Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases

Abstract

Background Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. Methods We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. Results GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. Conclusions GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

A Formative Study of the Implementation of Whole Genome Sequencing in Northern Ireland

“…A multidisciplinary, collaborative approach exploring additional variants based on prior genotype-phenotype relationships helps maximise diagnostic yield from WGS [ 50 ]. Advances in clinical decision support tools, such as machine learning/artificial intelligence models, will also facilitate variant classification and accelerate rare disease diagnoses [ 51 , 52 ].…”

New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

“…FABRIC GEM works as a complete variant prioritization platform and has been shown to perform better than other solutions such as VAAST [ 39 ], Phevor [ 40 ] and Exomizer [ 41 ]. It has also sped up the interpretation by reducing the time taken to clinically review pathogenic variants within genes by reducing the number of genes in review to an average of just two genes per case instead of tens of genes in the case of competing tools [ 9 , 42 ].…”

“…Although the diagnostic rate has improved due to HTS, because of these challenges, there are vast troves of underexplored genomic datasets, leading to an expensive non-diagnosis and lack of actionable insights for patients. Therefore, more efforts are being made to solve previously unsolved rare diseases by reanalyzing previously generated sequencing data using new methodologies [ 9 , 10 ]. One of the first reanalysis studies showed an increase in diagnostic yield by 18% (absolute diagnostic yield increased from 25.4 to 31.4%) [ 11 ], indicating the possibility of gathering new insights into the underpinnings of rare diseases.…”

Automated Prioritization of Sick Newborns for Whole Genome Sequencing Using Clinical Natural Language Processing and Machine Learning

“…Our clinical dataset consisted of 293 probands who underwent rWGS at Rady Children’s Hospital in San Diego (RCHSD), 84 of which received a molecular diagnosis of Mendelian disorder. The diagnosed individuals represent a real-world population comprised of different Mendelian conditions resulting from diverse modes of disease inheritance and disease-causing genotypes 3,5,14 . To this cohort, we added every NICU admission at RCHSD in the year 2018.…”

“…Natural Language Processing (NLP) is a class of computational methods for generating structured data from unstructured free text. Recent work has begun to explore the utility of using Clinical Natural Language Processing technologies (CNLP) to automatically generate descriptions directly from clinical notes, with several groups demonstrating that rWGS diagnosis rates using CNLP derived descriptions can equal or exceed those obtained using manually compiled ones 12,14 . This is a significant step towards scalability and automation.…”