Successful Computational Prediction of Novel Imprinted Genes from Epigenomic Features

Abstract

ABSTRACT Approximately 100 mouse genes undergo genomic imprinting, whereby one of the two parental alleles is epigenetically silenced. Imprinted genes influence processes including development, X chromosome inactivation, obesity, schizophrenia, and diabetes, motivating the identification of all imprinted loci. Local sequence features have been used to predict candidate imprinted genes, but rigorous testing using reciprocal crosses validated only three, one of which resided in previously identified imprinting clusters. Here we show that specific epigenetic features in mouse cells correlate with imprinting status in mice, and we identify hundreds of additional genes predicted to be imprinted in the mouse. We used a multitiered approach to validate imprinted expression, including use of a custom single nucleotide polymorphism array and traditional molecular methods. Of 65 candidates subjected to molecular assays for allele-specific expression, we found 10 novel imprinted genes that were maternally expressed in the placenta.