CAVaLRi: An Algorithm for Rapid Identification of Diagnostic Germline Variation

Clinical exome and genome sequencing (ES/GS) have become indispensable diagnostic tools for rare genetic diseases (RGD). However, the interpretation of ES/GS presents a substantial operational challenge in clinical settings. Test interpretation requires the review of hundreds of genetic variants, a task that has become increasingly challenging given the rising use of ES/GS. In response, we present Clinical Assessment of Variants by Likelihood Ratios (CAVaLRi), which employ a modified likelihood ratio (LR) framework to assign diagnostic probabilities to candidate germline disease genes. CAVaLRi models aspects of the clinical variant assessment process, taking into consideration the predicted impact of the variant, the proband and parental genotypes, and the proband's clinical characteristics. It also factors in computational phenotype noise and weighs the relative significance of genotype, phenotype, and variant segregation information. We trained and tested CAVaLRi on variant and phenotype data from an internal cohort of 655 clinical ES cases. For validation, CAVaLRi's performance was benchmarked against four leading gene prioritization algorithms (Exomiser's hiPHIVE and PhenIX prioritizers, LIRICAL, and XRare) using a distinct cohort of 12,832 ES cases. Our findings reveal that CAVaLRi significantly outperforms its counterparts when clinician-curated phenotype sets are used, as evidenced by its superior precision-recall curve (PR AUC: 0.701) and average diagnostic gene rank (1.59). Notably, even when substituting highly focused clinician-curated phenotype sets with large and potentially nonspecific computationally derived phenotypes, CAVaLRi retains its precision (PR AUC: 0.658; diagnostic gene average rank: 1.68) and markedly outperforms other tools. In a large, heterogeneous validation cohort, CAVaLRi stood out as the most precise prioritization algorithm (PR AUC: 0.335; average diagnostic rank: 1.91). In conclusion, CAVaLRi presents a robust solution for prioritizing diagnostic genes, surpassing current methods. It demonstrates resilience to noisy, computationally-derived phenotypes, providing a scalable strategy to help labs focus on the most diagnostically relevant variants, thus addressing the growing demand for ES/GS interpretation.