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1. Stroke genetics informs drug discovery and risk prediction across ancestries

Author: Mishra, A, Malik, R, Hachiya, T, Jurgenson, T, Namba, S, Posner, DC, Kamanu, FK, Koido, M, Le Grand, Q, Shi, M, He, Y, Georgakis, MK, Caro, I, Krebs, K, Liaw, Y-C, Vaura, FC, Lin, K, Winsvold, BS, Srinivasasainagendra, V, Parodi, L, Bae, H-J, Chauhan, G, Chong, MR, Tomppo, L, Akinyemi, R, Roshchupkin, GV, Habib, N, Jee, YH, Thomassen, JQ, Abedi, V, Carcel-Marquez, J, Nygaard, M, Leonard, HL, Yang, C, Yonova-Doing, E, Knol, MJ, Lewis, AJ, Judy, RL, Ago, T, Amouyel, P, Armstrong, ND, Bakker, MK, Bartz, TM, Bennett, DA, Bis, JC, Bordes, C, Borte, S, Cain, A, Ridker, PM, Cho, K, Chen, Z, Cruchaga, C, Cole, JW, de Jager, PL, de Cid, R, Endres, M, Ferreira, LE, Geerlings, MI, Gasca, NC, Gudnason, V, Hata, J, He, J, Heath, AK, Ho, Y-L, Havulinna, AS, Hopewell, JC, Hyacinth, HI, Inouye, M, Jacob, MA, Jeon, CE, Jern, C, Kamouchi, M, Keene, KL, Kitazono, T, Kittner, SJ, Konuma, T, Kumar, A, Lacaze, P, Launer, LJ, Lee, K-J, Lepik, K, Li, J, Li, L, Manichaikul, A, Markus, HS, Marston, NA, Meitinger, T, Mitchell, BD, Montellano, FA, Morisaki, T, Mosley, TH, Nalls, MA, Nordestgaard, BG, O'Donnell, MJ, Okada, Y, Onland-Moret, NC, Ovbiagele, B, Peters, A, Psaty, BM, Rich, SS, Rosand, J, Sabatine, MS, Sacco, RL, Saleheen, D, Sandset, EC, Salomaa, V, Sargurupremraj, M, Sasaki, M, Satizabal, CL, Schmidt, CO, Shimizu, A, Smith, NL, Sloane, KL, Sutoh, Y, Sun, YV, Tanno, K, Tiedt, S, Tatlisumak, T, Torres-Aguila, NP, Tiwari, HK, Tregouet, D-A, Trompet, S, Tuladhar, AM, Tybjaerg-Hansen, A, van Vugt, M, Vibo, R, Verma, SS, Wiggins, KL, Wennberg, P, Woo, D, Wilson, PWF, Xu, H, Yang, Q, Yoon, K, Millwood, IY, Gieger, C, Ninomiya, T, Grabe, HJ, Jukema, JW, Rissanen, IL, Strbian, D, Kim, YJ, Chen, P-H, Mayerhofer, E, Howson, JMM, Irvin, MR, Adams, H, Wassertheil-Smoller, S, Christensen, K, Ikram, MA, Rundek, T, Worrall, BB, Lathrop, GM, Riaz, M, Simonsick, EM, Korv, J, Franca, PHC, Zand, R, Prasad, K, Frikke-Schmidt, R, de Leeuw, F-E, Liman, T, Haeusler, KG, Ruigrok, YM, Heuschmann, PU, Longstreth, WT, Jung, KJ, Bastarache, L, Pare, G, Damrauer, SM, Chasman, DI, Rotter, JI, Anderson, CD, Zwart, J-A, Niiranen, TJ, Fornage, M, Liaw, Y-P, Seshadri, S, Fernandez-Cadenas, I, Walters, RG, Ruff, CT, Owolabi, MO, Huffman, JE, Milani, L, Kamatani, Y, Dichgans, M, Debette, S, Lee, J-M, Cheng, Y-C, Meschia, JF, Chen, WM, Sale, MM, Zonderman, AB, Evans, MK, Wilson, JG, Correa, A, Traylor, M, Lewis, CM, Reiner, A, Haessler, J, Langefeld, CD, Gottesman, RF, Yaffe, K, Liu, YM, Kooperberg, C, Lange, LA, Furie, KL, Arnett, DK, Benavente, OR, Grewal, RP, Peddareddygari, LR, Hveem, K, Lindstrom, S, Wang, L, Smith, EN, Gordon, W, Vlieg, AVH, de Andrade, M, Brody, JA, Pattee, JW, Brumpton, BM, Suchon, P, Chen, M-H, Frazer, KA, Turman, C, Germain, M, MacDonald, J, Braekkan, SK, Armasu, SM, Pankratz, N, Jackson, RD, Nielsen, JB, Giulianin, F, Puurunen, MK, Ibrahim, M, Heckbert, SR, Bammler, TK, McCauley, BM, Taylor, KD, Pankow, JS, Reiner, AP, Gabrielsen, ME, Deleuze, J-F, O'Donnell, CJ, Kim, J, McKnight, B, Kraft, P, Hansen, J-B, Rosendaal, FR, Heit, JA, Tang, W, Morange, P-E, Johnson, AD, Kabrhel, C, van Dijk, EJ, Koudstaal, PJ, Luijckx, G-J, Nederkoorn, PJ, van Oostenbrugge, RJ, Visser, MC, Wermer, MJH, Kappelle, LJ, Esko, T, Metspalu, A, Magi, R, Nelis, M, Levi, CR, Maguire, J, Jimenez-Conde, J, Sharma, P, Sudlow, CLM, Rannikmae, K, Schmidt, R, Slowik, A, Pera, J, Thijs, VNS, Lindgren, AG, Ilinca, A, Melander, O, Engstrom, G, Rexrode, KM, Rothwell, PM, Stanne, TM, Johnson, JA, Danesh, J, Butterworth, AS, Heitsch, L, Boncoraglio, GB, Kubo, M, Pezzini, A, Rolfs, A, Giese, A-K, Weir, D, Ross, OA, Lemmons, R, Soderholm, M, Cushman, M, Jood, K, McDonough, CW, Bell, S, Linkohr, B, Lee, T-H, Putaala, J, Lopez, OL, Carty, CL, Jian, X, Schminke, U, Cullell, N, Delgado, P, Ibanez, L, Krupinski, J, Lioutas, V, Matsuda, K, Montaner, J, Muino, E, Roquer, J, Sarnowski, C, Sattar, N, Sibolt, G, Teumer, A, Rutten-Jacobs, L, Kanai, M, Gretarsdottir, S, Rost, NS, Yusuf, S, Almgren, P, Ay, H, Bevan, S, Brown, RD, Carrera, C, Buring, JE, Chen, W-M, Cotlarciuc, I, de Bakker, PIW, DeStefano, AL, den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gustafsson, S, Hassan, A, Holliday, EG, Howard, G, Hsu, F-C, Ingelsson, E, Harris, TB, Kissela, BM, Kleindorfer, DO, Langenberg, C, Leys, D, Lin, W-Y, Lorentzen, E, Magnusson, PK, McArdle, PF, Pulit, SL, Rice, K, Sakaue, S, Sapkota, BR, Tanislav, C, Thorleifsson, G, Thorsteinsdottir, U, Tzourio, C, van Duijn, CM, Walters, M, Wareham, NJ, Amin, N, Aparicio, HJ, Attia, J, Beiser, AS, Berr, C, Bustamante, M, Caso, V, Choi, SH, Chowhan, A, Dartigues, J-F, Delavaran, H, Dorr, M, Ford, I, Gurpreet, WS, Hamsten, A, Hozawa, A, Ingelsson, M, Iwasaki, M, Kaffashian, S, Kalra, L, Kjartansson, O, Kloss, M, Labovitz, DL, Laurie, CC, Lind, L, Lindgren, CM, Makoto, H, Minegishi, N, Morris, AP, Mueller-Nurasyid, M, Norrving, B, Ogishima, S, Parati, EA, Pedersen, NL, Perola, M, Jousilahti, P, Pileggi, S, Rabionet, R, Riba-Llena, I, Ribases, M, Romero, JR, Rudd, AG, Sarin, A-P, Sarju, R, Satoh, M, Sawada, N, Sigurdsson, A, Smith, A, Stine, OC, Stott, DJ, Strauch, K, Takai, T, Tanaka, H, Touze, E, Tsugane, S, Uitterlinden, AG, Valdimarsson, EM, van der Lee, SJ, Wakai, K, Williams, SR, Wolfe, CDA, Wong, Q, Yamaji, T, Sanghera, DK, Stefansson, K, Martinez-Majander, N, Sobue, K, Soriano-Tarraga, C, Volzke, H, Akpa, O, Sarfo, FS, Akpalu, A, Obiako, R, Wahab, K, Osaigbovo, G, Owolabi, L, Komolafe, M, Jenkins, C, Arulogun, O, Ogbole, G, Adeoye, AM, Akinyemi, J, Agunloye, A, Fakunle, AG, Uvere, E, Olalere, A, Adebajo, OJ, Chen, J, Clarke, R, Collins, R, Guo, Y, Wang, C, Lv, J, Peto, R, Chen, Y, Fairhurst-Hunter, Z, Hill, M, Pozarickij, A, Schmidt, D, Stevens, B, Turnbull, I, Yu, C, Nagai, A, Murakami, Y, Shiroma, EJ, Sigurdsson, S, Ghanbari, M, Boerwinkle, E, Fongang, B, Wang, R, Ikram, MK, Volker, U, de Laat, KF, van Norden, AGW, de Kort, PL, Vermeer, SE, Brouwers, PJAM, Gons, RAR, den Heijer, T, van Dijk, GW, van Rooij, FGW, Aamodt, AH, Skogholt, AH, Willer, CJ, Heuch, I, Hagen, K, Fritsche, LG, Pedersen, LM, Ellekjaer, H, Zhou, W, Martinsen, AE, Kristoffersen, ES, Thomas, LF, Kleinschnitz, C, Frantz, S, Ungethum, K, Gallego-Fabrega, C, Lledos, M, Llucia-Carol, L, Sobrino, T, Campos, F, Castillo, J, Freijo, M, Arenillas, JF, Obach, V, Alvarez-Sabin, J, Molina, CA, Ribo, M, Munoz-Narbona, L, Lopez-Cancio, E, Millan, M, Diaz-Navarro, R, Vives-Bauza, C, Serrano-Heras, G, Segura, T, Dhar, R, Delgado-Mederos, R, Prats-Sanchez, L, Camps-Renom, P, Blay, N, Sumoy, L, Marti-Fabregas, J, Schnohr, P, Jensen, GB, Benn, M, Afzal, S, Kamstrup, PR, van Setten, J, van der Laan, SW, Vonk, JMJ, Kim, B-J, Curtze, S, Tiainen, M, Kinnunen, J, Menon, V, Sung, YJ, Saillour-Glenisson, F, Gravel, S, Mishra, A, Malik, R, Hachiya, T, Jurgenson, T, Namba, S, Posner, DC, Kamanu, FK, Koido, M, Le Grand, Q, Shi, M, He, Y, Georgakis, MK, Caro, I, Krebs, K, Liaw, Y-C, Vaura, FC, Lin, K, Winsvold, BS, Srinivasasainagendra, V, Parodi, L, Bae, H-J, Chauhan, G, Chong, MR, Tomppo, L, Akinyemi, R, Roshchupkin, GV, Habib, N, Jee, YH, Thomassen, JQ, Abedi, V, Carcel-Marquez, J, Nygaard, M, Leonard, HL, Yang, C, Yonova-Doing, E, Knol, MJ, Lewis, AJ, Judy, RL, Ago, T, Amouyel, P, Armstrong, ND, Bakker, MK, Bartz, TM, Bennett, DA, Bis, JC, Bordes, C, Borte, S, Cain, A, Ridker, PM, Cho, K, Chen, Z, Cruchaga, C, Cole, JW, de Jager, PL, de Cid, R, Endres, M, Ferreira, LE, Geerlings, MI, Gasca, NC, Gudnason, V, Hata, J, He, J, Heath, AK, Ho, Y-L, Havulinna, AS, Hopewell, JC, Hyacinth, HI, Inouye, M, Jacob, MA, Jeon, CE, Jern, C, Kamouchi, M, Keene, KL, Kitazono, T, Kittner, SJ, Konuma, T, Kumar, A, Lacaze, P, Launer, LJ, Lee, K-J, Lepik, K, Li, J, Li, L, Manichaikul, A, Markus, HS, Marston, NA, Meitinger, T, Mitchell, BD, Montellano, FA, Morisaki, T, Mosley, TH, Nalls, MA, Nordestgaard, BG, O'Donnell, MJ, Okada, Y, Onland-Moret, NC, Ovbiagele, B, Peters, A, Psaty, BM, Rich, SS, Rosand, J, Sabatine, MS, Sacco, RL, Saleheen, D, Sandset, EC, Salomaa, V, Sargurupremraj, M, Sasaki, M, Satizabal, CL, Schmidt, CO, Shimizu, A, Smith, NL, Sloane, KL, Sutoh, Y, Sun, YV, Tanno, K, Tiedt, S, Tatlisumak, T, Torres-Aguila, NP, Tiwari, HK, Tregouet, D-A, Trompet, S, Tuladhar, AM, Tybjaerg-Hansen, A, van Vugt, M, Vibo, R, Verma, SS, Wiggins, KL, Wennberg, P, Woo, D, Wilson, PWF, Xu, H, Yang, Q, Yoon, K, Millwood, IY, Gieger, C, Ninomiya, T, Grabe, HJ, Jukema, JW, Rissanen, IL, Strbian, D, Kim, YJ, Chen, P-H, Mayerhofer, E, Howson, JMM, Irvin, MR, Adams, H, Wassertheil-Smoller, S, Christensen, K, Ikram, MA, Rundek, T, Worrall, BB, Lathrop, GM, Riaz, M, Simonsick, EM, Korv, J, Franca, PHC, Zand, R, Prasad, K, Frikke-Schmidt, R, de Leeuw, F-E, Liman, T, Haeusler, KG, Ruigrok, YM, Heuschmann, PU, Longstreth, WT, Jung, KJ, Bastarache, L, Pare, G, Damrauer, SM, Chasman, DI, Rotter, JI, Anderson, CD, Zwart, J-A, Niiranen, TJ, Fornage, M, Liaw, Y-P, Seshadri, S, Fernandez-Cadenas, I, Walters, RG, Ruff, CT, Owolabi, MO, Huffman, JE, Milani, L, Kamatani, Y, Dichgans, M, Debette, S, Lee, J-M, Cheng, Y-C, Meschia, JF, Chen, WM, Sale, MM, Zonderman, AB, Evans, MK, Wilson, JG, Correa, A, Traylor, M, Lewis, CM, Reiner, A, Haessler, J, Langefeld, CD, Gottesman, RF, Yaffe, K, Liu, YM, Kooperberg, C, Lange, LA, Furie, KL, Arnett, DK, Benavente, OR, Grewal, RP, Peddareddygari, LR, Hveem, K, Lindstrom, S, Wang, L, Smith, EN, Gordon, W, Vlieg, AVH, de Andrade, M, Brody, JA, Pattee, JW, Brumpton, BM, Suchon, P, Chen, M-H, Frazer, KA, Turman, C, Germain, M, MacDonald, J, Braekkan, SK, Armasu, SM, Pankratz, N, Jackson, RD, Nielsen, JB, Giulianin, F, Puurunen, MK, Ibrahim, M, Heckbert, SR, Bammler, TK, McCauley, BM, Taylor, KD, Pankow, JS, Reiner, AP, Gabrielsen, ME, Deleuze, J-F, O'Donnell, CJ, Kim, J, McKnight, B, Kraft, P, Hansen, J-B, Rosendaal, FR, Heit, JA, Tang, W, Morange, P-E, Johnson, AD, Kabrhel, C, van Dijk, EJ, Koudstaal, PJ, Luijckx, G-J, Nederkoorn, PJ, van Oostenbrugge, RJ, Visser, MC, Wermer, MJH, Kappelle, LJ, Esko, T, Metspalu, A, Magi, R, Nelis, M, Levi, CR, Maguire, J, Jimenez-Conde, J, Sharma, P, Sudlow, CLM, Rannikmae, K, Schmidt, R, Slowik, A, Pera, J, Thijs, VNS, Lindgren, AG, Ilinca, A, Melander, O, Engstrom, G, Rexrode, KM, Rothwell, PM, Stanne, TM, Johnson, JA, Danesh, J, Butterworth, AS, Heitsch, L, Boncoraglio, GB, Kubo, M, Pezzini, A, Rolfs, A, Giese, A-K, Weir, D, Ross, OA, Lemmons, R, Soderholm, M, Cushman, M, Jood, K, McDonough, CW, Bell, S, Linkohr, B, Lee, T-H, Putaala, J, Lopez, OL, Carty, CL, Jian, X, Schminke, U, Cullell, N, Delgado, P, Ibanez, L, Krupinski, J, Lioutas, V, Matsuda, K, Montaner, J, Muino, E, Roquer, J, Sarnowski, C, Sattar, N, Sibolt, G, Teumer, A, Rutten-Jacobs, L, Kanai, M, Gretarsdottir, S, Rost, NS, Yusuf, S, Almgren, P, Ay, H, Bevan, S, Brown, RD, Carrera, C, Buring, JE, Chen, W-M, Cotlarciuc, I, de Bakker, PIW, DeStefano, AL, den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gustafsson, S, Hassan, A, Holliday, EG, Howard, G, Hsu, F-C, Ingelsson, E, Harris, TB, Kissela, BM, Kleindorfer, DO, Langenberg, C, Leys, D, Lin, W-Y, Lorentzen, E, Magnusson, PK, McArdle, PF, Pulit, SL, Rice, K, Sakaue, S, Sapkota, BR, Tanislav, C, Thorleifsson, G, Thorsteinsdottir, U, Tzourio, C, van Duijn, CM, Walters, M, Wareham, NJ, Amin, N, Aparicio, HJ, Attia, J, Beiser, AS, Berr, C, Bustamante, M, Caso, V, Choi, SH, Chowhan, A, Dartigues, J-F, Delavaran, H, Dorr, M, Ford, I, Gurpreet, WS, Hamsten, A, Hozawa, A, Ingelsson, M, Iwasaki, M, Kaffashian, S, Kalra, L, Kjartansson, O, Kloss, M, Labovitz, DL, Laurie, CC, Lind, L, Lindgren, CM, Makoto, H, Minegishi, N, Morris, AP, Mueller-Nurasyid, M, Norrving, B, Ogishima, S, Parati, EA, Pedersen, NL, Perola, M, Jousilahti, P, Pileggi, S, Rabionet, R, Riba-Llena, I, Ribases, M, Romero, JR, Rudd, AG, Sarin, A-P, Sarju, R, Satoh, M, Sawada, N, Sigurdsson, A, Smith, A, Stine, OC, Stott, DJ, Strauch, K, Takai, T, Tanaka, H, Touze, E, Tsugane, S, Uitterlinden, AG, Valdimarsson, EM, van der Lee, SJ, Wakai, K, Williams, SR, Wolfe, CDA, Wong, Q, Yamaji, T, Sanghera, DK, Stefansson, K, Martinez-Majander, N, Sobue, K, Soriano-Tarraga, C, Volzke, H, Akpa, O, Sarfo, FS, Akpalu, A, Obiako, R, Wahab, K, Osaigbovo, G, Owolabi, L, Komolafe, M, Jenkins, C, Arulogun, O, Ogbole, G, Adeoye, AM, Akinyemi, J, Agunloye, A, Fakunle, AG, Uvere, E, Olalere, A, Adebajo, OJ, Chen, J, Clarke, R, Collins, R, Guo, Y, Wang, C, Lv, J, Peto, R, Chen, Y, Fairhurst-Hunter, Z, Hill, M, Pozarickij, A, Schmidt, D, Stevens, B, Turnbull, I, Yu, C, Nagai, A, Murakami, Y, Shiroma, EJ, Sigurdsson, S, Ghanbari, M, Boerwinkle, E, Fongang, B, Wang, R, Ikram, MK, Volker, U, de Laat, KF, van Norden, AGW, de Kort, PL, Vermeer, SE, Brouwers, PJAM, Gons, RAR, den Heijer, T, van Dijk, GW, van Rooij, FGW, Aamodt, AH, Skogholt, AH, Willer, CJ, Heuch, I, Hagen, K, Fritsche, LG, Pedersen, LM, Ellekjaer, H, Zhou, W, Martinsen, AE, Kristoffersen, ES, Thomas, LF, Kleinschnitz, C, Frantz, S, Ungethum, K, Gallego-Fabrega, C, Lledos, M, Llucia-Carol, L, Sobrino, T, Campos, F, Castillo, J, Freijo, M, Arenillas, JF, Obach, V, Alvarez-Sabin, J, Molina, CA, Ribo, M, Munoz-Narbona, L, Lopez-Cancio, E, Millan, M, Diaz-Navarro, R, Vives-Bauza, C, Serrano-Heras, G, Segura, T, Dhar, R, Delgado-Mederos, R, Prats-Sanchez, L, Camps-Renom, P, Blay, N, Sumoy, L, Marti-Fabregas, J, Schnohr, P, Jensen, GB, Benn, M, Afzal, S, Kamstrup, PR, van Setten, J, van der Laan, SW, Vonk, JMJ, Kim, B-J, Curtze, S, Tiainen, M, Kinnunen, J, Menon, V, Sung, YJ, Saillour-Glenisson, F, and Gravel, S
Abstract: Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.
Published: 2022

2. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Author: Grapotte M., Saraswat M., Bessiere C., Menichelli C., Ramilowski J. A., Severin J., Hayashizaki Y., Itoh M., Tagami M., Murata M., Kojima-Ishiyama M., Noma S., Noguchi S., Kasukawa T., Hasegawa A., Suzuki H., Nishiyori-Sueki H., Frith M. C., Abugessaisa I., Aitken S., Aken B. L., Alam I., Alam T., Alasiri R., Alhendi A. M. N., Alinejad-Rokny H., Alvarez M. J., Andersson R., Arakawa T., Araki M., Arbel T., Archer J., Archibald A. L., Arner E., Arner P., Asai K., Ashoor H., Astrom G., Babina M., Baillie J. K., Bajic V. B., Bajpai A., Baker S., Baldarelli R. M., Balic A., Bansal M., Batagov A. O., Batzoglou S., Beckhouse A. G., Beltrami A. P., Beltrami C. A., Bertin N., Bhattacharya S., Bickel P. J., Blake J. A., Blanchette M., Bodega B., Bonetti A., Bono H., Bornholdt J., Bttcher M., Bougouffa S., Boyd M., Breda J., Brombacher F., Brown J. B., Bult C. J., Burroughs A. M., Burt D. W., Busch A., Caglio G., Califano A., Cameron C. J., Cannistraci C. V., Carbone A., Carlisle A. J., Carninci P., Carter K. W., Cesselli D., Chang J. -C., Chen J. C., Chen Y., Chierici M., Christodoulou J., Ciani Y., Clark E. L., Coskun M., Dalby M., Dalla E., Daub C. O., Davis C. A., de Hoon M. J. L., de Rie D., Denisenko E., Deplancke B., Detmar M., Deviatiiarov R., Di Bernardo D., Diehl A. D., Dieterich L. C., Dimont E., Djebali S., Dohi T., Dostie J., Drablos F., Edge A. S. B., Edinger M., Ehrlund A., Ekwall K., Elofsson A., Endoh M., Enomoto H., Enomoto S., Faghihi M., Fagiolini M., Farach-Carson M. C., Faulkner G. J., Favorov A., Fernandes A. M., Ferrai C., Forrest A. R. R., Forrester L. M., Forsberg M., Fort A., Francescatto M., Freeman T. C., Frith M., Fukuda S., Funayama M., Furlanello C., Furuno M., Furusawa C., Gao H., Gazova I., Gebhard C., Geier F., Geijtenbeek T. B. H., Ghosh S., Ghosheh Y., Gingeras T. R., Gojobori T., Goldberg T., Goldowitz D., Gough J., Greco D., Gruber A. J., Guhl S., Guigo R., Guler R., Gusev O., Gustincich S., Ha T. J., Haberle V., Hale P., Hallstrom B. M., Hamada M., Handoko L., Hara M., Harbers M., Harrow J., Harshbarger J., Hase T., Hashimoto K., Hatano T., Hattori N., Hayashi R., Herlyn M., Hettne K., Heutink P., Hide W., Hitchens K. J., Sui S. H., 't Hoen P. A. C., Hon C. C., Hori F., Horie M., Horimoto K., Horton P., Hou R., Huang E., Huang Y., Hugues R., Hume D., Ienasescu H., Iida K., Ikawa T., Ikemura T., Ikeo K., Inoue N., Ishizu Y., Ito Y., Ivshina A. V., Jankovic B. R., Jenjaroenpun P., Johnson R., Jorgensen M., Jorjani H., Joshi A., Jurman G., Kaczkowski B., Kai C., Kaida K., Kajiyama K., Kaliyaperumal R., Kaminuma E., Kanaya T., Kaneda H., Kapranov P., Kasianov A. S., Katayama T., Kato S., Kawaguchi S., Kawai J., Kawaji H., Kawamoto H., Kawamura Y. I., Kawasaki S., Kawashima T., Kempfle J. S., Kenna T. J., Kere J., Khachigian L., Kiryu H., Kishima M., Kitajima H., Kitamura T., Kitano H., Klaric E., Klepper K., Klinken S. P., Kloppmann E., Knox A. J., Kodama Y., Kogo Y., Kojima M., Kojima S., Komatsu N., Komiyama H., Kono T., Koseki H., Koyasu S., Kratz A., Kukalev A., Kulakovskiy I., Kundaje A., Kunikata H., Kuo R., Kuo T., Kuraku S., Kuznetsov V. A., Kwon T. J., Larouche M., Lassmann T., Law A., Le-Cao K. -A., Lecellier C. -H., Lee W., Lenhard B., Lennartsson A., Li K., Li R., Lilje B., Lipovich L., Lizio M., Lopez G., Magi S., Mak G. K., Makeev V., Manabe R., Mandai M., Mar J., Maruyama K., Maruyama T., Mason E., Mathelier A., Matsuda H., Medvedeva Y. A., Meehan T. F., Mejhert N., Meynert A., Mikami N., Minoda A., Miura H., Miyagi Y., Miyawaki A., Mizuno Y., Morikawa H., Morimoto M., Morioka M., Morishita S., Moro K., Motakis E., Motohashi H., Mukarram A. K., Mummery C. L., Mungall C. J., Murakawa Y., Muramatsu M., Nagasaka K., Nagase T., Nakachi Y., Nakahara F., Nakai K., Nakamura K., Nakamura Y., Nakazawa T., Nason G. P., Nepal C., Nguyen Q. H., Nielsen L. K., Nishida K., Nishiguchi K. M., Nishiyori H., Nitta K., Notredame C., Ogishima S., Ohkura N., Ohno H., Ohshima M., Ohtsu T., Okada Y., Okada-Hatakeyama M., Okazaki Y., Oksvold P., Orlando V., Ow G. S., Ozturk M., Pachkov M., Paparountas T., Parihar S. P., Park S. -J., Pascarella G., Passier R., Persson H., Philippens I. H., Piazza S., Plessy C., Pombo A., Ponten F., Poulain S., Poulsen T. M., Pradhan S., Prezioso C., Pridans C., Qin X. -Y., Quackenbush J., Rackham O., Ramilowski J., Ravasi T., Rehli M., Rennie S., Rito T., Rizzu P., Robert C., Roos M., Rost B., Roudnicky F., Roy R., Rye M. B., Sachenkova O., Saetrom P., Sai H., Saiki S., Saito M., Saito A., Sakaguchi S., Sakai M., Sakaue S., Sakaue-Sawano A., Sandelin A., Sano H., Sasamoto Y., Sato H., Saxena A., Saya H., Schafferhans A., Schmeier S., Schmidl C., Schmocker D., Schneider C., Schueler M., Schultes E. A., Schulze-Tanzil G., Semple C. A., Seno S., Seo W., Sese J., Sheng G., Shi J., Shimoni Y., Shin J. W., SimonSanchez J., Sivertsson A., Sjostedt E., Soderhall C., Laurent G. S., Stoiber M. H., Sugiyama D., Summers K. M., Suzuki A. M., Suzuki K., Suzuki M., Suzuki N., Suzuki T., Swanson D. J., Swoboda R. K., Taguchi A., Takahashi H., Takahashi M., Takamochi K., Takeda S., Takenaka Y., Tam K. T., Tanaka H., Tanaka R., Tanaka Y., Tang D., Taniuchi I., Tanzer A., Tarui H., Taylor M. S., Terada A., Terao Y., Testa A. C., Thomas M., Thongjuea S., Tomii K., Triglia E. T., Toyoda H., Tsang H. G., Tsujikawa M., Uhlen M., Valen E., van de Wetering M., van Nimwegen E., Velmeshev D., Verardo R., Vitezic M., Vitting-Seerup K., von Feilitzen K., Voolstra C. R., Vorontsov I. E., Wahlestedt C., Wasserman W. W., Watanabe K., Watanabe S., Wells C. A., Winteringham L. N., Wolvetang E., Yabukami H., Yagi K., Yamada T., Yamaguchi Y., Yamamoto M., Yamamoto Y., Yamanaka Y., Yano K., Yasuzawa K., Yatsuka Y., Yo M., Yokokura S., Yoneda M., Yoshida E., Yoshida Y., Yoshihara M., Young R., Young R. S., Yu N. Y., Yumoto N., Zabierowski S. E., Zhang P. G., Zucchelli S., Zwahlen M., Chatelain C., Brehelin L., Grapotte, M., Saraswat, M., Bessiere, C., Menichelli, C., Ramilowski, J. A., Severin, J., Hayashizaki, Y., Itoh, M., Tagami, M., Murata, M., Kojima-Ishiyama, M., Noma, S., Noguchi, S., Kasukawa, T., Hasegawa, A., Suzuki, H., Nishiyori-Sueki, H., Frith, M. C., Abugessaisa, I., Aitken, S., Aken, B. L., Alam, I., Alam, T., Alasiri, R., Alhendi, A. M. N., Alinejad-Rokny, H., Alvarez, M. J., Andersson, R., Arakawa, T., Araki, M., Arbel, T., Archer, J., Archibald, A. L., Arner, E., Arner, P., Asai, K., Ashoor, H., Astrom, G., Babina, M., Baillie, J. K., Bajic, V. B., Bajpai, A., Baker, S., Baldarelli, R. M., Balic, A., Bansal, M., Batagov, A. O., Batzoglou, S., Beckhouse, A. G., Beltrami, A. P., Beltrami, C. A., Bertin, N., Bhattacharya, S., Bickel, P. J., Blake, J. A., Blanchette, M., Bodega, B., Bonetti, A., Bono, H., Bornholdt, J., Bttcher, M., Bougouffa, S., Boyd, M., Breda, J., Brombacher, F., Brown, J. B., Bult, C. J., Burroughs, A. M., Burt, D. W., Busch, A., Caglio, G., Califano, A., Cameron, C. J., Cannistraci, C. V., Carbone, A., Carlisle, A. J., Carninci, P., Carter, K. W., Cesselli, D., Chang, J. -C., Chen, J. C., Chen, Y., Chierici, M., Christodoulou, J., Ciani, Y., Clark, E. L., Coskun, M., Dalby, M., Dalla, E., Daub, C. O., Davis, C. A., de Hoon, M. J. L., de Rie, D., Denisenko, E., Deplancke, B., Detmar, M., Deviatiiarov, R., Di Bernardo, D., Diehl, A. D., Dieterich, L. C., Dimont, E., Djebali, S., Dohi, T., Dostie, J., Drablos, F., Edge, A. S. B., Edinger, M., Ehrlund, A., Ekwall, K., Elofsson, A., Endoh, M., Enomoto, H., Enomoto, S., Faghihi, M., Fagiolini, M., Farach-Carson, M. C., Faulkner, G. J., Favorov, A., Fernandes, A. M., Ferrai, C., Forrest, A. R. R., Forrester, L. M., Forsberg, M., Fort, A., Francescatto, M., Freeman, T. C., Frith, M., Fukuda, S., Funayama, M., Furlanello, C., Furuno, M., Furusawa, C., Gao, H., Gazova, I., Gebhard, C., Geier, F., Geijtenbeek, T. B. H., Ghosh, S., Ghosheh, Y., Gingeras, T. R., Gojobori, T., Goldberg, T., Goldowitz, D., Gough, J., Greco, D., Gruber, A. J., Guhl, S., Guigo, R., Guler, R., Gusev, O., Gustincich, S., Ha, T. J., Haberle, V., Hale, P., Hallstrom, B. M., Hamada, M., Handoko, L., Hara, M., Harbers, M., Harrow, J., Harshbarger, J., Hase, T., Hashimoto, K., Hatano, T., Hattori, N., Hayashi, R., Herlyn, M., Hettne, K., Heutink, P., Hide, W., Hitchens, K. J., Sui, S. H., 't Hoen, P. A. C., Hon, C. C., Hori, F., Horie, M., Horimoto, K., Horton, P., Hou, R., Huang, E., Huang, Y., Hugues, R., Hume, D., Ienasescu, H., Iida, K., Ikawa, T., Ikemura, T., Ikeo, K., Inoue, N., Ishizu, Y., Ito, Y., Ivshina, A. V., Jankovic, B. R., Jenjaroenpun, P., Johnson, R., Jorgensen, M., Jorjani, H., Joshi, A., Jurman, G., Kaczkowski, B., Kai, C., Kaida, K., Kajiyama, K., Kaliyaperumal, R., Kaminuma, E., Kanaya, T., Kaneda, H., Kapranov, P., Kasianov, A. S., Katayama, T., Kato, S., Kawaguchi, S., Kawai, J., Kawaji, H., Kawamoto, H., Kawamura, Y. I., Kawasaki, S., Kawashima, T., Kempfle, J. S., Kenna, T. J., Kere, J., Khachigian, L., Kiryu, H., Kishima, M., Kitajima, H., Kitamura, T., Kitano, H., Klaric, E., Klepper, K., Klinken, S. P., Kloppmann, E., Knox, A. J., Kodama, Y., Kogo, Y., Kojima, M., Kojima, S., Komatsu, N., Komiyama, H., Kono, T., Koseki, H., Koyasu, S., Kratz, A., Kukalev, A., Kulakovskiy, I., Kundaje, A., Kunikata, H., Kuo, R., Kuo, T., Kuraku, S., Kuznetsov, V. A., Kwon, T. J., Larouche, M., Lassmann, T., Law, A., Le-Cao, K. -A., Lecellier, C. -H., Lee, W., Lenhard, B., Lennartsson, A., Li, K., Li, R., Lilje, B., Lipovich, L., Lizio, M., Lopez, G., Magi, S., Mak, G. K., Makeev, V., Manabe, R., Mandai, M., Mar, J., Maruyama, K., Maruyama, T., Mason, E., Mathelier, A., Matsuda, H., Medvedeva, Y. A., Meehan, T. F., Mejhert, N., Meynert, A., Mikami, N., Minoda, A., Miura, H., Miyagi, Y., Miyawaki, A., Mizuno, Y., Morikawa, H., Morimoto, M., Morioka, M., Morishita, S., Moro, K., Motakis, E., Motohashi, H., Mukarram, A. K., Mummery, C. L., Mungall, C. J., Murakawa, Y., Muramatsu, M., Nagasaka, K., Nagase, T., Nakachi, Y., Nakahara, F., Nakai, K., Nakamura, K., Nakamura, Y., Nakazawa, T., Nason, G. P., Nepal, C., Nguyen, Q. H., Nielsen, L. K., Nishida, K., Nishiguchi, K. M., Nishiyori, H., Nitta, K., Notredame, C., Ogishima, S., Ohkura, N., Ohno, H., Ohshima, M., Ohtsu, T., Okada, Y., Okada-Hatakeyama, M., Okazaki, Y., Oksvold, P., Orlando, V., Ow, G. S., Ozturk, M., Pachkov, M., Paparountas, T., Parihar, S. P., Park, S. -J., Pascarella, G., Passier, R., Persson, H., Philippens, I. H., Piazza, S., Plessy, C., Pombo, A., Ponten, F., Poulain, S., Poulsen, T. M., Pradhan, S., Prezioso, C., Pridans, C., Qin, X. -Y., Quackenbush, J., Rackham, O., Ramilowski, J., Ravasi, T., Rehli, M., Rennie, S., Rito, T., Rizzu, P., Robert, C., Roos, M., Rost, B., Roudnicky, F., Roy, R., Rye, M. B., Sachenkova, O., Saetrom, P., Sai, H., Saiki, S., Saito, M., Saito, A., Sakaguchi, S., Sakai, M., Sakaue, S., Sakaue-Sawano, A., Sandelin, A., Sano, H., Sasamoto, Y., Sato, H., Saxena, A., Saya, H., Schafferhans, A., Schmeier, S., Schmidl, C., Schmocker, D., Schneider, C., Schueler, M., Schultes, E. A., Schulze-Tanzil, G., Semple, C. A., Seno, S., Seo, W., Sese, J., Sheng, G., Shi, J., Shimoni, Y., Shin, J. W., Simonsanchez, J., Sivertsson, A., Sjostedt, E., Soderhall, C., Laurent, G. S., Stoiber, M. H., Sugiyama, D., Summers, K. M., Suzuki, A. M., Suzuki, K., Suzuki, M., Suzuki, N., Suzuki, T., Swanson, D. J., Swoboda, R. K., Taguchi, A., Takahashi, H., Takahashi, M., Takamochi, K., Takeda, S., Takenaka, Y., Tam, K. T., Tanaka, H., Tanaka, R., Tanaka, Y., Tang, D., Taniuchi, I., Tanzer, A., Tarui, H., Taylor, M. S., Terada, A., Terao, Y., Testa, A. C., Thomas, M., Thongjuea, S., Tomii, K., Triglia, E. T., Toyoda, H., Tsang, H. G., Tsujikawa, M., Uhlen, M., Valen, E., van de Wetering, M., van Nimwegen, E., Velmeshev, D., Verardo, R., Vitezic, M., Vitting-Seerup, K., von Feilitzen, K., Voolstra, C. R., Vorontsov, I. E., Wahlestedt, C., Wasserman, W. W., Watanabe, K., Watanabe, S., Wells, C. A., Winteringham, L. N., Wolvetang, E., Yabukami, H., Yagi, K., Yamada, T., Yamaguchi, Y., Yamamoto, M., Yamamoto, Y., Yamanaka, Y., Yano, K., Yasuzawa, K., Yatsuka, Y., Yo, M., Yokokura, S., Yoneda, M., Yoshida, E., Yoshida, Y., Yoshihara, M., Young, R., Young, R. S., Yu, N. Y., Yumoto, N., Zabierowski, S. E., Zhang, P. G., Zucchelli, S., Zwahlen, M., Chatelain, C., Brehelin, L., Institute of Biotechnology, Biosciences, Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), National Institute of Advanced Industrial Science and Technology (AIST), SANOFI Recherche, University of British Columbia (UBC), Experimental Immunology, Infectious diseases, AII - Infectious diseases, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)
Subjects: 0301 basic medicine, General Physics and Astronomy, Genome, Mice, 0302 clinical medicine, Transcription (biology), Promoter Regions, Genetic, Transcription Initiation, Genetic, 0303 health sciences, Multidisciplinary, 1184 Genetics, developmental biology, physiology, High-Throughput Nucleotide Sequencing, Neurodegenerative Diseases, 222 Other engineering and technologies, Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], humanities, Enhancer Elements, Genetic, Microsatellite Repeat, Transcription Initiation Site, Sequence motif, Transcription Initiation, Human, Enhancer Elements, Neural Networks, Science, 610 Medicine & health, Computational biology, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Promoter Regions, 03 medical and health sciences, Computer, Deep Learning, Tandem repeat, Genetic, Clinical Research, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Machine learning, Genetics, Animals, Humans, Polymorphism, Enhancer, Transcriptomics, Gene, A549 Cell, 030304 developmental biology, Polymorphism, Genetic, Neurodegenerative Disease, Base Sequence, Animal, Genome, Human, Human Genome, Computational Biology, Promoter, General Chemistry, 113 Computer and information sciences, Cap analysis gene expression, 030104 developmental biology, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Cardiovascular and Metabolic Diseases, A549 Cells, Minion, Generic health relevance, 3111 Biomedicine, Neural Networks, Computer, 610 Medizin und Gesundheit, 030217 neurology & neurosurgery, FANTOM consortium, Microsatellite Repeats
Abstract: Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism., Nature Communications, 12 (1), ISSN:2041-1723
Published: 2020
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3. GA4GH: International policies and standards for data sharing across genomic research and healthcare

