TogoVar: A comprehensive Japanese genetic variation database

Mitsuhashi, Nobutaka; Toyo-Oka, Licht; Katayama, Toshiaki; Kawashima, M.; Kawashima, Seiichiro; Miyazaki, Kazunori; Takagi, Toshihisa

doi:10.1038/s41439-022-00222-9

Cited by 23 publications

(14 citation statements)

References 31 publications

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“…The pathogenic variants so obtained were searched across 19 global population datasets for allele frequencies including GnomAD populations and subpopulations, the China Metabolic Analytics Project (ChinaMAP) 21 , the Hong Kong Cantonese population (HKG) 22 , TogoVar (Japanese population) 23 , Korea1K Variome (KGP) 24 , Korean Variant Archive 2 (KOVA 2) 25 , Qatar 26 , Taiwan Biobank 27 , GME populations and subpopulations, 1KG, the Gambian dataset 28 (Gambian), the GenomeAsia100K Project 29 (GenomeAsia), Human Genome Diversity Project (HGDP) 30 , the Andamanese population 31 (Andamanese), the Simons Genome Diversity Project 32 (Simons), the Singapore Sequencing Indian Project (SSIP) 33 , and the Singapore Sequencing Malay Project (SSMP) 34 . Additionally, the Vietnamese Genetic Variation Database 35 , and the Iranome 36 were also queried.…”

Section: Methodsmentioning

confidence: 99%

Understanding the genetic epidemiology of hereditary breast cancer in India using whole genome data from 1029 healthy individuals

Vatsyayan,

Mathur,

Bhoyar

et al. 2023

Preprint

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Aim: Breast cancer is the most highly reported cancer in India as well as globally (Globocan 2020). Genetic testing could help tackle the increasing cancer burden by enabling carriers of pathogenic variants obtain an early diagnosis through increased surveillance, and help guide treatment, and in some cases enable prevention. However, accurate interpretation of variant pathogenicity must be established in a population-specific manner to ensure effective use of genetic testing. Further, since BRCA1 and BRCA2 are importance breast cancer genes, even rare variants must be studied for their potential effect on the disease. Materials and Methods: We query the IndiGen data obtained from whole genome sequencing of 1029 Indian individuals, and perform variant classification of all reported BRCA variants according to the gold-standard ACMG/AMP guidelines to establish disease epidemiology. We further implement machine learning approaches to classify all reported non-benign variants, and create a ready-reference of variants classified in a manner close to ACMG guidelines at scale. Results: We determined the genetic prevalence to be the following: 1 in nearly 341 individuals for BRCA1, and 1 in nearly 170 individuals for BRCA2 are likely to be carriers of pathogenic mutations. Overall, 1 in nearly 114 individuals are likely to be carriers of pathogenic BRCA mutations. Further, using the brca-NOVUS tool, we classified 1,54,045 genetic variants across 18 population sets and 4 large variant repositories as either pathogenic or benign. Conclusion: The high population prevalence indicates a greater need of studying genetic variants linked with breast cancer in an Indian population specific manner. To the best of our knowledge, this is the first and most comprehensive population-scale genetic epidemiological study of BRCA-linked breast cancer variants reported from India.

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Section: Methodsmentioning

confidence: 99%

Understanding the genetic epidemiology of hereditary breast cancer in India using whole genome data from 1029 healthy individuals

Vatsyayan,

Mathur,

Bhoyar

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…We mapped global as well as subpopulation allele frequencies across all variants that had OncoKB annotations. We queried 14 global populations including IndiGen, GnomAD 10 , Human Genome Diversity Project (HGDP) 11 , TogoVar (Japanese population) 12 , the China Metabolic Analytics Project (ChinaMAP) 13 , Qatar 14 , the Singapore Sequencing Indian Project (SSIP) 15 , the Singapore Sequencing Malay Project (SSMP) 16 , 1000Genomes 17 , the Greater Middle East (GME) Variome Project 18 , Korea1K Variome 19 , Taiwan Biobank 20 , the Vietnamese Genetic Variation Database 21 , and the Iranome 22 . We performed the Fisher’s exact test across each of these populations and sub-populations with respect to the IndiGen population to determine whether the allele frequencies were significantly different in any population.…”

Section: Methodsmentioning

confidence: 99%

Understanding the prevalence of germline oncogenic biomarker variants across the Indian population

