外显子组测序
表型
外显子组
计算生物学
遗传学
生物信息学
生物
基因
作者
Brian Lee,Lily Nasanovsky,Li Shen,Dennis T. Maglinte,Yachen Pan,Xiaowu Gai,Ryan J. Schmidt,Gordana Raca,Jaclyn A. Biegel,Megan Roytman,Paul An,Carol Saunders,Emily Farrow,Soheil Shams,Jianling Ji
标识
DOI:10.1016/j.jmoldx.2024.01.009
摘要
Several in silico annotation-based methods have been developed to prioritize variants in exome sequencing analysis. This study introduces a novel metric, the 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 explaining the patient’s clinical features. SAP scores were calculated, and the rank of the disease-causing variant was compared to a known method, Exomiser. Using the SAP score, the known causative variant was ranked in the top 10 retained variants for 94% (77/82) of the family-based exomes and in the 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% (28/35) of the cases. The SAP score, which is independent of detected variants, demonstrates comparable performance to 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 utilized in conjunction with standard variant filtration and annotation to enhance variant prioritization in exome analysis.
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