MAGE enables population level RNAseq driven genotyping and (differential) allelic divergence detection in healthy kidney and carcinoma

Stroobandt, C.; Goovaerts, T.; Coussement, L.; De Graeve, F.; Voorthuijzen, F.; Van Steenbergen, L.; Galle, J.; Van Criekinge, W.; De Meyer, T.

doi:10.1101/2022.09.06.506720

bioRxiv · 10.1101/2022.09.06.506720

MAGE enables population level RNAseq driven genotyping and (differential) allelic divergence detection in healthy kidney and carcinoma

Stroobandt, C.·Goovaerts, T.·Coussement, L.·De Graeve, F.·Voorthuijzen, F.·Van Steenbergen, L.·Galle, J.·Van Criekinge, W.·De Meyer, T.

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Abstract

Decreasing sequencing costs have instigated large-scale RNAseq experiments, yet genetic polymorphisms in such data remain poorly exploited. Currently, allele-specific expression (ASE) studies focus almost exclusively on genetic variants explaining expression differences (cis-eQTLs), largely ignoring other ASE effects. The latter are typically associated with higher variance in expression of both copies of a gene, here called Allelic Divergence (AD). We therefore developed an RNAseq-driven population-level beta-binomial mixture model for (differential) AD detection. The model simultaneously enables RNAseq-driven genotyping, which outperforms alternative RNA genotyping methods when applied on healthy kidney data from The Cancer Genome Atlas. Moreover, we identify well-known non-cis-eQTL ASE, e.g. random monoallelic expression of HLA and immunoglobulin genes in healthy kidney, as well as allele-specific aberrations in clear cell kidney carcinoma, including long-range epigenetic silencing of protocadherins, copy-number alterations, and loss of imprinting. These methods are available as the Modeller of Allelic Gene Expression (MAGE) tool suite: https://biobix.github.io/MAGE/.

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Stroobandt, C., Goovaerts, T., Coussement, L., De Graeve, F., Voorthuijzen, F., Van Steenbergen, L., Galle, J., Van Criekinge, W., De Meyer, T.. 2022-09-06. MAGE enables population level RNAseq driven genotyping and (differential) allelic divergence detection in healthy kidney and carcinoma. https://doi.org/10.1101/2022.09.06.506720

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