Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny.

The SARS-CoV-2 genome occupies a unique place in infection biology - it is the most highly sequenced genome on earth (making up over 20% of public sequencing datasets) with fine scale information on sampling date and geography, and has been subject to unprecedented intense analysis. As a result, these phylogenetic data are an incredibly valuable resource for science and public health. However, the vast majority of the data was sequenced by tiling amplicons across the full genome, with amplicon schemes that changed over the pandemic as mutations in the viral genome interacted with primer binding sites. In combination with the disparate set of genome assembly workflows and lack of consistent quality control (QC) processes, the current genomes have many systematic errors that have evolved with the virus and amplicon schemes. These errors have significant impacts on the phylogeny, and therefore over the last few years, many thousands of hours of researchers time has been spent in "eyeballing" trees, looking for artefacts, and then patching the tree. Given the huge value of this dataset, we therefore set out to reprocess the complete set of public raw sequence data in a rigorous amplicon-aware manner, and build a cleaner phylogeny. Here we provide a global tree of 3,960,704 samples, built from a consistently assembled set of high quality consensus sequences from all available public data as of March 2023, viewable at https://viridian.taxonium.org . Each genome was constructed using a novel assembly tool called Viridian ( https://github.com/iqbal-lab-org/viridian ), developed specifically to process amplicon sequence data, eliminating artefactual errors and mask the genome at low quality positions. We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. Phase 2 of our project will address the fact that the data in the public archives is heavily geographically biased towards the Global North. We therefore have contributed new raw data to ENA/SRA from many countries including Ghana, Thailand, Laos, Sri Lanka, India, Argentina and Singapore. We will incorporate these, along with all public raw data submitted between March 2023 and the current day, into an updated set of assemblies, and phylogeny. We hope the tree, consensus sequences and Viridian will be a valuable resource for researchers.

The genomic landscape of CYP2D6 variation in the Indian population.

Aim: The CYP2D6 gene is highly polymorphic, causing large interindividual variability in the metabolism of several clinically important drugs. Materials & methods: The authors investigated the diversity and distribution of CYP2D6 alleles in Indians using whole genome sequences (N = 1518). Functional consequences were assessed using pathogenicity scores and molecular dynamics simulations. Results: The analysis revealed population-specific CYP2D6 alleles (*86, *7, *111, *112, *113, *99) and remarkable differences in variant and phenotype frequencies with global populations. The authors observed that one in three Indians could benefit from a dose alteration for psychiatric drugs with accurate CYP2D6 phenotyping. Molecular dynamics simulations revealed large conformational fluctuations, confirming the predicted reduced function of *86 and *113 alleles. Conclusion: The findings emphasize the utility of comprehensive CYP2D6 profiling for aiding precision public health.

Landscape of pharmacogenetic variants associated with non-insulin antidiabetic drugs in the Indian population.

INTRODUCTION: Genetic variants contribute to differential responses to non-insulin antidiabetic drugs (NIADs), and consequently to variable plasma glucose control. Optimal control of plasma glucose is paramount to minimizing type 2 diabetes-related long-term complications. India's distinct genetic architecture and its exploding burden of type 2 diabetes warrants a population-specific survey of NIAD-associated pharmacogenetic (PGx) variants. The recent availability of large-scale whole genomes from the Indian population provides a unique opportunity to generate a population-specific map of NIAD-associated PGx variants. RESEARCH DESIGN AND METHODS: We mined 1029 Indian whole genomes for PGx variants, drug-drug interaction (DDI) and drug-drug-gene interactions (DDGI) associated with 44 NIADs. Population-wise allele frequencies were estimated and compared using Fisher's exact test. RESULTS: Overall, we found 76 known and 52 predicted deleterious common PGx variants associated with response to type 2 diabetes therapy among Indians. We report remarkable interethnic differences in the relative cumulative counts of decreased and increased response-associated alleles across NIAD classes. Indians and South Asians showed a significant excess of decreased metformin response-associated alleles compared with other global populations. Network analysis of shared PGx genes predicts high DDI risk during coadministration of NIADs with other metabolic disease drugs. We also predict an increased CYP2C19-mediated DDGI risk for CYP3A4/3A5-metabolized NIADs, saxagliptin, linagliptin and glyburide when coadministered with proton-pump inhibitors (PPIs). CONCLUSIONS: Indians and South Asians have a distinct PGx profile for antidiabetes drugs, marked by an excess of poor treatment response-associated alleles for various NIAD classes. This suggests the possibility of a population-specific reduced drug response in atleast some NIADs. In addition, our findings provide an actionable resource for accelerating future diabetes PGx studies in Indians and South Asians and reconsidering NIAD dosing guidelines to ensure maximum efficacy and safety in the population.

