Integrated in silico and experimental assessment of disease relevance of PCDH19 missense variants.

PCDH19 is a nonclustered protocadherin molecule involved in axon bundling, synapse function, and transcriptional coregulation. Pathogenic variants in PCDH19 cause infantile-onset epilepsy known as PCDH19-clustering epilepsy or PCDH19-CE. Recent advances in DNA-sequencing technologies have led to a significant increase in the number of reported PCDH19-CE variants, many of uncertain significance. We aimed to determine the best approaches for assessing the disease relevance of missense variants in PCDH19. The application of the American College of Medical Genetics and Association for Molecular Pathology (ACMG-AMP) guidelines was only 50% accurate. Using a training set of 322 known benign or pathogenic missense variants, we identified MutPred2, MutationAssessor, and GPP as the best performing in silico tools. We generated a protein structural model of the extracellular domain and assessed 24 missense variants. We also assessed 24 variants using an in vitro reporter assay. A combination of these tools was 93% accurate in assessing known pathogenic and benign PCDH19 variants. We increased the accuracy of the ACMG-AMP classification of 45 PCDH19 variants from 50% to 94%, using these tools. In summary, we have developed a robust toolbox for the assessment of PCDH19 variant pathogenicity to improve the accuracy of PCDH19-CE variant classification.

Genotype-phenotype correlation in 18 Egyptian patients with glutaric acidemia type I.

Glutaric acidemia I (GAI) is an autosomal recessive metabolic disease caused by a deficiency of glutaryl-CoA dehydrogenase enzyme (GCDH). Patients with GAI are characterized by macrocephaly, acute encephalitis-like crises, dystonia and frontotemporal atrophy. In this study, we investigated 18 Egyptian patients that were diagnosed with GAI based on their clinical, neuroradiological, and biochemical profiles. Of the 18 patients, 16 had developmental delay and/or regression, dystonia was prominent in 75% of the cases, and three patients died. Molecular genetics analysis identified 14 different mutations in the GCDH gene in the 18 patients, of the 14 mutations, nine were missense, three were in the 3'-Untranslated Region (3'-UTR), one was nonsense, and one was a silent mutation. Four novel mutations were identified (c.148 T > A; p.Trp50Arg, c.158C > A; p.Pro53Gln, c.1284C > G; p.Ile428Met, and c.1189G > T; p.Glu397*) that were all absent in 300 normal chromosomes. The 3'-UTR mutation (c.*165A > G; rs8012), was the most frequent mutation observed (0.5; 18/36), followed by the most common mutation among Caucasian patients (p.Arg402Trp; rs121434369) with allele frequency of 0.36 (13/36), and the 3'-UTR mutation (c.*288G > T; rs9384, 0.22; 8/16). The p.Arg257Gln mutation was found with allele frequency of ~0.17 (6/36). The marked homozygosity observed in our patients is probably due to the high level of consanguinity that is observed in 100% of the cases. We used nine in silico prediction tools to predict the pathogenicity (SIFT, PhD-SNP, SNAP, Meta-SNP, PolyPhen2, and Align GVGD) and protein stability (I-Mutant2.0, Mupro, and istable) of the nine missense mutants. The mutant p.Arg402Trp was predicted to be most deleterious by all the six pathogenicity prediction tools and destabilizing by all the three-stability prediction tools, and highly conserved by the ConSurf server. Using the clinical, biochemical, family history of the 18 patients, and the in silico analysis of the missense mutations, our study showed a mix of conclusive and inconclusive genotype-phenotype correlations among our patient's cohort and suggests the usefulness of using various sophisticated computational analysis to be utilized for future variant classifications in the genetic clinics.

A systematic approach to assessing the clinical significance of genetic variants.

