ArticlePLoS genetics2020
ASEP: Gene-based detection of allele-specific expression across individuals in a population by RNA sequencing.
Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- TIGER: The gene expression regulatory variation landscape of human pancreatic islets.Cell reports · 2021Pooled it
- The landscape of allele-specific expression in human kidneys.Science advances · 2026Article
- Allele-specific expression in the brain links genetic risk and cortical thinning in psychiatric disorders.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
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- The G→C rs590352 in the Protein-Coding Region of ATXN7L3B Gene Upregulates Its Expression In Vivo.Biochemical genetics · 2026Article
- Germline regulation of tumor evolutionary dynamics shapes multiple myeloma progression.bioRxiv : the preprint server for biology · 2026Article
- Divergent cis-regulatory haplotypes at Tlr2 are associated with immune responsiveness.Molecular biology and evolution · 2026Article
- Assessment of forensic individual identification and kinship analysis using transcript SNPs derived from public transcriptome sequencing data.BMC genomics · 2026Article
- Monoallelic expression characterizes a distinct molecular and clinical group of breast tumors.Research square · 2026Article
- The Camellia sinensis var. sinensis cv. Fuding Dabaicha genome unveils structural variation-driven metabolic innovation.Nature communications · 2026Article
- Self-organizing maps for allele specific expression data reconstruction and identification of anomalous genomic regions.Frontiers in bioinformatics · 2026Article
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- The Single Nucleotide Substitution T → A rs2072580 Damages the CREB1 Binding Site in the BidirectionalGenes · 2025Article
- Bayesian estimation of allele-specific expression in the presence of phasing uncertainty.Bioinformatics (Oxford, England) · 2025Article
- Long-read RNA sequencing reveals allele-specificGenome research · 2025Article
- Probing the limits of cis-acting gene regulation using a model of allelic imbalance quantitative trait loci.PLoS genetics · 2025Article
- Statistical framework for calling allelic imbalance in high-throughput sequencing data.Nature communications · 2025Article
- A comprehensive allele specific expression resource for the equine transcriptome.BMC genomics · 2025Article
- Functional genomics of human skeletal development and the patterning of height heritability.Cell · 2025Article
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8 authors.
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Abstract
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.
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