Author: Rehm, HL, Page, AJH, Smith, L, Adams, JB, Alterovitz, G, Babb, LJ, Barkley, MP, Baudis, M, Beauvais, MJS, Beck, T, Beckmann, JS, Beltran, S, Bernick, D, Bernier, A, Bonfield, JK, Boughtwood, TF, Bourque, G, Bowers, SR, Brookes, AJ, Brudno, M, Brush, MH, Bujold, D, Burdett, T, Buske, OJ, Cabili, MN, Cameron, DL, Carroll, RJ, Casas-Silva, E, Chakravarty, D, Chaudhari, BP, Chen, SH, Cherry, JM, Chung, J, Cline, M, Clissold, HL, Cook-Deegan, RM, Courtot, M, Cunningham, F, Cupak, M, Davies, RM, Denisko, D, Doerr, MJ, Dolman, LI, Dove, ES, Dursi, LJ, Dyke, SOM, Eddy, JA, Eilbeck, K, Ellrott, KP, Fairley, S, Fakhro, KA, Firth, HV, Fitzsimons, MS, Fiume, M, Flicek, P, Fore, IM, Freeberg, MA, Freimuth, RR, Fromont, LA, Fuerth, J, Gaff, CL, Gan, W, Ghanaim, EM, Glazer, D, Green, RC, Griffith, M, Griffith, OL, Grossman, RL, Groza, T, Auvil, JMG, Guigo, R, Gupta, D, Haendel, MA, Hamosh, A, Hansen, DP, Hart, RK, Hartley, DM, Haussler, D, Hendricks-Sturrup, RM, Ho, CWL, Hobb, AE, Hoffman, MM, Hofmann, OM, Holub, P, Hsu, JS, Hubaux, J-P, Hunt, SE, Husami, A, Jacobsen, JO, Jamuar, SS, Janes, EL, Jeanson, F, Jene, A, Johns, AL, Joly, Y, Jones, SJM, Kanitz, A, Kato, K, Keane, TM, Kekesi-Lafrance, K, Kelleher, J, Kerry, G, Khor, S-S, Knoppers, BM, Konopko, MA, Kosaki, K, Kuba, M, Lawson, J, Leinonen, R, Li, S, Lin, MF, Linden, M, Liu, X, Liyanage, IU, Lopez, J, Lucassen, AM, Lukowski, M, Mann, AL, Marshall, J, Mattioni, M, Metke-Jimenez, A, Middleton, A, Milne, RJ, Molnar-Gabor, F, Mulder, N, Munoz-Torres, MC, Nag, R, Nakagawa, H, Nasir, J, Navarro, A, Nelson, TH, Niewielska, A, Nisselle, A, Niu, J, Nyronen, TH, O'Connor, BD, Oesterle, S, Ogishima, S, Wang, VO, Paglione, LAD, Palumbo, E, Parkinson, HE, Philippakis, AA, Pizarro, AD, Prlic, A, Rambla, J, Rendon, A, Rider, RA, Robinson, PN, Rodarmer, KW, Rodriguez, LL, Rubin, AF, Rueda, M, Rushton, GA, Ryan, RS, Saunders, GI, Schuilenburg, H, Schwede, T, Scollen, S, Senf, A, Sheffield, NC, Skantharajah, N, Smith, AV, Sofia, HJ, Spalding, D, Spurdle, AB, Stark, Z, Stein, LD, Suematsu, M, Tan, P, Tedds, JA, Thomson, AA, Thorogood, A, Tickle, TL, Tokunaga, K, Tomroos, J, Torrents, D, Upchurch, S, Valencia, A, Guimera, RV, Vamathevan, J, Varma, S, Vears, DF, Viner, C, Voisin, C, Wagner, AH, Wallace, SE, Walsh, BP, Williams, MS, Winkler, EC, Wold, BJ, Wood, GM, Woolley, JP, Yamasaki, C, Yates, AD, Yung, CK, Zass, LJ, Zaytseva, K, Zhang, J, Goodhand, P, North, K, Birney, E, Rehm, HL, Page, AJH, Smith, L, Adams, JB, Alterovitz, G, Babb, LJ, Barkley, MP, Baudis, M, Beauvais, MJS, Beck, T, Beckmann, JS, Beltran, S, Bernick, D, Bernier, A, Bonfield, JK, Boughtwood, TF, Bourque, G, Bowers, SR, Brookes, AJ, Brudno, M, Brush, MH, Bujold, D, Burdett, T, Buske, OJ, Cabili, MN, Cameron, DL, Carroll, RJ, Casas-Silva, E, Chakravarty, D, Chaudhari, BP, Chen, SH, Cherry, JM, Chung, J, Cline, M, Clissold, HL, Cook-Deegan, RM, Courtot, M, Cunningham, F, Cupak, M, Davies, RM, Denisko, D, Doerr, MJ, Dolman, LI, Dove, ES, Dursi, LJ, Dyke, SOM, Eddy, JA, Eilbeck, K, Ellrott, KP, Fairley, S, Fakhro, KA, Firth, HV, Fitzsimons, MS, Fiume, M, Flicek, P, Fore, IM, Freeberg, MA, Freimuth, RR, Fromont, LA, Fuerth, J, Gaff, CL, Gan, W, Ghanaim, EM, Glazer, D, Green, RC, Griffith, M, Griffith, OL, Grossman, RL, Groza, T, Auvil, JMG, Guigo, R, Gupta, D, Haendel, MA, Hamosh, A, Hansen, DP, Hart, RK, Hartley, DM, Haussler, D, Hendricks-Sturrup, RM, Ho, CWL, Hobb, AE, Hoffman, MM, Hofmann, OM, Holub, P, Hsu, JS, Hubaux, J-P, Hunt, SE, Husami, A, Jacobsen, JO, Jamuar, SS, Janes, EL, Jeanson, F, Jene, A, Johns, AL, Joly, Y, Jones, SJM, Kanitz, A, Kato, K, Keane, TM, Kekesi-Lafrance, K, Kelleher, J, Kerry, G, Khor, S-S, Knoppers, BM, Konopko, MA, Kosaki, K, Kuba, M, Lawson, J, Leinonen, R, Li, S, Lin, MF, Linden, M, Liu, X, Liyanage, IU, Lopez, J, Lucassen, AM, Lukowski, M, Mann, AL, Marshall, J, Mattioni, M, Metke-Jimenez, A, Middleton, A, Milne, RJ, Molnar-Gabor, F, Mulder, N, Munoz-Torres, MC, Nag, R, Nakagawa, H, Nasir, J, Navarro, A, Nelson, TH, Niewielska, A, Nisselle, A, Niu, J, Nyronen, TH, O'Connor, BD, Oesterle, S, Ogishima, S, Wang, VO, Paglione, LAD, Palumbo, E, Parkinson, HE, Philippakis, AA, Pizarro, AD, Prlic, A, Rambla, J, Rendon, A, Rider, RA, Robinson, PN, Rodarmer, KW, Rodriguez, LL, Rubin, AF, Rueda, M, Rushton, GA, Ryan, RS, Saunders, GI, Schuilenburg, H, Schwede, T, Scollen, S, Senf, A, Sheffield, NC, Skantharajah, N, Smith, AV, Sofia, HJ, Spalding, D, Spurdle, AB, Stark, Z, Stein, LD, Suematsu, M, Tan, P, Tedds, JA, Thomson, AA, Thorogood, A, Tickle, TL, Tokunaga, K, Tomroos, J, Torrents, D, Upchurch, S, Valencia, A, Guimera, RV, Vamathevan, J, Varma, S, Vears, DF, Viner, C, Voisin, C, Wagner, AH, Wallace, SE, Walsh, BP, Williams, MS, Winkler, EC, Wold, BJ, Wood, GM, Woolley, JP, Yamasaki, C, Yates, AD, Yung, CK, Zass, LJ, Zaytseva, K, Zhang, J, Goodhand, P, North, K, and Birney, E
Abstract: The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.
Published: 2021

4. A Map of Alzheimerʼs Disease-Signaling Pathways: A Hope for Drug Target Discovery

Author: Ogishima, S, Mizuno, S, Kikuchi, M, Miyashita, A, Kuwano, R, Tanaka, H, and Nakaya, J
Published: 2013
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5. The Human Phenotype Ontology in 2017

Author: Kohler, S., Vasilevsky, N.A., Engelstad, M., Foster, E., McMurry, J., Ayme, S., Baynam, G., Bello, S.M., Boerkoel, C.F., Boycott, K.M., Brudno, M., Buske, O.J., Chinnery, P.F., Cipriani, V., Connell, L.E., Dawkins, H.J., DeMare, L.E., Devereau, A.D., Vries, B.B. de, Firth, H.V., Freson, K., Greene, D., Hamosh, A., Helbig, I., Hum, C., Jahn, J.A., James, R., Krause, R., Laulederkind, S.J.F., Lochmuller, H., Lyon, G.J., Ogishima, S., Olry, A., Ouwehand, W.H., Pontikos, N., Rath, A., Schaefer, F., Scott, R.H., Segal, M., Sergouniotis, P.I., Sever, R., Smith, C.L., Straub, V., Thompson, R., Turner, C., Turro, E., Veltman, M.W., Vulliamy, T., Yu, J., Ziegenweidt, J. von, Zankl, A., Zuchner, S., Zemojtel, T., Jacobsen, J.O., Groza, T., Smedley, D., Mungall, C.J., Haendel, M., Robinson, P.N., Kohler, S., Vasilevsky, N.A., Engelstad, M., Foster, E., McMurry, J., Ayme, S., Baynam, G., Bello, S.M., Boerkoel, C.F., Boycott, K.M., Brudno, M., Buske, O.J., Chinnery, P.F., Cipriani, V., Connell, L.E., Dawkins, H.J., DeMare, L.E., Devereau, A.D., Vries, B.B. de, Firth, H.V., Freson, K., Greene, D., Hamosh, A., Helbig, I., Hum, C., Jahn, J.A., James, R., Krause, R., Laulederkind, S.J.F., Lochmuller, H., Lyon, G.J., Ogishima, S., Olry, A., Ouwehand, W.H., Pontikos, N., Rath, A., Schaefer, F., Scott, R.H., Segal, M., Sergouniotis, P.I., Sever, R., Smith, C.L., Straub, V., Thompson, R., Turner, C., Turro, E., Veltman, M.W., Vulliamy, T., Yu, J., Ziegenweidt, J. von, Zankl, A., Zuchner, S., Zemojtel, T., Jacobsen, J.O., Groza, T., Smedley, D., Mungall, C.J., Haendel, M., and Robinson, P.N.
Abstract: Contains fulltext : 169849.pdf (publisher's version ) (Open Access), Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.
Published: 2017

6. The human phenotype ontology in 2017

Author: Köhler, S, Vasilevsky, NA, Engelstad, M, Foster, E, McMurry, J, Aymé, S, Baynam, G, Bello, SM, Boerkoel, CF, Boycott, KM, Brudno, M, Buske, OJ, Chinnery, PF, Cipriani, V, Connell, LE, Dawkins, HJS, DeMare, LE, Devereau, AD, De Vries, BBA, Firth, HV, Freson, K, Greene, D, Hamosh, A, Helbig, I, Hum, C, Jähn, JA, James, R, Krause, R, Laulederkind, SJF, Lochmüller, H, Lyon, GJ, Ogishima, S, Olry, A, Ouwehand, WH, Pontikos, N, Rath, A, Schaefer, F, Scott, RH, Segal, M, Sergouniotis, PI, Sever, R, Smith, CL, Straub, V, Thompson, R, Turner, C, Turro, E, Veltman, MWM, Vulliamy, T, Yu, J, Von Ziegenweidt, J, Zankl, A, Züchner, S, Zemojtel, T, Jacobsen, JOB, Groza, T, Smedley, D, Mungall, CJ, Haendel, M, Robinson, PN, Köhler, S, Vasilevsky, NA, Engelstad, M, Foster, E, McMurry, J, Aymé, S, Baynam, G, Bello, SM, Boerkoel, CF, Boycott, KM, Brudno, M, Buske, OJ, Chinnery, PF, Cipriani, V, Connell, LE, Dawkins, HJS, DeMare, LE, Devereau, AD, De Vries, BBA, Firth, HV, Freson, K, Greene, D, Hamosh, A, Helbig, I, Hum, C, Jähn, JA, James, R, Krause, R, Laulederkind, SJF, Lochmüller, H, Lyon, GJ, Ogishima, S, Olry, A, Ouwehand, WH, Pontikos, N, Rath, A, Schaefer, F, Scott, RH, Segal, M, Sergouniotis, PI, Sever, R, Smith, CL, Straub, V, Thompson, R, Turner, C, Turro, E, Veltman, MWM, Vulliamy, T, Yu, J, Von Ziegenweidt, J, Zankl, A, Züchner, S, Zemojtel, T, Jacobsen, JOB, Groza, T, Smedley, D, Mungall, CJ, Haendel, M, and Robinson, PN
Abstract: © The Author(s) 2016. Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human PhenotypeOntology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.
Published: 2017

7. Data Descriptor: FANTOM5 CAGE profiles of human and mouse samples

Author: Noguchi, S, Arakawa, T, Fukuda, S, Furuno, M, Hasegawa, A, Hori, F, Ishikawa-Kato, S, Kaida, K, Kaiho, A, Kanamori-Katayama, M, Kawashima, T, Kojima, M, Kubosaki, A, Manabe, R-I, Murata, M, Nagao-Sato, S, Nakazato, K, Ninomiya, N, Nishiyori-Sueki, H, Noma, S, Saijyo, E, Saka, A, Sakai, M, Simon, C, Suzuki, N, Tagami, M, Watanabe, S, Yoshida, S, Arner, P, Axton, RA, Babina, M, Baillie, JK, Barnett, TC, Beckhouse, AG, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Carlisle, AJ, Clevers, HC, Davis, CA, Detmar, M, Dohi, T, Edge, ASB, Edinger, M, Ehrlund, A, Ekwall, K, Endoh, M, Enomoto, H, Eslami, A, Fagiolini, M, Fairbairn, L, Farach-Carson, MC, Faulkner, GJ, Ferrai, C, Fisher, ME, Forrester, LM, Fujita, R, Furusawa, J-I, Geijtenbeek, TB, Gingeras, T, Goldowitz, D, Guhl, S, Guler, R, Gustincich, S, Ha, TJ, Hamaguchi, M, Hara, M, Hasegawa, Y, Herlyn, M, Heutink, P, Hitchens, KJ, Hume, DA, Ikawa, T, Ishizu, Y, Kai, C, Kawamoto, H, Kawamura, YI, Kempfle, JS, Kenna, TJ, Kere, J, Khachigian, LM, Kitamura, T, Klein, S, Klinken, SP, Knox, AJ, Kojima, S, Koseki, H, Koyasu, S, Lee, W, Lennartsson, A, Mackay-sim, A, Mejhert, N, Mizuno, Y, Morikawa, H, Morimoto, M, Moro, K, Morris, KJ, Motohashi, H, Mummery, CL, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nozaki, T, Ogishima, S, Ohkura, N, Ohno, H, Ohshima, M, Okada-Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, DA, Passier, R, Patrikakis, M, Pombo, A, Pradhan-Bhatt, S, Qin, X-Y, Rehli, M, Rizzu, P, Roy, S, Sajantila, A, Sakaguchi, S, Sato, H, Satoh, H, Savvi, S, Saxena, A, Schmidl, C, Schneider, C, Schulze-Tanzil, GG, Schwegmann, A, Sheng, G, Shin, JW, Sugiyama, D, Sugiyama, T, Summers, KM, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tomoiu, A, Toyoda, H, van de Wetering, M, van den Berg, LM, Verardo, R, Vijayan, D, Wells, CA, Winteringham, LN, Wolvetang, E, Yamaguchi, Y, Yamamoto, M, Yanagi-Mizuochi, C, Yoneda, M, Yonekura, Y, Zhang, PG, Zucchelli, S, Abugessaisa, I, Arner, E, Harshbarger, J, Kondo, A, Lassmann, T, Lizio, M, Sahin, S, Sengstag, T, Severin, J, Shimoji, H, Suzuki, M, Suzuki, H, Kawai, J, Kondo, N, Itoh, M, Daub, CO, Kasukawa, T, Kawaji, H, Carninci, P, Forrest, ARR, Hayashizaki, Y, Noguchi, S, Arakawa, T, Fukuda, S, Furuno, M, Hasegawa, A, Hori, F, Ishikawa-Kato, S, Kaida, K, Kaiho, A, Kanamori-Katayama, M, Kawashima, T, Kojima, M, Kubosaki, A, Manabe, R-I, Murata, M, Nagao-Sato, S, Nakazato, K, Ninomiya, N, Nishiyori-Sueki, H, Noma, S, Saijyo, E, Saka, A, Sakai, M, Simon, C, Suzuki, N, Tagami, M, Watanabe, S, Yoshida, S, Arner, P, Axton, RA, Babina, M, Baillie, JK, Barnett, TC, Beckhouse, AG, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Carlisle, AJ, Clevers, HC, Davis, CA, Detmar, M, Dohi, T, Edge, ASB, Edinger, M, Ehrlund, A, Ekwall, K, Endoh, M, Enomoto, H, Eslami, A, Fagiolini, M, Fairbairn, L, Farach-Carson, MC, Faulkner, GJ, Ferrai, C, Fisher, ME, Forrester, LM, Fujita, R, Furusawa, J-I, Geijtenbeek, TB, Gingeras, T, Goldowitz, D, Guhl, S, Guler, R, Gustincich, S, Ha, TJ, Hamaguchi, M, Hara, M, Hasegawa, Y, Herlyn, M, Heutink, P, Hitchens, KJ, Hume, DA, Ikawa, T, Ishizu, Y, Kai, C, Kawamoto, H, Kawamura, YI, Kempfle, JS, Kenna, TJ, Kere, J, Khachigian, LM, Kitamura, T, Klein, S, Klinken, SP, Knox, AJ, Kojima, S, Koseki, H, Koyasu, S, Lee, W, Lennartsson, A, Mackay-sim, A, Mejhert, N, Mizuno, Y, Morikawa, H, Morimoto, M, Moro, K, Morris, KJ, Motohashi, H, Mummery, CL, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nozaki, T, Ogishima, S, Ohkura, N, Ohno, H, Ohshima, M, Okada-Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, DA, Passier, R, Patrikakis, M, Pombo, A, Pradhan-Bhatt, S, Qin, X-Y, Rehli, M, Rizzu, P, Roy, S, Sajantila, A, Sakaguchi, S, Sato, H, Satoh, H, Savvi, S, Saxena, A, Schmidl, C, Schneider, C, Schulze-Tanzil, GG, Schwegmann, A, Sheng, G, Shin, JW, Sugiyama, D, Sugiyama, T, Summers, KM, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tomoiu, A, Toyoda, H, van de Wetering, M, van den Berg, LM, Verardo, R, Vijayan, D, Wells, CA, Winteringham, LN, Wolvetang, E, Yamaguchi, Y, Yamamoto, M, Yanagi-Mizuochi, C, Yoneda, M, Yonekura, Y, Zhang, PG, Zucchelli, S, Abugessaisa, I, Arner, E, Harshbarger, J, Kondo, A, Lassmann, T, Lizio, M, Sahin, S, Sengstag, T, Severin, J, Shimoji, H, Suzuki, M, Suzuki, H, Kawai, J, Kondo, N, Itoh, M, Daub, CO, Kasukawa, T, Kawaji, H, Carninci, P, Forrest, ARR, and Hayashizaki, Y
Abstract: In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE (Cap Analysis of Gene Expression) coupled with single-molecule sequencing. Approximately three thousands of samples, consisting of a variety of primary cells, tissues, cell lines, and time series samples during cell activation and development, were subjected to a uniform pipeline of CAGE data production. The analysis pipeline started by measuring RNA extracts to assess their quality, and continued to CAGE library production by using a robotic or a manual workflow, single molecule sequencing, and computational processing to generate frequencies of transcription initiation. Resulting data represents the consequence of transcriptional regulation in each analyzed state of mammalian cells. Non-overlapping peaks over the CAGE profiles, approximately 200,000 and 150,000 peaks for the human and mouse genomes, were identified and annotated to provide precise location of known promoters as well as novel ones, and to quantify their activities.
Published: 2017

