VarChat: the generative AI assistant for the interpretation of human genomic variations.

MOTIVATION: In the modern era of genomic research, the scientific community is witnessing an explosive growth in the volume of published findings. While this abundance of data offers invaluable insights, it also places a pressing responsibility on genetic professionals and researchers to stay informed about the latest findings and their clinical significance. Genomic variant interpretation is currently facing a challenge in identifying the most up-to-date and relevant scientific papers, while also extracting meaningful information to accelerate the process from clinical assessment to reporting. Computer-aided literature search and summarization can play a pivotal role in this context. By synthesizing complex genomic findings into concise, interpretable summaries, this approach facilitates the translation of extensive genomic datasets into clinically relevant insights. RESULTS: To bridge this gap, we present VarChat (varchat.engenome.com), an innovative tool based on generative AI, developed to find and summarize the fragmented scientific literature associated with genomic variants into brief yet informative texts. VarChat provides users with a concise description of specific genetic variants, detailing their impact on related proteins and possible effects on human health. In addition, VarChat offers direct links to related scientific trustable sources, and encourages deeper research. AVAILABILITY AND IMPLEMENTATION: varchat.engenome.com.

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.

Molecular characterization of diffuse large B-cell lymphomas associated with hepatitis C virus infection.

Hepatitis C virus (HCV)-associated diffuse large B-cell lymphoma (DLBCL) displays peculiar clinicopathological characteristics, but its molecular landscape is not fully elucidated. In this study, we investigated the clinicopathological and molecular features of 54 patients with HCV-associated DLBCL. The median age was 71 years. An underlying marginal zone lymphoma component was detected in 14.8% of cases. FISH analysis showed rearrangements involving BCL6 in 50.9% of cases, MYC in 11.3% and BCL2 in 3.7%. Lymph2Cx-based assay was successful in 38 cases, recognizing 16 cases (42.1%) as ABC and 16 cases as GCB subtypes, while six resulted unclassified. ABC cases exhibited a higher lymphoma-related mortality (LRM). Next-generation sequencing analysis showed mutations in 158/184 evaluated genes. The most frequently mutated genes were KMT2D (42.6%), SETD1B (33.3%), RERE (29.4%), FAS and PIM1 (27.8%) and TBL1XR1 (25.9%). A mutation in the NOTCH pathway was detected in 25.9% of cases and was associated with worst LRM. Cluster analysis by LymphGen classified 29/54 cases within definite groups, including BN2 in 14 (48.2%), ST2 in seven (24.2%) and MCD and EZB in four each (13.8%). Overall, these results indicate a preferential marginal zone origin for a consistent subgroup of HCV-associated DLBCL cases and suggest potential implications for molecularly targeted therapies.