As prenatal genetic testing and imaging have advanced, the diagnosis of genetic disorders has moved from the postnatal to the prenatal time frame. This has largely been facilitated by the increasing use of exome sequencing (ES) in the prenatal setting. Two landmark trials published in January 2019 highlighted the overall diagnostic yields of prenatal ES as 8.5% and 10% in fetuses with normal karyotype and microarray.1,2
Although this is a huge step forward in prenatal diagnosis, ES is currently a manually curated, labor-intensive task. The process involves reviewing thousands of sequence variants for any given sample and prioritizing each variant based on bioinformatic data, prediction models, literature review, and specific patient characteristics. The patient characteristics, or phenotypic information, are critically important in prioritizing candidate variants.
To date, prenatal ES has been limited by the use of inconsistent terminology and the lack of well-understood prenatal phenotypes. In this Update, we highlight how recently published work draws attention to these critical gaps in prenatal diagnosis.
Standardizing phenotyping language in the prenatal setting
Tomar S, Sethi R, Lai PS. Specific phenotype semantics facilitate gene prioritization in clinical exome sequencing. Eur J Hum Genet. 2019;27:1389-1397.
Clinical ES in pediatric and adult populations is enhanced by the use of standardized vocabulary to describe disorders. Standardized language ensures that identified variants are filtered correctly and in a systematic fashion based on the patient characteristics that are provided. One commonly used platform is the Human Phenotype Ontology (HPO).
Tomar and colleagues assessed the impact of HPO-based clinical information on the performance of a gene prioritization tool.3 Gene prioritization (or simulation) tools are used for interpretation of ES data to help analysts efficiently sort through the thousands of variants in an individual’s genetic sequence. The performance, or accuracy, of a prioritization tool can be assessed by looking at the location of the disease-causing gene in the suggested gene list.
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