8. 503 Genome-wide association study identifies novel susceptibility loci for tanning ability in Japanese population

Author: Shido, K., primary, Kojima, K., additional, Hozawa, A., additional, Ogishima, S., additional, Minegishi, N., additional, Kawai, Y., additional, Tamiya, G., additional, Tanno, K., additional, Yamasaki, K., additional, Aiba, S., additional, Suzuki, Y., additional, and Nagasaki, M., additional
Published: 2017
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9. The statistical geometry of transcriptome divergence in cell-type evolution and cancer

Author: Liang, C, Alam, I, Albanese, D, Altschuler, G, Andersson, R, Arakawa, T, Archer, J, Arner, E, Arner, P, Babina, M, Baillie, K, Bajic, V, Baker, S, Balic, A, Balwierz, P, Beckhouse, A, Bertin, N, Blake, Ja, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Burroughs, M, Califano, A, Cannistraci, C, Carbajo, D, Carninci, P, Chen, Yang, Chierici, M, Ciani, Y, Clevers, H, Dalla, Emiliano, Daub, C, Davis, C, De Hoon, M, De Lima Morais, D, Dermar, M, Diehl, A, Dimont, E, Dohl, T, Drabros, F, Edge, A, Edinger, M, Ekwall, K, Endoh, M, Enomoto, H, Fagiolini, M, Fairbairn, L, Fang, H, Farach Carson, Mc, Faulkner, G, Favorov, A, Fisher, M, Forrest, A, Francescatto, M, Freeman, T, Frith, M, Fujita, R, Fukuda, S, Furlanello, C, Furuno, M, Furusawa, J, Geijtenbeek, Tb, Gibson, A, Gingeras, T, Goldowithz, D, Gough, J, Guhl, S, Guler, R, Gustincich, Stefano, Ha, T, Haberle, V, Hamaguchi, M, Hara, M, Harbers, M, Harshbarger, J, Hasegawa, A, Hasegawa, Y, Hashimoto, T, Hayashizaki, Y, Herlyn, M, Heutink, P, Hide, W, Hitchens, K, Ho Sui, S, Hofmann, O, Hoof, I, Hori, F, Hume, D, Huminiecki, L, Iida, K, Ikawa, T, Ishizu, Y, Itoh, M, Jankovic, B, Jia, H, Jorgensen, M, Joshi, A, Jurman, G, Kaczkowski, B, Kai, C, Kaida, K, Kaiho, A, Kajiyama, K, Kanamori Katayama, M, Kasianov, A, Kasukawa, T, Katayama, S, Kato Ishikawa, S, Kawaguchi, S, Kawai, J, Kawaji, H, Kawamoto, H, Kawamura, Y, Kawashima, T, Kempfle, J, Kenna, T, Kere, J, Khachigian, L, Kitamura, T, Klinken, P, Knox, A, Kojima, M, Kojima, S, Kondo, N, Koseki, H, Koyasu, S, Krampitz, S, Kubosaki, A, Kulakovskiy, I, Kwon, At, Laros, J, Lassmann, T, Lenhard, B, Lennartsson, A, Li, K, Lilji, B, Lipovich, L, Lizio, M, Mackay Sim, A, Makeev, V, Manabe, R, Mar, J, Marchand, B, Mathelier, A, Medvedeva, Y, Meehan, Tf, Mejhert, N, Meynert, A, Mizuno, Y, Morikawa, H, Morimoto, M, Moro, K, Motakis, E, Motohashi, H, Mummery, C, Mungall, Cj, Murata, M, Nagao Sato, S, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nakazato, K, Ninomiya Fukuda, N, Nishiyori Sueki, H, Noma, S, Nozaki, T, Ogishima, S, Ohkura, N, Ohmiya, H, Ohno, H, Ohshima, M, Okada Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, D, Pain, A, Passier, R, Persson, H, Piazza, Silvano, Plessy, C, Pradhan Bhatt, S, Prendergast, J, Rackham, O, Ramilowski, J, Rashid, M, Ravasi, T, Rehli, M, Rizzu, P, Roncador, M, Roy, S, Rye, M, Saijyo, E, Sajantila, A, Saka, A, Sakaguchi, S, Sakai, M, Sandelin, A, Sato, H, Satoh, H, Suzana, S, Alka, S, Schaefer, U, Schmeier, S, Schmidl, C, Schneider, C, Schultes, Ea, Schulze Tanzil, G, Schwegmann, A, Semple, C, Sengstag, T, Severin, J, Sheng, G, Shimoji, H, Shimoni, Y, Shin, J, Simon, C, Sugiyama, D, Sugiyama, T, Summers, K, Suzuki, H, Suzuki, M, Suzuki, N, Swoboda, R, Hoen P, T, Tagami, M, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tatum, Z, Taylor, M, Thompson, M, Toyoda, H, Toyoda, T, Valen, E, Van De Wetering, M, Van Den Berg, L, Van Nimwegen, E, Verardo, R, Vijayan, D, Vitezic, M, Vorontzov, I, Wasserman, W, Watanabe, S, Wells, C, Winteringham, L, Wolvetang, E, Wood, Ej, Yamaguchi, Y, Yamamoto, M, Yoneda, M, Yonekura, Y, Yoshida, Shin'Ichirou, Young, R, Zabierowski, Se, Zhang, P, Zhao, X, Zucchelli, Silvia, Forrest, Ar, Wagner, Gp, Hubrecht Institute for Developmental Biology and Stem Cell Research, AII - Amsterdam institute for Infection and Immunity, Infectious diseases, and Experimental Immunology
Subjects: Cell type, General Physics and Astronomy, rna-seq data, phylogenetic networks, Biology, ENCODE, General Biochemistry, Genetics and Molecular Biology, Divergence, Transcriptome, Models, Settore BIO/13 - Biologia Applicata, Neoplasms, Humans, Genetics, Models, Statistical, Multidisciplinary, Statistical model, General Chemistry, Statistical, Biological Evolution, Body plan, Tree structure, Evolutionary biology, Cancer cell
Abstract: In evolution, body plan complexity increases due to an increase in the number of individualized cell types. Yet, there is very little understanding of the mechanisms that produce this form of organismal complexity. One model for the origin of novel cell types is the sister cell-type model. According to this model, each cell type arises together with a sister cell type through specialization from an ancestral cell type. A key prediction of the sister cell-type model is that gene expression profiles of cell types exhibit tree structure. Here we present a statistical model for detecting tree structure in transcriptomic data and apply it to transcriptomes from ENCODE and FANTOM5. We show that transcriptomes of normal cells harbour substantial amounts of hierarchical structure. In contrast, cancer cell lines have less tree structure, suggesting that the emergence of cancer cells follows different principles from that of evolutionary cell-type origination.
Published: 2015
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10. The Pre-Eclampsia Ontology: A Disease Ontology Representing the Domain Knowledge Specific to Pre-Eclampsia

Author: Croy, A, Mizuno, S, Ogishima, S, Nishigori, H, Jamieson, DG, Verspoor, K, Tanaka, H, Yaegashi, N, Nakaya, J, Croy, A, Mizuno, S, Ogishima, S, Nishigori, H, Jamieson, DG, Verspoor, K, Tanaka, H, Yaegashi, N, and Nakaya, J
Abstract: Pre-eclampsia (PE) is a clinical syndrome characterized by new-onset hypertension and proteinuria at ≥20 weeks of gestation, and is a leading cause of maternal and perinatal morbidity and mortality. Previous studies have gathered abundant data about PE such as risk factors and pathological findings. However, most of these data are not semantically structured. Clinical data on PE patients are often generated with semantic heterogeneity such as using disparate terminology to describe the same phenomena. In clinical studies, interoperability of heterogenic clinical data is required in various situations. In such a situation, it is necessary to develop an interoperable and standardized semantic framework to research the pathology of PE more comprehensively and to achieve interoperability of heterogenic clinical data of PE patients. In this study, we developed an ontology representing clinical features, treatments, genetic factors, environmental factors, and other aspects of the current knowledge in the domain of PE. We call this pre-eclampsia ontology "PEO". To achieve interoperability with other ontologies, the core structure of PEO was compliant with the hierarchy of the Basic Formal Ontology (BFO). The PEO incorporates a wide range of key concepts and terms of PE from clinical and biomedical research in structuring the knowledge base that is specific to PE; therefore, PEO is expected to enhance PE-specific information retrieval and knowledge discovery in both clinical and biomedical research fields.
Published: 2016

11. Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation

Author: Morikawa, H, Ohkura, N, Vandenbon, A, Itoh, M, Nagao Sato, S, Kawaji, H, Lassmann, T, Carninci, P, Hayashizaki, Y, Forrest, Ar, Standley, Dm, Date, H, Sakaguchi, S, FANTOM Consortium (Forrest AR, Rehli, M, Baillie, Jk, de Hoon MJ, Haberle, V, Kulakovskiy, Iv, Lizio, M, Andersson, R, Mungall, Cj, Meehan, Tf, Schmeier, S, Bertin, N, Jørgensen, M, Dimont, E, Arner, E, Schmidl, C, Schaefer, U, Medvedeva, Ya, Plessy, C, Vitezic, M, Severin, J, Semple, Ca, Ishizu, Y, Francescatto, M, Alam, I, Albanese, D, Altschuler, Gm, Archer, Ja, Arner, P, Babina, M, Baker, S, Balwierz, Pj, Beckhouse, Ag, Pradhan Bhatt, S, Blake, Ja, Blumenthal, A, Bodega, B, Bonetti, A, Briggs, J, Brombacher, F, Burroughs, Am, Califano, A, Cannistraci, Cv, Carbajo, D, Chen, Y, Chierici, M, Ciani, Y, Clevers, Hc, Dalla, E, Davis, Ca, Deplancke, B, Detmar, M, Diehl, Ad, Dohi, T, Drabløs, F, Edge, As, Edinger, M, Ekwall, K, Endoh, M, Enomoto, H, Fagiolini, M, Fairbairn, L, Fang, H, Farach Carson MC, Faulkner, Gj, Favorov, Av, Fisher, Me, Frith, Mc, Fujita, R, Fukuda, S, Furlanello, C, Furuno, M, Furusawa, J, Geijtenbeek, Tb, Gibson, A, Gingeras, T, Goldowitz, D, Gough, J, Guhl, S, Guler, R, Gustincich, Stefano, Ha, Tj, Hamaguchi, M, Hara, M, Harbers, M, Harshbarger, J, Hasegawa, A, Hasegawa, Y, Hashimoto, T, Herlyn, M, Hitchens, Kj, Ho Sui SJ, Hofmann, Om, Hoof, I, Hori, F, Huminiecki, L, Iida, K, Ikawa, T, Jankovic, Br, Jia, H, Joshi, A, Jurman, G, Kaczkowski, B, Kai, C, Kaida, K, Kaiho, A, Kajiyama, K, Kanamori Katayama, M, Kasianov, As, Kasukawa, T, Katayama, S, Kato, S, Kawaguchi, S, Kawamoto, H, Kawamura, Yi, Kawashima, T, Kempfle, Js, Kenna, Tj, Kere, J, Khachigian, Lm, Kitamura, T, Klinken, Sp, Knox, Aj, Kojima, M, Kojima, S, Kondo, N, Koseki, H, Koyasu, S, Krampitz, S, Kubosaki, A, Kwon, At, Laros, Jf, Lee, W, Lennartsson, A, Li, K, Lilje, B, Lipovich, L, Mackay Sim, A, Manabe, R, Mar, Jc, Marchand, B, Mathelier, A, Mejhert, N, Meynert, A, Mizuno, Y, Morais, Da, Morimoto, M, Moro, K, Motakis, E, Motohashi, H, Mummery, Cl, Murata, M, Nakachi, Y, Nakahara, F, Nakamura, T, Nakamura, Y, Nakazato, K, van Nimwegen, E, Ninomiya, N, Nishiyori, H, Noma, S, Nozaki, T, Ogishima, S, Ohmiya, H, Ohno, H, Ohshima, M, Okada Hatakeyama, M, Okazaki, Y, Orlando, V, Ovchinnikov, Da, Pain, A, Passier, R, Patrikakis, M, Persson, H, Piazza, S, Prendergast, Jg, Rackham, Oj, Ramilowski, Ja, Rashid, M, Ravasi, T, Rizzu, P, Roncador, M, Roy, S, Rye, Mb, Saijyo, E, Sajantila, A, Saka, A, Sakai, M, Sato, H, Satoh, H, Savvi, S, Saxena, A, Schneider, C, Schultes, Ea, Schulze Tanzil GG, Schwegmann, A, Sengstag, T, Sheng, G, Shimoji, H, Shimoni, Y, Shin, Jw, Simon, C, Sugiyama, D, Sugiyama, T, Suzuki, M, Swoboda, Rk, 't Hoen PA, Tagami, M, Takahashi, N, Takai, J, Tanaka, H, Tatsukawa, H, Tatum, Z, Thompson, M, Toyoda, H, Toyoda, T, Valen, E, van de Wetering, M, van den Berg LM, Verardo, R, Vijayan, D, Vorontsov, Ie, Wasserman, Ww, Watanabe, S, Wells, Ca, Winteringham, Ln, Wolvetang, E, Wood, Ej, Yamaguchi, Y, Yamamoto, M, Yoneda, M, Yonekura, Y, Yoshida, S, Zabierowski, Se, Zhang, Pg, Zhao, X, Zucchelli, S, Summers, Km, Suzuki, H, Daub, Co, Kawai, J, Heutink, P, Hide, W, Freeman, Tc, Lenhard, B, Bajic, Vb, Taylor, Ms, Makeev, Vj, Sandelin, A, Hume, Da, Hayashizaki, Y., AII - Amsterdam institute for Infection and Immunity, Infectious diseases, Experimental Immunology, and Hubrecht Institute for Developmental Biology and Stem Cell Research
Subjects: Transcription, Genetic, Regulatory T cell, T-Lymphocytes, Down-Regulation, chemical and pharmacologic phenomena, Biology, Inbred C57BL, T-Lymphocytes, Regulatory, Epigenesis, Genetic, Mice, Genetic, Settore BIO/13 - Biologia Applicata, medicine, Transcriptional regulation, Animals, Epigenetics, Gene, Inbred BALB C, Genetics, Regulation of gene expression, Mice, Inbred BALB C, Multidisciplinary, Binding Sites, FOXP3, hemic and immune systems, Forkhead Transcription Factors, DNA Methylation, Biological Sciences, Regulatory, Cap analysis gene expression, Mice, Inbred C57BL, medicine.anatomical_structure, Gene Expression Regulation, DNA methylation, Transcription, Epigenesis
Abstract: Naturally occurring regulatory T (Treg) cells, which specifically express the transcription factor forkhead box P3 (Foxp3), are engaged in the maintenance of immunological self-tolerance and homeostasis. By transcriptional start site cluster analysis, we assessed here how genome-wide patterns of DNA methylation or Foxp3 binding sites were associated with Treg-specific gene expression. We found that Treg-specific DNA hypomethylated regions were closely associated with Treg up-regulated transcriptional start site clusters, whereas Foxp3 binding regions had no significant correlation with either up- or down-regulated clusters in nonactivated Treg cells. However, in activated Treg cells, Foxp3 binding regions showed a strong correlation with down-regulated clusters. In accordance with these findings, the above two features of activation-dependent gene regulation in Treg cells tend to occur at different locations in the genome. The results collectively indicate that Treg-specific DNA hypomethylation is instrumental in gene up-regulation in steady state Treg cells, whereas Foxp3 down-regulates the expression of its target genes in activated Treg cells. Thus, the two events seem to play distinct but complementary roles in Treg-specific gene expression.
Published: 2014
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12. The 3rd DBCLS BioHackathon: Improving life science data integration with Semantic Web technologies

Author: Katayama, T, Wilkinson, MD, Micklem, G, Kawashima, S, Yamaguchi, A, Nakao, M, Yamamoto, Y, Okamoto, S, Oouchida, K, Chun, HW, AERTS, Jan, Afzal, H, Antezana, E, Arakawa, K, Aranda, B, Belleau, F, Bolleman, J, Bonnal, RJP, Chapman, B, Cock, PJA, Eriksson, T, Gordon, PMK, Goto, N, Hayashi, K, Horn, H, Ishiwata, R, Kaminuma, E, Kasprzyk, A, Kawaji, H, Kido, N, Kim, YJ, Kinjo, AR, Konishi, F, Kwon, KH, Labarga, A, Lamprecht, AL, Lin, Y, Lindenbaum, P, McCarthy, L, Morita, H, Murakami, K, Nagao, K, Nishida, K, Nishimura, K, Nishizawa, T, Ogishima, S, Ono, K, Oshita, K, Park, KJ, Prins, P, Saito, TL, Samwald, M, Satagopam, VP, Shigemoto, Y, Smith, R, Splendiani, A, Sugawara, H, Taylor, J, Vos, RA, Withers, D, Yamasaki, C, Zmasek, CM, Kawamoto, S, Okubo, K, Asai, K, and Takagi, T
Subjects: Databases, Data visualization, BioHackathon, Interfaces, Data integration, Open source, Software, Semantic Web, Web services
Abstract: Background: BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research.Results: The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization.Conclusion: We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end consumer.
Published: 2013

13. Future Direction of IMIA Standardization

Author: Kimura, M., primary, Ogishima, S., primary, Shabo, A., primary, Kim, I. K., primary, Parisot, C., primary, de Faria Leao, B., primary, and Nakaya, J., additional
Published: 2014
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14. The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies

Author: Katayama, T., Wilkinson, D.W., Micklem, G., Kawashima, S., Yamaguchi, A., Nakao, M., Yamamoto, T., Okamoto, S., Oouchida, K., Chung, H., Aerts, J., Afzal, H., Antezana, E., Arakawa, K., Aranda, B., Belleau, F., Bolleman, J., Bonnal, R.J.P., Chapman, B., Cock, P.J.A., Eriksson, T., Gordon, P.M.K., Goto, N., Hayashida, K., Horn, H., Ishiwata, R., Kaminuma, E., Kasprzyk, A., Kawaji, H., Kido, N., Kim, Y., Kinjo, A.R., Konishi, F., Kwon, K.H., Labarga, A., Lamprecht, A., Lin, Y., Lindenbaum, P., McCarthy, L., Morita, H., Murakami, K., Nagao, K., Nishida, K., Nishimura, K., Nishizawa, T., Ogishima, S., Ono, K., Oshita, K., Park, K., Prins, J.C.P., Saito, T., Samwald, M., Satagopam, V.P., Shigemoto, Y., Smith, R., Splendiani, A., Sugawara, H., Taylor, J., Vos, R.A., Withers, D., Yamasaki, C., Zmasek, C.M., Kawamoto, S., Okubo, K., Asai, K., Takagi, T., Katayama, T., Wilkinson, D.W., Micklem, G., Kawashima, S., Yamaguchi, A., Nakao, M., Yamamoto, T., Okamoto, S., Oouchida, K., Chung, H., Aerts, J., Afzal, H., Antezana, E., Arakawa, K., Aranda, B., Belleau, F., Bolleman, J., Bonnal, R.J.P., Chapman, B., Cock, P.J.A., Eriksson, T., Gordon, P.M.K., Goto, N., Hayashida, K., Horn, H., Ishiwata, R., Kaminuma, E., Kasprzyk, A., Kawaji, H., Kido, N., Kim, Y., Kinjo, A.R., Konishi, F., Kwon, K.H., Labarga, A., Lamprecht, A., Lin, Y., Lindenbaum, P., McCarthy, L., Morita, H., Murakami, K., Nagao, K., Nishida, K., Nishimura, K., Nishizawa, T., Ogishima, S., Ono, K., Oshita, K., Park, K., Prins, J.C.P., Saito, T., Samwald, M., Satagopam, V.P., Shigemoto, Y., Smith, R., Splendiani, A., Sugawara, H., Taylor, J., Vos, R.A., Withers, D., Yamasaki, C., Zmasek, C.M., Kawamoto, S., Okubo, K., Asai, K., and Takagi, T.
Abstract: Background: BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. Results: The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. Conclusion: We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer. source provider, through middleware, to the end-consumer.
Published: 2013

15. 202 TNFR1 DEFICIENCY FACILITATES ARTICULAR CARTILAGE OSSIFICATION AND OSTEOPHYTE FORMATION

Author: Aini, H., primary, Yamauchi, Y., additional, Ochi, H., additional, Koga, D., additional, Okawa, A., additional, Ogishima, S., additional, Okazaki, M., additional, and Asou, Y., additional
Published: 2011
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16. 1P301 The evolutionary driving force of protein-protein interaction network

Author: Hase, T., primary, Ogishima, S., additional, Nakagawa, S., additional, and Tanaka, H., additional
Published: 2005
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17. 1P297 Relations between network topologies and degrees of self interaction proteins in evolution of yeast protein-protein interaction network

Author: Hase, T, primary, Ogishima, S, additional, Suzuki, Y, additional, Nakagawa, So, additional, and Tanaka, H, additional
Published: 2004
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18. 2M1600 Preliminary analysis of evolution of crossregulation of the Wnt signalling pathway based on domain structure

Author: Ogishima, S., primary and Tanaka, h., additional
Published: 2002
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19. A NEW ALGORITHM FOR ANALYSIS OF WITHIN-HOST HIV-1 EVOLUTION.

