Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Feb:66:10-19.
doi: 10.1016/j.gde.2020.10.007. Epub 2020 Dec 28.

Allele-specific expression: applications in cancer and technical considerations

Affiliations
Review

Allele-specific expression: applications in cancer and technical considerations

Carla Daniela Robles-Espinoza et al. Curr Opin Genet Dev. 2021 Feb.

Abstract

Allele-specific gene expression can influence disease traits. Non-coding germline genetic variants that alter regulatory elements can cause allele-specific gene expression and contribute to cancer susceptibility. In tumors, both somatic copy number alterations and somatic single nucleotide variants have been shown to lead to allele-specific expression of genes, many of which are considered drivers of tumor growth. Here, we review recent studies revealing the pervasive presence of this phenomenon in cancer susceptibility and progression. Furthermore, we underscore the importance of careful experimental design and computational analysis for accurate allelic expression quantification and avoidance of false positives. Finally, we discuss additional methodological challenges encountered in cancer studies and in the burgeoning field of single-cell transcriptomics.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Allele-specific expression. (a) Schemes depicting types of allelic expression. Allelic balance, when both gene alleles are equally expressed. Allele-specific expression or allelic imbalance when one allele is significantly more expressed than the other one. Monoallelic expression, when only one gene allele is expressed. (b) Scheme illustrating how allelic expression quantification is performed using RNA-seq, by taking advantage of exonic heterozygous sites and counting the number of reads mapping to each allele. (c) Scheme illustrating one hypothetical mechanism of how context-dependent allele-specific expression can occur.
Figure 2
Figure 2
The origins, types, and consequences of ASE in cancer. ASE can have both a germline or somatic origin (Left panel). The former refers to the case where genetic alterations are inherited from the parents, and the latter when these are acquired during the lifetime of the individual. Germline and somatic alterations can be non-coding, when they affect cis-regulatory elements or consist of an aberration in epigenetic mark configuration, or coding when stop-gained or splicing mutations lead to nonsense-mediated decay and preferential wild-type allele expression (middle panel). Copy number alterations, including gene gains and losses, can span both coding and non-coding genetic regions. These alterations can result in tumor-promoting mechanisms, such as higher expression of oncogenes or lower expression of functional tumor suppressors, which can then lead to aberrant cell cycle control, impaired DNA repair response, or other tumor-promoting mechanisms. However, they theoretically may also lead to compensatory mechanisms if the wild-type copy is expressed at higher levels than the mutant copies.
Figure 3
Figure 3
Guidelines for allele-specific expression analysis. Scheme depicting the main steps for allelic expression quantification, quality check, optional downstream analyses to study ASE, and special cases with extra challenges. Recommended tools and publications are cited [70,71].

Similar articles

Cited by

References

    1. Castel S.E., Levy-Moonshine A., Mohammadi P., Banks E., Lappalainen T. Tools and best practices for data processing in allelic expression analysis. Genome Biol. 2015;16:195. - PMC - PubMed
    1. Castel S.E., GTEx Consortium, Aguet F., Mohammadi P., Ardlie K.G., Lappalainen T. A vast resource of allelic expression data spanning human tissues. Genome Biol. 2020;21 - PMC - PubMed
    2. Using a large RNA-seq resource of 54 tissues from 838 individuals, the authors identify allele-specific expression across 15,253 samples using exemplary methods. They identify ASE at both the SNP level and haplotype level, and present a new tool to estimate effect sizes of cis-regulatory variants.

    1. Buil A., Brown A.A., Lappalainen T., Viñuela A., Davies M.N., Zheng H.-F., Richards J.B., Glass D., Small K.S., Durbin R. Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins. Nat Genet. 2015;47:88–91. - PMC - PubMed
    1. Lappalainen T., Sammeth M., Friedländer M.R., ’t Hoen P.A.C., Monlong J., Rivas M.A., Gonzàlez-Porta M., Kurbatova N., Griebel T., Ferreira P.G. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506–511. - PMC - PubMed
    1. Ferraro N.M., Strober B.J., Einson J., Abell N.S., Aguet F., Barbeira A.N., Brandt M., Bucan M., Castel S.E., Davis J.R. Transcriptomic signatures across human tissues identify functional rare genetic variation. Science. 2020;369 - PMC - PubMed

Publication types

MeSH terms