Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge

Precise identification of causative variants from whole-genome sequencing data, including both coding and non-coding variants, is challenging. The CAGI5 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 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 multi-phenotype-matching score. Mutations in the top scoring genes for each phenotype profile were ranked on a six-point scale of pathogenicity probability, resulting in an approximately equal number of top ranked coding and non-coding 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 post-submission phase, after careful screening of the genes in the correct genome we identified additional potential diagnostic variants, a high proportion of which are non-coding.