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22 results on '"Grossmann, V"'

1. Prognostic impact and landscape of NOTCH1 mutations in chronic lymphocytic leukemia (CLL): a study on 852 patients

Author

Weissmann, S., Roller, A., Jeromin, S., Hernández, M., Abáigar, M., Hernández Rivas, José Ángel, Grossmann, V., Haferlach, C., Kern, W., Haferlach, T., Schnittger, S., Kohlmann, A., Weissmann, S., Roller, A., Jeromin, S., Hernández, M., Abáigar, M., Hernández Rivas, José Ángel, Grossmann, V., Haferlach, C., Kern, W., Haferlach, T., Schnittger, S., and Kohlmann, A.

Abstract

Depto. de Bioquímica y Biología Molecular, Fac. de Farmacia, FALSE, pub

Published
2024

2. Fusion von Tauchroboter- und Satellitenmessungen über unterschiedliche Skalen, Messmodelle und spektrale Abtastungen

Author

Nakath, David, Grossmann, V., Kiko, R., Koch, Reinhard, Oppelt, Natascha, Köser, Kevin, Nakath, David, Grossmann, V., Kiko, R., Koch, Reinhard, Oppelt, Natascha, and Köser, Kevin

Abstract

In küstennahen Gewässern ist es von Vorteil, satellitengestützte optische Messungen des Meeres mit visuellen und sensorischen Beobachtungen von Tauchrobotern zu fusionieren. Obwohl Satelliten nur wenige Meter tief in Gewässer hineinschauen können, ist es möglich, generelle Wassereigenschaften oder den Bodenbewuchs von Küstengewässern zu bestimmen. Visuelle und sensorische Tauchroboterbeobachtungen sind hierzu komplementär und können auch tiefere Gewässer erreichen. Das mitgeführte künstliche Licht wird jedoch stark gestreut und erfordert andere Messmodelle. Zusätzlich sind die räumlichen und spektralen Auflösungen der Beobachtungen oftmals sehr unterschiedlich. Wir analysieren hier die damit verbundenen Problematiken und skizzieren Wege, wie die Fusion der grundverschiedenen Messungen dennoch gelingen könnte.

Published
2022
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3. Fusion von Tauchroboter- und Satellitenmessungen über unterschiedliche Skalen, Messmodelle und spektrale Abtastungen

Author

Nakath, David, Grossmann, V., Kiko, R., Koch, Reinhard, Oppelt, Natascha, Köser, Kevin, Nakath, David, Grossmann, V., Kiko, R., Koch, Reinhard, Oppelt, Natascha, and Köser, Kevin

Abstract

In küstennahen Gewässern ist es von Vorteil, satellitengestützte optische Messungen des Meeres mit visuellen und sensorischen Beobachtungen von Tauchrobotern zu fusionieren. Obwohl Satelliten nur wenige Meter tief in Gewässer hineinschauen können, ist es möglich, generelle Wassereigenschaften oder den Bodenbewuchs von Küstengewässern zu bestimmen. Visuelle und sensorische Tauchroboterbeobachtungen sind hierzu komplementär und können auch tiefere Gewässer erreichen. Das mitgeführte künstliche Licht wird jedoch stark gestreut und erfordert andere Messmodelle. Zusätzlich sind die räumlichen und spektralen Auflösungen der Beobachtungen oftmals sehr unterschiedlich. Wir analysieren hier die damit verbundenen Problematiken und skizzieren Wege, wie die Fusion der grundverschiedenen Messungen dennoch gelingen könnte.

Published
2022
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4. Fusion von Tauchroboter- und Satellitenmessungen über unterschiedliche Skalen, Messmodelle und spektrale Abtastungen

Author

Nakath, David, Grossmann, V., Kiko, R., Koch, Reinhard, Oppelt, Natascha, Köser, Kevin, Nakath, David, Grossmann, V., Kiko, R., Koch, Reinhard, Oppelt, Natascha, and Köser, Kevin

Abstract

In küstennahen Gewässern ist es von Vorteil, satellitengestützte optische Messungen des Meeres mit visuellen und sensorischen Beobachtungen von Tauchrobotern zu fusionieren. Obwohl Satelliten nur wenige Meter tief in Gewässer hineinschauen können, ist es möglich, generelle Wassereigenschaften oder den Bodenbewuchs von Küstengewässern zu bestimmen. Visuelle und sensorische Tauchroboterbeobachtungen sind hierzu komplementär und können auch tiefere Gewässer erreichen. Das mitgeführte künstliche Licht wird jedoch stark gestreut und erfordert andere Messmodelle. Zusätzlich sind die räumlichen und spektralen Auflösungen der Beobachtungen oftmals sehr unterschiedlich. Wir analysieren hier die damit verbundenen Problematiken und skizzieren Wege, wie die Fusion der grundverschiedenen Messungen dennoch gelingen könnte.

Published
2022
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5. Genome-wide Trans-ethnic Meta-analysis Identifies Seven Genetic Loci Influencing Erythrocyte Traits and a Role for RBPMS in Erythropoiesis

