1. Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project
- Author
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Sarah L. Stenton, Melanie C. O’Leary, Gabrielle Lemire, Grace E. VanNoy, Stephanie DiTroia, Vijay S. Ganesh, Emily Groopman, Emily O’Heir, Brian Mangilog, Ikeoluwa Osei-Owusu, Lynn S. Pais, Jillian Serrano, Moriel Singer-Berk, Ben Weisburd, Michael W. Wilson, Christina Austin-Tse, Marwa Abdelhakim, Azza Althagafi, Giulia Babbi, Riccardo Bellazzi, Samuele Bovo, Maria Giulia Carta, Rita Casadio, Pieter-Jan Coenen, Federica De Paoli, Matteo Floris, Manavalan Gajapathy, Robert Hoehndorf, Julius O. B. Jacobsen, Thomas Joseph, Akash Kamandula, Panagiotis Katsonis, Cyrielle Kint, Olivier Lichtarge, Ivan Limongelli, Yulan Lu, Paolo Magni, Tarun Karthik Kumar Mamidi, Pier Luigi Martelli, Marta Mulargia, Giovanna Nicora, Keith Nykamp, Vikas Pejaver, Yisu Peng, Thi Hong Cam Pham, Maurizio S. Podda, Aditya Rao, Ettore Rizzo, Vangala G. Saipradeep, Castrense Savojardo, Peter Schols, Yang Shen, Naveen Sivadasan, Damian Smedley, Dorian Soru, Rajgopal Srinivasan, Yuanfei Sun, Uma Sunderam, Wuwei Tan, Naina Tiwari, Xiao Wang, Yaqiong Wang, Amanda Williams, Elizabeth A. Worthey, Rujie Yin, Yuning You, Daniel Zeiberg, Susanna Zucca, Constantina Bakolitsa, Steven E. Brenner, Stephanie M. Fullerton, Predrag Radivojac, Heidi L. Rehm, and Anne O’Donnell-Luria
- Subjects
Rare disease ,Genome sequencing ,Genome interpretation ,Variant prioritization ,Best practices ,Medicine ,Genetics ,QH426-470 - Abstract
Abstract 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.
- Published
- 2024
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