Regional-specific calibration enables application of computational evidence for clinical classification of 5' cis-regulatory variants in Mendelian disease.

Autor:	Villani RM; Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia., McKenzie ME; Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia., Davidson AL; Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia., Spurdle AB; Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; University of Queensland, Brisbane, Queensland, Australia. Electronic address: amanda.spurdle@qimrberghofer.edu.au.
Jazyk:	angličtina
Zdroj:	American journal of human genetics [Am J Hum Genet] 2024 Jul 11; Vol. 111 (7), pp. 1301-1315. Date of Electronic Publication: 2024 May 29.
DOI:	10.1016/j.ajhg.2024.05.002
Abstrakt:	To date, clinical genetic testing for Mendelian disease variants has focused heavily on exonic coding and intronic gene regions. This multi-step study was undertaken to provide an evidence base for selecting and applying computational approaches for use in clinical classification of 5' cis-regulatory region variants. Curated datasets of clinically reported disease-causing 5' cis-regulatory region variants and variants from matched genomic regions in population controls were used to calibrate six bioinformatic tools as predictors of variant pathogenicity. Likelihood ratio estimates were aligned to code weights following ClinGen recommendations for application of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) classification scheme. Considering code assignment across all reference dataset variants, performance was best for CADD (81.2%) and REMM (81.5%). Optimized thresholds provided moderate evidence toward pathogenicity (CADD, REMM) and moderate (CADD) or supporting (REMM) evidence against pathogenicity. Both sensitivity and specificity of prediction were improved when further categorizing variants based on location in an EPDnew-defined promoter region. Combining predictions (CADD, REMM, and location in a promoter region) increased specificity at the expense of sensitivity. Importantly, the optimal CADD thresholds for assigning ACMG/AMP codes PP3 (≥10) and BP4 (≤8) were vastly different from recommendations for protein-coding variants (PP3 ≥25.3; BP4 ≤22.7); CADD <22.7 would incorrectly assign BP4 for >90% of reported disease-causing cis-regulatory region variants. Our results demonstrate the need to consider a tiered approach and tailored score thresholds to optimize bioinformatic impact prediction for clinical classification of 5' cis-regulatory region variants. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2024 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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