New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

Setty, Samarth Thonta; Scott‐Boyer, Marie‐Pier; Cuppens, Tania; Droit, Arnaud

doi:10.3390/ijms23126792

Cited by 10 publications

(3 citation statements)

References 83 publications

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“…[11] A powerful intron analysis tool, spliceAI [13] uses deep residual neural networks to identify splice-relevant mutations or splice mutations that result in aberrant isoforms, is available. SpliceAI could help diagnose rare diseases [14 15] Conversely, whole genome sequencing could be more potent than WES as a powerful genomic diagnostic method for diseases from unknown causes. [16] However, considering various cost aspects, re-analysing WES is still appropriate given its practicality.…”

Section: Introductionmentioning

confidence: 99%

Re-evaluation of whole exome sequencing, including intronic region, in combination with genetic intolerance score for detecting foetal structural anomalies in X-linked disorders

Taniguchi

Hasegawa

Okazaki

et al. 2023

Preprint

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Background: Whole-exome sequencing (WES) is a strong diagnostic tool for foetal structural abnormalities, but the causative gene for more than half abnormalities has not been identified. Therefore, it is essential to improve the diagnostic yield based on WES data. Methods: First, 138 foetuses with structural abnormalities were subjected to conventional WES and copy number variation (CNV) analyses. For undiagnosed cases, we employed a three-step approach for diagnosis. 1) We re-evaluated candidate variants using a loss-of-function observed/expected upper bound fraction (LOEUF) score, 2) we re-evaluated all variants on disease-causing genes for clinically diagnosed cases using spliceAI, and 3) we re-evaluated the rare variants in all low LOEUF scored genes (< 0.35) using spliceAI. Results: We identified molecular diagnoses in 53 of the 138 cases (38.4%) using conventional WES and CNV analysis. For undiagnosed cases, using the first step, we diagnosed two X-linked recessive diseases. Using the second step, we diagnosed Meckel-Gruber syndrome by detecting likely pathogenic intron variant in TMEM67. In the third step, we identified a de novo hemizygous pathogenic variant in one severe hydrops fetalis male, which could cause aberrant splicing in CASK. We found a novel phenotype, hydrops fetalis, in CASK-related X-linked dominant disorder. Moreover, we revealed that the LOEUF score of X-linked disease-causing genes was significantly lower than that of autosomal genes among all OMIM-registered genes. Conclusion: We showed that the evaluation of variants, including introns of WES data, in combination with the LOEUF score, could improve the WES diagnostic yield and detect new candidate genes, especially on chromosome X.

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Section: Introductionmentioning

confidence: 99%

Re-evaluation of whole exome sequencing, including intronic region, in combination with genetic intolerance score for detecting foetal structural anomalies in X-linked disorders

Taniguchi

Hasegawa

Okazaki

et al. 2023

Preprint

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“…The use of artificial intelligence (AI) and, particularly, machine learning (ML) algorithms has raised great interest in recent years due to its potential to uncover complex patterns in genomic data 8 . These ML algorithms have shown the capacity to learn from and act on large, heterogeneous datasets to extract new biological insights, improving the accuracy of the diagnosis of RDs [9][10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

A systematic review on machine learning approaches in the diagnosis of rare genetic diseases

Roman‐Naranjo

Parra-Perez

López-Escámez

2023

Preprint

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Background: The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases. Methods: We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis. Findings: Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations. Conclusions: ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases. Keywords: artificial intelligence, rare diseases, precision medicine, rare variants, DNA-sequencing, genomics

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“…The sheer re-analysis of exomic data after 1–3 years updating of the major disease variants and disease-gene association databases is reported to have increased the diagnosed cases by over 10% ( Wenger et al, 2017 ; Setty et al, 2022 ). Remarkably, a further improvement in the yields could be obtained by reanalysing the data in collaboration with the clinician who made the diagnosis ( Basel-Salmon et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Resources and tools for rare disease variant interpretation

Licata

Via

Turina

et al. 2023

Front. Mol. Biosci.

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Collectively, rare genetic disorders affect a substantial portion of the world’s population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

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New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

Cited by 10 publications

References 83 publications

Re-evaluation of whole exome sequencing, including intronic region, in combination with genetic intolerance score for detecting foetal structural anomalies in X-linked disorders

Re-evaluation of whole exome sequencing, including intronic region, in combination with genetic intolerance score for detecting foetal structural anomalies in X-linked disorders

A systematic review on machine learning approaches in the diagnosis of rare genetic diseases

Resources and tools for rare disease variant interpretation

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