Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Rare ACTN2 Frameshift Variants Resulting in Protein Extension Cause Distal Myopathy and Hypertrophic Cardiomyopathy through Protein Aggregation.

Distal myopathies are a group of rare, inherited muscular disorders characterized by progressive loss of muscle fibers that begins in the distal parts of arms and legs. Recently, variants in a new disease gene, ACTN2 , have been shown to cause distal myopathy. ACTN2 , a gene previously only associated with cardiomyopathies, encodes alpha-actinin-2, a protein expressed in both cardiac and skeletal sarcomeres. The primary function of alpha-actinin-2 is to link actin and titin to the sarcomere Z-disk. New ACTN2 variants are continuously discovered, however, the clinical significance of many variants remains unknown. Thus, lack of clear genotype-phenotype correlations in ACTN2 -related diseases, actininopathies, persists. Objective: The objective of the study is to characterize the pathomechanisms underlying actininopathies. Methods: Functional characterization in C2C12 cell models of several ACTN2 variants is conducted, including frameshift and missense variants associated with dominant actininopathies. We assess the genotype-phenotype correlations of actininopathies using clinical data from several patients carrying these variants. Results: The results show that the missense variants associated with a recessive form of actininopathy do not cause detectable alpha-actinin-2 aggregates in the cell model. Conversely, dominant frameshift variants causing a protein extension do produce alpha-actinin-2 aggregates. Interpretation: The results suggest that alpha-actinin-2 aggregation is the disease mechanism underlying some dominant actininopathies, and thus we recommend that protein-extending frameshift variants in ACTN2 should be classified as pathogenic. However, this mechanism is likely elicited by only a limited number of variants. Alternative functional characterization methods should be explored to further investigate other molecular mechanisms underlying actininopathies.