Vatsyayan,

Scaria

2023

Preprint

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Background: Germline variants have traditionally been used for determining carrier status in heritable cancer families. However, studies are increasingly finding use of germline variants as therapeutic, diagnostic and prognostic biomarkers used to guide therapy decisions in cancer treatment. Objectives: To study the prevalence of mutation specific oncogenic biomarkers in the Indian population and analyze their presence across disease cohorts Materials and Methods: We annotate the IndiGen data obtained from whole genome sequencing of 1029 self-declared healthy Indian individuals with biomarker information obtained from the OncoKB knowledgebase, a repository of evidence-based information about somatic biomarkers and structural alterations in patient tumors. Results: In our study we have discovered 34 biomarker variants of therapeutic actionability across 16 genes linked with 23 unique drugs or drug combinations in 23 unique types of cancer disease in the Indian population. In all, we have found 52 biomarker variants with 172 different biomarker types including therapeutic, resistance, diagnostic, and prognostic. We establish that 4.3% of the Indian population are carriers of therapeutic, and 2.13% are carriers of both diagnostic as well as prognostic germline biomarkers. Finally, we also establish the prevalence of 42 biomarker variants across the 23 genes in both AD and AR modes of inheritance in the Indian population.

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“…Upon classification, we collected all pathogenic/likely pathogenic variants obtained and compared their allele frequencies reported across 18 global populations datasets. These included 1KG, gnomAD, China Metabolic Analytics Project 18 (ChinaMAP), the Hong Kong Cantonese population 19 (HKG), TogoVar 20 (Japanese population), Korea1K Variome 21 (KGP), Korean Variant Archive 2 22 (KOVA 2), Qatar 23 , Taiwan Biobank 24 , GME 13 populations and subpopulations, the Gambian 25 dataset (Gambian), the GenomeAsia100K Project 26 (GenomeAsia), Human Genome Diversity Project 27 (HGDP), the Andamanese population 28 (Andamanese), the Simons Genome Diversity Project 29 (Simons), the Singapore Sequencing Indian Project 30 (SSIP), the Singapore Sequencing Malay Project 31 (SSMP), and the Iranome 32 .…”

Section: Methodsmentioning

confidence: 99%

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

Vatsyayan,

Bhardwaj,

Agrawal

et al. 2023

Preprint

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Aim: Multiple Endocrine Neoplasia (MEN) is a familial cancer syndrome that encompasses several different types of endocrine tumors. It has three main types, namely MEN1, MEN2 and MEN4 that may or may not overlap phenotypically, but are caused by genetic mutations in three different genes, namely RET, MEN1 and CDKN1B respectively. Genetic testing for effective diagnosis, improved prognosis, and treatment is recommended as part of of clinical practice guidelines, which makes establishment of accurate pathogenicity classification of variants across the three genes essential. However, few resources offer such classification, especially in a population specific manner. Materials and Methods: Using the gold-standard ACMG/AMP guidelines for variant classification, we have systematically classified variants reported across the RET, MEN1 and CDKN1B genes reported in the IndiGen dataset, and established the genetic epidemiology of MEN in the Indian population. We have additionally classified variants from ClinVar and Mastermind, and made all variant classifications freely accessible in the form of a database called MAPVar. Results: We have established the genetic prevalence of MEN in the Indian population to be the following: 1 in nearly 146 individuals are likely carriers of pathogenic RET mutations, and 1 in nearly 514 individuals are likely carriers of MEN1 pathogenic mutations. Overall, 1 in nearly 114 individuals are expected to be carriers of MEN-linked pathogenic mutations in the Indian population. We have compiled ACMG-classified variants from three large datasets to create an exhaustive compendium of MEN-linked variants called MEN-Associated Pathogenic Variants (MAPVar). The database is available at: https://clingen.igib.res.in/MAPVar/ Conclusion: Our work establishes that MEN is prevalent disorder in India, with MEN2 variants being the most reported of the three types. This indicates the need of more genomic studies of MEN variants to establish a more comprehensive variant landscape specific to Indian populations. Additionally, genetic testing is an effective tool used against MEN. Our resource MAPVar offers an exhaustive resource of ACMG-classified MEN variants, that can act as a ready resource to aid in interpretation of genetic testing results, as well as to better understanding genetic variants in clinical as well as research settings.

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TogoVar: A comprehensive Japanese genetic variation database

Cited by 23 publications

References 31 publications

Understanding the genetic epidemiology of hereditary breast cancer in India using whole genome data from 1029 healthy individuals

Understanding the genetic epidemiology of hereditary breast cancer in India using whole genome data from 1029 healthy individuals

Understanding the prevalence of germline oncogenic biomarker variants across the Indian population

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

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