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

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.

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

“…CAGI5 continued the emphasis on the interpretation of clinically relevant large‐scale sequence data, with a challenge on the risk of thrombosis in African‐American cohort given whole exome sequence (McInnes et al, ; Wang & Bromberg, ); the identification of variants contributing to intellectual disability phenotypes given gene panel sequence (Aspromonte et al, ; Carraro et al, ; Chen, ); and a challenge of matching whole genome sequences to clinical profiles for patients at Toronto's Hospital for Sick Children (SickKids) and identifying causal variants (Kasak, Hunter et al, ; Pal, Kundu, Yin, & Moult, ). The latter challenge is related to the CAGI4 SickKids challenge, also described in the assessment paper here.…”

CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases

“…Group 4 (Pal, Kundu, Yin, & Moult, ) : The bioinformatics approach used by the group 4 resulted in five correct genome to patient matches: 17 (H), 56(N), 93(F), 95(C), and 99(B), and yielded candidate variants for each (Table , Table S3). Of note, ethnicity was stated for three of the five cases.…”

“…A recent paper has demonstrated that the use of specific HPO terms improves gene‐ranking with the top 10% of HPO terms being sufficient to rank the causative gene (Tomar, Sethi, & Lai, ). Unlike other submissions, SID#4 weighed the clinical terms by scoring the most serious and definitive (to a presumed disease) term in the profile with the highest value (Pal et al, & Moult, ). SID#8 built eight gene sets related to the diseases of interest and classified each case as belonging to one of those categories, rather than using all the genes associated with any of the HPO terms derived from the clinical descriptions.…”

Genome interpretation using in silico predictors of variant impact

“…For example, FATHMM-XF, SIFT, PolyPhen2, and CADD were used to prioritize 190 candidate genes for driving neuroticism (Belonogova et al 2021 ) and similarly for other traits (Bacchelli et al 2016 ; Zhang et al 2018 ). In CAGI challenges, many participants predicted the risk of individuals based on genomic data and matched genotypes to phenotypes better than random (Kasak et al 2019b ; Katsonis and Lichtarge 2019 ; Pal et al 2017 , 2020 ; Wang and Bromberg 2019 ). The imputed Deviation in Evolutionary Action Load (iDEAL) approach used protein function predictions to discover trait drivers (Kim et al 2021 ).…”