Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility.

Autor:	Liang Q; Department of Computational & Systems Biology and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Human Genetics, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA., Abraham A; Children's Hospital of Philadelphia, Philadelphia, PA, USA., Capra JA; Department of Epidemiology & Biostatistics and Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA., Kostka D; Department of Computational & Systems Biology and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address: kostka@pitt.edu.
Jazyk:	angličtina
Zdroj:	HGG advances [HGG Adv] 2024 Jul 18; Vol. 5 (3), pp. 100310. Date of Electronic Publication: 2024 May 21.
DOI:	10.1016/j.xhgg.2024.100310
Abstrakt:	Non-protein-coding genetic variants are a major driver of the genetic risk for human disease; however, identifying which non-coding variants contribute to diseases and their mechanisms remains challenging. In silico variant prioritization methods quantify a variant's severity, but for most methods, the specific phenotype and disease context of the prediction remain poorly defined. For example, many commonly used methods provide a single, organism-wide score for each variant, while other methods summarize a variant's impact in certain tissues and/or cell types. Here, we propose a complementary disease-specific variant prioritization scheme, which is motivated by the observation that variants contributing to disease often operate through specific biological mechanisms. We combine tissue/cell-type-specific variant scores (e.g., GenoSkyline, FitCons2, DNA accessibility) into disease-specific scores with a logistic regression approach and apply it to ∼25,000 non-coding variants spanning 111 diseases. We show that this disease-specific aggregation significantly improves the association of common non-coding genetic variants with disease (average precision: 0.151, baseline = 0.09), compared with organism-wide scores (GenoCanyon, LINSIGHT, GWAVA, Eigen, CADD; average precision: 0.129, baseline = 0.09). Further on, disease similarities based on data-driven aggregation weights highlight meaningful disease groups, and it provides information about tissues and cell types that drive these similarities. We also show that so-learned similarities are complementary to genetic similarities as quantified by genetic correlation. Overall, our approach demonstrates the strengths of disease-specific variant prioritization, leads to improvement in non-coding variant prioritization, and enables interpretable models that link variants to disease via specific tissues and/or cell types. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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