Phenotype-driven gene target definition in clinical genome-wide sequencing data interpretation

Maver, Aleš; Lovrečić, Luca; Volk, Marija; Rudolf, G. de M.; Writzl, Karin; Blatnik, Ana; Hodžić, Alenka; Peterlin, Borut

doi:10.1038/gim.2016.22

Cited by 45 publications

(41 citation statements)

References 17 publications

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“…According to current European Society of Human Genetics recommendations for whole genome testing in health care, it is preferable to use a targeted gene panel approach first to avoid unsolicited findings or findings that cannot be interpreted . However, the selection of appropriate target genes is challenging and gene panels carry a risk of missing causative variation due to their restrictiveness . Indeed, there are many epilepsy gene panels, with a huge diversity, currently used for diagnostic purposes.…”

Section: Discussionmentioning

confidence: 99%

“…This was developed using a manual search through OMIM and PubMed databases and a Web tool for phenotype‐based gene panel generation (http://kimg.eu/generator). In our clinical practice, we have used this database‐based bioinformatic panel of 862 genes as a primary filtering system for exome sequencing data and time‐efficient exclusion of both false positive (by filtering out non‐epilepsy‐associated genes) and false negative (by inclusion of epilepsy‐associated genes that could be missed in the whole range of variants) findings. Even though the vast majority of causative variants could be captured by this gene panel, 3 of 42 variants (7%; SOX5 , SCN3A and UBA5 ) were identified during the second stage of untargeted interpretation of exome variants.…”

Section: Discussionmentioning

confidence: 99%

“…At first, a bioinformatic panel of 862 epilepsy‐ or seizure‐associated genes was generated by searching through OMIM (http://www.ncbi.nlm.nih.gov/omim) and PubMed (http://www.ncbi.nlm.nih.gov/pubmed) databases, and by using a Web tool for phenotype‐based gene panel generation (http://kimg.eu/generator). Genes associated with epilepsy through studies in model animals or through association studies were excluded and only genes associated with human monogenic diseases involving epilepsy or seizures were selected. Finally, a database of human monogenic diseases involving epilepsy and seizures, EpiGene, was comprised (http://kimg.eu/en/tools/epigene-database/).…”

Section: Methodsmentioning

confidence: 99%

“…40 However, the selection of appropriate target genes is challenging and gene panels carry a risk of missing causative variation due to their restrictiveness. 18 Indeed, there are many epilepsy gene panels, with a huge diversity, currently used for diagnostic purposes. projections.…”

Section: Methodsmentioning

confidence: 99%

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Diagnostic exome sequencing of syndromic epilepsy patients in clinical practice

et al. 2018

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Although genetic revolution of recent years has vastly expanded a list of genes implicated in epilepsies, complex architecture of epilepsy genetics is still largely unknown, consequently, universally accepted workflows for epilepsy genetic testing in a clinical practice are missing. We present a comprehensive NGS-based diagnostic approach addressing both the clinical and genetic heterogeneity of disorders involving epilepsy or seizures. A bioinformatic panel of 862 epilepsy- or seizure-associated genes was applied to Mendeliome (4813 genes) or whole-exome sequencing data as a first stage, while the second stage included untargeted variant interpretation. Eighty-six consecutive patients with epilepsy or seizures associated with neurodevelopmental disorders and/or congenital malformations were investigated. Of the 86 probands, 42 harbored pathogenic and likely pathogenic variants, giving a diagnostic yield of 49%. Two patients were diagnosed with pathogenic copy number variations and 2 had causative mitochondrial DNA variants. Eleven patients (13%) were diagnosed with diseases with specific treatments. Besides, genomic approach in diagnostics had multiple additional benefits due to mostly non-specific, overlapping, not full-blown phenotypes and abilities to diagnose novel and ultra rare epilepsy-associated diseases. Likely pathogenic variants were identified in SOX5 gene, not previously associated with epilepsy, and UBA5, a recently associated with epilepsy gene.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Diagnostic exome sequencing of syndromic epilepsy patients in clinical practice

et al. 2018

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“…Using HPO to generate individualized phenotype-driven gene panels for diagnostics led to an increase in the diagnostic yield (33). The ThromboGenomics Consortium reported that computational prioritization of candidate rare variants identified in patients with bleeding, thrombotic or platelet disorders using HPO-coded phenotypes assigned the highest scores to pathogenic or likely pathogenic variants in 85% of cases, demonstrating that HPO-based algorithms can make multidisciplinary diagnostic meetings more efficient (34).…”

Section: Clinical Utilitymentioning

confidence: 99%

The Human Phenotype Ontology in 2017

et al. 2016

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Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

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Modeling age‐specific facial development in Williams–Beuren‐, Noonan‐, and 22q11.2 deletion syndromes in cohorts of Czech patients aged 3–18 years: A cross‐sectional three‐dimensional geometric morphometry analysis of their facial gestalt

Čaplovičová

Moslerová

Dupej

et al. 2018

American J of Med Genetics Pt A

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Three-dimensional (3D) virtual facial models facilitate genotype-phenotype correlations and diagnostics in clinical dysmorphology. Within cross-sectional analysis of both genders we evaluated facial features in representative cohorts of Czech patients with Williams-Beuren-(WBS; 12 cases), Noonan-(NS; 14), and 22q11.2 deletion syndromes (22q11.2DS; 20) and compared their age-related developmental trajectories to 21 age, sex and ethnically matched controls in 3-18 years of age. Using geometric morphometry statistically significant differences in facial morphology were found in all cases compared to controls. The dysmorphic features observed in WBS were specific and manifested in majority of cases. During ontogenesis, dysmorphic features associated with increased facial convexity become more pronounced whereas other typical features remained relatively stable. Dysmorphic features observed in NS cases were mostly apparent during childhood and gradually diminished with age. Facial development had a similar progress as in controls, while there has been increased growth of patients' nose and chin in adulthood. Facial characteristics observed in 22q11.2DS, except for hypoplastic alae nasi, did not correspond with the standard description of its facial phenotype because of marked facial heterogeneity of this clinical entity. Because of the sensitivity of 3D facial morphometry we were able to reach statistical significance even in smaller retrospective patient cohorts, which proves its clinical utility within the routine setting. K E Y W O R D S 22q11.2 deletion syndrome, 3D facial gestalt analysis, 3D imaging, dysmorphology, facial shape and size, geometric morphometry, Noonan syndrome, ontogenetic trajectory, Williams-Beuren syndrome

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Phenotype-driven gene target definition in clinical genome-wide sequencing data interpretation

Abstract: In conclusion, we present a novel approach to a phenotype-driven diagnostic process of genome scale sequencing data that harnesses the sensitivity of these approaches while restricting the analysis to genes relevant to clinical presentation in patient.Genet Med 18 11, 1102-1110.

Cited by 45 publications

References 17 publications

Diagnostic exome sequencing of syndromic epilepsy patients in clinical practice

Diagnostic exome sequencing of syndromic epilepsy patients in clinical practice

The Human Phenotype Ontology in 2017

Modeling age‐specific facial development in Williams–Beuren‐, Noonan‐, and 22q11.2 deletion syndromes in cohorts of Czech patients aged 3–18 years: A cross‐sectional three‐dimensional geometric morphometry analysis of their facial gestalt

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