About MAEA variety of genetic and epigenetic factors affect the relative expression levels of the two copies of each given gene in diploid cells. A significant fraction of mammalian autosomal genes are subject to monoallelic expression (MAE), which reflects a mitotically stable allele-specific expression with different allelic states in clonal lineages. MAE is observed in olfactory receptor genes (Chess et al., 1994), as well as genes coding for immunoglobulins and some cytokines (Pernis et al., 1965; Bix and Locksley, 1998; Holländer et al., 1998). Genome-wide analyses of allele-specific expression have added a surprisingly large number of the autosomal genes in human and mouse to the MAE class (Gimelbrant et al., 2007; Zwemer et al., 2012; Eckersley-Maslin et al., 2014; Gendrel et al. 2014), including genes implicated in a number of human diseases, such as Alzheimer's disease (APP) (Bertram and Tanzi, 2012) and cancer (DAPK1) (Raval et al., 2007). MAE has been measured in approximately 10% of ∼4000 tested genes in human lymphoblastoid cells (LCLs) and about 15% of more than 1300 assessed genes in analogous mouse cells (Gimelbrant et al., 2007; Zwemer et al., 2012), with similar estimates for other cell types in mouse (Eckersley-Maslin et al., 2014; Gendrel et al. 2014)
A major technical bottleneck in addressing these questions is the clonal nature of MAE. Like X inactivation, MAE is masked in polyclonal samples, and obtaining monoclonal cell populations is challenging for most tissue types, particularly so in vivo. Moreover, genome-wide methods are limited by the availability of polymorphisms. To supplement direct observation, the Gimelbrant lab has developed a fundamentally new approach to the detection of monoallelic expression. In contrast to other methods, it does not require any allele-specific information, instead relying on a specific chromatin pattern as a proxy for MAE. According to this method, the tissue-specific prevalence of MAE is 15-30%, and > 40% of genes show MAE in at least one cell type.
(The above text is partially adapted from Nag et al. 2013)
View trend for "monoallelic expression" in Medline abstracts. Data from dan.corlan.net.