Motivation: A major cause of autosomal dominant disease is haploinsufficiency, whereby a single copy of a gene is not sufficient to maintain the normal function of the gene. A large proportion of existing methods for predicting haploinsufficiency incorporate biological networks, e.g. protein-protein interaction networks, that have recently been shown to introduce study bias. As a result, these methods tend to perform best on well studied genes, but underperform on less studied genes. The advent of large genome sequencing consortia, such as the 1,000 genomes project, NHLBI Exome Sequencing Project (ESP) and the Exome Aggregation Consortium (ExAC) creates an urgent need for unbiased haploinsufficiency prediction methods.
Results: Here, we describe a machine learning approach, called HIPred, that integrates genomic and evolutionary information from ENSEMBL, with functional annotations from the Encyclopaedia of DNA Elements (ENCODE) consortium and the NIH Roadmap Epigenomics Project to predict haploinsufficiency, without the study bias described above. We benchmark HIPred using several datasets and show that our unbiased method performs as well as, and in most cases, outperforms existing biased algorithms.
Availability: HIPred scores for all gene identifiers are available at: https://github.com/HAShihab/HIPred.