Consistent reanalysis of genome-wide imprinting studies in plants using generalized linear models increases concordance across datasets

• Verfasst von: Wyder, Stefan [VerfasserIn]
• Verfasst von: Raissig, Michael Thomas [VerfasserIn]
• Verfasst von: Grossniklaus, Ueli [VerfasserIn]
• Titel: Consistent reanalysis of genome-wide imprinting studies in plants using generalized linear models increases concordance across datasets
• Verf.angabe: Stefan Wyder, Michael T. Raissig & Ueli Grossniklaus
• E-Jahr: 2019
• Jahr: 04 February 2019
• Umfang: 13 S.
• Fussnoten: Gesehen am 12.09.2019
• Titel Quelle: Enthalten in: Scientific reports
• Ort Quelle: [London] : Macmillan Publishers Limited, part of Springer Nature, 2011
• Jahr Quelle: 2019
• Band/Heft Quelle: 9(2019) Artikel-Nummer 1320, 13 Seiten
• ISSN Quelle: 2045-2322
• DOI: doi:10.1038/s41598-018-36768-4
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1676788840

Abstract

Genomic imprinting leads to different expression levels of maternally and paternally derived alleles. Over the last years, major progress has been made in identifying novel imprinted candidate genes in plants, owing to affordable next-generation sequencing technologies. However, reports on sequencing the transcriptome of hybrid F1 seed tissues strongly disagree about how many and which genes are imprinted. This raises questions about the relative impact of biological, environmental, technical, and analytic differences or biases. Here, we adopt a statistical approach, frequently used in RNA-seq data analysis, which properly models count overdispersion and considers replicate information of reciprocal crosses. We show that our statistical pipeline outperforms other methods in identifying imprinted genes in simulated and real data. Accordingly, reanalysis of genome-wide imprinting studies in Arabidopsis and maize shows that, at least for Arabidopsis, an increased agreement across datasets could be observed. For maize, however, consistent reanalysis did not yield a larger overlap between the datasets. This suggests that the discrepancy across publications might be partially due to different analysis pipelines but that technical, biological, and environmental factors underlie much of the discrepancy between datasets. Finally, we show that the set of genes that can be characterized regarding allelic bias by all studies with minimal confidence is small (~8,000/27,416 genes for Arabidopsis and ~12,000/39,469 for maize). In conclusion, we propose to use biologically replicated reciprocal crosses, high sequence coverage, and a generalized linear model approach to identify differentially expressed alleles in developing seeds.