Whole genome sequencing followed by functional analysis of genomic deletion encompassing ERCC8 and NDUFAF2 genes in a non-consanguineous Indian family reveals dysfunctional mitochondrial bioenergetics leading to infant mortality.

Genomic investigations on an infant who presented with a putative mitochondrial disorder led to identification of compound heterozygous deletion with an overlapping region of ∼142 kb encompassing two nuclear encoded genes namely ERCC8 and NDUFAF2. Investigations on fetal-derived fibroblast culture demonstrated impaired bioenergetics and mitochondrial dysfunction, which explains the phenotype and observed infant mortality in the present study. The genetic findings from this study extended the utility of whole-genome sequencing as it led to development of a MLPA-based assay for carrier screening in the extended family and the prenatal testing aiding in the birth of two healthy children.

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.

The genome sequence of lumpy skin disease virus from an outbreak in India suggests a distinct lineage of the virus.

Although previously confined to regions within Africa, lumpy skin disease virus (LSDV) infections have caused significantly large outbreaks in several regions of the world in recent years. In 2019, an outbreak of the disease was reported in India with low rates of morbidity and no reported mortality. However, in 2022, an ongoing outbreak of LSDV spanning over seven states in India resulted in the loss of over 80,000 cattle over a period of three months. Here, we report complete genome sequences of six isolates of LSDV collected from affected cattle during an ongoing outbreak of the disease in Rajasthan, India. Analysis of these sequences showed that the genome isolates from the 2022 outbreak have a large number of genetic variations compared to the reference strain and that they form a distinct genetic lineage. This report thus highlights the importance of genome sequencing and surveillance of transboundary infectious agents to track the prevalence and emergence of variants.

Whole-genome sequencing of 1029 Indian individuals reveals unique and rare structural variants.

Structural variants contribute to genetic variability in human genomes and they can be presented in population-specific patterns. We aimed to understand the landscape of structural variants in the genomes of healthy Indian individuals and explore their potential implications in genetic disease conditions. For the identification of structural variants, a whole genome sequencing dataset of 1029 self-declared healthy Indian individuals from the IndiGen project was analysed. Further, these variants were evaluated for potential pathogenicity and their associations with genetic diseases. We also compared our identified variations with the existing global datasets. We generated a compendium of total 38,560 high-confident structural variants, comprising 28,393 deletions, 5030 duplications, 5038 insertions, and 99 inversions. Particularly, we identified around 55% of all these variants were found to be unique to the studied population. Further analysis revealed 134 deletions with predicted pathogenic/likely pathogenic effects and their affected genes were majorly enriched for neurological disease conditions, such as intellectual disability and neurodegenerative diseases. The IndiGenomes dataset helped us to understand the unique spectrum of structural variants in the Indian population. More than half of identified variants were not present in the publicly available global dataset on structural variants. Clinically important deletions identified in IndiGenomes might aid in improving the diagnosis of unsolved genetic diseases, particularly in neurological conditions. Along with basal allele frequency data and clinically important deletions, IndiGenomes data might serve as a baseline resource for future studies on genomic structural variant analysis in the Indian population.

Genetic landscape of human neutrophil antigen variants in India from population-scale genomes.

Antibodies against human neutrophil antigens (HNAs) play a significant role in various clinical conditions such as neonatal alloimmune neutropenia, transfusion-related acute lung injury, and other nonhemolytic transfusion reactions. This study aims to identify the genotype and allele frequencies of HNAs in the healthy Indian population. Ten genetic variants in four human genes encoding alleles of HNAs class I-V approved by the International Society of Blood Transfusion-Granulocyte Immunobiology Working Party were used in the analysis. Genetic variants from whole genome sequences of 1029 healthy Indian individuals corresponding to HNA alleles were analyzed. The frequencies of the variants were compared with global population datasets using an in-house computational pipeline. In HNA class I, allele frequencies of FCGR3B*01, FCGR3B*02, and FCGR3B*03 encoding HNA-1a, HNA-1b, and HNA-1c were 0.07, 0.8, and 0.13, respectively. HNA class 3 alleles namely SLC44A2*01 (encoding HNA-3a) and SLC44A2*02 (encoding HNA-3b) were found at allele frequencies of 0.78 and 0.22, respectively. The frequencies of ITGAM*01 encoding HNA-4a and ITGAM*02 encoding HNA-4a were 0.95 and 0.05, respectively. Furthermore, allele frequencies of HNA class 5 alleles were 0.32 for ITGAL*01 (encoding HNA-5a) and 0.68 for ITGAL*02 (encoding HNA-5b). Interestingly, it was also found that rs2230433 variant deciding the HNA class 5 alleles, was highly prevalent (78.2%) in the Indian population compared with other global populations. This study presents the first comprehensive report of HNA allele and genotype frequencies in the Indian population using population genome datasets of 1029 individuals. Significant difference was observed in the prevalence of HNA5a and HNA5b in India in comparison to other global populations.

