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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., (© 2024. The Author(s).)

Background: A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting., Methods: Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds., Results: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized 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 phenotype overlap with asparagine synthetase deficiency., Conclusions: By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation., Competing Interests: Competing interests. Authors S.Z., I.L., E.R., P.M., and R.B., own shares of enGenome srl. Authors F.D.P. and G.N. are employees of enGenome srl. Authors T.J., R.S., S.G.V., N.S., A.R., U.S., N.T., are employees of TCS Ltd. Authors P.J.C., C.K., K.N., and P.S. are employees of Invitae Ltd. H.L.R. receives support from Illumina and Microsoft for rare disease gene discovery and diagnosis. A.O’D-L. is a member of the scientific advisory board for Congenica Inc and the Simons Foundation SPARK for Autism study and co-chairs the clinical advisory board for CAGI. S.E.B receives support at UC Berkeley from a research agreement from TCS. All other authors report no competing interests.

SARS-CoV-2 rapidly spread from China until it was defined a pandemic by WHO in March 2020. Related scientific papers have rapidly extended information regarding the diagnosis, treatment and epidemiology of COVID-19 infection. To date, no vaccine or definitive treatment is available to defeat the virus and therapies are mainly based on existing drugs used to treat other conditions. Existing therapies used in several clinical trials work by affecting the biology of COVID-19 and/or counteracting the harmful host excessive immune response. Here, we have reviewed 526 ongoing clinical trials for COVID-19 to provide a perspective on the first 6 months of global efforts to identify an effective therapy. The drugs most actively tested in various centers include hydroxychloroquine, ritonavir, azithromycin, tocilizumab, lopinavir chloroquine and ivermectin. Our analysis shows that most clinical trials focus on a small number of candidate drugs (namely hydroxychloroquine and chloroquine representing 25% of total clinical trials) while underestimating the potential of other promising drugs. A global coordination in clinical trial management could avoid duplications and increase the effectiveness of the response to the global challenge., (Copyright © 2020 Idda, Soru and Floris.)