Author: REN, F., OGISHIMA, S., and TANAKA, H.
Subjects: HIV infection genetics, BIOLOGICAL evolution, GENETICS, VIRAL evolution, GENETIC algorithms, EVOLUTIONARY models
Published: 2000

20. Future Direction of IMIA Standardization

Author: Nakaya, J., Kimura, M., Ogishima, S., Shabo, A., Kim, I. K., Parisot, C., and de Faria Leao, B.
Published: 2014
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21. Publisher Correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes (Nature Genetics, (2018), 50, 4, (524-537), 10.1038/s41588-018-0058-3)

Author: Malik, R., Chauhan, G., Traylor, M., Sargurupremraj, M., Okada, Y., Mishra, A., Rutten-Jacobs, L., Giese, A. -K, Laan, S. W., Gretarsdottir, S., Anderson, C. D., Chong, M., Adams, H. H. H., Ago, T., Almgren, P., Amouyel, P., Ay, H., Bartz, T. M., Benavente, O. R., Bevan, S., Boncoraglio, G. B., Brown, R. D., Butterworth, A. S., Carrera, C., Carty, C. L., Chasman, D. I., Chen, W. -M, Cole, J. W., Correa, A., Cotlarciuc, I., Cruchaga, C., Danesh, J., Bakker, P. I. W., Destefano, A. L., Den Hoed, M., Duan, Q., Engelter, S. T., Falcone, G. J., Gottesman, R. F., Grewal, R. P., Gudnason, V., Gustafsson, S., Haessler, J., Harris, T. B., Hassan, A., Havulinna, A. S., Heckbert, S. R., Holliday, E. G., Howard, G., Hsu, F. -C, Hyacinth, H. I., Ikram, M. A., Ingelsson, E., Irvin, M. R., Jian, X., Jiménez-Conde, J., Johnson, J. A., Jukema, J. W., Kanai, M., Keene, K. L., Kissela, B. M., Kleindorfer, D. O., Kooperberg, C., Kubo, M., Lange, L. A., Langefeld, C. D., Langenberg, C., Launer, L. J., Lee, J. -M, Lemmens, R., Leys, D., Lewis, C. M., Lin, W. -Y, Lindgren, A. G., Lorentzen, E., Magnusson, P. K., Maguire, J., Manichaikul, A., Mcardle, P. F., Meschia, J. F., Mitchell, B. D., Mosley, T. H., Nalls, M. A., Ninomiya, T., O’donnell, M. J., Psaty, B. M., Pulit, S. L., Rannikmäe, K., Reiner, A. P., Rexrode, K. M., Rice, K., Rich, S. S., Ridker, P. M., Rost, N. S., Rothwell, P. M., Rotter, J. I., Rundek, T., Sacco, R. L., Sakaue, S., Sale, M. M., Salomaa, V., Sapkota, B. R., Schmidt, R., Schmidt, C. O., Schminke, U., Sharma, P., Slowik, A., Sudlow, C. L. M., Tanislav, C., Tatlisumak, T., Taylor, K. D., Thijs, V. N. S., Thorleifsson, G., Thorsteinsdottir, U., Tiedt, S., Trompet, S., Tzourio, C., Duijn, C. M., Walters, M., Wareham, N. J., Wassertheil-Smoller, S., Wilson, J. G., Wiggins, K. L., Yang, Q., Yusuf, S., Bis, J. C., Pastinen, T., Ruusalepp, A., Schadt, E. E., Koplev, S., Björkegren, J. L. M., Codoni, V., Civelek, M., Smith, N. L., Trégouët, D. A., Christophersen, I. E., Roselli, C., Lubitz, S. A., Ellinor, P. T., Tai, E. S., Kooner, J. S., Kato, N., He, J., Harst, P., Elliott, P., Chambers, J. C., Takeuchi, F., Johnson, A. D., Amin, N., Aparicio, H. S., Arnett, D. K., Attia, J., Beiser, A. S., Berr, C., Buring, J. E., Bustamante, M., Caso, V., Cheng, Y. -C, Choi, S. H., Chowhan, A., Cullell, N., Dartigues, J. -F, Delavaran, H., Delgado, P., Dörr, M., Engström, G., Ford, I., Gurpreet, W. S., Hamsten, A., Heitsch, L., Hozawa, A., Ibanez, L., Ilinca, A., Ingelsson, M., Iwasaki, M., Jackson, R. D., Jood, K., Jousilahti, P., Kaffashian, S., Kalra, L., Kamouchi, M., Kitazono, T., Kjartansson, O., Kloss, M., Koudstaal, P. J., Krupinski, J., Labovitz, D. L., Laurie, C. C., Levi, C. R., Li, L., Lind, L., Lindgren, C. M., Lioutas, V., Liu, Y. M., Lopez, O. L., Makoto, H., Martinez-Majander, N., Matsuda, K., Minegishi, N., Montaner, J., Morris, A. P., Muiño, E., Müller-Nurasyid, M., Norrving, B., Ogishima, S., Parati, E. A., Peddareddygari, L. R., Pedersen, N. L., Pera, J., Perola, M., Pezzini, A., Pileggi, S., Rabionet, R., Riba-Llena, I., Ribasés, M., Romero, J. R., Roquer, J., Rudd, A. G., Sarin, A. -P, Sarju, R., Sarnowski, C., Sasaki, M., Satizabal, C. L., Satoh, M., Sattar, N., Sawada, N., Sibolt, G., Sigurdsson, Á, Smith, A., Sobue, K., Soriano-Tárraga, C., Stanne, T., Stine, O. C., Stott, D. J., Strauch, K., Takai, T., Tanaka, H., Tanno, K., Teumer, A., Tomppo, L., Nuria P Torres-Aguila, Touze, E., Tsugane, S., Uitterlinden, A. G., Valdimarsson, E. M., Lee, S. J., Völzke, H., Wakai, K., Weir, D., Williams, S. R., Wolfe, C. D. A., Wong, Q., Xu, H., Yamaji, T., Sanghera, D. K., Melander, O., Jern, C., Strbian, D., Fernandez-Cadenas, I., Longstreth, W. T., Rolfs, A., Hata, J., Woo, D., Rosand, J., Pare, G., Hopewell, J. C., Saleheen, D., Stefansson, K., Worrall, B. B., Kittner, S. J., Seshadri, S., Fornage, M., Markus, H. S., Howson, J. M. M., Kamatani, Y., Debette, S., and Dichgans, M.

22. The 2nd DBCLS BioHackathon: interoperable bioinformatics Web services for integrated applications

Author: Katayama Toshiaki, Wilkinson Mark D, Vos Rutger, Kawashima Takeshi, Kawashima Shuichi, Nakao Mitsuteru, Yamamoto Yasunori, Chun Hong-Woo, Yamaguchi Atsuko, Kawano Shin, Aerts Jan, Aoki-Kinoshita Kiyoko F, Arakawa Kazuharu, Aranda Bruno, Bonnal Raoul JP, Fernández José M, Fujisawa Takatomo, Gordon Paul MK, Goto Naohisa, Haider Syed, Harris Todd, Hatakeyama Takashi, Ho Isaac, Itoh Masumi, Kasprzyk Arek, Kido Nobuhiro, Kim Young-Joo, Kinjo Akira R, Konishi Fumikazu, Kovarskaya Yulia, von Kuster Greg, Labarga Alberto, Limviphuvadh Vachiranee, McCarthy Luke, Nakamura Yasukazu, Nam Yunsun, Nishida Kozo, Nishimura Kunihiro, Nishizawa Tatsuya, Ogishima Soichi, Oinn Tom, Okamoto Shinobu, Okuda Shujiro, Ono Keiichiro, Oshita Kazuki, Park Keun-Joon, Putnam Nicholas, Senger Martin, Severin Jessica, Shigemoto Yasumasa, Sugawara Hideaki, Taylor James, Trelles Oswaldo, Yamasaki Chisato, Yamashita Riu, Satoh Noriyuki, and Takagi Toshihisa
Subjects: Computer applications to medicine. Medical informatics, R858-859.7
Abstract: Abstract Background The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperability initiatives are effectively deployed, end-user applications need to be aware of, and support, best practices and standards. Here, we report on an initiative in which software developers and genome biologists came together to explore and raise awareness of these issues: BioHackathon 2009. Results Developers in attendance came from diverse backgrounds, with experts in Web services, workflow tools, text mining and visualization. Genome biologists provided expertise and exemplar data from the domains of sequence and pathway analysis and glyco-informatics. One goal of the meeting was to evaluate the ability to address real world use cases in these domains using the tools that the developers represented. This resulted in i) a workflow to annotate 100,000 sequences from an invertebrate species; ii) an integrated system for analysis of the transcription factor binding sites (TFBSs) enriched based on differential gene expression data obtained from a microarray experiment; iii) a workflow to enumerate putative physical protein interactions among enzymes in a metabolic pathway using protein structure data; iv) a workflow to analyze glyco-gene-related diseases by searching for human homologs of glyco-genes in other species, such as fruit flies, and retrieving their phenotype-annotated SNPs. Conclusions Beyond deriving prototype solutions for each use-case, a second major purpose of the BioHackathon was to highlight areas of insufficiency. We discuss the issues raised by our exploration of the problem/solution space, concluding that there are still problems with the way Web services are modeled and annotated, including: i) the absence of several useful data or analysis functions in the Web service "space"; ii) the lack of documentation of methods; iii) lack of compliance with the SOAP/WSDL specification among and between various programming-language libraries; and iv) incompatibility between various bioinformatics data formats. Although it was still difficult to solve real world problems posed to the developers by the biological researchers in attendance because of these problems, we note the promise of addressing these issues within a semantic framework.
Published: 2011
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23. Serum magnesium and calcium levels in relation to ischemic stroke Mendelian randomization study

Author: Susanna C. Larsson, Matthew Traylor, Stephen Burgess, Giorgio B. Boncoraglio, Christina Jern, Karl Michaëlsson, Hugh S. Markus, Rainer Malik, Ganesh Chauhan, Muralidharan Sargurupremraj, Yukinori Okada, Aniket Mishra, Loes Rutten-Jacobs, Anne-Katrin Giese, Sander W van der Laan, Solveig Gretarsdottir, Christopher D Anderson, Michael Chong, Hieab HH Adams, Tetsuro Ago, Peter Almgren, Philippe Amouyel, Hakan Ay, raci M Bartz, Oscar R Benavente, Steve Bevan, Giorgio B Boncoraglio, Robert D Brown, Adam S Butterworth, Caty Carrera, Cara L Carty, Daniel I Chasman, Wei-Min Chen, John W Cole, Adolfo Correa, Ioana Cotlarciuc, Carlos Cruchaga, John Danesh, Paul IW de Bakker, Anita L DeStefano, Marcel den Hoed, Qing Duan, Stefan T Engelter, Guido J Falcone, Rebecca F Gottesman, Raji P Grewal, Vilmundur Gudnason, Stefan Gustafsson, Jeffrey Haessler, Tamara B Harris, Ahamad Hassan, Aki S Havulinna, Susan R Heckbert, Elizabeth G Holliday, George Howard, Fang-Chi Hsu, Hyacinth I Hyacinth, M Arfan Ikram, Erik Ingelsson, Marguerite R Irvin, Xueqiu Jian, Jordi Jiménez-Conde, Julie A Johnson, J Wouter Jukema, Masahiro Kanai, Keith L Keene, Brett M Kissela, Dawn O Kleindorfer, Charles Kooperberg, Michiaki Kubo, Leslie A Lange, Carl D Langefeld, Claudia Langenberg, Lenore J Launer, Jin-Moo Lee, Robin Lemmens, Didier Leys, Cathryn M Lewis, Wei-Yu Lin, Arne G Lindgren, Erik Lorentzen, Patrik K Magnusson, Jane Maguire, Ani Manichaikul, Patrick F McArdle, James F Meschia, Braxton D Mitchell, Thomas H Mosley, Michael A Nalls, Toshiharu Ninomiya, Martin J O'Donnell, Bruce M Psaty, Sara L Pulit, Kristiina Rannikmäe, Alexander P Reiner, Kathryn M Rexrode, Kenneth Rice, Stephen S Rich, Paul M Ridker, Natalia S Rost, Peter M Rothwell, Jerome I Rotter, Tatjana Rundek, Ralph L Sacco, Saori Sakaue, Michele M Sale, Veikko Salomaa, Bishwa R Sapkota, Reinhold Schmidt, Carsten O Schmidt, Ulf Schminke, Pankaj Sharma, Agnieszka Slowik, Cathie LM Sudlow, Christian Tanislav, Turgut Tatlisumak, Kent D Taylor, Vincent NS Thijs, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Steffen Tiedt, Stella Trompet, Christophe Tzourio, Cornelia M van Duijn, Matthew Walters, Nicholas J Wareham, Sylvia Wassertheil-Smoller, James G Wilson, Kerri L Wiggins, Qiong Yang, Salim Yusuf, Najaf Amin, Hugo S Aparicio, Donna K Arnett, John Attia, Alexa S Beiser, Claudine Berr, Julie E Buring, Mariana Bustamante, Valeria Caso, Yu-Ching Cheng, Seung Hoan Choi, Ayesha Chowhan, Natalia Cullell, Jean-François Dartigues, Hossein Delavaran, Pilar Delgado, Marcus Dörr, Gunnar Engström, Ian Ford, Wander S Gurpreet, Anders Hamsten, Laura Heitsch, Atsushi Hozawa, Laura Ibanez, Andreea Ilinca, Martin Ingelsson, Motoki Iwasaki, Rebecca D Jackson, Katarina Jood, Pekka Jousilahti, Sara Kaffashian, Lalit Kalra, Masahiro Kamouchi, Takanari Kitazono, Olafur Kjartansson, Manja Kloss, Peter J Koudstaal, Jerzy Krupinski, Daniel L Labovitz, Cathy C Laurie, Christopher R Levi, Linxin Li, Lars Lind, Cecilia M Lindgren, Vasileios Lioutas, Yong Mei Liu, Oscar L Lopez, Hirata Makoto, Nicolas Martinez-Majander, Koichi Matsuda, Naoko Minegishi, Joan Montaner, Andrew P Morris, Elena Muiño, Martina Müller-Nurasyid, Bo Norrving, Soichi Ogishima, Eugenio A Parati, Leema Reddy Peddareddygari, Nancy L Pedersen, Joanna Pera, Markus Perola, Alessandro Pezzini, Silvana Pileggi, Raquel Rabionet, Iolanda Riba-Llena, Marta Ribasés, Jose R Romero, Jaume Roquer, Anthony G Rudd, Antti-Pekka Sarin, Ralhan Sarju, Chloe Sarnowski, Makoto Sasaki, Claudia L Satizabal, Mamoru Satoh, Naveed Sattar, Norie Sawada, Gerli Sibolt, Ásgeir Sigurdsson, Albert Smith, Kenji Sobue, Carolina Soriano-Tárraga, Tara Stanne, O Colin Stine, David J Stott, Konstantin Strauch, Takako Takai, Hideo Tanaka, Kozo Tanno, Alexander Teumer, Liisa Tomppo, Nuria P Torres-Aguila, Emmanuel Touze, Shoichiro Tsugane, Andre G Uitterlinden, Einar M Valdimarsson, Sven J van der Lee, Henry Völzke, Kenji Wakai, David Weir, Stephen R Williams, Charles DA Wolfe, Quenna Wong, Huichun Xu, Taiki Yamaji, Dharambir K Sanghera, Olle Melander, Daniel Strbian, Israel Fernandez-Cadenas, W T Longstreth, Arndt Rolfs, Jun Hata, Daniel Woo, Jonathan Rosand, Guillaume Pare, Jemma C Hopewell, Danish Saleheen, Kari Stefansson, Bradford B Worrall, Steven J Kittner, Sudha Seshadri, Myriam Fornage, Hugh S Markus, Joanna MM Howson, Yoichiro Kamatani, Stephanie Debette, Martin Dichgans, Berr, Claudine, Unit of Cardiovascular and Nutritional Epidemiology [Stockholm, Sweden], Karolinska Institutet [Stockholm]-Institute of Environmental Medicine [Stockholm, Sweden], Stroke Research Group [London, UK] (Department of Brain Repair and Rehabilitation), University of London - UCL [London, UK], MRC Biostatistics Unit [Cambridge, UK], University of Cambridge [UK] (CAM), Department of Public Health and Primary Care [Cambridge, UK] (Institute of Public Health), Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Section of Clinical Immunology [Uppsala, Sweden] (Department of Immunology, Genetics and Pathology), Uppsala University, Department of Surgical Sciences [Uppsala, Sweden], This work was supported by the Swedish Research Council for Health, Working Life and Welfare (Forte) and the Swedish Research Council. Hugh Markus is supported by an NIHR Senior Investigator award. His and Matthew Traylor’s work is supported by infrastructural support from the Cambridge University Hospitals Trust NIHR Biomedical Research Centre., MEGASTROKE project of the International Stroke Genetics Consortium : Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, Rutten-Jacobs L, Giese AK, van der Laan SW, Gretarsdottir S, Anderson CD, Chong M, Adams HH, Ago T, Almgren P, Amouyel P, Ay H, Bartz RM, Benavente OR, Bevan S, Boncoraglio GB, Brown RD Jr, Butterworth AS, Carrera C, Carty CL, Chasman DI, Chen WM, Cole JW, Correa A, Cotlarciuc I, Cruchaga C, Danesh J, de Bakker PI, DeStefano AL, Hoed MD, Duan Q, Engelter ST, Falcone GJ, Gottesman RF, Grewal RP, Gudnason V, Gustafsson S, Haessler J, Harris TB, Hassan A, Havulinna AS, Heckbert SR, Holliday EG, Howard G, Hsu FC, Hyacinth HI, Ikram MA, Ingelsson E, Irvin MR, Jian X, Jiménez-Conde J, Johnson JA, Jukema JW, Kanai M, Keene KL, Kissela BM, Kleindorfer DO, Kooperberg C, Kubo M, Lange LA, Langefeld CD, Langenberg C, Launer LJ, Lee JM, Lemmens R, Leys D, Lewis CM, Lin WY, Lindgren AG, Lorentzen E, Magnusson PK, Maguire J, Manichaikul A, McArdle PF, Meschia JF, Mitchell BD, Mosley TH, Nalls MA, Ninomiya T, O'Donnell MJ, Psaty BM, Pulit SL, Rannikmäe K, Reiner AP, Rexrode KM, Rice K, Rich SS, Ridker PM, Rost NS, Rothwell PM, Rotter JI, Rundek T, Sacco RL, Sakaue S, Sale MM, Salomaa V, Sapkota BR, Schmidt R, Schmidt CO, Schminke U, Sharma P, Slowik A, Sudlow CL, Tanislav C, Tatlisumak T, Taylor KD, Thijs VN, Thorleifsson G, Thorsteinsdottir U, Tiedt S, Trompet S, Tzourio C, van Duijn CM, Walters M, Wareham NJ, Wassertheil-Smoller S, Wilson JG, Wiggins KL, Yang Q, Yusuf S, Amin N, Aparicio HS, Arnett DK, Attia J, Beiser AS, Berr C, Buring JE, Bustamante M, Caso V, Cheng YC, Choi SH, Chowhan A, Cullell N, Dartigues JF, Delavaran H, Delgado P, Dörr M, Engström G, Ford I, Gurpreet WS, Hamsten A, Heitsch L, Hozawa A, Ibanez L, Ilinca A, Ingelsson M, Iwasaki M, Jackson RD, Jood K, Jousilahti P, Kaffashian S, Kalra L, Kamouchi M, Kitazono T, Kjartansson O, Kloss M, Koudstaal PJ, Krupinski J, Labovitz DL, Laurie CC, Levi CR, Li L, Lind L, Lindgren CM, Lioutas V, Liu YM, Lopez OL, Makoto H, Martinez-Majander N, Matsuda K, Minegishi N, Montaner J, Morris AP, Muiño E, Müller-Nurasyid M, Norrving B, Ogishima S, Parati EA, Peddareddygari LR, Pedersen NL, Pera J, Perola M, Pezzini A, Pileggi S, Rabionet R, Riba-Llena I, Ribasés M, Romero JR, Roquer J, Rudd AG, Sarin AP, Sarju R, Sarnowski C, Sasaki M, Satizabal CL, Satoh M, Sattar N, Sawada N, Sibolt G, Sigurdsson Á, Smith A, Sobue K, Soriano-Tárraga C, Stanne T, Stine OC, Stott DJ, Strauch K, Takai T, Tanaka H, Tanno K, Teumer A, Tomppo L, Torres-Aguila NP, Touze E, Tsugane S, Uitterlinden AG, Valdimarsson EM, van der Lee SJ, Völzke H, Wakai K, Weir D, Williams SR, Wolfe CD, Wong Q, Xu H, Yamaji T, Sanghera DK, Melander O, Jern C, Strbian D, Fernandez-Cadenas I, Longstreth WT Jr, Rolfs A, Hata J, Woo D, Rosand J, Pare G, Hopewell JC, Saleheen D, Stefansson K, Worrall BB, Kittner SJ, Seshadri S, Fornage M, Markus HS, Howson JM, Kamatani Y, Debette S, Dichgans M., Larsson, Susanna C [0000-0003-0118-0341], Apollo - University of Cambridge Repository, and Neurology
Subjects: medicine.medical_specialty, Neurology, Heredity, Neurologi, [SDV]Life Sciences [q-bio], chemistry.chemical_element, Calcium, Polymorphism, Single Nucleotide, Gastroenterology, Article, Brain Ischemia, Brain ischemia, 03 medical and health sciences, 0302 clinical medicine, Human genetics, Internal medicine, Mendelian randomization, Medicine, Humans, Magnesium, 030212 general & internal medicine, Stroke, Herència (Biologia), Genètica humana, business.industry, Neurosciences, Mendelian Randomization Analysis, Odds ratio, medicine.disease, Confidence interval, 3. Good health, [SDV] Life Sciences [q-bio], Intracranial Embolism, chemistry, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Neurology (clinical), business, 030217 neurology & neurosurgery, Neurovetenskaper
Abstract: Comment inThe yin and yang of magnesium and calcium: New genetic insights for stroke? [Neurology. 2019]; International audience; Objective To determine whether serum magnesium and calcium concentrations are causally associated with ischemic stroke or any of its subtypes using the mendelian randomization approach. Methods Analyses were conducted using summary statistics data for 13 single-nucleotide polymorphisms robustly associated with serum magnesium (n = 6) or serum calcium (n = 7) concentrations. The corresponding data for ischemic stroke were obtained from the MEGASTROKE consortium (34,217 cases and 404,630 noncases). Results In standard mendelian randomization analysis, the odds ratios for each 0.1 mmol/L (about 1 SD) increase in genetically predicted serum magnesium concentrations were 0.78 (95% confidence interval [CI] 0.69-0.89; p = 1.3 × 10 −4) for all ischemic stroke, 0.63 (95% CI 0.50-0.80; p = 1.6 × 10 −4) for cardioembolic stroke, and 0.60 (95% CI 0.44-0.82; p = 0.001) for large artery stroke; there was no association with small vessel stroke (odds ratio 0.90, 95% CI 0.67-1.20; p = 0.46). Only the association with cardioembolic stroke was robust in sensitivity analyses. There was no association of genetically predicted serum calcium concentrations with all ischemic stroke (per 0.5 mg/dL [about 1 SD] increase in serum calcium: odds ratio 1.03, 95% CI 0.88-1.21) or with any subtype. Conclusions This study found that genetically higher serum magnesium concentrations are associated with a reduced risk of cardioembolic stroke but found no significant association of genetically higher serum calcium concentrations with any ischemic stroke subtype.
Published: 2019
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24. Identification of risk loci for postpartum depression in a genome-wide association study.

