Tohoku University Tohoku Medical Megabank Organization (Gen Tamiya)
2022.08.12

1.   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  
2.   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  
3.   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  
4.   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  
5.   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  
6.   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  
7.   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  
8.   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  
9.   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  
10.   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  
11.   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  
12.   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  
13.   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  
14.   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  
15.   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  
16.   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  
17.   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  
18.   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  
19.   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  
20.   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  
21.   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  
22.   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  
23.   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  
24.   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  
25.   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  
26.   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  
27.   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  
28.   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  
29.   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  
30.   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  
31.   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  
32.   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  
33.   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  
34.   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  
35.   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  
36.   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  
37.   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  
38.   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  
39.   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  
40.   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  
41.   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  
42.   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  
43.   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  
44.   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  
45.   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  
46.   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  
47.   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  
48.   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  
49.   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  
50.   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  
51.   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  
52.   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  
53.   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  
54.   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  
55.   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  
56.   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  
57.   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  
58.   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  
59.   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  
60.   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  
61.   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  
62.   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  
63.   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  
64.   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  
65.   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  
66.   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