Family-specific analysis of variant pathogenicity prediction tools

Whole exome sequencing reveals novel variants associated with diminished ovarian reserve in young women

Background: Diminished ovarian reserve is one of the most important causes of female infertility. In the etiology study of DOR, besides age, it is known that chromosomal abnormality, radiotherapy, chemotherapy and ovarian surgery can result in DOR. For young women without obvious risk factors, gene mutation should be considered as a possible cause. However, the specific molecular mechanism of DOR has not been fully elucidated.

Methods: In order to explore the pathogenic variants related to DOR, twenty young women under 35 years old affected by DOR without definite factors damaging ovarian reserve were recruited as the research subjects, and five women with normal ovarian reserve were recruited as the control group. Whole exome sequencing was applied as the genomics research tool.

Results: As a result, we obtained a set of mutated genes that may be related to DOR, where the missense variant on GPR84 was selected for further study. It is found that GPR84Y370H variant promotes the expression of proinflammatory cytokines (TNF-α, IL12B, IL-1β) and chemokines (CCL2, CCL5), as well as the activation of NF-κB signaling pathway.

Conclusion: In conclusion, GPR84Y370H variant was identified though analysis for WES results of 20 DOR patients. The deleterious variant of GPR84 could be the potential molecular mechanism of non-age-related pathological DOR through its role in promoting inflammation. The findings of this study can be used as a preliminary research basis for the development of early molecular diagnosis and treatment target selection of DOR.

Interpreting the spectrum of gamma-secretase complex missense variation in the context of hidradenitis suppurativa—An in-silico study

Hidradenitis suppurativa (HS) is a disease of the pilosebaceous unit characterized by recurrent nodules, abscesses and draining tunnels with a predilection to intertriginous skin. The pathophysiology of HS is complex. However, it is known that inflammation and hyperkeratinization at the hair follicle play crucial roles in disease manifestation. Genetic and environmental factors are considered the main drivers of these two pathophysiological processes. Despite a considerable proportion of patients having a positive family history of disease, only a minority of patients suffering from HS have been found to harbor monogenic variants which segregate to affected kindreds. Most of these variants are in the ɣ secretase complex (GSC) protein-coding genes. In this manuscript, we set out to characterize the burden of missense pathogenic variants in healthy reference population using large scale genomic dataset thereby providing a standard for comparing genomic variation in GSC protein-coding genes in the HS patient cohort.

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

Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools and availability of high throughput sequencing data have enabled the reanalysis of previously undiagnosed patients. In this review, we have systematically compiled the latest developments in the discovery of the genetic causes of rare diseases using machine learning methods. Importantly, we have detailed methods available to reanalyze existing whole exome sequencing data of unsolved rare diseases. We have identified different reanalysis methodologies to solve problems associated with sequence alterations/mutations, variation re-annotation, protein stability, splice isoform malfunctions and oligogenic analysis. In addition, we give an overview of new developments in the field of rare disease research using whole genome sequencing data and other omics.

DVPred: a disease-specific prediction tool for variant pathogenicity classification for hearing loss

Numerous computational prediction tools have been introduced to estimate the functional impact of variants in the human genome based on evolutionary constraints and biochemical metrics. However, their implementation in diagnostic settings to classify variants faced challenges with accuracy and validity. Most existing tools are pan-genome and pan-diseases, which neglected gene- and disease-specific properties and limited the accessibility of curated data. As a proof-of-concept, we developed a disease-specific prediction tool named Deafness Variant deleteriousness Prediction tool (DVPred) that focused on the 157 genes reportedly causing genetic hearing loss (HL). DVPred applied the gradient boosting decision tree (GBDT) algorithm to the dataset consisting of expert-curated pathogenic and benign variants from a large in-house HL patient cohort and public databases. With the incorporation of variant-level and gene-level features, DVPred outperformed the existing universal tools. It boasts an area under the curve (AUC) of 0.98, and showed consistent performance (AUC = 0.985) in an independent assessment dataset. We further demonstrated that multiple gene-level metrics, including low complexity genomic regions and substitution intolerance scores, were the top features of the model. A comprehensive analysis of missense variants showed a gene-specific ratio of predicted deleterious and neutral variants, implying varied tolerance or intolerance to variation in different genes. DVPred explored the utility of disease-specific strategy in improving the deafness variant prediction tool. It can improve the prioritization of pathogenic variants among massive variants identified by high-throughput sequencing on HL genes. It also shed light on the development of variant prediction tools for other genetic disorders.

Clinical, Biochemical, and Genetic Heterogeneity in Glutaric Aciduria Type II Patients

The variants of electron transfer flavoprotein (ETFA, ETFB) and ETF dehydrogenase (ETFDH) are the leading cause of glutaric aciduria type II (GA-II). In this study, we identified 13 patients harboring six variants of two genes associated with GA-II. Out of the six variants, four were missense, and two were frameshift mutations. A missense variant (ETFDH:p.Gln269His) was observed in a homozygous state in nine patients. Among nine patients, three had experienced metabolic crises with recurrent vomiting, abdominal pain, and nausea. In one patient with persistent metabolic acidosis, hypoglycemia, and a high anion gap, the ETFDH:p.Gly472Arg, and ETFB:p.Pro94Thrfs*8 variants were identified in a homozygous, and heterozygous state, respectively. A missense variant ETFDH:p.Ser442Leu was detected in a homozygous state in one patient with metabolic acidosis, hypoglycemia, hyperammonemia and liver dysfunction. The ETFDH:p.Arg41Leu, and ETFB:p.Ile346Phefs*19 variants were observed in a homozygous state in one patient each. Both these variants have not been reported so far. In silico approaches were used to evaluate the pathogenicity and structural changes linked with these six variants. Overall, the results indicate the importance of a newborn screening program and genetic investigations for patients with GA-II. Moreover, careful interpretation and correlation of variants of uncertain significance with clinical and biochemical findings are needed to confirm the pathogenicity of such variants.

L-Type Calcium Channel: Predicting Pathogenic/Likely Pathogenic Status for Variants of Uncertain Clinical Significance

(1) Background: Defects in gene CACNA1C, which encodes the pore-forming subunit of the human Cav1.2 channel (hCav1.2), are associated with cardiac disorders such as atrial fibrillation, long QT syndrome, conduction disorders, cardiomyopathies, and congenital heart defects. Clinical manifestations are known only for 12% of CACNA1C missense variants, which are listed in public databases. Bioinformatics approaches can be used to predict the pathogenic/likely pathogenic status for variants of uncertain clinical significance. Choosing a bioinformatics tool and pathogenicity threshold that are optimal for specific protein families increases the reliability of such predictions. (2) Methods and Results: We used databases ClinVar, Humsavar, gnomAD, and Ensembl to compose a dataset of pathogenic/likely pathogenic and benign variants of hCav1.2 and its 20 paralogues: voltage-gated sodium and calcium channels. We further tested the performance of sixteen in silico tools in predicting pathogenic variants. ClinPred demonstrated the best performance, followed by REVEL and MCap. In the subset of 309 uncharacterized variants of hCav1.2, ClinPred predicted the pathogenicity for 188 variants. Among these, 36 variants were also categorized as pathogenic/likely pathogenic in at least one paralogue of hCav1.2. (3) Conclusions: The bioinformatics tool ClinPred and the paralogue annotation method consensually predicted the pathogenic/likely pathogenic status for 36 uncharacterized variants of hCav1.2. An analogous approach can be used to classify missense variants of other calcium channels and novel variants of hCav1.2.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.