Back

Assessing the contribution of rare variants to congenital heart disease through a large-scale case-control exome study

Audain, E.; Wilsdon, A.; Dombrowsky, G.; Sifrim, A.; Breckpot, J.; Perez-Riverol, Y.; Loughna, S.; Daly, A.; Antoniou, P.; Hofmann, P.; Perez-Riverol, A.; Kahlert, A.-K.; Bauer, U.; Pickardt, T.; Klaassen, S.; Berger, F.; Daehnert, I.; Dittrich, S.; Stiller, B.; Abdul-Khaliq, H.; Bu'Lock, F.; Uebing, A.; Kramer, H.-H.; Iyer, V.; Larsen, L. A.; Brook, J. D.; Hitz, M.-P.

2023-12-28 genetic and genomic medicine
10.1101/2023.12.23.23300495 medRxiv
Show abstract

Several studies have demonstrated the value of large-scale human exome and genome data analysis, to maximise gene discovery in rare diseases. Using this approach, we have analysed the exomes of 4,747 cases and 52,881 controls, to identify single genes and digenic interactions which confer a substantial risk of congenital heart disease (CHD). We identified both rare loss-of-function and missense coding variants in ten genes which reached genome-wide significance (Bonferroni adjusted P < 0.05) and an additional four genes with a significant association at a false discovery rate (FDR) threshold of 5%. We highlight distinct genetic contributions to syndromic and non-syndromic CHD at both single gene and digenic level, by independently analysing probands from these two groups. In addition, by integrative analysis of exome data with single-cell transcriptomics data from human embryonic hearts, we identified cardiac-specific cells as well as putative biological processes underlying the pathogenesis of CHD. In summary, our findings strengthen the association of known CHD genes, and have identified additional novel disease genes and digenic interactions contributing to the aetiology of CHD.

Published in npj Genomic Medicine (predicted rank #2) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.