Author: Li X, Takahashi N, Narita A, Nakamura Y, Sakurai-Yageta M, Murakami K, Ishikuro M, Obara T, Kikuya M, Ueno F, Metoki H, Ohseto H, Takahashi I, Nakamura T, Warita N, Shoji T, Yu Z, Ono C, Kobayashi N, Kikuchi S, Matsuki T, Nagami F, Ogishima S, Sugawara J, Hoshiai T, Saito M, Fuse N, Kinoshita K, Yamamoto M, Yaegashi N, Ozaki N, Tamiya G, Kuriyama S, and Tomita H
Subjects: Humans, Female, Adult, Genetic Loci, Polymorphism, Single Nucleotide, Japan, Genetic Predisposition to Disease, Cohort Studies, Depression, Postpartum genetics, Genome-Wide Association Study
Abstract: Aim: Genome-wide association studies (GWAS) of postpartum depression (PPD) based on accumulated cohorts with multiple ethnic backgrounds have failed to identify significantly associated loci. Herein, we conducted a GWAS of Japanese perinatal women along with detailed confounding information to uncover PPD-associated loci., Methods: The first and second cohorts (n = 9260 and n = 8582 perinatal women enrolled in the Tohoku Medical Megabank Project) and the third cohort (n = 997), recruited at Nagoya University, underwent genotyping. Of them, 1421, 1264, and 225 were classified as PPD based on the Edinburgh Postnatal Depression Scale 1 month after delivery. The most influential confounding factors of genetic liability to PPD were selected, and logistic regression analyses were performed to evaluate genetic associations with PPD after adjusting for confounders., Results: A meta-analysis of GWAS results from the three cohorts identified significant associations between PPD and the following loci (P < 5 × 10 -8 ) by integrating the number of deliveries and the number of family members living together as the most influential confounders: rs377546683 at DAB1, rs11940752 near UGT8, rs141172317, rs117928019, rs76631412, rs118131805 at DOCK2, rs188907279 near ZNF572, rs504378, rs690150, rs491868, rs689917, rs474978, rs690118, rs690253 near DIRAS2, rs1435984417 at ZNF618, rs57705782 near PTPRM, and rs185293917 near PDGFB. Pathway analyses indicated that SNPs suggestively associated with PPD were mostly over-represented in categories including long-term depression, GnRH signaling, glutamatergic synapse, oxytocin signaling, and Rap1 signaling., Conclusion: The current GWAS study identified eight loci significantly associated with PPD, which may clarify the genetic structure underlying its pathogenesis., (© 2024 The Author(s). Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.)
Published: 2024
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25. The association of birth weight and current BMI on the risk of hypertension: the Tohoku medical megabank community-based cohort study.

Author: Himuro H, Kogure M, Nakaya N, Nakamura T, Hatanaka R, Chiba I, Nakaya K, Tsuchiya N, Hirata T, Orui M, Kobayashi T, Kodama EN, Hamanaka Y, Uruno A, Fuse N, Nagaie S, Ogishima S, Ishikuro M, Obara T, Izumi Y, Saito M, Kuriyama S, Hozawa A, and Sugawara J
Subjects: Humans, Female, Male, Adult, Middle Aged, Cross-Sectional Studies, Japan epidemiology, Cohort Studies, Aged, Blood Pressure physiology, Risk Factors, Young Adult, Infant, Low Birth Weight, Hypertension epidemiology, Body Mass Index, Birth Weight
Abstract: This study aimed to investigate the association of combination of birth weight and current body mass index (BMI) with the risk of hypertension in adulthood. This cross-sectional study used data from the Tohoku Medical Megabank Community-based Cohort Study conducted in Japan. A total of 10,688 subjects aged ≥20 years were eligible. We calculated the least square (LS) means of systolic blood pressure (SBP) and trend tests were performed to evaluate the linear relationships between birth weight categories and SBP. We also used a multivariate logistic regression analysis to assess the risk of hypertension associated with the combination of birth weight and current BMI. There was a statistically inverse association between birth weight and SBP in the 20-64 age group, but no significant association in the ≥65 age group. Low birth weight (LBW) with normal BMI group had a higher risk of hypertension than the normal or high birth weight groups with normal BMI. Furthermore, the group with LBW and BMI ≥25.0 kg/m 2 was the highest risk for hypertension (adjusted odds ratio: 2.73; 95% CI, 2.04-3.65) compared to the reference group (birth weight 2500-3499 g and BMI 18.5-24.9 kg/m 2 ). There was a significant association between LBW and subsequent risk of hypertension. In addition, participants with lower birth weights had a higher risk of hypertension than those with higher birth weights. However, even in participants with a lower birth weight, the risk of hypertension could be reduced when they maintained an optimal BMI., (© 2024. The Author(s).)
Published: 2024
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26. Depressive symptoms as risk factors for the onset of home hypertension: a prospective cohort study.

Author: Tokioka S, Nakaya N, Hatanaka R, Nakaya K, Kogure M, Chiba I, Nochioka K, Metoki H, Murakami T, Satoh M, Nakamura T, Ishikuro M, Obara T, Hamanaka Y, Orui M, Kobayashi T, Uruno A, Kodama EN, Nagaie S, Ogishima S, Izumi Y, Fuse N, Kuriyama S, and Hozawa A
Subjects: Humans, Female, Male, Middle Aged, Prospective Studies, Risk Factors, Adult, Aged, Japan epidemiology, Blood Pressure physiology, Blood Pressure Monitoring, Ambulatory, Cohort Studies, Hypertension epidemiology, Hypertension psychology, Hypertension complications, Depression epidemiology
Abstract: Depression is comorbid with somatic diseases; however, the relationship between depressive symptoms and hypertension (HT), a risk factor for cardiovascular events, remains unclear. Home blood pressure (BP) is more reproducible and accurately predictive of cardiovascular diseases than office BP. Therefore, we focused on home BP and investigated whether depressive symptoms contributed to the future onset of home HT. This prospective cohort study used data from the Tohoku Medical Megabank Community-Cohort Study (conducted in the Miyagi Prefecture, Japan) and included participants with home normotension (systolic blood pressure (SBP) < 135 mmHg and diastolic blood pressure (DBP) < 85 mmHg). Depressive symptoms were evaluated using the Center for Epidemiologic Studies Depression Scale-Japanese version at the baseline survey. In the secondary survey, approximately 4 years later, the onset of home HT was evaluated (SBP ≥ 135 mmHg or DBP ≥ 85 mmHg) and was compared in participants with and without depressive symptoms. Of the 3 082 (mean age: 54.2 years; females: 80.9%) participants, 729 (23.7%) had depressive symptoms at the baseline survey. During the 3.5-year follow-up, 124 (17.0%) and 388 (16.5%) participants with and without depressive symptoms, respectively, developed home HT. Multivariable adjusted odds ratios were 1.37 (95% confidence interval (CI): 1.02-1.84), 1.18 (95% CI: 0.86-1.61), and 1.66 (95% CI: 1.17-2.36) for home, morning, and evening HT, respectively. This relationship was consistent in the subgroup analyses according to age, sex, BP pattern, and drinking habit. Depressive symptoms increased the risk of new-onset home HT, particularly evening HT, among individuals with home normotension. This prospective cohort study revealed that depressive symptoms are risk factors for new-onset home hypertension, particularly evening hypertension among individuals with home normotension. Assessing home blood pressure in individuals with depressive symptoms is important for the prevention of hypertension and concomitant cardiovascular diseases., (© 2024. The Author(s).)
Published: 2024
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27. Associations between leisure time, non-leisure time physical activity, and kidney function in Japanese adults: a cross-sectional study.

Author: Chiba I, Nakaya N, Kogure M, Hatanaka R, Nakaya K, Tokioka S, Nakamura T, Nagaie S, Ogishima S, Obara T, Sato T, Fuse N, Izumi Y, Kuriyama S, and Hozawa A
Subjects: Humans, Cross-Sectional Studies, Male, Female, Middle Aged, Japan epidemiology, Adult, Aged, Kidney physiopathology, Kidney physiology, Cystatin C blood, Albuminuria epidemiology, Prevalence, East Asian People, Leisure Activities, Glomerular Filtration Rate, Exercise, Renal Insufficiency, Chronic epidemiology, Renal Insufficiency, Chronic physiopathology
Abstract: Background: Chronic kidney disease (CKD) contributes to decreased life expectancy. We examined the association between leisure-time physical activity (LTPA), non-leisure-time physical activity (non-LTPA) and kidney function., Methods: This was a cross-sectional study including 32 162 community-dwelling adults aged ≥ 20 years from the Tohoku Medical MegaBank community-based cohort study. Kidney function was evaluated using cystatin C-based estimated glomerular filtration rate (eGFR) as well as self-reported LTPA and non-LTPA. CKD was defined as either eGFR decline (≤ 60 mL/min/1.73 m 2 ) or presence of albuminuria (albumin-creatinine ≥ 30 mg/g). The association between domain-specific physical activity and kidney function, and CKD prevalence was examined using multivariable-adjusted ordinary least squares and modified Poisson models., Results: The mean eGFR was 98.1 (± 13.2) mL/min/1.73 m 2 . 3 185 (9.9%) participants were classified as having CKD. The mean LTPA and non-LTPA levels were 2.9 (± 4.2) and 16.6 (± 14.2) METs-hour/day, respectively. For LTPA, in the adjusted model, the quartile groups with higher levels had a higher kidney function (β, 0.36; 95% confidence intervals [CI], [0.06, 0.66]; p = 0.019 for the 2nd quartile, β, 0.82; 95% CI, [0.51, 1.14]; p < 0.001 for the 3rd quartile, and β, 1.16; 95% CI, [0.83, 1.49]; p < 0.001 for the 4th quartile), whereas there were no apparent associations for prevalence of CKD. For non-LTPA, 4th quartile was associated with decreased eGFR (β, -0.42; 95% CI, [-0.72, -0.11]; p = 0.007) and higher prevalence of CKD prevalence (Prevalence ratio, 1.12; 95% CI, [1.02, 1.24]; p = 0.022). These associations with kidney function remained consistent in the subgroup analyses divided by demographic and biological variables., Conclusions: We observed a positive association between higher LTPA levels and better kidney function, but not association with CKD prevalence. In contrast, higher non-LTPA was negatively associated with both kidney function and CKD prevalence. These findings suggest that promoting LTPA is beneficial for kidney function., (© 2024. The Author(s).)
Published: 2024
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28. Inconsistent embryo selection across polygenic score methods.

Author: Namba S, Akiyama M, Hamanoue H, Kato K, Kawashima M, Kushima I, Matsuda K, Nakatochi M, Ogishima S, Sonehara K, Suzuki K, Takata A, Tamiya G, Tanikawa C, Yamamoto K, Yamamoto N, Ozaki N, and Okada Y
Published: 2024
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29. Genetic Risk, Lifestyle Adherence, and Risk of Developing Hyperuricaemia in a Japanese Population.

Author: Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Tokioka S, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Ohseto H, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Matsuda BJPK, Izumi Y, Kinoshita K, Tamiya G, Hozawa A, and Yamamoto M
Abstract: Objective: To investigate the inter-relationships among genetic risk, healthy lifestyle adherence, and hyperuricaemia susceptibility., Methods: This prospective cohort study was conducted with 7,241 hyperuricaemia-free individuals aged ≥ 20 years from the Tohoku Medical Megabank Community-based cohort study. A comprehensive lifestyle score included body mass index, smoking, drinking, and physical activity, and a polygenic risk score (PRS) was constructed based on uric acid loci from a previous genome-wide association study meta-analysis. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and hyperuricaemia incidence and calculate the area under the receiver operating characteristic curve (AUROC). Hyperuricaemia was defined as a uric acid level ≥7.0 mg/dl or a self-reported history of hyperuricaemia., Results: Of the 7,241 adults (80.7% females; mean [SD] age: 57.7 [12.6] years), 217 (3.0%) developed hyperuricaemia during 3.5 years of follow-up. Genetic risk correlated with hyperuricaemia development (P for interaction = 0.287), and lifestyle risks were independently associated. Those with a high genetic risk and poor lifestyle had the highest risk (odds ratio: 5.34; 95% confidence interval [CI]: 2.61-12.10). Although not statistically significant, incorporating the PRS in the model with lifestyle information improved predictive ability (AUROC = 0.771, 95% CI: 0.736-0.806 for lifestyle; AUROC = 0.785, 95% CI: 0.751-0.819 for lifestyle and PRS; p = 0.07)., Conclusion: : A healthy lifestyle to prevent hyperuricaemia, irrespective of genetic risk, may mitigate the genetic risk. Genetic risk may complement lifestyle factors in identifying individuals at a heightened hyperuricaemia risk., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology.)
Published: 2024
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30. Progress Report of the Tohoku Medical Megabank Community-based Cohort Study: Study Profile of the Repeated Center-based Survey During Second Period in Miyagi Prefecture.

Author: Hozawa A, Nakaya K, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Chiba I, Kanno I, Sugawara J, Kodama E, Hamanaka Y, Kobayashi T, Uruno A, Tsuchiya N, Hirata T, Narita A, Tsuboi A, Tamahara T, Otsuki A, Goto M, Taira M, Shimizu R, Suzuki K, Obara T, Kikuya M, Metoki H, Ishikuro M, Danjoh I, Ogishima S, Nagaie S, Minegishi N, Hiratsuka M, Kumada K, Nishijima I, Nobukuni T, Yamaguchi-Kabata Y, Nagami F, Kure S, Fuse N, Kinoshita K, Izumi Y, Kuriyama S, and Yamamoto M
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Child, Female, Humans, Male, Middle Aged, Young Adult, Japan, Surveys and Questionnaires, Cohort Studies
Abstract: Background: The purpose of this study was to report the basic profile of the Miyagi Prefecture part of a repeated center-based survey during the second period of the Tohoku Medical Megabank Community-Based Cohort Study (TMM CommCohort Study), as well as the participants' characteristics based on their participation type in the baseline survey., Methods: The second period survey, conducted from June 2017 to March 2021, included participants of the TMM CommCohort Study (May 2013 to March 2016). In addition to the questionnaire, blood, urine, and physiological function tests were performed during the second period survey. There were three main ways of participation in the baseline survey: Type 1, Type 1 additional, or Type 2 survey. The second period survey was conducted in the same manner as the Type 2 survey, which was based on the community support center (CSC)., Results: In Miyagi Prefecture, 29,383 (57.7%) of 50,967 participants participated in the second period survey. The participation rate among individuals who had visited the CSC was approximately 80%. Although some factors differed depending on the participation type in the baseline survey, the second period survey respondents in the Type 1 and Type 2 survey groups at baseline had similar traits., Conclusion: The second period survey of the TMM CommCohort Study provided detailed follow-up information. Following up on the health conditions of the participants will clarify the long-term effects of disasters and contribute to personalized prevention.
Published: 2024
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31. Association of olfactory and cognitive function test scores with hippocampal and amygdalar grey matter volume: a cross-sectional study.

