Genetic variants affecting the risk of common diseases reside by and large outside the coding region. However so far, only a minor fraction of these could be shown to associate with variation in gene expression levels. Emerging evidence indicates that a significant fraction of disease-associated variants could exert its role by affecting splicing. Moreover, genetic studies on common diseases have been so far focusing – by design – on common genetic variants (found in > 5% of the population), leaving the larger number of rare variants – conferring potentially high risks – aside. It is therefore important to enable integrated genome-wide regulatory models of variants across the whole spectrum of allele frequency – particularly for those variants affecting splicing and expression levels.

Here, we set up a new research network, AbCD (Aberrant transcriptome influencing risk of common diseases), which brings together three e:Med groups with the required complementary expertise and resources to address this challenge: Julien Gagneur (mitOmics) has pioneered the detection of aberrant expression for diagnosis of rare diseases. Michael Ziller group (DiNGS) is developing computational methods to infer regulatory networks in order to interpret non-coding genetic variation. Heribert Schunkert (e:AtheroSysMed) overlooks globally the largest collection of individual level data on genomic variation and expression levels for a common disease, which enabled the discovery of numerous common and rare disease variants, as well as early attempts to integrate those into disease sub-networks.

Together, we will:
i) further exploit the methods to detect aberrant expression events
ii) integrate rare and common regulatory events on expression levels and isoform choices
iii) apply these methods to two major common diseases: coronary artery disease and schizophrenia
iv) provide software and training to the e:Med community


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