Genome-wide identification of allele-specific effects on gene expression for single and multiple individuals

Abstract

The analysis of allele-specific gene expression (ASE) is essential for the mapping of genetic variants that affect gene regulation, and for the identification of alleles that modify disease risk. Although RNA sequencing offers the opportunity to measure expression at allele levels, the availability of powerful statistical methods for mapping ASE in single or multiple individuals is limited. We developed a maximum likelihood model to characterize ASE in the human genome. Approximately 17% of genes displayed an allele-specific effect on gene expression in a single individual. Simulations using our model gave a better performance and improved robustness when compared with the binomial test, with different coverage levels, allelic expression fractions and random noise. In addition, our method can identify ASE in multiple individuals, with enhanced performance. This is helpful in understanding the mechanism of genetic regulation leading to expression changes, alternative splicing variants and even disease susceptibility.

Keywords: ASE; AUC; Allele-specific expression; CEU; CHB; Caucasians of Northern and Western European origin; Chinese individuals from Beijing University; FDR; HBV; HLA-DPA1; JPT; Japanese individuals from Tokyo; MHC; Maximum likelihood model; Populations; RNA-seq; ROC; RT-PCR; SNP; UTR; YRI; Yoruba from Ibadan, Nigeria; allele-specific gene expression; eQTLs; expression quantitative traits loci; false discovery rate; hepatitis B virus; high-throughput RNA sequencing; major histocompatibility complex; major histocompatibility complex, class II, DP alpha 1; real-time polymerase chain reaction; receiver operating characteristic; single nucleotide polymorphisms; the area under the curve; untranslated region.