Author: Sato S, Imaeda T, Mugikura S, Mori N, Takanashi M, Hayakawa K, Saito T, Taira M, Narita A, Kogure M, Chiba I, Hatanaka R, Nakaya K, Kanno I, Ishiwata R, Nakamura T, Motoike IN, Nakaya N, Koshiba S, Kinoshita K, Kuriyama S, Ogishima S, Nagami F, Fuse N, and Hozawa A
Subjects: Humans, Aged, Male, Female, Middle Aged, Cross-Sectional Studies, Aged, 80 and over, Adult, Cognitive Dysfunction pathology, Cognitive Dysfunction diagnosis, Cognitive Dysfunction diagnostic imaging, Neuropsychological Tests, Atrophy, Smell physiology, Organ Size, Gray Matter diagnostic imaging, Gray Matter pathology, Amygdala pathology, Amygdala diagnostic imaging, Hippocampus pathology, Hippocampus diagnostic imaging, Cognition physiology, Magnetic Resonance Imaging methods
Abstract: Few population-based studies including younger adults have examined the potential of olfactory function tests to capture the degree of atrophy in memory-associated brain regions, which cannot be adequately explained by cognitive function tests screening for cognitive impairment. This population-based study investigated associations between high-resolution olfactory test data with few odours and grey matter volumes (GMVs) of the left and right hippocampi, amygdala, parahippocampi, and olfactory cortex, while accounting for differences in cognitive decline, in 1444 participants (aged 31-91 years). Regression analyses included intracranial volume (ICV)-normalised GMVs of eight memory-related regions as objective variables and age, sex, education duration, smoking history, olfaction test score, and the Montreal Cognitive Assessment-Japanese version (MoCA-J) score as explanatory variables. Significant relationships were found between olfactory test scores and ICV-normalised GMVs of the left and right hippocampi and left amygdala (p = 0.020, 0.024, and 0.028, respectively), adjusting for the MoCA-J score. The olfactory test score was significantly related to the right amygdalar GMV (p = 0.020) in older adults (age ≥ 65 years). These associations remained significant after applying Benjamini-Hochberg multiple testing correction (false discovery rate < 0.1). Therefore, olfactory and cognitive function tests may efficiently capture the degree of atrophy in the hippocampi and amygdala, especially in older adults., (© 2024. The Author(s).)
Published: 2024
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32. Attitudes toward subsequent primary cancer prevention among survivors of childhood, adolescent, and young adult (CAYA) cancer in Japan: results of a comprehensive questionnaire survey on long-term women's health after CAYA cancer.

Author: Asai-Sato M, Takahashi T, Ota K, Komiya H, Iwasa T, Ogishima S, and Suzuki N
Abstract: Objective: Prevention of subsequent primary cancer (SPC) is crucial for cancer survivors, particularly those who developed the disease during childhood, adolescence, and young adulthood (CAYA). The aim of this study was to assess the current status of SPC prevention among female CAYA cancer survivors., Methods: A survey regarding long-term health issues after cancer treatment was conducted using questionnaires that targeted women aged ≥20 years who had developed cancer before the age of 40 years. The survey assessed various health issues, and this paper focuses on the items related to the respondents' perceptions and attitudes toward SPC prevention., Results: A total of 1,026 respondents were analyzed. Over 60% of respondents were aware of SPC and the need for screening. The percentages of respondents who underwent regular SPC screening were 68.3%, 68.4%, 49.7%, 58.6%, and 57.0% for cervical, breast, lung, and gastric cancers, respectively. After adjusting for age, type of first cancer, and current follow-up, we found that receiving recommendations for SPC screening was the most critical factor in SPC screening uptake (odds ratio=3.836; 95% confidence interval=2.281-6.451; p<0.001 by logistic regression analysis). However, only 40.4% of the respondents received recommendations for SPC screening from their physicians., Conclusion: Despite good awareness of SPC prevention, the uptake rate for cancer screening among cancer survivors was inadequate, indicating that preventive measures for SPC should be promoted. Because recommendations from others strongly influence SPC screening uptake, healthcare professionals should have accurate knowledge and provide guidance regarding SPC prevention., Competing Interests: No potential conflict of interest relevant to this article was reported., (© 2025. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology, and Japan Society of Gynecologic Oncology.)
Published: 2024
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33. Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS).

Author: Harada S, Iida M, Miyagawa N, Hirata A, Kuwabara K, Matsumoto M, Okamura T, Edagawa S, Kawada Y, Miyake A, Toki R, Akiyama M, Kawai A, Sugiyama D, Sato Y, Takemura R, Fukai K, Ishibashi Y, Kato S, Kurihara A, Sata M, Shibuki T, Takeuchi A, Kohsaka S, Sawano M, Shoji S, Izawa Y, Katsumata M, Oki K, Takahashi S, Takizawa T, Maruya H, Nishiwaki Y, Kawasaki R, Hirayama A, Ishikawa T, Saito R, Sato A, Soga T, Sugimoto M, Tomita M, Komaki S, Ohmomo H, Ono K, Otsuka-Yamasaki Y, Shimizu A, Sutoh Y, Hozawa A, Kinoshita K, Koshiba S, Kumada K, Ogishima S, Sakurai-Yageta M, Tamiya G, and Takebayashi T
Subjects: Humans, Middle Aged, Adult, Japan epidemiology, Female, Male, Aged, Cohort Studies, Biomarkers, Metabolomics
Abstract: The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study uses an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants, and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability and functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.
Published: 2024
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34. Associations of combined genetic and lifestyle risks with hypertension and home hypertension.

Author: Takase M, Hirata T, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Narita A, Metoki H, Satoh M, Obara T, Ishikuro M, Ohseto H, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Tamiya G, Hozawa A, and Yamamoto M
Subjects: Humans, Male, Female, Middle Aged, Cross-Sectional Studies, Aged, Adult, Japan epidemiology, Genome-Wide Association Study, Blood Pressure genetics, Risk Factors, Exercise, Genetic Predisposition to Disease, Multifactorial Inheritance, Body Mass Index, Alcohol Drinking genetics, Hypertension genetics, Life Style
Abstract: No study, to our knowledge, has constructed a polygenic risk score based on clinical blood pressure and investigated the association of genetic and lifestyle risks with home hypertension. We examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. In a cross-sectional study of 7027 Japanese individuals aged ≥20 years, we developed a lifestyle score based on body mass index, alcohol consumption, physical activity, and sodium-to-potassium ratio, categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score was constructed with the target data (n = 1405) using publicly available genome-wide association study summary statistics from BioBank Japan. Using the test data (n = 5622), we evaluated polygenic risk score performance and examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertension and home hypertension were defined as blood pressure measured at a community-support center ≥140/90 mmHg or at home ≥135/85 mmHg, respectively, or self-reported treatment for hypertension. In the test data, 2294 and 2322 participants had hypertension and home hypertension, respectively. Both polygenic risk and lifestyle scores were independently associated with hypertension and home hypertension. Compared with those of participants with low genetic risk and an ideal lifestyle, the odds ratios for hypertension and home hypertension in the low genetic risk and poor lifestyle group were 1.94 (95% confidence interval, 1.34-2.80) and 2.15 (1.60-2.90), respectively. In summary, lifestyle is important to prevent hypertension; nevertheless, participants with high genetic risk should carefully monitor their blood pressure despite a healthy lifestyle., (© 2024. The Author(s).)
Published: 2024
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35. Early prediction of hypertensive disorders of pregnancy toward preventive early intervention.

Author: Mizuno S, Nagaie S, Sugawara J, Tamiya G, Obara T, Ishikuro M, Kuriyama S, Yaegashi N, Tanaka H, Yamamoto M, and Ogishima S
Abstract: Background: Various disease prediction models have been developed, capitalizing on the wide use of electronic health records, but environmental factors that are important in the development of noncommunicable diseases are rarely included in the prediction models. Hypertensive disorders of pregnancy are leading causes of maternal morbidity and mortality and are known to cause several serious complications later in life., Objective: This study aims to develop early hypertensive disorders of pregnancy prediction models using comprehensive environmental factors based on self-report questionnaires in early pregnancy., Study Design: We developed machine learning and artificial intelligence models for the early prediction of hypertensive disorders of pregnancy using early pregnancy data from approximately 23,000 pregnancies in the Tohoku Medical Megabank Birth and Three Generation Cohort Study. We clarified the important features for prediction based on regression coefficients or Gini coefficients of the interpretable artificial intelligence models (i.e., logistic regression, random forest and XGBoost models) among our developed models., Results: The performance of the early hypertensive disorders of pregnancy prediction models reached an area under the receiver operating characteristic curve of 0.93, demonstrating that the early hypertensive disorders of pregnancy prediction models developed in this study retain sufficient performance in hypertensive disorders of pregnancy prediction. Among the early prediction models, the best performing model was based on self-reported questionnaire data in early pregnancy (mean of 20.2 gestational weeks at filling) which consist of comprehensive lifestyles. The interpretation of the models reveals that both eating habits were dominantly important for prediction., Conclusion: We have developed high-performance models for early hypertensive disorders of pregnancy prediction using large-scale cohort data from the Tohoku Medical Megabank project. Our study clearly revealed that the use of comprehensive lifestyles from self-report questionnaires led us to predict hypertensive disorders of pregnancy risk at the early stages of pregnancy, which will aid early intervention to reduce the risk of hypertensive disorders of pregnancy., (© 2024 The Authors.)
Published: 2024
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36. How Healthy Lifestyle Habits Have Interacted with SARS-CoV-2 Infection and the Effectiveness of COVID-19 Vaccinations: Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study.

Author: Orui M, Obara T, Ishikuro M, Noda A, Shinoda G, Murakami K, Nakamura T, Metoki H, Ogishima S, Izumi Y, Nakaya N, Hozawa A, Ishii T, Nagami F, Yamamoto M, and Kuriyama S
Abstract: Introduction: To examine the interaction between lifestyle habits and the COVID-19 vaccinations for preventing SARS-CoV-2 infection, we analyzed 11,016 adult participants registered in the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study., Methods: Lifestyle variables, including regular exercise, smoking and drinking habits, sleep status, body mass index, and daily breakfast consumption, were assessed from 2014 to 2019 using baseline questionnaires. Information on SARS-CoV-2 infection and the COVID-19 vaccination were also collected from March 2020 to May 2023. The study period was divided into two in the postvaccination phase: the first period (the beginning of the vaccination program) and the second period (the fourth shot onward)., Results: In the Cox proportional-hazards model analysis, the five-time vaccinations group showed a significantly lower risk of SARS-CoV-2 infection adjusted age, sex, underlying health condition, and lifestyle variables (hazard ratio [HR] 0.81, 95% confidence interval [CI] 0.76-0.86). Logistic regression analysis revealed that a higher number of vaccinations was significantly associated with a low risk of SARS-CoV-2 infection regardless of lifestyle habits (three times in the first period: odds ratio [OR] 0.19, 95% CI 0.15-0.24; five times in the second period: OR 0.07, 95% CI 0.05-0.11 vs. none). Regarding lifestyle habits, the risk reduction in those who had sleep satisfaction (OR 0.12, 95% CI 0.08-0.18) was slightly larger than in those who had sleep dissatisfaction (OR 0.23, 95% CI 0.17-0.32) in the group with the highest number of vaccinations in the first period; however, this interaction was hardly confirmed in the second period when the number of infected cases significantly increased., Conclusions: Our findings indicated that a higher number of COVID-19 vaccinations was associated with reduced risk of SARS-CoV-2 infection; otherwise, we may need to understand the advantages and limitations of a healthy lifestyle for preventing infection depending on the situation with vaccinations and infection spreading., Competing Interests: None, (Copyright © Japan Medical Association.)
Published: 2024
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37. Genetic Risk, Healthy Lifestyle Adherence, and Risk of Developing Diabetes in the Japanese Population.

Author: Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Matsuda K, Izumi Y, Kinoshita K, Tamiya G, Hozawa A, and Yamamoto M
Abstract: Aim: This study examined the relationship between genetic risk, healthy lifestyle, and risk of developing diabetes., Methods: This prospective cohort study included 11,014 diabetes-free individuals ≥ 20 years old from the Tohoku Medical Megabank Community-based cohort study. Lifestyle scores, including the body mass index, smoking, physical activity, and gamma-glutamyl transferase (marker of alcohol consumption), were assigned, and participants were categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score (PRS) was constructed based on the type 2 diabetes loci from the BioBank Japan study. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and diabetes incidence and to calculate the area under the receiver operating characteristic curve (AUROC)., Result: Of the 11,014 adults included (67.8% women; mean age [standard deviation], 59.1 [11.3] years old), 297 (2.7%) developed diabetes during a mean 4.3 (0.8) years of follow-up. Genetic and lifestyle score is independently associated with the development of diabetes. Compared with the low genetic risk and ideal lifestyle groups, the odds ratio was 3.31 for the low genetic risk and poor lifestyle group. When the PRS was integrated into a model including the lifestyle and family history, the AUROC significantly improved to 0.719 (95% confidence interval [95% CI]: 0.692-0.747) compared to a model including only the lifestyle and family history (0.703 [95% CI, 0.674-0.732])., Conclusion: Our findings indicate that adherence to a healthy lifestyle is important for preventing diabetes, regardless of genetic risk. In addition, genetic risk might provide information beyond lifestyle and family history to stratify individuals at high risk of developing diabetes.
Published: 2024
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38. Relationships of Fat Mass Index and Fat-Free Mass Index with Low-Density Lipoprotein Cholesterol Levels in the Tohoku Medical Megabank Community-Based Cohort Study.

Author: Takase M, Nakamura T, Nakaya N, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Obara T, Ishikuro M, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Izumi Y, Kuriyama S, and Hozawa A
Subjects: Humans, Male, Female, Cross-Sectional Studies, Middle Aged, Body Composition, Body Mass Index, Aged, Adult, Cohort Studies, Adipose Tissue metabolism, Dyslipidemias blood, Dyslipidemias metabolism, Dyslipidemias epidemiology, Prognosis, Follow-Up Studies, Risk Factors, Cholesterol, LDL blood, Cholesterol, LDL metabolism
Abstract: Aims: Although fat mass (FM) and fat-free mass (FFM) have an impact on lipid metabolism, the relationship between different body composition phenotypes and lipid profiles is still unclear. By dividing the FM and FFM by the square of the height, respectively, the fat mass index (FMI) and fat-free mass index (FFMI) can be used to determine the variations in body composition. This study aimed to investigate the relationship of combined FMI and FFMI with low-density lipoprotein cholesterol (LDL-C) levels., Methods: This cross-sectional study comprised 5,116 men and 13,630 women without cardiovascular disease and without treatment for hypertension, and diabetes. Following sex-specific quartile classification, FMI and FFMI were combined into 16 groups. Elevated LDL-C levels were defined as LDL-C ≥ 140 mg/dL and/or dyslipidemia treatment. Multivariable logistic regression models were used to examine the relationships between combined FMI and FFMI and elevated LDL-C levels., Results: Overall, elevated LDL-C levels were found in 1,538 (30.1%) men and 5,434 (39.9%) women. In all FFMI subgroups, a higher FMI was associated with elevated LDL-C levels. Conversely, FFMI was inversely associated with elevated LDL-C levels in most FMI subgroups. Furthermore, the groups with the highest FMI and lowest FFMI had higher odds ratios for elevated LDL-C levels than those with the lowest FMI and highest FFMI., Conclusions: Regardless of FFMI, FMI was positively associated with elevated LDL-C levels. Conversely, in the majority of FMI subgroups, FFMI was inversely associated with elevated LDL-C levels.
Published: 2024
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39. Relationship between traditional risk factors for hypertension and systolic blood pressure in the Tohoku Medical Megabank Community-based Cohort Study.

Author: Takase M, Nakaya N, Tanno K, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Nakamura T, Hirata T, Obara T, Ishikuro M, Kotozaki Y, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Ohmomo H, Fuse N, Sugawara J, Shimizu A, Izumi Y, Kuriyama S, and Hozawa A
Subjects: Humans, Female, Male, Middle Aged, Risk Factors, Japan epidemiology, Cross-Sectional Studies, Aged, Cohort Studies, Adult, Body Mass Index, Potassium urine, Smoking adverse effects, Alcohol Drinking adverse effects, Hypertension epidemiology, Blood Pressure physiology, gamma-Glutamyltransferase blood
Abstract: Risk factors for hypertension have been emphasized in the Japanese Society of Hypertension Guidelines for the Management of Hypertension. However, large-scale studies on the association of smoking, potassium excretion, and gamma-glutamyl transferase level with BP in the Japanese population are limited. We conducted a cross-sectional study to examine the association between hypertension risk factors and systolic blood pressure in the Tohoku Medical Megabank Community-based Cohort Study (23,446 men and 38,921 women aged ≥20 years). A model adjusted for age, body mass index, smoking status, drinking status, estimated daily salt intake, potassium excretion, (or urinary sodium-to-potassium ratio), gamma-glutamyl transferase, physical activity, education level, status of damage to homes during the Great East Japan Earthquake, and residential areas was used. The average age and systolic blood pressure were 62.5 (10.3) years for men and 59.6 (11.3) years for women, 128.9 (16.7) mmHg for men and 124.7 (17.5) mmHg for women, respectively. Body mass index estimated daily salt intake, urinary sodium-to-potassium ratio and gamma-glutamyl transferase levels were positively associated with systolic blood pressure. Compared with never-drinkers, current drinkers who consumed 23-45 g/day and ≥46.0 g/day had significantly increased systolic blood pressure. Conversely, current smokers (1-10 cigarettes/day and 11-20 cigarettes/day) were inversely associated with systolic blood pressure compared to never-smokers. Overall, systolic blood pressure was associated with gamma-glutamyl transferase and hypertension risk factors, including body mass index, alcohol consumption, estimated daily salt intake, urinary sodium-to-potassium ratio, and potassium excretion. Our findings support the notion that lifestyle modifications should be attempted to prevent hypertension., (© 2024. The Author(s).)
Published: 2024
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40. Factors Associated With the Prevalence of Irritable Bowel Syndrome: The Miyagi Part of the Tohoku Medical Megabank Project Community-based Cohort Study.

Author: Nakaya K, Nakaya N, Kogure M, Hatanaka R, Chiba I, Kanno I, Nagaie S, Nakamura T, Kanazawa M, Ogishima S, Fuse N, Fukudo S, and Hozawa A
Abstract: Background/aims: The objective of this research is to examine factors related to irritable bowel syndrome (IBS) prevalence in a large population-based study., Methods: A cross-sectional study was conducted with participants in the Miyagi part of the Tohoku Medical Megabank Project Community-Based cohort study who completed the Rome II Modular Questionnaire. Multivariate odds ratios (ORs) for the presence of IBS and 95% confidence intervals (95% CIs) for the reference group were calculated for each factor. Additionally, a stratified analysis was performed by sex and age group (20-49 years, 50-64 years, and ≥ 65 years)., Results: Among 16 252 participants, 3025 (18.6%) had IBS, comprising 750 men (15.5%) and 2275 women (19.9%). Multivariate ORs for the presence of IBS decreased significantly with each year of age (OR, 0.98; 95% CI, 0.98-0.99). Moreover, compared with the reference group, ORs for the presence of IBS were significantly higher in individuals whose home was partially damaged by the Great East Japan Earthquake, those with < 16 years of education, those who spent less time walking, those with high perceived stress (1.77, 1.57-2.01), those with high psychological distress (1.58, 1.36-1.82), and those with high symptoms of depression (1.76, 1.60-1.94). In stratified analyses, a significant relationship was found between psychological factors and IBS prevalence in all sex and age groups., Conclusions: This large cross-sectional population-based cohort study identified several factors associated with IBS prevalence. Psychological factors were significantly associated with IBS prevalence across all age groups and sexes.
Published: 2024
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41. Fractional exhaled nitric oxide distribution and its relevant factors in the general adult population and its healthy subpopulation.

Author: Yamada M, Takase M, Nakaya K, Nakamura T, Kogure M, Nakaya N, Fujino N, Tamada T, Iwasaki C, Suzuki M, Matsumoto S, Fuse N, Uruno A, Kumada K, Ogishima S, Kuriyama S, Ichinose M, Sugiura H, and Hozawa A
Abstract: Background: Measurement of fractional exhaled nitric oxide (Feno) has been used in the diagnosis and management of asthma. Understanding the distribution of Feno in a larger resident population and its "healthy" subpopulation would contribute to the interpretation of Feno in clinical practice., Objective: This study aimed to investigate the distribution and its associated factors in the adult population and its healthy subpopulations., Methods: We conducted a cross-sectional study of 8,638 men and 17,288 women aged 20 years or older living in Miyagi prefecture, Japan. We investigated the distribution of Feno and its associated factors in all subjects, a subpopulation with no history of upper and lower airway diseases (healthy subpopulation 1), and a subpopulation with no history of upper and lower airway diseases, normal lung function, and no positivity for other biomarkers of type 2 inflammation (healthy subpopulation 2)., Results: The distribution of Feno in healthy subpopulations, especially in healthy subpopulation 2 (median [interquartile range], 17 [12-23] with 95th percentile of 36 ppb) was lower than in all subjects (19 [13-26] ppb with 95th percentile of 47 ppb). In healthy subpopulation 1, 10.3% had elevated Feno (≥35 ppb), and elevated Feno was positively associated with factors including obstructive ventilatory defect, blood eosinophilia, house dust mite-specific IgE positivity, and history of hypertension. Male sex was associated with elevated Feno in all subjects and healthy subpopulations., Conclusion: The distribution of Feno in the healthy subpopulation supports the validity of the criteria (≥35 ppb) currently used in Japan for the diagnosis of asthma., (© 2024 The Author(s).)
Published: 2024
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42. Low Hemoglobin Level and Elevated Inflammatory Hematological Ratios Associated With Depression and Sleep Disturbance.

