Tohoku University Tohoku Medical Megabank Organization (Gen Tamiya)
2024.03.29

1.   Ohseto Hisashi, Takahashi Ippei, Narita Akira, et al. Risk Factors, Prognosis, Influence on the Offspring, and Genetic Architecture of Perinatal Depression Classified Based on the Depressive Symptom Trajectory. Depression and Anxiety. 2024; 2024 : 1-13. doi:10.1155/2024/6622666  
2.   Harada Sei, Iida Miho, Miyagawa Naoko, et al. Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS). Journal of Epidemiology. 2024; : JE20230192. doi:10.2188/jea.JE20230192  
3.   Yaoita Hisao, Kawai Eiichiro, Takayama Jun, et al. Genetic etiology of truncus arteriosus excluding 22q11.2 deletion syndrome and identification of c.1617del, a prevalent variant in TMEM260, in the Japanese population. Journal of Human Genetics. 2024; : . doi:10.1038/s10038-024-01223-y  
4.   Tadaka Shu, Kawashima Junko, Hishinuma Eiji, et al. jMorp: Japanese Multi-Omics Reference Panel update report 2023. Nucleic Acids Research. 2024; 52 (D1): D622-D632. doi:10.1093/nar/gkad978  
5.   Mizuno Satoshi, Wagata Maiko, Nagaie Satoshi, et al. Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant women. Scientific Reports. 2024; 14 (1): 6292. doi:10.1038/s41598-024-55914-9  
6.   Mizuno Satoshi, Nagaie Satoshi, Tamiya Gen, et al. Establishment of the early prediction models of low-birth-weight reveals influential genetic and environmental factors: a prospective cohort study. BMC Pregnancy and Childbirth. 2023; 23 (1): 628. doi:10.1186/s12884-023-05919-5  
7.   Li Xue, Ono Chiaki, Warita Noriko, et al. Comprehensive evaluation of machine learning algorithms for predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability. Frontiers in Psychiatry. 2023; 14 : . doi:10.3389/fpsyt.2023.1104222  
8.   Takase Masato, Nakaya Naoki, Nakamura Tomohiro, et al. Influence of Diabetes Family History on the Associations of Combined Genetic and Lifestyle Risks with Diabetes in the Tohoku Medical Megabank Community-Based Cohort Study. Journal of Atherosclerosis and Thrombosis. 2023; 30 (12): 64425. doi:10.5551/jat.64425  
9.   Saito-Hakoda Akiko, Kikuchi Atsuo, Takahashi Tadahisa, et al. Familial Paget’s disease of bone with ocular manifestations and a novel TNFRSF11A duplication variant (72dup27). Journal of Bone and Mineral Metabolism. 2023; 41 (2): 193-202. doi:10.1007/s00774-022-01392-w  
10.   Sugawara Yuka, Hirakawa Yosuke, Nagasu Hajime, et al. Genome-wide association study of the risk of chronic kidney disease and kidney-related traits in the Japanese population: J-Kidney-Biobank. Journal of Human Genetics. 2023; 68 (2): 55-64. doi:10.1038/s10038-022-01094-1  
11.   Nishioka Masaki, Takayama Jun, Sakai Naomi, et al. Deep exome sequencing identifies enrichment of deleterious mosaic variants in neurodevelopmental disorder genes and mitochondrial tRNA regions in bipolar disorder. Molecular Psychiatry. 2023; 28 (10): 4294-4306. doi:10.1038/s41380-023-02096-x  
12.   Uneoka Saki, Kobayashi Tomoko, Numata-Uematsu Yurika, et al. A Case Series of Patients With MYBPC1 Gene Variants Featuring Undulating Tongue Movements as Myogenic Tremor. Pediatric Neurology. 2023; 146 : 16-20. doi:10.1016/j.pediatrneurol.2023.06.002  
