Predicting molecular events underlying rare diseases using variant annotation, aberrant gene expression events, and human phenotype ontology

Abstract

Abstract Rare genetic diseases often pose significant challenges for diagnosis. Over the past years, RNA sequencing and other omics modalities have emerged as complementary strategies to DNA sequencing to enhance diagnostic success. In the 6th round of the Critical Assessment of Genome Interpretation (CAGI), the SickKids clinical genomes and transcriptomes challenge aimed to evaluate the diagnostic potential of multi-omics approaches in identifying and resolving undiagnosed genetic disorders. Here, we present our participation in that challenge, where we leveraged genomic, transcriptomic, and clinical data from 79 children with diverse suspected Mendelian disorders to develop a model predicting the causal gene. We employed a machine learning model trained on a cohort of 93 solved mitochondrial disease samples to prioritize candidate genes. In our analysis of the SickKids cohort, we successfully prioritized the causal genes in 2 out of the 3 diagnosed individuals exhibiting abnormalities at the RNA-seq level and 6 cases out of the 12 where no effect on RNA was seen making our solution one of the winning ones. The challenge and our approach highlight the invaluable contributions of an integrative analysis of genetic, transcriptomic, and clinical data to pinpoint the disease-causing gene. The challenge was evaluated using three previously diagnosed individuals in which RNA-seq data proved helpful for diagnostics together with twelve individuals diagnosed solely through DNA analysis. Some of those cases were reported after the challenge by Deshwar et al. Our model was able to prioritize 2 out of the 3 RNA-seq supported cases on the top 3 ranks (Table 1), while reaching a recall of over 50% under the top 100 genes across all 15 cases (Fig. 4).