Impact of genome build on RNA-seq interpretation and diagnostics.

Autor:	Ungar RA; Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA., Goddard PC; Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA., Jensen TD; Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA., Degalez F; PEGASE, INRAE, Institut Agro, Rennes, Bretagne, France., Smith KS; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA., Jin CA; Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA., Bonner DE; Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA, USA; Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA., Bernstein JA; Stanford Center for Undiagnosed Diseases, Stanford University, Stanford, CA, USA., Wheeler MT; Department of Cardiovascular Medicine, School of Medicine, Stanford University, Stanford, CA, USA., Montgomery SB; Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. Electronic address: smontgom@stanford.edu.
Jazyk:	angličtina
Zdroj:	American journal of human genetics [Am J Hum Genet] 2024 Jul 11; Vol. 111 (7), pp. 1282-1300. Date of Electronic Publication: 2024 Jun 03.
DOI:	10.1016/j.ajhg.2024.05.005
Abstrakt:	Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network and Genomics Research to Elucidate the Genetics of Rare Disease Consortium. Across six routinely collected biospecimens, 61% of quantified genes were not influenced by genome build. However, we identified 1,492 genes with build-dependent quantification, 3,377 genes with build-exclusive expression, and 9,077 genes with annotation-specific expression across six routinely collected biospecimens, including 566 clinically relevant and 512 known OMIM genes. Further, we demonstrate that between builds for a given gene, a larger difference in quantification is well correlated with a larger change in expression outlier calling. Combined, we provide a database of genes impacted by build choice and recommend that transcriptomics-guided analyses and diagnoses are cross referenced with these data for robustness. Competing Interests: Declaration of interests During this project R.A.U. was employed for an internship by Vertex Pharmaceuticals. P.C.G. is a consultant for BioMarin. S.B.M. is an advisor to BioMarin, MyOme, and Tenaya Therapeutics. (Copyright © 2024 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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