13.   Katata Yu, Uneoka Saki, Saijyo Naoya, et al. The longest reported sibling survivors of a severe form of congenital myasthenic syndrome with the ALG14 pathogenic variant. American Journal of Medical Genetics Part A. 2022; 188 (4): 1293-1298. doi:10.1002/ajmg.a.62629  
14.   Shibuya Moriei, Yaoita Hisao, Kodama Kaori, et al. A patient with early-onset SMAX3 and a novel variant of ATP7A. Brain and Development. 2022; 44 (1): 63-67. doi:10.1016/j.braindev.2021.08.004  
15.   Otsuki Akihito, Okamura Yasunobu, Ishida Noriko, et al. Construction of a trio-based structural variation panel utilizing activated T lymphocytes and long-read sequencing technology. Communications Biology. 2022; 5 (1): 991. doi:10.1038/s42003-022-03953-1  
16.   Li Xue, Ono Chiaki, Warita Noriko, et al. Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women. Frontiers in Psychiatry. 2022; 12 : . doi:10.3389/fpsyt.2021.799029  
17.   Kanoni Stavroula, Graham Sarah E., Wang Yuxuan, et al. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis. Genome Biology. 2022; 23 (1): 268. doi:10.1186/s13059-022-02837-1  
18.   Shiga Naomi, Yamaguchi-Kabata Yumi, Igeta Saori, et al. Pathological variants in genes associated with disorders of sex development and central causes of hypogonadism in a whole-genome reference panel of 8380 Japanese individuals. Human Genome Variation. 2022; 9 (1): 34. doi:10.1038/s41439-022-00213-w  
19.   Sugawara Junichi, Ishikuro Mami, Obara Taku, et al. Maternal Baseline Characteristics and Perinatal Outcomes: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. Journal of epidemiology. 2022; 32 (2): 69-79. doi:10.2188/jea.JE20200338  
20.   Kanno Miyako, Suzuki Mitsuyoshi, Tanikawa Ken, et al. Heterozygous calcyclin-binding protein/Siah1-interacting protein (CACYBP/SIP) gene pathogenic variant linked to a dominant family with paucity of interlobular bile duct. Journal of Human Genetics. 2022; 67 (7): 393-397. doi:10.1038/s10038-022-01017-0  
21.   Ramdas Shweta, Judd Jonathan, Graham Sarah E., et al. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids. The American Journal of Human Genetics. 2022; 109 (8): 1366-1387. doi:10.1016/j.ajhg.2022.06.012  
22.   Narishige Yuta, Yaoita Hisao, Shibuya Moriei, et al. Two Siblings with Cerebellar Ataxia, Mental Retardation, and Disequilibrium Syndrome 4 and a Novel Variant of ATP8A2. The Tohoku Journal of Experimental Medicine. 2022; 256 (4): 2022.J010. doi:10.1620/tjem.2022.J010  
23.   Uchida Yasuo, Higuchi Tomoya, Shirota Matsuyuki, et al. Identification and Validation of Combination Plasma Biomarker of Afamin, Fibronectin and Sex Hormone-Binding Globulin to Predict Pre-eclampsia. Biological and Pharmaceutical Bulletin. 2021; 44 (6): 804-815. doi:10.1248/bpb.b20-01043  
24.   Suzuki Shiori, Goto Atsushi, Nakatochi Masahiro, et al. Body mass index and colorectal cancer risk: A Mendelian randomization study. Cancer Science. 2021; 112 (4): 1579-1588. doi:10.1111/cas.14824  
25.   Ueki Masao, Tamiya Gen. Smooth-threshold multivariate genetic prediction incorporating gene–environment interactions. G3 Genes|Genomes|Genetics. 2021; 11 (12): . doi:10.1093/g3journal/jkab278  
26.   Ogishima Soichi, Nagaie Satoshi, Mizuno Satoshi, et al. dbTMM: an integrated database of large-scale cohort, genome and clinical data for the Tohoku Medical Megabank Project. Human Genome Variation. 2021; 8 (1): 44. doi:10.1038/s41439-021-00175-5  
