Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases

Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes

PURPOSE

This study aimed to provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids program.

METHODS

Extensive analyses of 960 families with suspected genetic disorders included short-read exome sequencing and short-read genome sequencing (srGS); PacBio HiFi long-read genome sequencing (HiFi-GS); variant calling for single nucleotide variants (SNV), structural variant (SV), and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants, and pedigrees were stored in PhenoTips database, with data sharing through controlled access the database of Genotypes and Phenotypes.

RESULTS

Diagnostic rates ranged from 11% in patients with prior negative genetic testing to 34.5% in naive patients. Incorporating SVs from genome sequencing added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs compared with srGS. Variants and genes of unknown significance remain the most common finding (58% of nondiagnostic cases).

CONCLUSION

Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated using HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation and by providing HiFi variant (SNV/SV) resources from >1000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases

It’s challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient’s genotype and phenotype information, or phenotype information only as input to rank the candidate’s pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

Human Phenotype Ontology (HPO)-based analysis has become standard for genomic diagnostics of rare diseases. Current algorithms use a variety of semantic and statistical approaches to prioritize the typically long lists of genes with candidate pathogenic variants. These algorithms do not provide robust estimates of the strength of the predictions beyond the placement in a ranked list, nor do they provide measures of how much any individual phenotypic observation has contributed to the prioritization result. However, given that the overall success rate of genomic diagnostics is only around 25%-50% or less in many cohorts, a good ranking cannot be taken to imply that the gene or disease at rank one is necessarily a good candidate. Here, we present an approach to genomic diagnostics that exploits the likelihood ratio (LR) framework to provide an estimate of (1) the posttest probability of candidate diagnoses, (2) the LR for each observed HPO phenotype, and (3) the predicted pathogenicity of observed genotypes. LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) placed the correct diagnosis within the first three ranks in 92.9% of 384 case reports comprising 262 Mendelian diseases, and the correct diagnosis had a mean posttest probability of 67.3%. Simulations show that LIRICAL is robust to many typically encountered forms of genomic and phenomic noise. In summary, LIRICAL provides accurate, clinically interpretable results for phenotype-driven genomic diagnostics.

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 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.

Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks

BACKGROUND

One of the major goals of genomic medicine is the identification of causal genomic variants in a patient and their relation to the observed clinical phenotypes. Prioritizing the genomic variants by considering only the genotype information usually identifies a few hundred potential variants. Narrowing it down further to find the causal disease genes and relating them to the observed clinical phenotypes remains a significant challenge, especially for rare diseases.

METHODS

We propose a phenotype-driven gene prioritization approach using heterogeneous networks in the context of rare diseases. Towards this, we first built a heterogeneous network consisting of ontological associations as well as curated associations involving genes, diseases, phenotypes and pathways from multiple sources. Motivated by the recent progress in spectral graph convolutions, we developed a graph convolution based technique to infer new phenotype-gene associations from this initial set of associations. We included these inferred associations in the initial network and termed this integrated network HANRD (Heterogeneous Association Network for Rare Diseases). We validated this approach on 230 recently published rare disease clinical cases using the case phenotypes as input.

RESULTS

When HANRD was queried with the case phenotypes as input, the causal genes were captured within Top-50 for more than 31% of the cases and within Top-200 for more than 56% of the cases. The results showed improved performance when compared to other state-of-the-art tools.

CONCLUSIONS

In this study, we showed that the heterogeneous network HANRD, consisting of curated, ontological and inferred associations, helped improve causal gene identification in rare diseases. HANRD allows future enhancements by supporting incorporation of new entity types and additional information sources.