Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework

Genetics in Medicine

Volumn 20, Issue 9, 2018, Pages 1054-1060

  Tavtigian, Sean V ^a   Greenblatt, Marc S ^b   Harrison, Steven M ^c   Nussbaum, Robert L ^d   Prabhu, Snehit A ^e   Boucher, Kenneth M ^a   Biesecker, Leslie G ^f  

^a UNIVERSITY OF UTAH   (United States)

^b UNIVERSITY OF VERMONT COLLEGE OF MEDICINE   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d Clinical Molecular Geneticist and Cytogeneticist Invitae Corportation   (United States)

^e STANFORD UNIVERSITY   (United States)

^f NATIONAL HUMAN GENOME RESEARCH INSTITUTE   (United States)

Author keywords

Bayesian framework; medical genetics; unclassified variants; variant classification; variants of uncertain significance

Indexed keywords

ARTICLE; BAYESIAN LEARNING; DISEASE CLASSIFICATION; HEALTH CARE ORGANIZATION; HEURISTICS; INTERNAL CONSISTENCY; MATHEMATICAL MODEL; PATHOGENICITY; PRACTICE GUIDELINE; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; BAYES THEOREM; BIOLOGY; DNA SEQUENCE; GENETIC SCREENING; GENETIC VARIATION; GENETICS; GENOMICS; HIGH THROUGHPUT SEQUENCING; HUMAN; HUMAN GENOME; PROCEDURES; SOFTWARE; STANDARDS;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; GENETIC TESTING; GENETIC VARIATION; GENOME, HUMAN; GENOMICS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; SEQUENCE ANALYSIS, DNA; SOFTWARE;

EID: 85049273706     PISSN: 10983600     EISSN: 15300366     Source Type: Journal    
DOI: 10.1038/gim.2017.210     Document Type: Article

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