27.   Nagaoka Shinichi, Yamaguchi-Kabata Yumi, Shiga Naomi, et al. Estimation of the carrier frequencies and proportions of potential patients by detecting causative gene variants associated with autosomal recessive bone dysplasia using a whole-genome reference panel of Japanese individuals. Human Genome Variation. 2021; 8 (1): 2. doi:10.1038/s41439-020-00133-7  
28.   Hozawa Atsushi, Tanno Kozo, Nakaya Naoki, et al. Study profile of the tohoku medical megabank community-based cohort study. Journal of Epidemiology. 2021; 31 (1): 65-76. doi:10.2188/jea.JE20190271  
29.   Mitsui Tetsuo, Makino Satoshi, Tamiya Gen, et al. ALOX12 mutation in a family with dominantly inherited bleeding diathesis. Journal of Human Genetics. 2021; 66 (8): 753-759. doi:10.1038/s10038-020-00887-6  
30.   Narita Akira, Ueki Masao, Tamiya Gen. Artificial intelligence powered statistical genetics in biobanks. Journal of Human Genetics. 2021; 66 (1): 61-65. doi:10.1038/s10038-020-0822-y  
31.   Shido Kosuke, Kojima Kaname, Shirota Matsuyuki, et al. GWAS Identified IL4R and the Major Histocompatibility Complex Region as the Associated Loci of Total Serum IgE Levels in 9,260 Japanese Individuals. Journal of Investigative Dermatology. 2021; 141 (11): 2749-2752. doi:10.1016/j.jid.2021.02.762  
32.   Graham Sarah E., Clarke Shoa L., Wu Kuan-Han H., et al. The power of genetic diversity in genome-wide association studies of lipids. Nature. 2021; 600 (7890): 675-679. doi:10.1038/s41586-021-04064-3  
33.   Takayama Jun, Tadaka Shu, Yano Kenji, et al. Construction and integration of three de novo Japanese human genome assemblies toward a population-specific reference. Nature Communications. 2021; 12 (1): 226. doi:10.1038/s41467-020-20146-8  
34.   Gharahkhani Puya, Jorgenson Eric, Hysi Pirro, et al. Genome-wide meta-analysis identifies 127 open-angle glaucoma loci with consistent effect across ancestries. Nature Communications. 2021; 12 (1): 1258. doi:10.1038/s41467-020-20851-4  
35.   Sakaue Saori, Kanai Masahiro, Tanigawa Yosuke, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nature Genetics. 2021; 53 (10): 1415-1424. doi:10.1038/s41588-021-00931-x  
36.   Tadaka Shu, Hishinuma Eiji, Komaki Shohei, et al. jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population. Nucleic Acids Research. 2021; 49 (D1): D536-D544. doi:10.1093/nar/gkaa1034  
37.   Kojima Kaname, Shido Kosuke, Tamiya Gen, et al. Facial UV photo imaging for skin pigmentation assessment using conditional generative adversarial networks. Scientific Reports. 2021; 11 (1): 1213. doi:10.1038/s41598-020-79995-4  
38.   Sakurai-Yageta Mika, Kumada Kazuki, Gocho Chinatsu, et al. Japonica Array NEO with increased genome-wide coverage and abundant disease risk SNPs. The Journal of Biochemistry. 2021; 170 (3): 399-410. doi:10.1093/jb/mvab060  
39.   Shigemizu Daichi, Mitsumori Risa, Akiyama Shintaro, et al. Ethnic and trans-ethnic genome-wide association studies identify new loci influencing Japanese Alzheimer’s disease risk. Translational Psychiatry. 2021; 11 (1): 151. doi:10.1038/s41398-021-01272-3  
40.   Okuda Hiroshi, Okamoto Koji, Abe Michiaki, et al. Genome-wide association study identifies new loci for albuminuria in the Japanese population. Clinical and Experimental Nephrology. 2020; 24 (8): 1-9. doi:10.1007/s10157-020-01884-x  