1029 genomes of self-declared healthy individuals from India reveal prevalent and clinically relevant cardiac ion channelopathy variants.

BACKGROUND: The prevalence and genetic spectrum of cardiac channelopathies exhibit population-specific differences. We aimed to understand the spectrum of cardiac channelopathy-associated variations in India, which is characterised by a genetically diverse population and is largely understudied in the context of these disorders. RESULTS: We utilised the IndiGenomes dataset comprising 1029 whole genomes from self-declared healthy individuals as a template to filter variants in 36 genes known to cause cardiac channelopathies. Our analysis revealed 186,782 variants, of which we filtered 470 variants that were identified as possibly pathogenic (440 nonsynonymous, 30 high-confidence predicted loss of function ). About 26% (124 out of 470) of these variants were unique to the Indian population as they were not reported in the global population datasets and published literature. Classification of 470 variants by ACMG/AMP guidelines unveiled 13 pathogenic/likely pathogenic (P/LP) variants mapping to 19 out of the 1029 individuals. Further query of 53 probands in an independent cohort of cardiac channelopathy, using exome sequencing, revealed the presence of 3 out of the 13 P/LP variants. The identification of p.G179Sfs*62, p.R823W and c.420 + 2 T > C variants in KCNQ1, KCNH2 and CASQ2 genes, respectively, validate the significance of the P/LP variants in the context of clinical applicability as well as for large-scale population analysis. CONCLUSION: A compendium of ACMG/AMP classified cardiac channelopathy variants in 1029 self-declared healthy Indian population was created. A conservative genotypic prevalence was estimated to be 0.9-1.8% which poses a huge public health burden for a country with large population size like India. In the majority of cases, these disorders are manageable and the risk of sudden cardiac death can be alleviated by appropriate lifestyle modifications as well as treatment regimens/clinical interventions. Clinical utility of the obtained variants was demonstrated using a cardiac channelopathy patient cohort. Our study emphasises the need for large-scale population screening to identify at-risk individuals and take preventive measures. However, we suggest cautious clinical interpretation to be exercised by taking other cardiac channelopathy risk factors into account.

Variants of concern responsible for SARS-CoV-2 vaccine breakthrough infections from India.

Emerging reports of SARS-CoV-2 breakthrough infections entail methodical genomic surveillance for determining the efficacy of vaccines. This study elaborates genomic analysis of isolates from breakthrough infections following vaccination with AZD1222/Covishield and BBV152/Covaxin. Variants of concern B.1.617.2 and B.1.1.7 responsible for cases surge in April-May 2021 in Delhi, were the predominant lineages among breakthrough infections.

ESC: a comprehensive resource for SARS-CoV-2 immune escape variants.

Ever since the breakout of COVID-19 disease, ceaseless genomic research to inspect the epidemiology and evolution of the pathogen has been undertaken globally. Large scale viral genome sequencing and analysis have uncovered the functional impact of numerous genetic variants in disease pathogenesis and transmission. Emerging evidence of mutations in spike protein domains escaping antibody neutralization is reported. We have built a database with precise collation of manually curated variants in SARS-CoV-2 from literature with potential escape mechanisms from a range of neutralizing antibodies. This comprehensive repository encompasses a total of 5258 variants accounting for 2068 unique variants tested against 230 antibodies, patient convalescent plasma and vaccine breakthrough events. This resource enables the user to gain access to an extensive annotation of SARS-CoV-2 escape variants which would contribute to exploring and understanding the underlying mechanisms of immune response against the pathogen. The resource is available at http://clingen.igib.res.in/esc/.

Genetic epidemiology of autoinflammatory disease variants in Indian population from 1029 whole genomes.

BACKGROUND: Autoinflammatory disorders are the group of inherited inflammatory disorders caused due to the genetic defect in the genes that regulates innate immune systems. These have been clinically characterized based on the duration and occurrence of unprovoked fever, skin rash, and patient's ancestry. There are several autoinflammatory disorders that are found to be prevalent in a specific population and whose disease genetic epidemiology within the population has been well understood. However, India has a limited number of genetic studies reported for autoinflammatory disorders till date. The whole genome sequencing and analysis of 1029 Indian individuals performed under the IndiGen project persuaded us to perform the genetic epidemiology of the autoinflammatory disorders in India. RESULTS: We have systematically annotated the genetic variants of 56 genes implicated in autoinflammatory disorder. These genetic variants were reclassified into five categories (i.e., pathogenic, likely pathogenic, benign, likely benign, and variant of uncertain significance (VUS)) according to the American College of Medical Genetics and Association of Molecular pathology (ACMG-AMP) guidelines. Our analysis revealed 20 pathogenic and likely pathogenic variants with significant differences in the allele frequency compared with the global population. We also found six causal founder variants in the IndiGen dataset belonging to different ancestry. We have performed haplotype prediction analysis for founder mutations haplotype that reveals the admixture of the South Asian population with other populations. The cumulative carrier frequency of the autoinflammatory disorder in India was found to be 3.5% which is much higher than reported. CONCLUSION: With such frequency in the Indian population, there is a great need for awareness among clinicians as well as the general public regarding the autoinflammatory disorder. To the best of our knowledge, this is the first and most comprehensive population scale genetic epidemiological study being reported from India.

