Whole genome sequencing of families diagnosed with cardiac channelopathies reveals structural variants missed by whole exome sequencing.

Cardiac channelopathies are a group of heritable disorders that affect the heart's electrical activity due to genetic variations present in genes coding for ion channels. With the advent of new sequencing technologies, molecular diagnosis of these disorders in patients has paved the way for early identification, therapeutic management and family screening. The objective of this retrospective study was to understand the efficacy of whole-genome sequencing in diagnosing patients with suspected cardiac channelopathies who were reported negative after whole exome sequencing and analysis. We employed a 3-tier analysis approach to identify nonsynonymous variations and loss-of-function variations missed by exome sequencing, and structural variations that are better resolved only by sequencing whole genomes. By performing whole genome sequencing and analyzing 25 exome-negative cardiac channelopathy patients, we identified 3 pathogenic variations. These include a heterozygous likely pathogenic nonsynonymous variation, CACNA1C:NM_000719:exon19:c.C2570G:p. P857R, which causes autosomal dominant long QT syndrome in the absence of Timothy syndrome, a heterozygous loss-of-function variation CASQ2:NM_001232.4:c.420+2T>C classified as pathogenic, and a 9.2 kb structural variation that spans exon 2 of the KCNQ1 gene, which is likely to cause Jervell-Lange-Nielssen syndrome. In addition, we also identified a loss-of-function variation and 16 structural variations of unknown significance (VUS). Further studies are required to elucidate the role of these identified VUS in gene regulation and decipher the underlying genetic and molecular mechanisms of these disorders. Our present study serves as a pilot for understanding the utility of WGS over clinical exomes in diagnosing cardiac channelopathy disorders.

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.

Genomic characterization and epidemiology of an emerging SARS-CoV-2 variant in Delhi, India.

Delhi, the national capital of India, experienced multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks in 2020 and reached population seropositivity of >50% by 2021. During April 2021, the city became overwhelmed by COVID-19 cases and fatalities, as a new variant, B.1.617.2 (Delta), replaced B.1.1.7 (Alpha). A Bayesian model explains the growth advantage of Delta through a combination of increased transmissibility and reduced sensitivity to immune responses generated against earlier variants (median estimates: 1.5-fold greater transmissibility and 20% reduction in sensitivity). Seropositivity of an employee and family cohort increased from 42% to 87.5% between March and July 2021, with 27% reinfections, as judged by increased antibody concentration after a previous decline. The likely high transmissibility and partial evasion of immunity by the Delta variant contributed to an overwhelming surge in Delhi.