Tohoku University Tohoku Medical Megabank Organization (Gen Tamiya)
2019.09.20

1.   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  
2.   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  
3.   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. 2019; : . doi:10.1093/ije/dyz169  
4.   Sakurai Rieko, Ueki Masao, Makino Satoshi, et al. Outlier detection for questionnaire data in biobanks. International Journal of Epidemiology. 2019; : . doi:10.1093/ije/dyz012  
5.   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  
6.   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  
7.   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  
8.   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  
9.   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  
10.   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  
11.   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  
12.   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  
13.   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  
14.   Kuroha Takeshi, Nagai Keisuke, Gamuyao Rico, et al. Ethylene-gibberellin signaling underlies adaptation of rice to periodic flooding. Science (New York, N.Y.). 2018; 361 (6398): 181-186. doi:10.1126/science.aat1577  
15.   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  
16.   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  
17.   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  
18.   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  
19.   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  
20.   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  
21.   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  
22.   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  
23.   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  
24.   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  
25.   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  
26.   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  
27.   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): 156-8. doi:10.1016/j.jdermsci.2015.07.015  
28.   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  
29.   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): 233-5. doi:10.1111/pcmr.12337  
30.   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): 1211-2. doi:10.1111/1346-8138.13095  
31.   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  
32.   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): 636-49. doi:10.1105/tpc.113.121350  
33.   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