Article ; Online: Significance Associated with Phenotype Score Aids in Variant Prioritization for Exome Sequencing Analysis.
The Journal of molecular diagnostics : JMD
2024 Volume 26, Issue 5, Page(s) 337–348
Abstract: Several in silico annotation-based methods have been developed to prioritize variants in exome sequencing analysis. This study introduced a novel metric Significance Associated with Phenotypes (SAP) score, which generates a statistical score by comparing ...
Abstract | Several in silico annotation-based methods have been developed to prioritize variants in exome sequencing analysis. This study introduced a novel metric Significance Associated with Phenotypes (SAP) score, which generates a statistical score by comparing an individual's observed phenotypes against existing gene-phenotype associations. To evaluate the SAP score, a retrospective analysis was performed on 219 exomes. Among them, 82 family-based and 35 singleton exomes had at least one disease-causing variant that explained the patient's clinical features. SAP scores were calculated, and the rank of the disease-causing variant was compared with a known method, Exomiser. Using the SAP score, the known causative variant was ranked in the top 10 retained variants for 94% (77 of 82) of the family-based exomes and in first place for 73% of these cases. For singleton exomes, the SAP score analysis ranked the known pathogenic variants within the top 10 for 80% (28 of 35) of cases. The SAP score, which is independent of detected variants, demonstrates comparable performance with Exomiser, which considers both phenotype and variant-level evidence simultaneously. Among 102 cases with negative results or variants of uncertain significance, SAP score analysis revealed two cases with a potential new diagnosis based on rank. The SAP score, a phenotypic quantitative metric, can be used in conjunction with standard variant filtration and annotation to enhance variant prioritization in exome analysis. |
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MeSH term(s) | Humans ; Exome Sequencing ; Retrospective Studies ; Databases, Genetic ; Genetic Testing ; Phenotype |
Language | English |
Publishing date | 2024-02-13 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 2000060-1 |
ISSN | 1943-7811 ; 1525-1578 |
ISSN (online) | 1943-7811 |
ISSN | 1525-1578 |
DOI | 10.1016/j.jmoldx.2024.01.009 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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