Instrumental variable methods use a local expression quantitative trait locus (eQTL) as a randomized instrument for a gene's expression level, and assign target genes based on distal eQTL associations. Mediation-based methods additionally require that distal eQTL associations are mediated by the source gene. A detailed comparison between these methods has not yet been conducted, due to the lack of a standardized implementation of different methods, the limited sample size of most multi-omics datasets, and the absence of ground-truth networks for most organisms. Here we used Findr, a software package providing uniform implementations of instrumental variable, mediation, and coexpression-based methods, a recent dataset of 1012 segregants from a cross between two budding yeast strains, and the YEASTRACT database of known transcriptional interactions to compare causal gene network inference methods. We found that causal inference methods result in a significant overlap with the ground-truth, whereas coexpression did not perform better than random. A subsampling analysis revealed that the performance of mediation saturates at large sample sizes, due to a loss of sensitivity when residual correlations become significant. Instrumental variable methods on the other hand contain false positive predictions, due to genomic linkage between eQTL instruments. Instrumental variable and mediation-based methods also have complementary roles for identifying causal genes underlying transcriptional hotspots. Instrumental variable methods correctly predicted STB5 targets for a hotspot centred on the transcription factor STB5, whereas mediation failed due to Stb5p auto-regulating its own expression. Mediation suggests a new candidate gene, DNM1, for a hotspot on Chr XII, whereas instrumental variable methods could not distinguish between multiple genes located within the hotspot. In conclusion, causal inference from genomics and transcriptomics data is a powerful approach for reconstructing causal gene networks, which could be further improved by the development of methods to control for residual correlations in mediation analyses, and for genomic linkage and pleiotropic effects from transcriptional hotspots in instrumental variable analyses.en_US
dc.language.isoengen_US
dc.publisherRoyal Society of Chemistryen_US
dc.titleComparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeasten_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright The Royal Society of Chemistry 2021en_US
dc.source.articlenumber241en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.doi10.1039/d0mo00140f
dc.identifier.cristin1882738
dc.source.journalMolecular Biosystemsen_US
dc.identifier.citationMolecular Biosystems. 2021, 17, 241en_US
dc.source.volume17en_US


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