Research ID
hum0014-v37Release info
Latest
Research title
Bio Bank Japan project
Research overview
- Aims
- Identify disease-related genes and mobile element variations in Japanese/Development for Japanese population-specific reference panels
- Methods
- Genomic DNA samples were genotyped by following methods: Human610-Quad BeadChip, HumanHap550v3 Genotyping BeadChip, HumanOmniExpress-12 BeadChip, HumanExome BeadChip, OmniExpressExome BeadChip (Illumina), high-density oligonucleotide arrays (Perlegen Sciences), or Invader (Hologic Japan). Genome-Wide Association Studies (GWAS) for myocardial infarction (MI) , type II diabetes mellitus (T2DM), Atopic dermatitis (AD), atrial fibrillation (AF), Body Mass Index (BMI), primary open-angle glaucoma (POAG), 58 quantitative traits, age at menarche / menopause, smoking behaviour, height, 42 diseases (among them, the samples of 4 diseases were partially overlapped with those of previous release), dietary habits, and coronary artery disease were performed using about 500-2700K variants. Meta analyses for T2DM with diabetic nephropathy and for T2DM were also performed. SNP array analysis for 51 diseases registered in Biobank Japan were performed. Whole-genome sequencing analyses for 1,026 + 1,007 patients, who were registered Bio Bank Japan from 2003 - 2007, 1,765 myocardial infarction patients, 199 dementia patients, 256 + 2,067 gastric cancer patients, 617 colorectal cancer patients and 2,162 diabetes patients were performed with Illumina HiSeq 2500/X Five. Target sequencing analyses of 11 hereditary breast cancer genes in 7,104 breast cancer patients and 23,731 controls, 8 hereditary prostate cancer genes in 7,636 prostate cancer patients and 12,366 controls, 23 genes related to clonal hematopoiesis in 11,234 subjects extracted from approximately 200,000 subjects registered in Biobank Japan between fiscal years 2003 to 2007, 27 cancer-predisposing genes in 1,009 pancreatic cancer patients, 12,606 colorectal cancer patients, 740 renal cell cancer patients, 1,982 lymphoma patients, 10,366 gastric cancer patients and 23,780 + 5,996 + 37,592 controls and 13 renal cell carcinoma-related genes in 740 renal cell cancer patients and 5,996 controls were performed with Illumina HiSeq 2500. Also targeted sequencing was performed on the coding regions of TP53 in 140,597 individuals, including those with breast cancer, stomach cancer, colon cancer, heart failure, stroke, etc. SNP array analysis for 11,234 subjects was performed. A new reference panel was build with WGS data of the biobank Japan project (N=7,472 or 3,256) and the 1KGPp3v5 ALL (N=2,504). Sex-stratified genome-wide association studies using a Cox proportional hazard model under the assumption of the additive genetic model were performed. Associations of genetic variants estimated by saddle point estimation using SPACox software were also evaluated. A mobile element variation (MEV) search tool, MEGAnE, was applied to 4,880 WGS conducted in BBJ and 24,933 MEVs were found. Genome-wide association study for atrial fibrillation was performed in 9,826 cases and 140,446 controls. A subsequent cross-ancestry meta-analysis with European GWAS (60,620 cases and 970,216 controls; http://csg.sph.umich.edu/willer/public/afib2018) and Finnish GWAS (7,244 cases and 56,378 controls; FinnGenn; https://www.finngen.fi/en) was performed (77,690 cases and 1,167,040 controls in total). Polygenic risk score was constructed based on the cross-ancestry meta-analysis of atrial fibrillation.
- Participants/materials
- Participants for the Tailor-made Medical Treatment Program (BioBank Japan: BBJ)
Datasets
| Cart | Dataset ID | Type of data | Analysis method | Access criteria | Date published |
|---|---|---|---|---|---|
| NHA000001 | GWAS for MI |
| Unrestricted-access | 2014-09-30 | |
| NHA000008 | Genotype frequencies in 934 healthy individuals (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000031 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000014 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000040 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000017 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000035 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000028 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000032 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000041 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000039 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000011 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000012 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000019 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000015 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000030 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000037 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000013 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000018 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000027 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000016 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000025 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000023 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000036 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000043 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000033 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000042 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000022 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000034 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000038 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000010 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000026 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000009 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000020 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000029 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000021 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000024 | Genotype frequencies in each disease (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000006 | Genotype frequencies in 182 esophageal cancer patients (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000007 | Genotype frequencies in 92 amyotrophic lateral sclerosis (ALS) patients (JSNP data) |
