Mitochondrial Encephalohepatopathy (MEH) is an autosomal recessive neurodevelopmental disorder usually accompanied by microcephaly, white matter changes, cardiac and hepatic failure. Here, we applied the whole-exome sequencing (WES) framework on a trio family data with unaffected non-consanguineous parents and proband (neonate girl) with this inherited disorder. A total of 2,928,402 variants were observed with 2,613,746 SNPs, 112,336 multiple nucleotide polymorphisms (MNPs), 72,610 insertions, 113,207 deletions and 16,503 mixed variants. These variations are responsible for 82,813,631 effects on various genomic regions. Our pipeline uncovered candidate gene mutations from these variants and retained a handful of 5,277 variants harboring 3,598 genes, out of which, 8 genes codes for non-coding RNA while 178 genes are those with high impact severity. Among these 178 variants, 125 are de-novo variants that are not previously reported in the ClinVar database. Consistent to previous studies, the leftover high impact severity genes are involved in encephalopathy, Leigh syndrome, Charcot–Marie–Tooth disease, global developmental disorder, seizures, spastic paraplegia, premature ovarian failure, mitochondrial myopathy-cerebellar, ataxia-pigmentary, retinopathy syndrome, ocular and retinal degeneration, deafness, intellectual disability, cardiofacioneurodevelopmental syndrome etc. All these clinical features were also observed in the patient studied. The current analysis highlights and expands the genetic architecture of the MEH phenotype. Furthermore, this pipeline on trio family data significantly broadens the concept of its usefulness as a first-tier diagnostic method in the detection of complex multisystem phenotypic disorders.

An in-silico WES approach using the Galaxy platform was adopted in the current study to predict the genetic basis of Premature Ovarian Failure (POF), where three affected patients in a Saudi Arabian family of seven, found associated with X-linked recessive mutations. The current analysis discovered 518,054 variants using FreeBayes variant caller that had 1,461,864 effects on variable sites in the genome revealed by SnpEff software. The causal genetic mutations were filtered and annotated with the ClinVar database using the GEMINI tool. This tool retained 369 pathogenic mutations harboring 130 genes. Among the total, 268 variants positioned on 69 genes are shared with three affected individuals, 61 variants on 23 genes are shared by any two of the affected individuals, and 40 of the variants on 38 genes are present in any one of the affected sample. Two mutations in one of the already POF-associated, POF1B gene were also observed e.g. (i) g.84563135T>A; p.M349L and (ii) g.84563194C>T; p.R329Q in the two affected individuals i.e. IV-I-C & IV-6 in the current data. This gene consists of 17 exons that span the region of >100 kb. The putative function of this gene in regulating the actin cytoskeleton due to homology with myosin tail and maintains a number of oocytes during fetal ovary development. In a nutshell, this Galaxy pipeline facilitates all-in-one to pinpoint not only the known pathogenic gene mutations for this disorder but few other novel genetic variants as well, whose gene-disease association may be validated by further experimental studies.

ClinVar is a web platform that stores around 774k curated entries, which allows exploring genetic variants and their associations with complex phenotypes. A partial set of ClinVar’s genetic associations were reported with conflict of interpretation or uncertain clinical impact significance, which currently challenges clinicians and geneticists. Here, we evaluate the performance of data pre-processing methods combined with classical prediction methods, such as Naive Bayes, Random Forest, and Support Vector Machine to build a meta-prediction model aiming to improve genetic pathogenicity interpretation. Models were trained with ClinVar data (September 2020), and genetic variants were annotated with eight functional impact predictors catalogued with SnpEff/SnpSift (v4.3). A 10-fold cross-validation strategy was performed for evaluation by accuracy, F1-Score, Receiver Operating Characteristic, Area Under Curve. The best meta-prediction model raises by combining one-hot encoding with tree-based classifiers as Random Forest, which shows Area Under Curve ≥ 0,93. We predict pathogenicity for 109k genetic variants, which were found labeled as uncertain significance or conflict of interpretation. Additionally, we implemented AmazonForest (https://www.lghm.ufpa.br/amazonforest), a web tool to query data for a set of 5k variants that were predicted with high pathogenic probability (RFprob >= 0.9).