NGS technologies have enabled large-scale genome sequencing to capture variants at low frequencies. However, existing databases do not contain sufficient variant frequency data for Japanese populations. To address this problem, TogoVar seeks to provide a reliable frequency information by assembling variant information from the NBDC Human Database/Japanese Genotype-phenotype Archive (JGA) and public Japanese genomic variation data.

In addition, since the reference human genome sequence can be updated, the genomic position may vary for the same variant if it refers to a different genome build. Thus, in our database, a unique ID is assigned to the same variant, thereby preventing the need to convert the positional information of the variant among different reference sequences, as done by researchers.

See TogoTV. Videos on TogoVar are here.

You can use the TogoVar service for free. For the use of the database, refer to the Terms.

Refer to the attribution (credit) display examples in Terms.

You are free to browse the TogoVar websites and use the docker images to be released for commercial purposes. For data created by third parties and imported in TogoVar, please follow the terms of use of each organization. For more details, refer to Terms.

TogoVar database does not keep private information, and we do not currently provide phenotype information for each individual. For more details about phenotype information, please directly contact the research groups described in the JGA-NGS dataset and JGA-SNP dataset.

Not yet but planned.

Refer to How JGA dataset was generated.

There are five targeted variant types for TogoVar: SNV, Insertion, Deletion, Indel, and Substitution. Of 5 types that HGVS enumerated as the types of variants, Duplication is classified as Insertion, and Indels, for which the lengths of Insertion and Deletion are the same, are classified as Substitution. To identify the type of the variant, Variant Effect Predictor (VEP) is used.

The value of variant consequence in Variant Effect Predictor (VEP) is displayed. If there are multiple transcripts for a single variant, only the most critical consequence may be displayed.

The value of clinical significance in ClinVar is displayed.

AlphaMissense, SIFT and PolyPhen predict the impact of variants on protein function when an amino acid sequence is altered. These programs were performed using Variant Effect Predictor (VEP). The icons below represent the threshold scores used for qualitative predictions.

• AlphaMissense
  
  > 0.564

  Likely pathogenic
  ≥ 0.340

  Ambiguous
  < 0.340

  Likely benign
  The score thresholds for "Likely pathogenic" and "Likely benign" were chosen to classify pathogenic and benign ClinVar variants with 90% precision, respectively. See AlphaMissense in VEP Plugins for details.
• SIFT
  
  < 0.05

  Deleterious
  ≥ 0.05

  Tolerated
  The score represents the normalized probability of an amino acid change being tolerated. Scores closer to zero indicate a higher likelihood of the change being deleterious. The qualitative prediction is based on this score. Substitutions with a score less than 0.05 are classified as "Deleterious", while all others are considered "Tolerated". See SIFT in Pathogenicity prediction of Ensembl Variation for details.
• PolyPhen
  
  > 0.908

  Probably Damaging
  > 0.446

  Possibly Damaging
  ≤ 0.446

  Benign
  Unknown

  Unknown
  Variants are predicted as "Probably Damaging" if they have posterior probability scores indicating an estimated false positive rate (FPR) of 5% or below. Those with FPRs at or below 10% are "Possibly Damaging", while variants with FPRs above 10% are classified as "Benign". See Polyphen in Pathogenicity prediction of Ensembl Variation for details.

Yes. Some of the GRCh38 datasets have been created by lifting over their GRCh37 datasets. Please refer to the Liftover GRCh37 column in the TogoVar datasets (GRCh38) pagefor the target datasets. A liftover tool transannohas been used since the release on November 16, 2023. CrossMapwas used until the release on July 14, 2023.