| Unrestricted-access | 2015-12-28 | |
| NHA000044 | GWAS for T2DM [1] |
| Unrestricted-access | 2016-01-28 | |
| NHA000045 | GWAS for T2DM [2] |
| Unrestricted-access | 2016-01-28 | |
| NHA000046 | GWAS for AD |
| Unrestricted-access | 2016-02-02 | |
| JGAD000101 | Genotype and phenotype data for 8180 AF patients |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000102 | Genotype and phenotype data for 8180 AF patients |
| Controlled-access (Type I) | 2020-09-28 | |
| NHA000052 | GWAS for AF |
| Unrestricted-access | 2017-05-18 | |
| JGAD000124 | BMI data for 158,284 individuals Genotype data for 182,505 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000123 | BMI data for 158,284 individuals Genotype data for 182,505 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| NHA000053 | GWAS for BMI |
| Unrestricted-access | 2017-09-08 | |
| JGAD000144 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000145 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000146 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000147 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000148 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000149 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000150 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000151 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000152 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000153 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000154 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000155 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000156 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000157 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000158 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000159 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000160 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000161 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000162 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000163 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000164 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000165 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000166 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000167 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000168 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000169 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000170 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000171 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000172 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000173 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000174 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000175 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000176 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000177 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000178 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000179 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000180 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000181 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000182 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000183 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000184 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000185 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000186 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000187 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000188 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000189 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000190 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000191 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000192 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000193 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000194 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000195 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000196 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000197 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000198 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000199 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000200 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000201 | 58 quantitative traits data for 200,849 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000220 | WGS for 1,026 individuals |
| Controlled-access (Type I) | 2020-09-28 | |
| JGAD000410 | bam/gvcf data of WGS (JGAD000220) |
| Controlled-access (Type I) | 2021-06-21 | |
| JGAD000679 | Processed data of JGAD000220 (reference panel) by JGA (data for the TogoImputation reference panel) |
| Controlled-access (Type I) | 2023-01-26 | |
| JGAD000690 | Processed data of JGAD000220 (WGS for 1,026 individuals) by JGA (CRAM, gVCF) |
| Controlled-access (Type I) | 2023-07-30 | |
| JGAD000758 | Processed data (joint call) of JGAD000220 (WGS for 1,026 individuals) by JGA (aggregate VCF) |
| Controlled-access (Type I) | 2023-07-30 | |
| JGAD000867 | Processed data of JGAD000220 (reference panel) by JGA (data for the TogoImputation reference panel) |
| Controlled-access (Type I) | 2024-09-04 | |
| JGAD000885 | Processed data of JGAD000220 (WGS for 1,026 individuals) by JGA (mitochondrial variant calls) |
| Controlled-access (Type I) | 2024-10-24 | |
| NHA000068 | GWAS for POAG |
