Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge.
Lipika R PalKunal KunduYizhou YinJohn MoultPublished in: Human mutation (2019)
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.
Keyphrases
- genome wide
- copy number
- dna methylation
- bioinformatics analysis
- genome wide identification
- end stage renal disease
- healthcare
- magnetic resonance
- ejection fraction
- newly diagnosed
- oxidative stress
- chronic kidney disease
- social media
- staphylococcus aureus
- escherichia coli
- blood brain barrier
- biofilm formation
- brain injury
- magnetic resonance imaging
- big data
- genetic diversity