PEDIA: prioritization of exome data by image analysis
PEDIA: prioritization of exome data by image analysis
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DOI：
10.1038/s41436-019-0566-2
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发表时间：
2019-12-01
影响因子：
8.8
通讯作者：
Krawitz, Peter M.
中科院分区：
文献类型：
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作者：
Hsieh, Tzung-Chien;Mensah, Martin A.;Krawitz, Peter M.
目的：表型信息对于解释基因组变异至关重要。到目前为止，它只可用于生物信息学的工作流程后，编码成临床术语由专家dysmorphologist.Methods：在这里，我们介绍了一种人工智能驱动的方法，使用肖像照片的解释临床外显子组数据。我们测量了计算机辅助图像分析对由679名患有105种不同单基因疾病的个体组成的队列的诊断率的附加值。对于队列中的每个病例，我们汇编了正面照片、临床特征和致病变异，并模拟了不同种族背景的多个外显子组。结果：额外使用来自正面照片的计算机辅助分析的相似性评分，使致病基因的前1名准确率提高了20-89%以上，前10名准确率提高了5-99%以上。通过深度学习算法进行的图像分析可用于量化表型相似性（美国医学遗传学和基因组学学会指南的PP 4标准），并提高外显子组分析的生物信息学管道的性能。
Purpose: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.Methods: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds.Results: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene.Conclusion: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.