| Unrestricted-access | 2018-04-04 | |
| NHA000070 | GWAS for 58 quantitative traits |
| Unrestricted-access | 2018-05-01 | |
| NHA000073 | GWAS for age at menarche and menopause |
| Unrestricted-access | 2018-08-07 | |
| NHA000072 | GWAS for age at menarche and menopause |
| Unrestricted-access | 2018-08-07 | |
| JGAD000209 | target sequencing of 11 hereditary breast cancer genes in 7,104 breast cancer patients and 23,731 controls |
| Controlled-access (Type I) | 2020-09-28 | |
| NHA000075 | meta analysis of 2 GWASs for T2DM with diabetic nephropathy |
| Unrestricted-access | 2018-12-10 | |
| NHA000078 | meta analysis of 4 GWASs for T2DM |
| Unrestricted-access | 2019-01-25 | |
| NHA000084 | GWAS for smoking behaviour |
| Unrestricted-access | 2019-03-26 | |
| NHA000080 | GWAS for smoking behaviour |
| Unrestricted-access | 2019-03-26 | |
| NHA000081 | GWAS for smoking behaviour |
| Unrestricted-access | 2019-03-26 | |
| NHA000082 | GWAS for smoking behaviour |
| Unrestricted-access | 2019-03-26 | |
| NHA000083 | GWAS for smoking behaviour |
| Unrestricted-access | 2019-03-26 | |
| NHA000088 | GWAS for height |
| Unrestricted-access | 2019-09-27 | |
| JGAD000288 | target sequencing of 8 hereditary prostate cancer genes in 7,636 prostate cancer patients and 12,366 controls |
| Controlled-access (Type I) | 2020-09-28 | |
| NHA000090 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000091 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000089 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000107 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000098 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000092 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000099 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000094 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000095 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000097 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000100 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000093 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000110 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000101 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000126 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000104 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000105 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000103 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000106 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000109 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000108 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000111 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000115 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000113 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000096 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000114 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000116 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000117 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000119 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000118 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000123 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000121 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000120 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000112 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000124 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000122 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000125 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000102 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000128 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000127 | GWAS for 40 diseases |
| Unrestricted-access | 2019-10-08 | |
| NHA000129 | GWAS for 40 diseases |
| Unrestricted-access | 2019-11-26 | |
| NHA000152 | GWAS for 40 diseases |
| Unrestricted-access | 2020-08-25 | |
| NHA000139 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000138 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000136 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000145 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000142 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000148 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000137 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000143 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000146 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000140 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000144 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000147 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000141 | GWAS for dietary habits |
| Unrestricted-access | 2020-04-20 | |
| NHA000149 | GWAS for coronary artery disease |
| Unrestricted-access | 2020-08-17 | |
| NHA000150 | GWAS for coronary artery disease |
| Unrestricted-access | 2020-08-17 | |
| NHA000151 | GWAS for coronary artery disease |
| Unrestricted-access | 2020-08-17 | |
| JGAD000399 | target sequencing of 23 genes related to clonal hematopoiesis and SNP array in 11,234 subjects extracted from approximately 200,000 subjects registered in Biobank Japan between fiscal years 2003 to 2007 |
| Controlled-access (Type I) | 2021-05-21 | |
| JGAD000400 | target sequencing of 23 genes related to clonal hematopoiesis and SNP array in 11,234 subjects extracted from approximately 200,000 subjects registered in Biobank Japan between fiscal years 2003 to 2007 |
| Controlled-access (Type I) | 2021-05-21 | |
| JGAD000438 | target sequencing of 27 cancer-predisposing genes in 1,005 pancreatic cancer patients |
| Controlled-access (Type I) | 2021-12-24 | |
| JGAD000458 | target sequencing of 27 cancer-predisposing genes in 12,503 colorectal cancer patients and 23,705 controls |
| Controlled-access (Type I) | 2021-12-28 | |
