Understanding the variant landscape, and genetic epidemiology of Multiple Endocrine Neoplasia in India.

PURPOSE: Multiple Endocrine Neoplasia (MEN) is a group of familial cancer syndromes that encompasses several types of endocrine tumors differentiated by genetic mutations in RET, MEN1 and CDKN1B genes. Accurate diagnosis of MEN subtypes can thus be performed through genetic testing. However, MEN variants remain largely understudied in Indian populations. Additionally, few dedicated resources to understand these disorders currently exist. METHODS: Using the gold-standard ACMG/AMP guidelines, we systematically classified variants reported across the three genes in the IndiGen dataset, and established the genetic epidemiology of MEN in the Indian population. We further classified ClinVar and Mastermind variants and compiled all into a database. Finally, we designed a multiplex primer panel for rapid variant identification. RESULTS: We have established the genetic prevalence of MEN as the following: 1 in 1026 individuals is likely to be afflicted with MEN linked with pathogenic RET mutations. We have further created the MAPVar database containing 3280 ACMG-classified variants freely accessible at: https://clingen.igib.res.in/MAPVar/ . Finally, our NGS primer panel covers 33 exonic regions across two pools through 38 amplicons with a total amplified region of 65 kb. CONCLUSION: Our work establishes that MEN is a prevalent disorder in India. The rare nature of Indian variants underscores the need of genomic and functional studies to establish a more comprehensive variant landscape. Additionally, our panel offers a means of cost-effective genetic testing, and the MAPVar database a ready reference to aid in a better understanding of variant pathogenicity in clinical as well as research settings.

WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease.

BACKGROUND: Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG-AMP) have issued a set of guidelines for variant classification. However, given the vast number of variants associated with any disorder, it is impossible to manually apply these guidelines to all known variants. Machine learning methodologies offer a rapid way to classify large numbers of variants, as well as variants of uncertain significance as either pathogenic or benign. Here we classify ATP7B genetic variants by employing ML and AI algorithms trained on our well-annotated WilsonGen dataset. METHODS: We have trained and validated two algorithms: TabNet and XGBoost on a high-confidence dataset of manually annotated, ACMG & AMP classified variants of the ATP7B gene associated with Wilson's Disease. RESULTS: Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings. CONCLUSION: We have created a ready to deploy tool, that can classify variants linked with Wilson's disease as pathogenic or benign, which can be utilized by both clinicians and researchers to better understand the disease through the nature of genetic variants associated with it.

Multi-omics Analysis Classifies Colorectal Cancer into Distinct Methylated Immunogenic and Angiogenic Subtypes Based on Anatomical Laterality.

We employed supervised machine learning algorithms to a cohort of colorectal cancer patients from the NCI to differentiate and classify the heterogenous disease based on anatomical laterality and multi-omics stratification, in a first of its kind. Multi-omics integrative analysis shows distinct clustering of left and right colorectal cancer with disentangled representation of methylome and delineation of transcriptome and genome. We present novel multi-omics findings consistent with augmented hypermethylation of genes in right CRC, epigenomic biomarkers on the right in conjunction with immune-mediated pathway signatures, and lymphocytic invasion which unlocks unique therapeutic avenues. Contrarily, left CRC multi-omics signature is found to be marked by angiogenesis, cadherins, and epithelial-mesenchymal transition (EMT). An integrated multi-omics molecular signature of RNF217-AS1, hsa-miR-10b, and panel of FBX02, FBX06, FBX044, MAD2L2, and MIIP copy number altered genes have been found by the study. Overall survival analysis reveals genomic biomarkers ABCA13 and TTN in 852 LCRC cases, and SOX11 in 170 RCRC cases that predicts a significant survival benefit. Our study exemplifies the translational competence and robustness of machine learning in effective translational bridging of research and clinic. Supplementary Information: The online version contains supplementary material available at 10.1007/s13193-023-01760-6.

Genetic landscape of human platelet antigen variants in the Indian population analysed from 1029 whole genomes.

