Paired human macrophage RNA sequencing data

Allele-specific expression (ASE) analysis, which quantifies the relative
expression of two alleles in a diploid individual, is a powerful tool for
identifying cis-regulated gene expression variations that underlie
phenotypic differences among individuals. Existing methods for gene-level
ASE detection analyze one individual at a time, therefore failing to
account for shared information across individuals. Failure to accommodate
such shared information not only reduces power, but also makes it
difficult to interpret results across individuals. However, when only RNA
sequencing (RNA-seq) data are available, ASE detection across individuals
is challenging because the data often include individuals that are either
heterozygous or homozygous for the unobserved cis-regulatory SNP, leading
to sample heterogeneity as only those heterozygous individuals are
informative for ASE, whereas those homozygous individuals have balanced
expression. To simultaneously model multi-individual information and
account for such heterogeneity, we developed ASEP, a mixture model with
subject-specific random effect to account for multi-SNP correlations
within the same gene. ASEP only requires RNA-seq data, and is able to
detect gene-level ASE under one condition and differential ASE between two
conditions (e.g., pre- versus post- treatment). Extensive simulations
demonstrated the convincing performance of ASEP under a wide range of
scenarios. We applied ASEP to a human kidney RNA-seq dataset, identified
ASE genes and validated our results with two published eQTL studies. We
further applied ASEP to a human macrophage RNA-seq dataset, identified
genes showing evidence of differential ASE between M0 and M1 macrophages,
and confirmed our findings by results from cardiometabolic trait-relevant
genome-wide association studies. To the best of our knowledge, ASEP is the
first method for gene-level ASE detection at the population level that
only requires the use of RNA-seq data. With the growing adoption of
RNA-seq, we believe ASEP will be well-suited for various ASE studies for
human diseases.