| JGAD000459 | target sequencing of 27 cancer-predisposing genes in 12,503 colorectal cancer patients and 23,705 controls |
| Controlled-access (Type I) | 2021-12-28 | |
| JGAD000531 | target sequencings of 27 cancer-predisposing genes and 13 renal cell carcinoma-related genes in 740 renal cell cancer patients and 5,996 controls |
| Controlled-access (Type I) | 2022-04-07 | |
| JGAD000460 | target sequencing of 27 cancer-predisposing genes in 1,982 lymphoma patients |
| Controlled-access (Type I) | 2023-05-01 | |
| JGAD000720 | target sequencing of 27 cancer-predisposing genes in 10,366 gastric cancer patients |
| Controlled-access (Type I) | 2023-05-01 | |
| JGAD000721 | target sequencing of 27 cancer-predisposing genes in 37,592 controls |
| Controlled-access (Type I) | 2023-05-01 | |
| JGAD000495 | WGS for 1,765 myocardial infarction patients and 199 dementia patients |
| Controlled-access (Type I) | 2022-01-27 | |
| JGAD000496 | WGS for 1,765 myocardial infarction patients and 199 dementia patients |
| Controlled-access (Type I) | 2022-01-27 | |
| JGAD000868 | Processed data of JGAD000495 (reference panel) by JGA (data for the TogoImputation reference panel) |
| Controlled-access (Type I) | 2024-08-28 | |
| JGAD000893 | Processed data of JGAD000495 (WGS for 1,964 individuals) by JGA (mitochondrial variant calls) |
| Controlled-access (Type I) | 2024-10-18 | |
| NHA000180 | GWAS for survival time in 137,693 individuals from BBJ 1st cohort |
| Unrestricted-access | 2022-12-31 | |
| NHA000183 | mobile element variations in 4,880 individuals from BBJ 1st cohort |
| Unrestricted-access | 2023-04-05 | |
| NHA000184 | GWAS for 9,826 AF patients and 140,446 controls from BBJ 1st cohort GWAS meta-analysis for 77,690 AF patients and 1,167,040 controls |
| Unrestricted-access | 2023-04-05 | |
| JGAD000777 | WGS for 1,007 individuals |
| Controlled-access (Type I) | 2023-11-09 | |
| JGAD000831 | WGS for 256 gastric cancer patients |
| Controlled-access (Type I) | 2024-05-27 | |
| JGAD000836 | SNP array for 269,000 patients (51 diseases) in BBJ 1st and 2nd cohort |
| Controlled-access (Type I) | 2024-05-27 | |
| JGAD000832 | WGS for 617 colorectal cancer patients |
| Controlled-access (Type I) | 2024-05-27 | |
| JGAD000833 | low-depth WGS for 2,162 diabetes patients |
| Controlled-access (Type I) | 2024-05-27 | |
| JGAD000834 | low-depth WGS for 2,067 gastric cancer patients |
| Controlled-access (Type I) | 2024-05-27 | |
| JGAD000873 | Imputation reference panel for 7,472 Japanese WGS and 2,504 1000 Genome Project data |
| Controlled-access (Type I) | 2024-11-29 | |
| JGAD000881 | Imputation reference panel for 3,256 Japanese WGS and 2,504 1000 Genome Project data |
| Controlled-access (Type I) | 2024-12-04 | |
| JGAD000924 | target sequencings on the coding regions of TP53 in 140,597 individuals, including those with breast cancer, stomach cancer, colon cancer, heart failure, stroke, etc. |
| Controlled-access (Type I) | 2025-06-05 |
Data provider
- Principal investigator
- Michiaki Kubo
- Affiliation
- RIKEN Center for Integrative Medical Sciences
Research projects
| Name | URL |
|---|---|
Tailor-made Medical Treatment Program (Bio Bank Japan: BBJ) |
Grants
| Name | Title | Project number |
|---|---|---|
Core Research and Evolutional Science and Technology, Advanced Research & Development Programs for Medical Innovation, Japan Agency for Medical Research and Development (AMED-CREST) | Research on altered tissue functions caused by clonal expansion and remodeling of apparently normal tissues related to normal aging or exposure to chronic inflammation and other lifestyles |
|
KAKENHI Grant-in-Aid for Scientific Research (S) | Comprehensive studies on the molecular basis of early development and clonal evolution in cancer using advanced genomics. |
|
Program for Promoting Platform of Genomics based Drug Discovery, Project for Genome and Health Related Data, Japan Agency for Medical Research and Development (AMED) | Development of a large-scale database for effective drug treatment for breast, colorectal, and pancreas cancers |
|
KAKENHI Grant-in-Aid for Early-Career Scientists | Genome-wide association study integrating mobile genetic elements |
|
KAKENHI Grant-in-Aid for Scientific Research (B) | Elucidation of genetic factors that define myocardial vulnerability as a basis for the development of heart failure |
|
KAKENHI Grant-in-Aid for Scientific Research (S) | Genome immunity: elucidation of the antiviral activity of endogenous bornaviruses and their utilization as functional resources |
|
KAKENHI Grant-in-Aid for Scientific Research (B) | Integration and reactivation of human herpesvirus 6: association with diseases |
|
Biobank - Construction and Utilization biobank for genomic medicine REalization (B-Cure), Japan Agency for Medical Research and Development (AMED) | Management of the Japanese biobank |
|
Practical Research Project for Life-Style related Diseases including Cardiovascular Diseases and Diabetes Mellitus, Japan Agency for Medical Research and Development (AMED) | Multi-layered and integrated research for prevention of atrial fibrillation and serious complications |
|
Biobank - Construction and Utilization biobank for genomic medicine REalization, Japan Agency for Medical Research and Development (AMED) | Understanding pathogenesis of atrial fibrillation and implementation of precision medicine by WGS and multi-omics |
|
Biobank - Construction and Utilization biobank for genomic medicine REalization, Japan Agency for Medical Research and Development (AMED) | Implementation of next-generation precision medicine for cardiovascular disease by multi-omics |
|
Practical Research Project for Rare / Intractable Diseases, Japan Agency for Medical Research and Development (AMED) | Understanding pathology and implementation of precision medicine for intractable cardiovascular disease by multi-omics analysis |
