Abstract
Haploinsufficiency is a major mechanism of genetic risk in developmental disorders (DD). Accurate prediction of haploinsufficient genes is essential for prioritizing and interpreting deleterious variants. Current methods based on mutation intolerance in population data suffer from inadequate power for genes with short transcripts. Here we showed haploinsufficiency is strongly associated with epigenomic patterns, and then developed a new computational method (Episcore) to predict haploinsufficiency from epigenomic data using a Random Forest model. Based on data from recent exome sequencing studies of DD, we show that Episcore performs favorably to current methods in prioritizing loss of function de novo variants. Our method enables new applications of epigenomic data, and facilitates discovery and interpretation of novel candidate risk variants in genetic studies of DD.