41.   Koshiba Seizo, Motoike Ikuko N., Saigusa Daisuke, et al. Identification of critical genetic variants associated with metabolic phenotypes of the Japanese population. Communications Biology. 2020; 3 (1): 662. doi:10.1038/s42003-020-01383-5  
42.   Kuriyama Shinichi, Metoki Hirohito, Kikuya Masahiro, et al. Cohort Profile: Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study): rationale, progress and perspective. International Journal of Epidemiology. 2020; 49 (1): 18-19m. doi:10.1093/ije/dyz169  
43.   Kojima Kaname, Tadaka Shu, Katsuoka Fumiki, et al. A genotype imputation method for de-identified haplotype reference information by using recurrent neural network. PLOS Computational Biology. 2020; 16 (10): e1008207. doi:10.1371/journal.pcbi.1008207  
44.   Takahashi Yuta, Yoshizoe Kazuki, Ueki Masao, et al. Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms. Scientific Reports. 2020; 10 (1): 21726. doi:10.1038/s41598-020-78966-z  
45.   Tsuboi Akito, Matsui Hiroyuki, Shiraishi Naru, et al. Design and Progress of Oral Health Examinations in the Tohoku Medical Megabank Project. The Tohoku Journal of Experimental Medicine. 2020; 251 (2): 97-115. doi:10.1620/tjem.251.97  
46.   Narita Akira, Nagai Masato, Mizuno Satoshi, et al. Clustering by phenotype and genome-wide association study in autism. Translational Psychiatry. 2020; 10 (1): 290. doi:10.1038/s41398-020-00951-x  
47.   Takahashi Yuta, Ueki Masao, Yamada Makoto, et al. Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection. Translational Psychiatry. 2020; 10 (1): 157. doi:10.1038/s41398-020-0831-9  
48.   Takahashi Yuta, Ueki Masao, Tamiya Gen, et al. Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes. Translational Psychiatry. 2020; 10 (1): 294. doi:10.1038/s41398-020-00957-5  
49.   Akatsuka Jun, Yamamoto Yoichiro, Sekine Tetsuro, et al. Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches. Biomolecules. 2019; 9 (11): 673. doi:10.3390/biom9110673  
50.   Yamaguchi-Kabata Yumi, Yasuda Jun, Uruno Akira, et al. Estimating carrier frequencies of newborn screening disorders using a whole-genome reference panel of 3552 Japanese individuals. Human Genetics. 2019; 138 (4): 389-409. doi:10.1007/s00439-019-01998-7  
51.   Tadaka Shu, Katsuoka Fumiki, Ueki Masao, et al. 3.5KJPNv2: an allele frequency panel of 3552 Japanese individuals including the X chromosome. Human Genome Variation. 2019; 6 (1): 28. doi:10.1038/s41439-019-0059-5  
52.   Sakurai Rieko, Ueki Masao, Makino Satoshi, et al. Outlier detection for questionnaire data in biobanks. International Journal of Epidemiology. 2019; 48 (4): 1305-1315. doi:10.1093/ije/dyz012  
53.   Fuse Nobuo, Sakurai-Yageta Mika, Katsuoka Fumiki, et al. Establishment of Integrated Biobank for Precision Medicine and Personalized Healthcare: The Tohoku Medical Megabank Project. JMA Journal. 2019; 2 (2): 113-122. doi:10.31662/jmaj.2019-0014  
54.   Numakura Chikahiko, Tamiya Gen, Ueki Masao, et al. Growth impairment in individuals with citrin deficiency. Journal of Inherited Metabolic Disease. 2019; 42 (3): 501-508. doi:10.1002/jimd.12051  
55.   Shido Kosuke, Kojima Kaname, Yamasaki Kenshi, et al. Susceptibility Loci for Tanning Ability in the Japanese Population Identified by a Genome-Wide Association Study from the Tohoku Medical Megabank Project Cohort Study. Journal of Investigative Dermatology. 2019; 139 (7): 1605-1608.e13. doi:10.1016/j.jid.2019.01.015  