|
Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS), Japan Agency for Medical Research and Development (AMED) | Support for large-scale functional genomics and development for the platform of evaluating functions of human immunological systems |
|
Practical Research for Innovative Cancer Control, Japan Agency for Medical Research and Development (AMED) | Risk estimation of each cancer risk by integrating genetic, environmental, and lifestyle factors in 140,000 samples of 23 cancer types |
|
BioBank Japan Project for Genomic and Clinical Research, Japan Agency for Medical Research and Development (AMED) | Management of the Japanese biobank |
|
Ministry of Education, Culture, Sports, Science and Technology in Japan | Tailor-made Medical Treatment Program (the 3rd phase) | N/A |
Tailor-Made Medical Treatment with the BioBank Japan Project (BBJ), Japan Agency for Medical Research and Development (AMED) | Generating large-scale data of genetic polymorphism to identify disease-related genes |
|
Project for Cancer Research and Therapeutic Evolution (P-CREATE), Japan Agency for Medical Research and Development (AMED) | Exploration of special and temporal diversity in genome and epigenome of hematological malignancies based on large-scale sequencing analyses. |
|
Related publications
| Title | DOI | Dataset ID |
|---|---|---|
A genome-wide association study identifies PLCL2 and AP3D1-DOT1L-SF3A2 as new susceptibility loci for myocardial infarction in Japanese. | ||
A functional variant in ZNF512B is associated with susceptibility to amyotrophic lateral sclerosis in Japanese. | ||
Functional variants in ADH1B and ALDH2 coupled with alcohol and smoking synergistically enhance esophageal cancer risk. | ||
SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations. | ||
Common variants in a novel gene, FONG on chromosome 2q33.1 confer risk of osteoporosis in Japanese. | ||
Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes. | ||
Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. | ||
Genome-wide association study identifies eight new susceptibility loci for atopic dermatitis in the Japanese population. | ||
Identification of six new genetic loci associated with atrial fibrillation in the Japanese population. | ||
Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. | ||
Genome-wide association study identifies seven novel susceptibility loci for primary open-angle glaucoma. | ||
Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. | ||
Elucidating the genetic architecture of reproductive ageing in the Japanese population | ||
Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese. | ||
Germline pathogenic variants of 11 breast cancer genes in 7,051 Japanese patients and 11,241 controls. | ||
A Variant within the FTO confers susceptibility to diabetic nephropathy in Japanese patients with type 2 diabetes | ||
Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population | ||
GWAS of smoking behaviour in 165,436 Japanese people reveals seven new loci and shared genetic architecture. | ||
Characterizing rare and low-frequency height-associated variants in the Japanese population | ||
Germline pathogenic variants in 7,636 Japanese patients with prostate cancer and 12,366 controls. | ||
Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases | ||
GWAS of 165,084 Japanese individuals identified nine loci associated with dietary habits | ||
Population-specific and transethnic genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. | ||
Genetic characterization of pancreatic cancer patients and prediction of carrier status of germline pathogenic variants in cancer-predisposing genes | ||
Population-based Screening for Hereditary Colorectal Cancer Variants in Japan | ||
Genome-wide association study reveals BET1L associated with survival time in the 137,693 Japanese individuals | ||
Cross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction | ||
Association between germline pathogenic variants in cancer-predisposing genes and lymphoma risk | ||
Helicobacter pylori, Homologous-Recombination Genes, and Gastric Cancer | ||
Germ line DDX41 mutations define a unique subtype of myeloid neoplasms | ||
Combined landscape of single-nucleotide variants and copy number alterations in clonal hematopoiesis | ||
Chromosomal alterations among age-related haematopoietic clones in Japan | ||
Detection of trait-associated structural variations using short-read sequencing | ||
Population-specific non-coding and coding putative causal variants shape quantitative traits | ||
Population-specific reference panel improves imputation quality for genome-wide association studies conducted on the Japanese population |
Controlled access users
| Principal investigator | Affiliation | Country/Region | Research title | Period of data use | Dataset ID |
|---|---|---|---|---|---|
| Mark Daly | Broad Institute of MIT and Harvard | Massachusetts, United States | BioBank Japan (BBJ) Dataset | 2018-09-11 – 2028-07-31 | |
| Yukinori Okada | Department of Statistical Genetics, Osaka University Graduate School of Medicine | Japan | Development of statistical genetic analysis methods using the whole-genome data from B cell lines of Japanese individuals | 2018-09-20 – 2029-03-31 | |
| SHIGEO KAMITSUJI | Statistical Analysis Division, StaGen Co., Ltd. | Japan | Pharmcogenomics study: Study on genetic risks of an adverse event by a new drug | 2018-10-04 – 2019-08-29 | |
| Katsushi Tokunaga | Department of Human Genetics, Graduate School of Medicine, The University of Tokyo | Japan | Development and application of bioinformatics methods to facilitate the detection of genes associated with multifactorial disorders based on large-scale whole genome sequencing data in Japanese individuals | 2018-11-13 – 2027-03-31 | |