Molecular genetic testing informs diagnosis, prognosis, and risk assessment for patients and their family members. Recent advances in low-cost, high-throughput DNA sequencing and computing technologies have enabled the rapid expansion of genetic test content, resulting in dramatically increased numbers of DNA variants identified per test. To address this challenge, our laboratory has developed a systematic approach to thorough and efficient assessments of variants for pathogenicity determination. We first search for existing data in publications and databases including internal, collaborative and public resources. We then perform full evidence-based assessments through statistical analyses of observations in the general population and disease cohorts, evaluation of experimental data from in vivo or in vitro studies, and computational predictions of potential impacts of each variant. Finally, we weigh all evidence to reach an overall conclusion on the potential for each variant to be disease causing. In this report, we highlight the principles of variant assessment, address the caveats and pitfalls, and provide examples to illustrate the process. By sharing our experience and providing a framework for variant assessment, including access to a freely available customizable tool, we hope to help move towards standardized and consistent approaches to variant assessment.

aRgus: Multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment.

The widespread use of high-throughput sequencing techniques is leading to a rapidly increasing number of disease-associated variants of unknown significance and candidate genes. Integration of knowledge concerning their genetic, protein as well as functional and conservational aspects is necessary for an exhaustive assessment of their relevance and for prioritization of further clinical and functional studies investigating their role in human disease. To collect the necessary information, a multitude of different databases has to be accessed and data extraction from the original sources commonly is not user-friendly and requires advanced bioinformatics skills. This leads to a decreased data accessibility for a relevant number of potential users such as clinicians, geneticist, and clinical researchers. Here, we present aRgus (https://argus.urz.uni-heidelberg.de/), a standalone webtool for simple extraction and intuitive visualization of multi-layered gene, protein, variant, and variant effect prediction data. aRgus provides interactive exploitation of these data within seconds for any known gene of the human genome. In contrast to existing online platforms for compilation of variant data, aRgus complements visualization of chromosomal exon-intron structure and protein domain annotation with ClinVar and gnomAD variant distributions as well as position-specific variant effect prediction score modeling. aRgus thereby enables timely assessment of protein regions vulnerable to variation with single amino acid resolution and provides numerous applications in variant and protein domain interpretation as well as in the design of in vitro experiments.

Joint estimation and imputation of variant functional effects using high throughput assay data.

Deep mutational scanning assays enable the functional assessment of variants in high throughput. Phenotypic measurements from these assays are broadly concordant with clinical outcomes but are prone to noise at the individual variant level. We develop a framework to exploit related measurements within and across experimental assays to jointly estimate variant impact. Drawing from a large corpus of deep mutational scanning data, we collectively estimate the mean functional effect per AA residue position within each gene, normalize observed functional effects by substitution type, and make estimates for individual allelic variants with a pipeline called FUSE (Functional Substitution Estimation). FUSE improves the correlation of functional screening datasets covering the same variants, better separates estimated functional impacts for known pathogenic and benign variants (ClinVar BRCA1, p=2.24×10-51), and increases the number of variants for which predictions can be made (2,741 to 10,347) by inferring additional variant effects for substitutions not experimentally screened. For UK Biobank patients who carry a rare variant in TP53, FUSE significantly improves the separation of patients who develop cancer syndromes from those without cancer (p=1.77×10-6). These approaches promise to improve estimates of variant impact and broaden the utility of screening data generated from functional assays.

Utilizing iVariantGuide for Variant Assessment of Next-Generation Sequencing.

Molecular genetic testing provides the capability for personalized prediction, diagnosis, and pharmacological treatments of disease and disorders. Variant assessment of next-generation sequencing (NGS) is a crucial component of genetic testing for clinicians to counsel patients on risk and management. The iVariantGuide application is a dynamic Web-based application made for the tertiary analysis of NGS. Along with variant assessment, iVariantGuide provides a unique interactive pathway impact analysis of genetic variants, as well as a unique Gene Ontology (GO) analysis. Here we provide a step-by-step guide on how to utilize iVariantGuide, employing a publicly available NGS dataset consisting of a cohort of germline DNAs from high-risk serous ovarian cancer (OVCA) patients. The application will be used to exhibit the ease in filtering down to a set of compelling novel variants and their impact on biological pathways and GO terms. © 2019 by John Wiley & Sons, Inc.