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.

MitoLink: A generic integrated web-based workflow system to evaluate genotype-phenotype correlations in human mitochondrial diseases: Observations from the GenomeAsia Pilot project.

MitoLink is a generic, scalable and modular web-based workflow system developed to study genotype-phenotype correlations in human mitochondrial diseases. MitoLink integrates applications for assessment of genomic variation and currently houses genome-wide datasets from GenomeAsia Pilot project, gnomAD, ClinVar and DisGenNet. In this study, a reference list of nearly 3975 proteins (both nuclear and mitochondrial encoded) with mitochondrial function is reported. This protein set is mapped to disease associated variants in the GenomeAsia Pilot Project and DisGenNet and evaluated for pathogenicity as defined by ClinVar. Observations of shared genetic components in potential comorbidities are discussed from gene-disease network in Asian population, however, the platform is generic and can be applied to any population dataset. MitoLink is a unique customized workflow system that allows for systematic storage, extraction, analysis and visualization of genomic variation to understand genotype-phenotype correlations for mitochondrial diseases. Given the modularity of tool and data integration, MitoLink is a scalable system that can accommodate a diverse set of applications linked via standard data structure within the framework of Galaxy. MitoLink is built on FAIR principles and supports creation of reproducible workflows towards understanding genotype-phenotype correlations across several disease phenotypes globally.

An optimized, amplicon-based approach for sequencing of SARS-CoV-2 from patient samples using COVIDSeq assay on Illumina MiSeq sequencing platforms.

Sequencing of SARS-CoV-2 genomes is crucial for understanding the genetic epidemiology of the COVID-19 pandemic. It is also critical for understanding the evolution of the virus and also for the rapid development of diagnostic tools. The present protocol is a modification of the Illumina COVIDSeq test. We describe an amplicon-based next-generation sequencing approach with short turnaround time, adapted for bench-top sequencers like MiSeq, iSeq, and MiniSeq. For complete details on the use and execution of this protocol, please refer to Bhoyar et al. (2021).

Computational Analysis and Phylogenetic Clustering of SARS-CoV-2 Genomes.

COVID-19, the disease caused by the novel SARS-CoV-2 coronavirus, originated as an isolated outbreak in the Hubei province of China but soon created a global pandemic and is now a major threat to healthcare systems worldwide. Following the rapid human-to-human transmission of the infection, institutes around the world have made efforts to generate genome sequence data for the virus. With thousands of genome sequences for SARS-CoV-2 now available in the public domain, it is possible to analyze the sequences and gain a deeper understanding of the disease, its origin, and its epidemiology. Phylogenetic analysis is a potentially powerful tool for tracking the transmission pattern of the virus with a view to aiding identification of potential interventions. Toward this goal, we have created a comprehensive protocol for the analysis and phylogenetic clustering of SARS-CoV-2 genomes using Nextstrain, a powerful open-source tool for the real-time interactive visualization of genome sequencing data. Approaches to focus the phylogenetic clustering analysis on a particular region of interest are detailed in this protocol.

Pharmacogenomic landscape of COVID-19 therapies from Indian population genomes.

Aim: Numerous drugs are being widely prescribed for COVID-19 treatment without any direct evidence for the drug safety/efficacy in patients across diverse ethnic populations. Materials & methods: We analyzed whole genomes of 1029 Indian individuals (IndiGen) to understand the extent of drug-gene (pharmacogenetic), drug-drug and drug-drug-gene interactions associated with COVID-19 therapy in the Indian population. Results: We identified 30 clinically significant pharmacogenetic variants and 73 predicted deleterious pharmacogenetic variants. COVID-19-associated pharmacogenes were substantially overlapped with those of metabolic disorder therapeutics. CYP3A4, ABCB1 and ALB are the most shared pharmacogenes. Fifteen COVID-19 therapeutics were predicted as likely drug-drug interaction candidates when used with four CYP inhibitor drugs. Conclusion: Our findings provide actionable insights for future validation studies and improved clinical decisions for COVID-19 therapy in Indians.