| Tatsuhiko Tsunoda | Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical andDental University | Japan | Research on big data analysis for precision medicine | 2018-12-18 – 2021-06-18 | |
| Liming Liang | Department of Epidemiology, Harvard T.H. Chan School of Public Health | Massachusetts, United States | Genetic effect, genetic correlation and interaction with environmental exposure for complex traits and diseases across populations | 2019-01-21 – 2028-12-31 | |
| Masao Nagasaki | Department of Integrative Genomics, Tohoku Medical Megabank Organization,Tohoku University | Japan | Development and application of bioinformatics methods to facilitate the detection of genes associated with multifactorial disordersbased on large-scale whole genome sequencing data in Japanese individuals | 2019-01-31 – 2021-03-17 | |
| Seishi Ogawa | Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University | Japan | Comprehensive genome analysis in solid tumors | 2019-02-04 – 2021-07-30 | |
| SHIGEO KAMITSUJI | Statistical Analysis Division, StaGen Co., Ltd. | Japan | Genome-wide association study for comitant strabismus susceptibility in Japanese patients | 2019-03-13 – 2022-03-29 | |
| Takashi Kohno | Division of genome biology, National Cancer Research Institute | Japan | Elucidation of immune-system networks between host and tumor based on genomic analysis | 2019-04-15 – 2029-03-31 | |
| Shigeo Horie | Department of Urology, Juntendo University, Graduate School of Medicine | Japan | Disease risk analysis of mosaic loss of chromosome Y in blood cells | 2019-05-14 – 2029-03-31 | |
| Tatsuhiko Tsunoda | Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, Tokyo University | Japan | Research on sequence, image data analysis for precision medicine | 2019-06-06 – 2023-08-29 | |
| Masayuki Yamamoto | Tohoku Medical Megabank Organization | Japan | Construction of Japanese whole genome database | 2019-06-24 – 2022-04-15 | |
| Kouya Shiraishi | Division of Genome Biology, National Cancer Research Institute | Japan | Elucidation of immune-system networks between host and tumor based on genomic analysis | 2019-08-05 – 2029-03-31 | |
| SHIGEO KAMITSUJI | Statistical Analysis Division, StaGen Co., Ltd. | Japan | Mendelian randomization study using genetic markers of uric acid levels as an instrumental variable | 2019-08-16 – 2024-03-13 | |
| SHIGEO KAMITSUJI | Statistical Analysis Division, StaGen Co., Ltd. | Japan | Mendelian randomization study using 58 clinical laboratory tests and SNP genotype data. | 2019-08-22 – 2024-03-13 | |
| Osamu Ogasawara | Bioinformation and DDBJ Center, National Institute of Genetics | Japan | Evaluation of human genome analysis workflow using JGA/AGD genome data. | 2019-10-11 – 2024-07-05 | |
| Seishi Ogawa | Department of Medical science, Kyoto University | Japan | Comprehensive analysis of genetic alterarions in hematological malignancies | 2019-11-14 – 2024-07-11 | |
| Yasushi Okazaki | Diagnostics and Therapeutics of Intractable Diseases, Graduate School of Medicine, Juntendo University | Japan | Identification of disease biomarkers by disease cohort research network -Whole genome sequencing of epilepsy- | 2020-06-04 – 2023-06-30 | |
| Hata Chihiro | Bioinformation and DDBJ Center, National Institute of Genetics | Japan | Identification of hypomorphic mutations in Japanese breast cancer patients | 2020-06-04 – 2028-03-31 | |
| Yosuke Kawai | Genome Medical Science Project, National Center for Global Health and Medicine | Japan | Large scale genome analysis of modern human genomes to infer the origin of Yaponesians | 2020-06-19 – 2028-03-31 | |
| Nakao Iwata | Department of Psychiatry, Fujita Health University School of Medicine | Japan | Research for investigating susceptibility of mental state, mental disorders, drug efficacy and side effects through genetic analysis | 2020-08-17 – 2024-07-22 | |
| Charleston Chiang | Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California | California, United States | Investigating the evolution of complex genetic architecture in participants of Biobank Japan | 2022-03-03 – 2030-07-01 | |
| Hae Kyung Im | Biological Sciences Division, University of Chicago | Illinois, United States | Predicted Gene Expression: High Power, Mechanism, and Direction of Effect | 2020-09-15 – 2023-11-06 | |
| Atray Dixit | Coral Genomics, Inc. | California, United States | Derivation and Evaluation of Functional Response Scores | 2020-08-24 – 2022-01-17 | |
| SHIGEO KAMITSUJI | Statistical Analysis Division, StaGen Co., Ltd. | Japan | Identifying the genetic risk factors for Stent Thrombosis by genome-wide association study | 2022-03-07 – 2025-02-17 | |
| Kazuhiro Nakayama | Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo | Japan | Investigation of genome variation influening activity of brown adipose tissues | 2022-03-07 – 2027-09-18 | |
| Taisei Mushiroda | RIKEN Center for Integrative Medical Sciences | Japan | SNP analysis of wheat allergy | 2021-03-26 – 2028-03-31 | |
| Hongyu Zhao | Department of Biostatistics, Yale School of Public Health | United States | Leveraging multi-ethnic data and functional annotations in casual variant identification, genetic correlation estimation, and genetic risk prediction | 2024-12-25 – 2030-03-01 | |
| KEISHI FUJIO | Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, University of Tokyo | Japan | Integrative analysis of immune-cell eQTL data and large-scaled GWAS data in Japanese | 2020-12-16 – 2026-09-29 | |
| Masataka Kikuchi | Department of Genome Informatics, Graduate School of Medicine, Osaka University | Japan | Imputation analysis using a Japanese reference panel | 2020-12-16 – 2022-10-03 | |
| Fumihiko Matsuda | Center for Genomic Medicine, Kyoto University | Japan | Elucidation of Japanese genetic diversity | 2021-02-08 – 2025-03-31 | |