56.   Hashimoto Taeko, Harita Yutaka, Takizawa Keiichi, et al. In Vivo Expression of NUP93 and Its Alteration by NUP93 Mutations Causing Focal Segmental Glomerulosclerosis. Kidney International Reports. 2019; 4 (9): 1312-1322. doi:10.1016/j.ekir.2019.05.1157  
57.   Iwasawa Shinya, Kikuchi Atsuo, Wada Yoichi, et al. The prevalence of GALM mutations that cause galactosemia: A database of functionally evaluated variants. Molecular Genetics and Metabolism. 2019; 126 (4): 362-367. doi:10.1016/j.ymgme.2019.01.018  
58.   Yamamoto Yoichiro, Tsuzuki Toyonori, Akatsuka Jun, et al. Automated acquisition of explainable knowledge from unannotated histopathology images. Nature Communications. 2019; 10 (1): 5642. doi:10.1038/s41467-019-13647-8  
59.   Ueki Masao, Fujii Masahiro, Tamiya Gen. Quick assessment for systematic test statistic inflation/deflation due to null model misspecifications in genome-wide environment interaction studies. PLOS ONE. 2019; 14 (7): e0219825. doi:10.1371/journal.pone.0219825  
60.   Miura Emiri, Tsuchiya Naho, Igarashi Yu, et al. Respiratory resistance among adults in a population-based cohort study in Northern Japan. Respiratory Investigation. 2019; 57 (3): 274-281. doi:10.1016/j.resinv.2018.12.008  
61.   Yasuda Jun, Kinoshita Kengo, Katsuoka Fumiki, et al. Genome analyses for the Tohoku Medical Megabank Project towards establishment of personalized healthcare. The Journal of Biochemistry. 2019; 165 (2): 139-158. doi:10.1093/jb/mvy096  
62.   Yasuda Jun, Katsuoka Fumiki, Danjoh Inaho, et al. Regional genetic differences among Japanese populations and performance of genotype imputation using whole-genome reference panel of the Tohoku Medical Megabank Project. BMC Genomics. 2018; 19 (1): 551. doi:10.1186/s12864-018-4942-0  
63.   Koshiba Seizo, Motoike Ikuko, Saigusa Daisuke, et al. Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project. Genes to Cells. 2018; 23 (6): 406-417. doi:10.1111/gtc.12588  
64.   Yamaguchi-Kabata Yumi, Yasuda Jun, Tanabe Osamu, et al. Evaluation of reported pathogenic variants and their frequencies in a Japanese population based on a whole-genome reference panel of 2049 individuals. Journal of Human Genetics. 2018; 63 (2): 213-230. doi:10.1038/s10038-017-0347-1  
65.   Kuroha Takeshi, Nagai Keisuke, Gamuyao Rico, et al. Ethylene-gibberellin signaling underlies adaptation of rice to periodic flooding. Science. 2018; 361 (6398): 181-186. doi:10.1126/science.aat1577  
66.   Hiyama Gen, Mizushima Shusei, Matsuzaki Mei, et al. Female Japanese quail visually differentiate testosterone-dependent male attractiveness for mating preferences. Scientific Reports. 2018; 8 (1): 10012. doi:10.1038/s41598-018-28368-z  
67.   Obara Taku, Ishikuro Mami, Tamiya Gen, et al. Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods. Scientific Reports. 2018; 8 (1): 14840. doi:10.1038/s41598-018-33110-w  
68.   Takai-Igarashi Takako, Kinoshita Kengo, Nagasaki Masao, et al. Security controls in an integrated Biobank to protect privacy in data sharing: rationale and study design. BMC Medical Informatics and Decision Making. 2017; 17 (1): 100. doi:10.1186/s12911-017-0494-5  
69.   Ueki Masao, Kawasaki Yoshinori, Tamiya Gen. Detecting genetic association through shortest paths in a bidirected graph. Genetic Epidemiology. 2017; 41 (6): 481-497. doi:10.1002/gepi.22051  
