Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype‐weighted knowledge in the CAGI SickKids5 clinical genomes challenge. Issue 2 (15th November 2019) - Pal, Lipika R.; Kundu, Kunal; Yin, Yizhou; Moult, John
Abstract: Precise identification of causative variants from whole‐genome sequencing data, including both coding and noncoding variants, is challenging. The Critical Assessment of Genome Interpretation 5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of the 24 whole‐genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state‐of‐the‐art pipeline. The patients have a range of eye, neurological, and connective‐tissue disorders. We used a gene‐centric approach to address this problem, assigning each gene a multiphenotype‐matching score. Mutations in the top‐scoring genes for each phenotype profile were ranked on a 6‐point scale of pathogenicity probability, resulting in an approximately equal number of top‐ranked coding and noncoding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the postsubmission phase, after careful screening of the genes in the correct genome, we identified additional potential diagnostic variants, a high proportion of which are noncoding. Abstract : We describe a pipeline for identifying rare disease causative variants from the whole‐genome sequence. The pipeline was developed for the Critical Assessment of Genome Interpretation 5 (CAGI5) challenge of matching clinical phenotypes to genomes for a set of pediatric cases but has general applicability. Novel features include a phenotype‐gene matching algorithm using weighted Human Phenotype Ontology terms and six categories of potential impact variants. The pipeline was demonstrated to be effective with the CAGI data.