Genetic variants in human platelet antigens (HPAs) considered allo- or auto antigens are associated with various disorders, including neonatal alloimmune thrombocytopenia, platelet transfusion refractoriness and post-transfusion purpura. Although global differences in genotype frequencies were observed, the distributions of HPA variants in the Indian population are largely unknown. This study aims to explore the landscape of HPA variants in India to provide a basis for risk assessment and management of related complications. Population-specific frequencies of genetic variants associated with the 35 classes of HPAs (HPA-1 to HPA-35) were estimated by systematically analysing genomic variations of 1029 healthy Indian individuals as well as from global population genome datasets. Allele frequencies of the most clinically relevant HPA systems in the Indian population were found as follows, HPA-1a - 0.884, HPA-1b - 0.117, HPA-2a - 0.941, HPA-2b - 0.059, HPA-3a - 0.653, HPA-3b - 0.347, HPA-4a - 0.999, HPA-4b - 0.0010, HPA-5a - 0.923, HPA-5b - 0.077, HPA-6a - 0.998, HPA-6b - 0.002, HPA-15a - 0.582 and HPA-15b - 0.418. This study provides the first comprehensive analysis of HPA allele and genotype frequencies using large scale representative whole genome sequencing data of the Indian population.

Systematic In-Silico Evaluation of the Diagnostic Impact of Mpox Genome Variants in the Current Outbreak.

BACKGROUND AND OBJECTIVE: The rapid rate at which the current mpox virus outbreak has spread across the globe has led the World Health Organization to declare it a Public Health Emergency of International Concern. Polymerase chain reaction-based methods are one of the cornerstones for effective molecular detection of viruses including mpox virus. Genetic variants in primer binding sites are known to impact the efficiency of polymerase chain reaction and therefore diagnosis. Here we have analyzed the genetic variants and their impact on efficient binding of oligonucleotides used in diagnostics. METHODS: In this study, we have systematically collected primers and probes used in the detection of mpox virus from published literature and public resources, and assessed the impact of primer binding region genetic variants in the detection of mpox virus by analysing the thermodynamic parameters, Gibbs free energy and melting temperature. These were calculated using the nearest neighbour method for variants in mpox virus genomes available and the deviation in parameters was computed with respect to the reference genome sequence. RESULTS: We have identified 170 genetic variations that fall within the oligo binding region in 1176 mpox virus genomes out of which five oligos showed at least a 2 °C decrease in melting temperature, which could potentially affect the diagnostic efficacy. CONCLUSIONS: Our analysis shows the importance of continuous monitoring of mpox virus detection primer efficacy and provides the list of oligos with potentially reduced detection efficiency in the current mpox virus outbreak.

MUSTARD-a comprehensive resource of mutation-specific therapies in cancer.

The steady increase in global cancer burden has fuelled the development of several modes of treatment for the disease. In the presence of an actionable mutation, targeted therapies offer a method to selectively attack cancer cells, increasing overall efficacy and reducing harmful side effects. However, different drug molecules are in different stages of development, with new molecules obtaining approvals from regulatory agencies each year. To augment clinical impact, it is important that this information reaches clinicians, patients and researchers swiftly and in a structured, well-annotated manner. To this end, we have developed Mutation-Specific Therapies Resource and Database in Cancer (MUSTARD), a database that is designed to be a centralized resource with diverse information such as cancer subtype, associated mutations, therapy offered and its effect observed, along with links to external resources for a more comprehensive annotation. In its current version, MUSTARD comprises over 2105 unique entries, including associations between 418 unique drug therapies, 189 cancer subtypes and 167 genes curated and annotated from over 862 different publications. To the best of our knowledge, it is the only resource that offers comprehensive information on mutation-specific, gene fusions and overexpressed gene-targeted therapies for cancer. Database URL: http://clingen.igib.res.in/mustard/.

DALIA- a comprehensive resource of Disease Alleles in Arab population.

The Arab population encompasses over 420 million people characterized by genetic admixture and a consequent rich genetic diversity. A number of genetic diseases have been reported for the first time from the population. Additionally a high prevalence of some genetic diseases including autosomal recessive disorders such as hemoglobinopathies and familial mediterranean fever have been found in the population and across the region. There is a paucity of databases cataloguing genetic variants of clinical relevance from the population. The availability of such a catalog could have implications in precise diagnosis, genetic epidemiology and prevention of disease. To fill in the gap, we have compiled DALIA, a comprehensive compendium of genetic variants reported in literature and implicated in genetic diseases reported from the Arab population. The database aims to act as an effective resource for population-scale and sub-population specific variant analyses, enabling a ready reference aiding clinical interpretation of genetic variants, genetic epidemiology, as well as facilitating rapid screening and a quick reference for evaluating evidence on genetic diseases.