Statistical framework for calling allelic imbalance in high-throughput sequencing data
High-throughput sequencing facilitates large-scale studies of gene regulation and allows tracing the associations of individual genomic variants with changes in gene regulation and expression. Compared to classic association studies, the assessment of an allelic imbalance at heterozygous variants captures functional variant effects with smaller sample sizes, higher sensitivity, and better resolution. Yet, identification of allele-specific variants from allelic read counts remains challenging due to data-dependent biases and overdispersion arising from technical and biological variability. We present MIXALIME, a novel computational framework for calling allele-specific variants in diverse omics data with a repertoire of statistical models accounting for read mapping bias and copy number variation. We benchmark MIXALIME with DNase-Seq, ATAC-Seq, and CAGE-Seq data, and we demonstrate that the allelic imbalance highlights causal variants in GWAS results. Finally, as a showcase of the large-scale practical application of MIXALIME, we present an atlas of variants exhibiting allele-specific chromatin accessibility, built from thousands of available datasets obtained from diverse cell types.
Authors
- Vsevolod J. Makeev (ORCID: https://orcid.org/0000-0001-9405-9748)
- Semyon Kolmykov (ORCID: https://orcid.org/0000-0002-9259-0495)
- G. A. Meshcheryakov (ORCID: https://orcid.org/0000-0003-0751-8286)
- Alexandr Boytsov (ORCID: https://orcid.org/0000-0002-2712-8368)
- Jeff Vierstra (ORCID: https://orcid.org/0000-0001-6406-8563)
- Eugene F. Baulin (ORCID: https://orcid.org/0000-0003-4694-9783)
- Vladimir Nozdrin (ORCID: https://orcid.org/0009-0004-3053-1387)
- Andrey Buyan (ORCID: https://orcid.org/0000-0001-9105-4028)
- Ivan V. Kulakovskiy (ORCID: https://orcid.org/0000-0002-6554-8128)
- Fedor Kolpakov (ORCID: https://orcid.org/0000-0002-0396-0256)
- В В Сафронов
- Sergey Abramov
Institutions
- Lomonosov Moscow State University (RU)
- Sirius University of Science and Technology (RU)
- Ufa Institute of Chemistry (RU)
- Altius Institute for Biomedical Sciences (US)
- International Institute of Molecular and Cell Biology (PL)
- Cancer Research UK Manchester Institute (GB)
- Academician V.P.Makeyev State Rocket Centre (Russia) (RU)
- Institute of Protein Research (RU)
- Federal Research Center for Information and Computational Technologies (RU)
- Vavilov Institute of General Genetics (RU)
- Medical Technologies (Czechia) (CZ)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1038/s41467-024-55513-2
- Citations
- 5
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 9.09
Funders
- Russian Science Foundation
- Ministry of Science and Higher Education of the Russian Federation