AbCD-Net

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

Publications

de Jong, L., Bobeldijk-Pastorova, I., Erdmann, J., Bijker-Schreurs, M., Schunkert, H., Kuivenhoven, J. A. and van Gool, A. J. (2020). "Sharing lessons learnt across European cardiovascular research consortia." Drug Discovery Today. www.sciencedirect.com/science/article/pii/S1359644620300350.

Erdmann, J., Kessler, T., Munoz Venegas, L. and Schunkert, H. (2018). "A decade of genome-wide association studies for coronary artery disease: the challenges ahead." Cardiovascular Research 114(9): 1241-1257. www.ncbi.nlm.nih.gov/pubmed/29617720.

Kessler, T. and Schunkert, H. (2019). "Genetics of Recovery After Stroke." Circulation Research 124(1): 18-20. www.ncbi.nlm.nih.gov/pubmed/30605410.

Ntalla, I., Kanoni, S., Zeng, L., Giannakopoulou, O., Danesh, J., Watkins, H., Samani, N. J., Deloukas, P., Schunkert, H. and Group, U. K. B. C. C. C. W. (2019). "Genetic Risk Score for Coronary Disease Identifies Predispositions to Cardiovascular and Noncardiovascular Diseases." J Am Coll Cardiol 73(23): 2932-2942. www.ncbi.nlm.nih.gov/pubmed/31196449.

Schunkert, H. and Samani, N. J. (2018). "Into the great wide open: 10 years of genome-wide association studies." Cardiovascular Research 114(9): 1189-1191. www.ncbi.nlm.nih.gov/pubmed/29688283.

Schunkert, H., von Scheidt, M., Kessler, T., Stiller, B., Zeng, L. and Vilne, B. (2018). "Genetics of coronary artery disease in the light of genome-wide association studies." Clinical Research in Cardiology: Official Journal of the German Cardiac Society 107(Suppl 2): 2-9. www.ncbi.nlm.nih.gov/pubmed/30022276.

Zeng, L., Talukdar, H. A., Koplev, S., Giannarelli, C., Ivert, T., Gan, L. M., Ruusalepp, A., Schadt, E. E., Kovacic, J. C., Lusis, A. J., Michoel, T., Schunkert, H. and Bjorkegren, J. L. M. (2019). "Contribution of Gene Regulatory Networks to Heritability of Coronary Artery Disease." J Am Coll Cardiol 73(23): 2946-2957. www.ncbi.nlm.nih.gov/pubmed/31196451.