Mendel,MD: A user-friendly open-source web tool for analyzing WES and WGS in the diagnosis of patients with Mendelian disorders

<div><p>Whole exome and whole genome sequencing have both become widely adopted methods for investigating and diagnosing human Mendelian disorders. As pangenomic agnostic tests, they are capable of more accurate and agile diagnosis compared to traditional sequencing methods. This article describes new software called Mendel,MD, which combines multiple types of filter options and makes use of regularly updated databases to facilitate exome and genome annotation, the filtering process and the selection of candidate genes and variants for experimental validation and possible diagnosis. This tool offers a user-friendly interface, and leads clinicians through simple steps by limiting the number of candidates to achieve a final diagnosis of a medical genetics case. A useful innovation is the “1-click” method, which enables listing all the relevant variants in genes present at OMIM for perusal by clinicians. Mendel,MD was experimentally validated using clinical cases from the literature and was tested by students at the Universidade Federal de Minas Gerais, at GENE–Núcleo de Genética Médica in Brazil and at the Children’s University Hospital in Dublin, Ireland. We show in this article how it can simplify and increase the speed of identifying the culprit mutation in each of the clinical cases that were received for further investigation. Mendel,MD proved to be a reliable web-based tool, being open-source and time efficient for identifying the culprit mutation in different clinical cases of patients with Mendelian Disorders. It is also freely accessible for academic users on the following URL: <a href="https://mendelmd.org/" target="_blank">https://mendelmd.org</a>.</p></div

Upload System.

<p>This figure shows the interface for submission of VCF files in the system using a library called JQuery File-Upload. As soon the user selects the VCF files from his computer, the upload starts automatically and there is an estimated time showing that it is updated constantly until the completion of the upload.</p

Filter analysis.

<p>Using this method, a user can quickly define multiple options in order to exclude variants according to different criteria. This enables you to quickly repeat the filtering step using different options to adjust your analysis according to your preferences.</p

Feature grid of tools for human genome annotation and analysis.

<p>This table shows the comparison of multiple tools and platforms that can be used for doing variant annotation, prioritization and clinical genome analysis.</p

VCF summary.

<p>Here you can see some metrics for each VCF file such as the total number of variants and the number of novel variants.</p

Pynnotator annotation framework.

<p>This figure shows the processes we perform on each VCF file after a user uploads it to the system. First we perform a vcf-validation and a sanity check to make sure the file is ready to be annotated. After it, we send each checked VCF file to be annotated in parallel by SNPEFF, SNPSIFT, VEP, Decipher, and two other python scripts we developed in-house. The first one is capable of using other VCF files to annotate a VCF file so we use the VCFs from 1000Genomes, dbSNP and NHLBI GO Exome Sequencing Project to annotate a VCF and the second python script uses data from dbNSFP to add functional annotation and prediction scores to our annotation. After merging the results of all tasks we compress the file and prepare it to be inserted to the database.</p

VCF comparison.

<p>This method searches for variants at common positions in samples (Ec. Two VCF files), and compares the genotypes in all these common positions. Here we show that two siblings had 84.2% of genotypes in common in the positions their VCF have in common.</p

1p13.2 deletion displays clinical features overlapping Noonan syndrome, likely related to NRAS gene haploinsufficiency

Abstract Deletion-induced hemizygosity may unmask deleterious autosomal recessive variants and be a cause of the phenotypic variability observed in microdeletion syndromes. We performed complete exome sequencing (WES) analysis to examine this possibility in a patient with 1p13.2 microdeletion. Since the patient displayed clinical features suggestive of Noonan Syndrome (NS), we also used WES to rule out the presence of pathogenic variants in any of the genes associated with the different types of NS. We concluded that the clinical findings could be attributed solely to the 1p13.2 haploinsufficiency. Retrospective analysis of other nine reported patients with 1p13.2 microdeletions showed that six of them also presented some characteristics of NS. In all these cases, the deleted segment included the NRAS gene. Gain-of-function mutations of NRAS gene are causally related to NS type 6. Thus, it is conceivable that NRAS haploinsufficiency and gain-of-function mutations may have similar clinical consequences. The same phenomenon has been described for two other genes belonging to the Ras/MAPK pathway: MAP2K2 and SHOC2. In conclusion, we here report genotype-phenotype correlations in patients with chromosome 1p13.2 microdeletions and we propose that NRAS may be a critical gene for the NS characteristics in the patients

X chromosome dosage and presence of SRY shape sex-specific differences in DNA methylation at an autosomal region in human cells

Abstract Background Sexual dimorphism in DNA methylation levels is a recurrent epigenetic feature in different human cell types and has been implicated in predisposition to disease, such as psychiatric and autoimmune disorders. To elucidate the genetic origins of sex-specific DNA methylation, we examined DNA methylation levels in fibroblast cell lines and blood cells from individuals with different combinations of sex chromosome complements and sex phenotypes focusing on a single autosomal region––the differentially methylated region (DMR) in the promoter of the zona pellucida binding protein 2 (ZPBP2) as a reporter. Results Our data show that the presence of the sex determining region Y (SRY) was associated with lower methylation levels, whereas higher X chromosome dosage in the absence of SRY led to an increase in DNA methylation levels at the ZPBP2 DMR. We mapped the X-linked modifier of DNA methylation to the long arm of chromosome X (Xq13-q21) and tested the impact of mutations in the ATRX and RLIM genes, located in this region, on methylation levels. Neither ATRX nor RLIM mutations influenced ZPBP2 methylation in female carriers. Conclusions We conclude that sex-specific methylation differences at the autosomal locus result from interaction between a Y-linked factor SRY and at least one X-linked factor that acts in a dose-dependent manner