Author: Akaishi T, Nakaya K, Nakaya N, Kogure M, Hatanaka R, Chiba I, Tokioka S, Nagaie S, Ogishima S, and Hozawa A
Abstract: Background: The relationship between blood cell profiles, including hemoglobin (Hb) levels and inflammatory hematological ratios, and mental health problems currently remains unclear., Aim: This study aimed to investigate the relationship between blood cell profiles and mental health issues, including depressive state and sleep disturbance, while adjusting for potential demographic confounders., Methodology: This retrospective, cross-sectional, observational study used a population-based medical database from the Tohoku Medical Megabank Project with more than 60,000 volunteers. Data on age, sex, daily tobacco use, body mass index, and self-reported scores on the Kessler Psychological Distress Scale (K6), Athens Insomnia Scale (AIS), and the Center for Epidemiologic Studies Depression Scale (CES-D) were collected., Results: A total of 62,796 volunteers (23,663 males and 39,133 females), aged ≥20 years at the time of the blood test, agreed to participate in this study. Among the evaluated blood cell profiles, Hb, hematocrit, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were significantly correlated with the K6, AIS, and CES-D scores, with strong statistical significance (p<0.0001 for all) in bivariate correlation analyses. A significant adjusted odds ratio (aOR) of the Hb level for elevated CES-D scores (aOR=0.965 [95% CI: 0.949-0.981], p<0.0001) was confirmed after adjusting for demographic data and daily tobacco use using a logistic regression model. Sensitivity analyses revealed that these associations existed in both males and females but were more prominent in the former. In male participants, a low Hb level was significantly associated with an elevated AIS score. The evaluated inflammatory hematological ratios, including NLR, PLR, and monocyte-to-lymphocyte ratio (MLR), also showed significant aORs with the K6, AIS, and CES-D scores after adjusting for demographic background., Conclusion: Low Hb levels and elevated inflammatory hematological ratios (NLR, MLR, and PLR) were associated with depressive state and sleep disturbances in the general population., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Akaishi et al.)
Published: 2024
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43. Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant women.

Author: Mizuno S, Wagata M, Nagaie S, Ishikuro M, Obara T, Tamiya G, Kuriyama S, Tanaka H, Yaegashi N, Yamamoto M, Sugawara J, and Ogishima S
Subjects: Pregnancy, Female, Humans, Pregnant Women, Cohort Studies, Prospective Studies, Hypertension, Pregnancy-Induced diagnosis, Pre-Eclampsia
Abstract: Recently, many phenotyping algorithms for high-throughput cohort identification have been developed. Prospective genome cohort studies are critical resources for precision medicine, but there are many hurdles in the precise cohort identification. Consequently, it is important to develop phenotyping algorithms for cohort data collection. Hypertensive disorders of pregnancy (HDP) is a leading cause of maternal morbidity and mortality. In this study, we developed, applied, and validated rule-based phenotyping algorithms of HDP. Two phenotyping algorithms, algorithms 1 and 2, were developed according to American and Japanese guidelines, and applied into 22,452 pregnant women in the Birth and Three-Generation Cohort Study of the Tohoku Medical Megabank project. To precise cohort identification, we analyzed both structured data (e.g., laboratory and physiological tests) and unstructured clinical notes. The identified subtypes of HDP were validated against reference standards. Algorithms 1 and 2 identified 7.93% and 8.08% of the subjects as having HDP, respectively, along with their HDP subtypes. Our algorithms were high performing with high positive predictive values (0.96 and 0.90 for algorithms 1 and 2, respectively). Overcoming the hurdle of precise cohort identification from large-scale cohort data collection, we achieved both developed and implemented phenotyping algorithms, and precisely identified HDP patients and their subtypes from large-scale cohort data collection., (© 2024. The Author(s).)
Published: 2024
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44. The association between depressive symptoms and masked hypertension in participants with normotension measured at research center.

Author: Tokioka S, Nakaya N, Nakaya K, Kogure M, Hatanaka R, Chiba I, Kanno I, Nochioka K, Metoki H, Murakami T, Satoh M, Nakamura T, Ishikuro M, Obara T, Hamanaka Y, Orui M, Kobayashi T, Uruno A, Kodama EN, Nagaie S, Ogishima S, Izumi Y, Fuse N, Kuriyama S, and Hozawa A
Subjects: Humans, Male, Female, Adult, Middle Aged, Aged, Blood Pressure physiology, Depression complications, Cohort Studies, Cross-Sectional Studies, Blood Pressure Monitoring, Ambulatory, Masked Hypertension, Hypertension complications, Hypertension epidemiology, Hypertension diagnosis
Abstract: Masked hypertension is a risk factor for cardiovascular diseases. However, masked hypertension is sometimes overlooked owing to the requirement for home blood pressure measurements for diagnosing. Mental status influences blood pressure. To reduce undiagnosed masked hypertension, this study assessed the association between depressive symptoms and masked hypertension. This cross-sectional study used data from the Tohoku Medical Megabank Project Community-Based Cohort Study (conducted in Miyagi Prefecture, Japan, from 2013) and included participants with normotension measured at the research center (systolic blood pressure<140 mmHg and diastolic blood pressure <90 mmHg). Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (Japanese version). Masked hypertension was defined as normotension measured at the research center and home hypertension (home systolic blood pressure ≥135 mmHg or home diastolic blood pressure ≥85 mmHg). The study comprised 6705 participants (mean age: 55.7 ± 13.7 years). Of these participants, 1106 (22.1%) without depressive symptoms and 393 (23.2%) with depressive symptoms were categorized to have masked hypertension. Sex-specific and age-adjusted least mean squares for home blood pressure, not for research blood pressure were significantly higher in the group with depressive symptoms in both sex categories. The multivariate odds ratio for masked hypertension in the patients with depressive symptoms was 1.72 (95% confidence interval: 1.26-2.34) in male participants and 1.30 (95% confidence interval: 1.06-1.59) in female ones. Depressive symptoms were associated with masked hypertension in individuals with normotension measured at the research center. Depressive symptoms may be one of the risk factors for masked hypertension. Depressive symptoms were associated with masked hypertension in individuals with normotension measured at research center., (© 2023. The Author(s).)
Published: 2024
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45. The Human Phenotype Ontology in 2024: phenotypes around the world.

Author: Gargano MA, Matentzoglu N, Coleman B, Addo-Lartey EB, Anagnostopoulos AV, Anderton J, Avillach P, Bagley AM, Bakštein E, Balhoff JP, Baynam G, Bello SM, Berk M, Bertram H, Bishop S, Blau H, Bodenstein DF, Botas P, Boztug K, Čady J, Callahan TJ, Cameron R, Carbon SJ, Castellanos F, Caufield JH, Chan LE, Chute CG, Cruz-Rojo J, Dahan-Oliel N, Davids JR, de Dieuleveult M, de Souza V, de Vries BBA, de Vries E, DePaulo JR, Derfalvi B, Dhombres F, Diaz-Byrd C, Dingemans AJM, Donadille B, Duyzend M, Elfeky R, Essaid S, Fabrizzi C, Fico G, Firth HV, Freudenberg-Hua Y, Fullerton JM, Gabriel DL, Gilmour K, Giordano J, Goes FS, Moses RG, Green I, Griese M, Groza T, Gu W, Guthrie J, Gyori B, Hamosh A, Hanauer M, Hanušová K, He YO, Hegde H, Helbig I, Holasová K, Hoyt CT, Huang S, Hurwitz E, Jacobsen JOB, Jiang X, Joseph L, Keramatian K, King B, Knoflach K, Koolen DA, Kraus ML, Kroll C, Kusters M, Ladewig MS, Lagorce D, Lai MC, Lapunzina P, Laraway B, Lewis-Smith D, Li X, Lucano C, Majd M, Marazita ML, Martinez-Glez V, McHenry TH, McInnis MG, McMurry JA, Mihulová M, Millett CE, Mitchell PB, Moslerová V, Narutomi K, Nematollahi S, Nevado J, Nierenberg AA, Čajbiková NN, Nurnberger JI Jr, Ogishima S, Olson D, Ortiz A, Pachajoa H, Perez de Nanclares G, Peters A, Putman T, Rapp CK, Rath A, Reese J, Rekerle L, Roberts AM, Roy S, Sanders SJ, Schuetz C, Schulte EC, Schulze TG, Schwarz M, Scott K, Seelow D, Seitz B, Shen Y, Similuk MN, Simon ES, Singh B, Smedley D, Smith CL, Smolinsky JT, Sperry S, Stafford E, Stefancsik R, Steinhaus R, Strawbridge R, Sundaramurthi JC, Talapova P, Tenorio Castano JA, Tesner P, Thomas RH, Thurm A, Turnovec M, van Gijn ME, Vasilevsky NA, Vlčková M, Walden A, Wang K, Wapner R, Ware JS, Wiafe AA, Wiafe SA, Wiggins LD, Williams AE, Wu C, Wyrwoll MJ, Xiong H, Yalin N, Yamamoto Y, Yatham LN, Yocum AK, Young AH, Yüksel Z, Zandi PP, Zankl A, Zarante I, Zvolský M, Toro S, Carmody LC, Harris NL, Munoz-Torres MC, Danis D, Mungall CJ, Köhler S, Haendel MA, and Robinson PN
Subjects: Humans, Phenotype, Genomics, Algorithms, Rare Diseases, Biological Ontologies
Abstract: The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)
Published: 2024
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46. Effect of Nicotinamide Mononucleotide Concentration in Human Milk on Neurodevelopmental Outcome: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study.

Author: Saito Y, Sato K, Jinno S, Nakamura Y, Nobukuni T, Ogishima S, Mizuno S, Koshiba S, Kuriyama S, Ohneda K, and Morifuji M
Subjects: Child, Preschool, Female, Infant, Humans, NAD, Cohort Studies, Nucleotides, Milk, Human, Nicotinamide Mononucleotide
Abstract: (1) Background: Breast milk is the only source of nutrition for breastfed infants, but few studies have examined the relationship between breast milk micronutrients and infant neurodevelopmental outcome in exclusively breastfed infants. The aim of this study was to characterize the association between nicotinamide adenine dinucleotide (NAD)-related compounds in the breast milk of Japanese subjects and infant neurodevelopmental outcome. (2) Methods: A total of 150 mother-child pairs were randomly selected from the three-generation cohort of the Tohoku Medical Megabank in Japan. Infants were exclusively breastfed for up to 6 months. Breast milk was collected at 1 month postpartum, and the quantity of NAD-related substances in the breast milk was quantified. The mothers also completed developmental questionnaires at 6, 12, and 24 months. The relationship between the concentration of NAD-related substances in breast milk and developmental indicators was evaluated via ordinal logistic regression analysis. (3) Results: Nicotinamide mononucleotide (NMN) was quantified as the major NAD precursor in breast milk. The median amount of NMN in the breast milk was 9.2 μM. The NMN concentration in breast milk was the only NAD-related substance in breast milk that showed a significant positive correlation with neurodevelopmental outcome in infants at 24 months. (4) Conclusions: The results suggest that NMN in human milk may be an important nutrient for early childhood development.
Published: 2023
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47. Association of Central Blood Pressure and Carotid Intima Media Thickness with New-Onset Hypertension in People with High Normal Blood Pressure.

Author: Tokioka S, Nakaya N, Nakaya K, Takase M, Kogure M, Hatanaka R, Chiba I, Kanno I, Nochioka K, Metoki H, Murakami T, Satoh M, Nakamura T, Obara T, Hamanaka Y, Kobayashi T, Uruno A, Sugawara J, N Kodama E, Ogishima S, Izumi Y, Fuse N, Kuriyama S, Tsuji I, and Hozawa A
Subjects: Humans, Female, Middle Aged, Blood Pressure physiology, Cohort Studies, Prospective Studies, Risk Factors, Carotid Intima-Media Thickness, Hypertension complications
Abstract: Aim: People with high normal blood pressure (BP) have a higher risk of cardiovascular events than those with normal BP; therefore, progression to hypertension (HT) should be prevented. We aimed to assess the HT risk using central BP and carotid intima media thickness (CIMT) in people with high normal BP., Methods: This prospective cohort study used the Tohoku Medical Megabank Community-Based Project Cohort Study (conducted from 2013 in Miyagi Prefecture in Japan). The participants had a high normal BP, defined as a systolic BP of 120-139 mmHg and diastolic BP ＜90 mmHg using brachial BP measurement during the baseline survey. The outcome was new-onset HT during the secondary survey, conducted four years after the baseline survey., Results: Overall, 4,021 participants with high normal BP during the baseline survey, with an average age of 58.7 years, were included; 1,030 (26%) were diagnosed with new-onset HT during the secondary survey, 3.5±0.7 years after the baseline survey. The multivariable odds ratio (95% confidence interval) for HT in the highest versus lowest quartile of central BP was 1.7 (1.2-2.4, p=0.0030), and that of CIMT was 1.8 (1.4-2.4, p＜0.001). Subgroup analysis according to age (＜60 and ≥ 60 years) and sex revealed that the central BP was influential in groups with younger age and female individuals; CIMT was influential in all groups., Conclusions: Higher central BP and thicker CIMT at the baseline were correlated with new-onset HT in individuals with high normal BP, independent of brachial systolic BP and other cardiovascular risk factors.
Published: 2023
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48. 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.

Author: Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Uruno A, Kobayashi T, N Kodama E, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Tsuji I, Tamiya G, Hozawa A, and Yamamoto M
Subjects: Humans, Cohort Studies, Cross-Sectional Studies, Genetic Predisposition to Disease, Risk Factors, Life Style, Genome-Wide Association Study, Diabetes Mellitus epidemiology, Diabetes Mellitus genetics
Abstract: Aim: The influence of family history of diabetes, probably reflecting genetic and lifestyle factors, on the association of combined genetic and lifestyle risks with diabetes is unknown. We examined these associations., Methods: This cross-sectional study included 9,681 participants in the Tohoku Medical Megabank Community-based Cohort Study. A lifestyle score, which was categorized into ideal, intermediate, and poor lifestyles, was given. Family history was obtained through a self-reported questionnaire. A polygenic risk score (PRS) was constructed in the target data (n=1,936) using publicly available genome-wide association study summary statistics from BioBank Japan. For test data (n=7,745), we evaluated PRS performance and examined the associations of combined family history and genetic and lifestyle risks with diabetes. Diabetes was defined as non-fasting blood glucose ≥ 200 mmHg, HbA1c ≥ 6.5%, and/or self-reported diabetes treatment., Results: In test data, 467 (6.0%) participants had diabetes. Compared with a low genetic risk and an ideal lifestyle without a family history, the odds ratio (OR) was 3.73 (95% confidence interval [CI], 1.92-7.00) for a lower genetic risk and a poor lifestyle without a family history. Family history was significantly associated with diabetes (OR, 3.58 [95% CI, 1.73-6.98]), even in those with a low genetic risk and an ideal lifestyle. Even among participants who had an ideal lifestyle without a family history, a high genetic risk was associated with diabetes (OR, 2.49 [95% CI, 1.65-3.85]). Adding PRS to family history and conventional lifestyle risk factors improved the prediction ability for diabetes., Conclusions: Our findings support the notion that a healthy lifestyle is important to prevent diabetes regardless of genetic risk.
Published: 2023
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49. Establishment of the early prediction models of low-birth-weight reveals influential genetic and environmental factors: a prospective cohort study.

Author: Mizuno S, Nagaie S, Tamiya G, Kuriyama S, Obara T, Ishikuro M, Tanaka H, Kinoshita K, Sugawara J, Yamamoto M, Yaegashi N, and Ogishima S
Subjects: Infant, Newborn, Female, Pregnancy, Humans, Cohort Studies, Prospective Studies, Fetal Development, Mothers, Premature Birth epidemiology, Premature Birth genetics
Abstract: Background: Low birth weight (LBW) is a leading cause of neonatal morbidity and mortality, and increases various disease risks across life stages. Prediction models of LBW have been developed before, but have limitations including small sample sizes, absence of genetic factors and no stratification of neonate into preterm and term birth groups. In this study, we challenged the development of early prediction models of LBW based on environmental and genetic factors in preterm and term birth groups, and clarified influential variables for LBW prediction., Methods: We selected 22,711 neonates, their 21,581 mothers and 8,593 fathers from the Tohoku Medical Megabank Project Birth and Three-Generation cohort study. To establish early prediction models of LBW for preterm birth and term birth groups, we trained AI-based models using genetic and environmental factors of lifestyles. We then clarified influential environmental and genetic factors for predicting LBW in the term and preterm groups., Results: We identified 2,327 (10.22%) LBW neonates consisting of 1,077 preterm births and 1,248 term births. Our early prediction models archived the area under curve 0.96 and 0.95 for term LBW and preterm LBW models, respectively. We revealed that environmental factors regarding eating habits and genetic features related to fetal growth were influential for predicting LBW in the term LBW model. On the other hand, we identified that genomic features related to toll-like receptor regulations and infection reactions are influential genetic factors for prediction in the preterm LBW model., Conclusions: We developed precise early prediction models of LBW based on lifestyle factors in the term birth group and genetic factors in the preterm birth group. Because of its accuracy and generalisability, our prediction model could contribute to risk assessment of LBW in the early stage of pregnancy and control LBW risk in the term birth group. Our prediction model could also contribute to precise prediction of LBW based on genetic factors in the preterm birth group. We then identified parental genetic and maternal environmental factors during pregnancy influencing LBW prediction, which are major targets for understanding the LBW to address serious burdens on newborns' health throughout life., (© 2023. BioMed Central Ltd., part of Springer Nature.)
Published: 2023
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50. Association between low levels of anti-inflammatory cytokines during pregnancy and postpartum depression.

Author: Ono CT, Yu Z, Obara T, Ishikuro M, Murakami K, Kikuya M, Kikuchi S, Kobayashi N, Kudo H, Ogishima S, Minegishi N, Sugawara J, Kuriyama S, Yamamoto M, Yaegashi N, and Tomita H
Subjects: Pregnancy, Female, Humans, Interleukin-10, Interleukin-12 Subunit p40, Cytokines, Tumor Necrosis Factor-alpha, Case-Control Studies, Cross-Sectional Studies, Interleukin-4, Interleukin-6, Risk Factors, Depression, Postpartum
Abstract: Aim: Previous studies based on a relatively limited number of subjects have indicated potential associations between plasma cytokine concentrations in perinatal women and postpartum depression (PPD). This report aimed to examine alterations in cytokine levels during pregnancy and after delivery by measuring nine cytokines in prenatal and postnatal plasma samples in a large cohort., Methods: A nested, case-control study was conducted using plasma samples from 247 women with PPD (Edinburgh Postnatal Depression Scale: EPDS ≥9) and 243 age-matched control (EPDS ≤2) women from among perinatal women who participated in the Tohoku Medical Megabank three-generation cohort. Concentrations of nine plasma cytokines (IFN-γ, IL-1β, IL-4, IL-6, IL-10, IL-12p40, IL-12p70, IL-13, and TNF-α) in plasma collected at the time of enrollment during pregnancy and 1 month after delivery were determined using an immunoassay kit., Results: Cross-sectional comparisons of cytokine levels during pregnancy and after delivery indicated that the PPD group maintained significantly lower plasma IL-4 levels during pregnancy and after delivery than the control group, and that plasma IL-4 levels decreased significantly during pregnancy regardless of PPD status. Plasma IL-10 levels were significantly higher during pregnancy than after delivery only among healthy controls, and plasma IL-10 levels were significantly higher in the control group than in the PPD group. Moreover, IFN-γ, IL-6, IL-12p40, and TNF-α levels were significantly lower during pregnancy compared with after delivery regardless of PPD status., Conclusions: These results suggest a potential protective effect of the anti-inflammatory cytokines IL-4 and IL-10 during pregnancy against the development of PPD., (© 2023 The Authors. Psychiatry and Clinical Neurosciences © 2023 Japanese Society of Psychiatry and Neurology.)
Published: 2023
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