Evaluation of an automated genome interpretation model for rare disease routinely used in a clinical genetic laboratory.

Linyan Meng¹, Ruben Attali², Tomer Talmy³, Yakir Regev², Niv Mizrahi², Pola Smirin-Yosef², Liesbeth Vossaert¹, Christian Taborda⁴, Michael Santana⁴, Ido Machol¹, Rui Xiao¹, Hongzheng Dai¹, Christine Eng¹, Fan Xia¹, Shay Tzur⁵

¹Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX; Baylor Genetics, Houston, TX.

²Genomic Research Department, Emedgene, an Illumina Company, Tel Aviv, Israel.

³Genomic Research Department, Emedgene, an Illumina Company, Tel Aviv, Israel; Institute of Research in Military Medicine, The Faculty of Medicine, The Hebrew University of Jerusalem, Hadassah Medical Center, Jerusalem, Israel.

⁴Baylor Genetics, Houston, TX.

⁵Genomic Research Department, Emedgene, an Illumina Company, Tel Aviv, Israel. Electronic address: stzur@illumina.com.

PubMed DOI

Abstract

PURPOSE: The analysis of exome and genome sequencing data for the diagnosis of rare diseases is challenging and time-consuming. In this study, we evaluated an artificial intelligence model, based on machine learning for automating variant prioritization for diagnosing rare genetic diseases in the Baylor Genetics clinical laboratory. METHODS: The automated analysis model was developed using a supe…

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