| Emiko Noguchi | Department of Medical Genetics, Faculty of Medicine, University of Tsukuba | Japan | Exploratory study of genetic factors in allergic diseases | 2021-03-26 – 2032-03-31 | |
| Gil McVean | King Charles House, Genomics plc | United Kingdom | Development of polygenic risk scores in diverse ancestries for diseases, traits and conditions | 2022-07-19 – 2025-06-17 | |
| Masao Nagasaki | Center for Genomic Medicine, Graduate School of Medicine Center for the Promotion of Interdisciplinary Education and Research, Kyoto University | Japan | Development and application of bioinformatics methods to facilitate the detection of genes associated with multifactorial disorders based on large-scale whole genome sequencing data of Japanese individuals | 2021-04-01 – 2024-08-23 | |
| Noriko Sato | Department of Molecular Epidemiology Medical Research Institute, Tokyo Medical and Dental University | Japan | Analysis of genetic and environmental risks of obesity and diabetes based on regional cohort longitudinal data | 2021-04-13 – 2022-03-17 | |
| Takashi Kohno | Division of Genome Biology, National Cancer Center Research Institute | Japan | Identification of genetic risk factors in AYA(Adolescence and Young Adult) cancer | 2021-05-26 – 2028-12-31 | |
| Akihiro Fujimoto | School of Integrated Health Sciences, Faculty of Medicine, The University of Tokyo | Japan | Comprehensive analysis of mutations and genetic diversity by analyzing whole-genome sequence data | 2021-09-21 – 2024-12-02 | |
| Yasunobu Nagata | Department of hematology, Nippon Medical School | Japan | Identification of the mechanisms for pathogenesis of hematologic tumors based on novel genetic abnormalities | 2021-06-08 – 2026-03-31 | |
| Atsushi Kawakami | リウマチ・膠原病内科, 長崎大学病院 | Japan | An exploratory study to determine the genetic polymorphisms or mutations associated with type 1 diabetes and interstitial lung disease induced by immune checkpoint inhibitor; nivolumab | 2021-06-16 – 2024-04-02 | |
| Yoshihiro Asano | Department of Cardiovascular Medicine Graduate School of Medicine, Osaka University | Japan | Sensitive gene analysis of hereditary cardiovascular disease | 2021-07-26 – 2027-05-31 | |
| Hironori Masuko | Department of Pulmonary Medicine, University of Tsukuba | Japan | Search for susceptibility genes for chronic inflammatory airway diseases | 2021-08-16 – 2027-03-31 | |
| Takashi Kohno | Division of Genome Biology, National Cancer Center Research Institute | Japan | Identification of genetic risk factors in AYA(Adolescence and Young Adult) cancer | 2021-09-28 – 2028-12-31 | |
| Fumihiko Matsuda | Center for Genomic Medicine, Kyoto University | Japan | Development of personalized medicine | 2021-09-16 – 2028-03-31 | |
| Takashi Matsuda | Advanced Informatics & Analytics, Astellas Pharma Inc. | Japan | Investigation of the correlation between Liver cancer/Hepatitis B and polymorphism | 2021-11-11 – 2022-08-01 | |
| Masanori Arita | Bioinformation and DDBJ Center, National Institute of Genetics | Japan | Provision of processed JGA data analyzed by DDBJ Center and NBDC | 2021-10-07 – 2030-03-31 | |
| Emiko Noguchi | Department of Medical Genetics, Faculty of Medicine, University of Tsukuba | Japan | Identification of the pathogenic factors for food allergy | 2021-12-08 – 2029-03-31 | |
| Joshua Chiou | Internal Medicine Research Unit, Pfizer | Massachusetts, United States | Evaluating GWAS associations from Biobank Japan to Support Confidence in Rationale for Therapeutic Targets | 2022-02-03 – 2025-12-21 | |
| Yosuke Kawai | 人癌病因遺伝子分野, 東京大学医科学研究所 | Japan | Population Genetic Analysis of the Origin of Japanese Populations | 2021-12-15 – 2026-01-22 | |
| Masanori Arita | Bioinformation and DDBJ Center, National Institute of Genetics | Japan | Development of the imputation analysis program and running environment in the NIG supercomputer for personal genome analysis. | 2021-11-29 – 2030-03-31 | |
| Toshiharu Ninomiya | Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University | Japan | Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD) | 2021-12-13 – 2029-07-31 | |
| Hirofumi Nakaoka | Department of Cancer Genome Research, Sasaki Institute | Japan | Analysis of hypomorphic variants in breast cancer-associated genes by using large-scale sequencing data sets | 2022-08-18 – 2026-07-23 | |
| Gil McVean | King Charles House, Genomics plc | United Kingdom | Using large-scale reference panels for imputation and ancestry analysis to support target discovery and polygenic risk score models | 2022-08-04 – 2025-07-23 | |
| Emiko Noguchi | Department of Medical Genetics, Faculty of Medicine, University of Tsukuba | Japan | Research on genetic predisposition to inflammatory lung disease | 2022-09-21 – 2027-03-31 | |
| Keiko Yamazaki | Department of Public Health, Chiba University Graduate School of Medicine | Japan | Prediction of effectiveness to molecular target drugs in Japanese patients with inflammatory bowel disease | 2022-11-15 – 2030-03-31 | |
| Nuria Lopez-Bigas | Biomedical Genomics Lab Cancer Science, Institute for Research in Biomedicine (IRB Barcelona) | Spain | Study of the genetic basis of clonal hematopoiesis | 2022-11-08 – 2027-09-12 | |
| Masataka Kikuchi | Department of Computational Biology and Medical Sciences, The University of Tokyo | Japan | Imputation analysis using a Japanese reference panel | 2022-10-03 – 2025-10-09 | |
| ryosuke kitoh | 医学部 耳鼻咽喉科頭頸部外科, 信州大学 | Japan | Genome-wide association study of the sudden sensorineural hearing loss | 2022-12-22 – 2027-03-31 | |
| Kei Yura | Natural Science Division, Faculty of Core Research, Ochanomizu University | Japan | Data Analysis for Phenotype Prediction of Cancer Suppressor Gene BRCA1 variants | 2023-03-17 – 2028-03-31 | |
