Rapid and accurate interpretation of clinical exomes using Phenoxome: a computational phenotype-driven approach

Chao Wu, Batsal Devkota, Perry Evans, Xiaonan Zhao, Samuel W. Baker, Rojeen Niazi, Kajia Cao, Michael A. Gonzalez, Pushkala Jayaraman, Laura K. Conlin, Bryan L. Krock, Matthew A. Deardorff, Nancy B. Spinner, Ian D. Krantz, Avni B. Santani, Ahmad N.Abou Tayoun, Mahdi Sarmady

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Clinical exome sequencing (CES) has become the preferred diagnostic platform for complex pediatric disorders with suspected monogenic etiologies. Despite rapid advancements, the major challenge still resides in identifying the casual variants among the thousands of variants detected during CES testing, and thus establishing a molecular diagnosis. To improve the clinical exome diagnostic efficiency, we developed Phenoxome, a robust phenotype-driven model that adopts a network-based approach to facilitate automated variant prioritization. Phenoxome dissects the phenotypic manifestation of a patient in concert with their genomic profile to filter and then prioritize variants that are likely to affect the function of the gene (potentially pathogenic variants). To validate our method, we have compiled a clinical cohort of 105 positive patient samples that represent a wide range of genetic heterogeneity. Phenoxome identifies the causative variants within the top 5, 10, or 25 candidates in more than 50%, 71%, or 88% of these exomes, respectively. Furthermore, we show that our method is optimized for clinical testing by outperforming the current state-of-art method. We have demonstrated the performance of Phenoxome using a clinical cohort and showed that it enables rapid and accurate interpretation of clinical exomes. Phenoxome is available at https://phenoxome.chop.edu/.

Original languageEnglish
Pages (from-to)612-620
Number of pages9
JournalEuropean Journal of Human Genetics
Issue number4
StatePublished - 1 Apr 2019
Externally publishedYes


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