70.   Shido K, Kojima K, Hozawa A, et al. 503 Genome-wide association study identifies novel susceptibility loci for tanning ability in Japanese population. Journal of Investigative Dermatology. 2017; 137 (5): S86. doi:10.1016/j.jid.2017.02.523  
71.   Hachiya Tsuyoshi, Komaki Shohei, Hasegawa Yutaka, et al. Genome-wide meta-analysis in Japanese populations identifies novel variants at the TMC6–TMC8 and SIX3–SIX2 loci associated with HbA1c. Scientific Reports. 2017; 7 (1): 16147. doi:10.1038/s41598-017-16493-0  
72.   Ueki Masao, Tamiya Gen. Smooth-Threshold Multivariate Genetic Prediction with Unbiased Model Selection. Genetic Epidemiology. 2016; 40 (3): 233-243. doi:10.1002/gepi.21958  
73.   Araki Yuta, Okamura Ken, Munkhbat Batmunkh, et al. Whole-exome sequencing confirmation of multiple MC1R variants associated with extensive freckles and red hair: Analysis of a Mongolian family. Journal of Dermatological Science. 2016; 84 (2): 216-219. doi:10.1016/j.jdermsci.2016.08.009  
74.   Kuriyama Shinichi, Yaegashi Nobuo, Nagami Fuji, et al. The Tohoku Medical Megabank Project: Design and Mission. Journal of Epidemiology. 2016; 26 (9): 493-511. doi:10.2188/jea.JE20150268  
75.   Ogino Daisuke, Hashimoto Taeko, Hattori Motoshi, et al. Analysis of the genes responsible for steroid-resistant nephrotic syndrome and/or focal segmental glomerulosclerosis in Japanese patients by whole-exome sequencing analysis. Journal of Human Genetics. 2016; 61 (2): 137-141. doi:10.1038/jhg.2015.122  
76.   Okamura Ken, Ohe Rintaro, Abe Yuko, et al. Immunohistopathological analysis of frizzled-4-positive immature melanocytes from hair follicles of patients with Rhododenol-induced leukoderma. Journal of Dermatological Science. 2015; 80 (2): 156-158. doi:10.1016/j.jdermsci.2015.07.015  
77.   Sato Hiroko, Uchida Toshihiko, Toyota Kentaro, et al. Association of neonatal hyperbilirubinemia in breast-fed infants with UGT1A1 or SLCOs polymorphisms. Journal of Human Genetics. 2015; 60 (1): 35-40. doi:10.1038/jhg.2014.98  
78.   Shimanuki Miwa, Abe Yuko, Tamiya Gen, et al. Positive selection with diversity in oculocutaneous albinisms type 2 gene ( OCA2 ) among Japanese. Pigment Cell & Melanoma Research. 2015; 28 (2): 233-235. doi:10.1111/pcmr.12337  
79.   Okamura Ken, Oiso Naoki, Tamiya Gen, et al. Waardenburg syndrome type IIE in a Japanese patient caused by a novel missense mutation in the SOX10 gene. The Journal of Dermatology. 2015; 42 (12): 1211-1212. doi:10.1111/1346-8138.13095  
80.   Yoshizawa Junko, Abe Yuko, Oiso Naoki, et al. Variants in melanogenesis-related genes associate with skin cancer risk among Japanese populations. The Journal of Dermatology. 2014; 41 (4): 296-302. doi:10.1111/1346-8138.12432  
81.   Iwano Megumi, Igarashi Motoko, Tarutani Yoshiaki, et al. A Pollen Coat–Inducible Autoinhibited Ca2+-ATPase Expressed in Stigmatic Papilla Cells Is Required for Compatible Pollination in the Brassicaceae. The Plant Cell. 2014; 26 (2): 636-649. doi:10.1105/tpc.113.121350  
82.   Shibata Kyoko, Hozawa Atsushi, Tamiya Gen, et al. The confounding effect of cryptic relatedness for environmental risks of systolic blood pressure on cohort studies. Molecular Genetics & Genomic Medicine. 2013; 1 (1): 45-53. doi:10.1002/mgg3.4