Evaluation of a whole‐exome sequencing pipeline and benchmarking of causal germline variant prioritizers

Tosco-Herrera, Eva; Muñoz-Barrera, Adrián; Jáspez, David; Rubio-Rodríguez, Luis A.; Mendoza-Alvarez, Alejandro; Rodríguez‐Pérez, Hector; Jou, Jonathan; Íñigo-Campos, Antonio; Corrales, Almudena; Ciuffreda, Laura; MártinezBugallo, F.; Prieto-Morín, Carol; García‐Olivares, Víctor; González-Montelongo, Rafaela; Lorenzo-Salazar, José M.; Marcelino-Rodríguez, Itahisa; Flores, Carlos

doi:10.1002/humu.24459

Cited by 10 publications

(7 citation statements)

References 46 publications

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“…A research team in Spain benchmarked causal-gene prioritization tools with WES data of 61 unrelated singleton cases 41 , and a comparison between their study and ours was made (Supplementary Sect. S5 ).…”

Section: Discussionmentioning

confidence: 99%

Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases

Yuan,

Su,

Wang

et al. 2024

Sci Rep

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Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3–91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.

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Section: Discussionmentioning

confidence: 99%

Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases

Yuan,

Su,

Wang

et al. 2024

Sci Rep

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“…Based on the benchmark tests and code availability mentioned from the previous literature [ 54 ], we selected Exomiser and LIRICAL as benchmark models to compare the variant prioritization performance. Exomiser version 13.0.1 with data version 2109 was executed using Java version 18.0.1.1.…”

Section: Methodsmentioning

confidence: 99%

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

Kim,

Woo

et al. 2024

Hum Genomics

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Background In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions. Results We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data. Conclusions 3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

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“…Whole-genome sequencing (WGS) of DNA from a test sub-cohort of 100 BA cases, all recruited at CHP and analysis of rare variants was performed using the GD Cross variant ranking and prioritization algorithm as described previously by us and others 33 – 35 (see supplementary methods ). The input diagnosis for each BA case was biliary atresia.…”

Section: Methodsmentioning

confidence: 99%

Biliary atresia is associated with polygenic susceptibility in ciliogenesis and planar polarity effector genes

Glessner

Ningappa

Ngo

et al. 2023

Journal of Hepatology

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Evaluation of a whole‐exome sequencing pipeline and benchmarking of causal germline variant prioritizers

Cited by 10 publications

References 46 publications

Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases

Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

Biliary atresia is associated with polygenic susceptibility in ciliogenesis and planar polarity effector genes

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