| Shigeo Kamitsuji | Statistical Analysis Division, StaGen Co., Ltd. | Japan | Genome-Wide Association Study to identify genetic factors for strabismus in Japanese population | 2023-02-14 – 2027-02-28 | |
| Yoshihiro Onouchi | Department of Public Health, Chiba University Graduate School of Medicine | Japan | A Multicenter Study to Identify Genetic Factors in Kawasaki Disease | 2023-04-06 – 2030-03-31 | |
| Yoshihiro Onouchi | Department of Public Health, Chiba University Graduate School of Medicine | Japan | A study of the genetic background of differences in antibody response to COVID-19 vaccine. | 2023-04-24 – 2030-03-31 | |
| Masaki Kato | 精神神経科, 関西医科大学 | Japan | Exploratory and validation study of genetic and biological factors for the development of precision medicine algorithms for psychiatric disorders. | 2023-08-25 – 2028-06-30 | |
| Hiroki Kimura | Department of Psychiatry, Nagoya University Graduate school of medicine | Japan | Research on elucidation of susceptibility to brain and mental illness (vulnerability to disease onset) and efficacy and side effects of drugs (treatment responsiveness) through genetic analysis | 2023-11-17 – 2028-10-28 | |
| Hiroyuki Mishima | Department of Human Genetics, Atomic Bomb Disease Institute, Nagasaki University | Japan | Development of Methods to Mitigate Batch Effects in Human Whole Genome Sequencing | 2024-04-16 – 2027-03-31 | |
| yasuhiro mochida | Shonan Kamakura General Hospital | Japan | Association between Clonal hematopoiesis of indeterminate potential and Chronic Kidney Disease in Japanese cohort study | 2024-02-07 – 2027-03-31 | |
| Chikashi Terao | Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences | Japan | Research on personalized medicine based on genomics information | 2023-11-21 – 2026-07-10 | |
| Chikashi Terao | Immunology Research, Clinical Research Center, Shizuoka General Hospital | Japan | Investigation of Genetic Factors Associated with Human Phenotypic Traits | 2024-04-25 – 2028-12-03 | |
| Masao Nagasaki | Division of Biomedical Information Analysis, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University | Japan | Development and application of bioinformatics methods to facilitate the detection of genes associated with multifactorial disorders based on large-scale whole genome sequencing data of Japanese individuals | 2024-06-24 – 2027-03-31 | |
| Koichi Matsuda | Clinical genome sequencing, The University of Tokyo | Japan | Disease Cohort Research Network for Disease Marker Exploratory Studies | 2024-06-17 – 2029-03-31 | |
| Norihiro Kato | Gene Diagnostics and Therapeutics, National Center for Global Health and Medicine | Japan | Study of genetic predisposition of primary aldosteronism and its clinical significance | 2024-06-24 – 2029-03-31 | |
| Kouya Shiraishi | Department of Clinical Genomics, National Cancer Center Research Institute | Japan | Search for genes involved in susceptibility to lung cancer | 2024-06-24 – 2025-12-15 | |
| Taisei Mushiroda | Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences | Japan | Search of genomic biomarkers associated with drug-induced eruptions | 2024-08-01 – 2028-03-31 | |
| Masahiro Nakatochi | Public Health Informatics Unit Department of Integrated Health Sciences, Nagoya University | Japan | Exploration of factors involved in the onset, progression, and prognosis of amyotrophic lateral sclerosis. | 2024-08-27 – 2030-03-31 | |
| Masanori Arita | Bioinformation and DDBJ Center, National Institute of Genetics | Japan | Provision of processed JGA data analyzed by DDBJ Center and DBCLS | 2025-04-28 – 2030-03-31 | |
| Atsushi Ono | Hiroshima University | Japan | Utilizing Genomic Information to Address Challenges in Liver Diseases | 2025-06-25 – 2034-03-31 | |
| Kouya Shiraishi | Department of Clinical Genomics, National Cancer Center Research Institute | Japan | AYA (Adolescence and Young Adult) Generation Cancer Research Aiming to Identify Genetic Factors that Contribute to Personalized Prevention | 2025-05-02 – 2026-04-07 | |
| Masahiro Miyake | Kyoto University | Japan | Genetic research on ophthalmic diseases | 2025-06-02 – 2027-03-31 | |
| Norihiro Kato | Gene Diagnostics and Therapeutics, National Center for Global Health and Medicine | Japan | Clinical Application of Polygenic Risk Scores for Glaucoma in the Japanese Population | 2025-11-17 – 2030-03-31 | |
| Taku Nakashima | Department of Molecular and Internal Medicine, Hiroshima University | Japan | A Study on the Relationship Between Pulmonary Fibrosis and Clonal Hematopoiesis | 2025-12-11 – 2028-03-31 | |
| Jian Huang | Institute for Human Development and Potential (IHDP), Agency for Science, Technology and Research (A*STAR) | Singapore | Multi-omic causal inference strategy for drug repurposing and pharmacovigilance across the lifespan | 2026-04-13 – 2028-03-31 | |
| Hirofumi Nakaoka | Department of Biomedical Data Science, Kagoshima University Graduate School of Medical and Dental Sciences | Japan | Analysis of hypomorphic variants in breast cancer-associated genes by using large-scale sequencing data sets | 2026-04-13 – 2030-03-31 | |
| Taisuke Ishikawa | Kagoshima University | Japan | Elucidation of the genetic basis of arrhythmia syndromes | 2026-08-05 – 2029-03-31 | |
| Ryota Hashimoto | Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry | Japan | Exploratory research on new diagnostic classification and pathology elucidation of neuropsychiatric disorders based on biological data | 2026-09-10 – 2028-07-31 | |
| Chikashi Terao | Center for Genomic Medicine, Research Promotion Headquarters, Fujita Health University | Japan | Research on Personalized Medicine Leveraging Integrated Genomic Data | 2026-09-03 – 2036-03-31 | |
| Masataka Kikuchi | Department of Molecular Genetics, Niigata University | Japan | Imputation analysis using a Japanese reference panel | 2026-09-10 – 2028-03-31 |