Notwithstanding important advances in the context of single-variant pathogenicity identification, novel breakthroughs in discerning the origins of many rare diseases require methods able to identify more complex genetic models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (called digenic or bilocus variant combinations). We show that the results produced by this method are highly accurate and precise, an efficacy that is endorsed when validating the method on recently published independent disease-causing data. Confidence labels of 95% and 99% are identified, representing the probability of a bilocus combination being a true pathogenic result, providing geneticists with rational markers to evaluate the most relevant pathogenic combinations and limit the search space and time. Finally, the VarCoPP has been designed to act as an interpretable method that can provide explanations on why a bilocus combination is predicted as pathogenic and which biological information is important for that prediction. This work provides an important step toward the genetic understanding of rare diseases, paving the way to clinical knowledge and improved patient care.

A tremendous amount of DNA sequencing data is being produced around the world with the ambition to capture in more detail the mechanisms underlying human diseases. While numerous bioinformatics tools exist that allow the discovery of causal variants in Mendelian diseases, little to no support is provided to do the same for variant combinations, an essential task for the discovery of the causes of oligogenic diseases. ORVAL (the Oligogenic Resource for Variant AnaLysis), which is presented here, provides an answer to this problem by focusing on generating networks of candidate pathogenic variant combinations in gene pairs, as opposed to isolated variants in unique genes. This online platform integrates innovative machine learning methods for combinatorial variant pathogenicity prediction with visualization techniques, offering several interactive and exploratory tools, such as pathogenic gene and protein interaction networks, a ranking of pathogenic gene pairs, as well as visual mappings of the cellular location and pathway information. ORVAL is the first web-based exploration platform dedicated to identifying networks of candidate pathogenic variant combinations with the sole ambition to help in uncovering oligogenic causes for patients that cannot rely on the classical disease analysis tools. ORVAL is available at https://orval.ibsquare.be.

DNA methylation controls gene expression, and once established, DNA methylation patterns are faithfully copied during DNA replication by the maintenance DNA methyltransferase Dnmt1. In vivo, Dnmt1 interacts with Uhrf1, which recognizes hemimethylated CpGs. Recently, we reported that Uhrf1‐catalyzed K18‐ and K23‐ubiquitinated histone H3 binds to the N‐terminal region (the replication focus targeting sequence, RFTS) of Dnmt1 to stimulate its methyltransferase activity. However, it is not yet fully understood how ubiquitinated histone H3 stimulates Dnmt1 activity. Here, we show that monoubiquitinated histone H3 stimulates Dnmt1 activity toward DNA with multiple hemimethylated CpGs but not toward DNA with only a single hemimethylated CpG, suggesting an influence of ubiquitination on the processivity of Dnmt1. The Dnmt1 activity stimulated by monoubiquitinated histone H3 was additively enhanced by the Uhrf1 SRA domain, which also binds to RFTS. Thus, Dnmt1 activity is regulated by catalysis (ubiquitination)‐dependent and ‐independent functions of Uhrf1.