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Background & Aims: Genetic factors underlie a substantial proportion of paediatric liver diseases. Hereditary liver diseases have considerable genetic heterogeneity and variable clinical manifestations, which bring great challenges to clinical and molecular diagnoses. In this study, we investigated a group of paediatric patients with varying degrees of liver dysfunction using a hierarchical genetic testing strategy. Methods: We first applied a panel encompassing 166 known causal genes of liver disease. We then used exome sequencing (ES) in those patients whose cases remained undiagnosed to identify the genetic aetiology of their symptoms. Results: In total, we enrolled 131 unrelated paediatric patients with liver disease of Chinese Han ethnicity. We first applied targeted gene sequencing of 166 genes to all patients and yielded a diagnostic rate of 35.9% (47 of 131). Eighty‐four patients who remained undiagnosed after target gene sequencing were subjected to ES. As a result, eight (8/84, 9.5%) of them obtained molecular diagnoses, including four patients suspected of abnormal bilirubin metabolism and four idiopathic cases. Non‐typical genetic findings, including digenic inheritance and dual molecular diagnosis, were also identified. Through a comprehensive assessment of novel candidate variants of uncertain disease association, 11 patients of the remaining undiagnosed patients were able to obtain likely molecular diagnoses. Conclusions: Our study presents evidence for the diagnostic utility of sequential genetic testing in a cohort of patients with paediatric liver disease. Our findings expand the understanding of the phenotypic and mutational spectrum underlying this heterogeneous group of diseases. Lay Summary: Genetic factors underlie a substantial proportion of paediatric liver diseases. The genetic architecture of paediatric liver diseases was insufficiently described by previous research. We demonstrated the utility of hierarchical genetic testing, i.e., gene panel sequencing supplemented by exome sequencing, in paediatric liver disease. Additionally, we identified clinically relevant candidate variants in 11 patients highly suspected of having a specific liver disease but without a definitive molecular diagnosis. The hierarchical genetic testing provides a cost‐effective and accurate genetic diagnosis in paediatric liver disease. It also provides opportunities for the discovery of novel candidate variants. [ABSTRACT FROM AUTHOR]

In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous filtering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapid identification of causative genes. Since the interpretations of ES data require comprehensive clinical analyses, taking clinical expertise into consideration can speed the molecular diagnoses of Mendelian disorders. To leverage clinical expertise to prioritize candidate genes, we developed PhenoApt, a phenotype-driven gene prioritization tool that allows users to assign a customized weight to each phenotype, via a machine-learning algorithm. Using the ability to rank causative genes in top-10 lists as an evaluation metric, baseline analysis demonstrated that PhenoApt outperformed previous phenotype-driven gene prioritization tools by a relative increase of 22.7%–140.0% in three independent, real-world, multi-center cohorts (cohort 1, n = 185; cohort 2, n = 784; and cohort 3, n = 208). Additional trials showed that, by adding weights to clinical indications, which should be explained by the causative gene, PhenoApt performance was improved by a relative increase of 37.3% in cohort 2 (n = 471) and 21.4% in cohort 3 (n = 208). Moreover, PhenoApt could assign an intrinsic weight to each phenotype based on the likelihood of its being a Mendelian trait using term frequency-inverse document frequency techniques. When clinical indications were assigned with intrinsic weights, PhenoApt performance was improved by a relative increase of 23.7% in cohort 2 and 15.5% in cohort 3. For the integration of PhenoApt into clinical practice, we developed a user-friendly website and a command-line tool. [ABSTRACT FROM AUTHOR]

Background: We previously reported a novel clinically distinguishable subtype of congenital scoliosis (CS), namely, TBX6-associated congenital scoliosis (TACS). We further developed the TBX6-associated CS risk score (TACScore), a multivariate phenotype-based model to predict TACS according to the patient's clinical manifestations. In this study, we aimed to evaluate whether using the TACScore as a screening method prior to performing whole-exome sequencing (WES) is more cost-effective than using WES as the first-line genetic test for CS.Methods: We retrospectively collected the molecular data of 416 CS patients in the Deciphering disorders Involving Scoliosis and COmorbidities (DISCO) study. A decision tree was constructed to estimate the cost and the diagnostic time required for the two alternative strategies (TACScore versus WES). Bootstrapping simulations and sensitivity analyses were performed to examine the distributions and robustness of the estimates. The economic evaluation considered both the health care payer and the personal budget perspectives.Results: From the health care payer perspective, the strategy of using the TACScore as the primary screening method resulted in an average cost of $1074.2 (95%CI: $1044.8 to $1103.5) and an average diagnostic duration of 38.7d (95%CI: 37.8d to 39.6d) to obtain a molecular diagnosis for each patient. In contrast, the corresponding values were $1169.6 (95%CI: $1166.9 to $1172.2) and 41.4d (95%CI: 41.1d to 41.7d) taking WES as the first-line test (P < 0.001). From the personal budget perspective, patients who were predicted to be positive by the TACScore received a result with an average cost of $715.1 (95%CI: $594.5 to $835.7) and an average diagnostic duration of 30.4d (95%CI: 26.3d to 34.6d). Comparatively, the strategy of WES as the first-line test was estimated to have significantly longer diagnostic time with an average of 44.0d (95%CI: 43.2d to 44.9d), and more expensive with an average of $1193.4 (95%CI: $1185.5 to $1201.3) (P < 0.001). In 100% of the bootstrapping simulations, the TACScore strategy was significantly less costly and more time-saving than WES. The sensitivity analyses revealed that the TACScore strategy remained cost-effective even when the cost per WES decreased to $8.8.Conclusions: This retrospective study provides clinicians with economic evidence to integrate the TACScore into clinical practice. The TACScore can be considered a cost-effective tool when it serves as a screening test prior to performing WES. [ABSTRACT FROM AUTHOR]