Author

Rooij, F.J.A. (Frank) van, Qayyum, Q. (Rehan), Smith, A.V. (Albert Vernon), Zhou, Y. (Yi), Trompet, S. (Stella), Tanaka, T. (Toshiko), Keller, M.F. (Margaux), Chang, L.-C. (Li-Ching), Schmidt, H. (Helena), Yang, M.-L. (Min-Lee), Chen, M.-H. (Ming-Huei), Hayes, J. (James), Johnson, A.D. (Andrew), Yanek, L.R. (Lisa), Mueller, C. (Christian), Lange, L.A. (Leslie), Floyd, J. (James), Ghanbari, M. (Mohsen), Zonderman, A.B., Jukema, J.W., Hofman, A. (Albert), Duijn, C.M. (Cornelia) van, Desch, K.C. (Karl C.), Saba, Y. (Yasaman), Ozel, A.B. (Ayse), Snively, B.M. (Beverly M.), Wu, J.-Y. (Jer-Yuarn), Schmidt, R. (Reinhold), Fornage, M. (Myriam), Klein, R.J. (Robert J.), Fox, C.S. (Caroline), Matsuda, K. (Koichi), Kamatani, N. (Naoyuki), Wild, P.S. (Philipp S.), Stott, D.J. (David J.), Ford, I., Slagboom, P.E. (Eline), Yang, J. (Jaden), Chu, A.Y. (Audrey Y), Lambert, A.J. (Amy J.), Uitterlinden, A.G. (André), Franco, O.H. (Oscar), Hofer, E. (Edith), Ginsburg, D. (David), Hu, B. (Bella), Keating, J. (John), Schick, U.M. (Ursula), Brody, J.A. (Jennifer A.), Li, J.Z. (Jun Z.), Chen, Z. (Zhao), Zeller, T. (Tanja), Guralnik, J.M. (Jack M.), Chasman, D.I. (Daniel), Peters, L.L. (Luanne L.), Kubo, M. (Michiaki), Becker, D.M. (Diane M.), Li, J. (Jin), Eiriksdottir, G. (Gudny), Rotter, J.I. (Jerome I.), Levy, D. (Daniel), Grossmann, V. (Vera), Patel, K.V. (Kushang V.), Chen, C.-H., Ridker, P.M. (Paul M.), Tang, H. (Hua), Launer, L.J. (Lenore), Rice, K.M. (Kenneth M.), Li-Gao, R. (Ruifang), Ferrucci, L. (Luigi), Evans, M.K. (Michelle K.), Choudhuri, A. (Avik), Trompouki, E. (Eirini), Abraham, B.J. (Brian J.), Yang, S. (Song), Takahashi, A. (Atsushi), Kamatani, Y. (Yoichiro), Kooperberg, C. (Charles), Harris, T.B. (Tamara), Jee, S.H. (Sun Ha), Coresh, J. (Josef), Tsai, F.-J. (Fuu-Jen), Longo, D.L. (Dan L.), Chen, Y.-T. (Yuan-Tsong), Felix, J.F. (Janine), Yang, Q. (Qiong), Psaty, B.M. (Bruce), Boerwinkle, E.A. (Eric), Becker, L.C. (Lewis C.), Mook-Kanamori, D.O. (Dennis), Wilson, J.F. (James), Gudnason, V. (Vilmundur), O'Donnell, C.J. (Christopher J.), Dehghan, A. (Abbas), Cupples, L.A. (Adrienne), Nalls, M.A. (Michael), Morris, A.P. (Andrew), Okada, Y. (Yukinori), Reiner, A.P. (Alexander P.), Zon, L.I. (Leonard), Ganesh, S.K. (Santhi), Rooij, F.J.A. (Frank) van, Qayyum, Q. (Rehan), Smith, A.V. (Albert Vernon), Zhou, Y. (Yi), Trompet, S. (Stella), Tanaka, T. (Toshiko), Keller, M.F. (Margaux), Chang, L.-C. (Li-Ching), Schmidt, H. (Helena), Yang, M.-L. (Min-Lee), Chen, M.-H. (Ming-Huei), Hayes, J. (James), Johnson, A.D. (Andrew), Yanek, L.R. (Lisa), Mueller, C. (Christian), Lange, L.A. (Leslie), Floyd, J. (James), Ghanbari, M. (Mohsen), Zonderman, A.B., Jukema, J.W., Hofman, A. (Albert), Duijn, C.M. (Cornelia) van, Desch, K.C. (Karl C.), Saba, Y. (Yasaman), Ozel, A.B. (Ayse), Snively, B.M. (Beverly M.), Wu, J.-Y. (Jer-Yuarn), Schmidt, R. (Reinhold), Fornage, M. (Myriam), Klein, R.J. (Robert J.), Fox, C.S. (Caroline), Matsuda, K. (Koichi), Kamatani, N. (Naoyuki), Wild, P.S. (Philipp S.), Stott, D.J. (David J.), Ford, I., Slagboom, P.E. (Eline), Yang, J. (Jaden), Chu, A.Y. (Audrey Y), Lambert, A.J. (Amy J.), Uitterlinden, A.G. (André), Franco, O.H. (Oscar), Hofer, E. (Edith), Ginsburg, D. (David), Hu, B. (Bella), Keating, J. (John), Schick, U.M. (Ursula), Brody, J.A. (Jennifer A.), Li, J.Z. (Jun Z.), Chen, Z. (Zhao), Zeller, T. (Tanja), Guralnik, J.M. (Jack M.), Chasman, D.I. (Daniel), Peters, L.L. (Luanne L.), Kubo, M. (Michiaki), Becker, D.M. (Diane M.), Li, J. (Jin), Eiriksdottir, G. (Gudny), Rotter, J.I. (Jerome I.), Levy, D. (Daniel), Grossmann, V. (Vera), Patel, K.V. (Kushang V.), Chen, C.-H., Ridker, P.M. (Paul M.), Tang, H. (Hua), Launer, L.J. (Lenore), Rice, K.M. (Kenneth M.), Li-Gao, R. (Ruifang), Ferrucci, L. (Luigi), Evans, M.K. (Michelle K.), Choudhuri, A. (Avik), Trompouki, E. (Eirini), Abraham, B.J. (Brian J.), Yang, S. (Song), Takahashi, A. (Atsushi), Kamatani, Y. (Yoichiro), Kooperberg, C. (Charles), Harris, T.B. (Tamara), Jee, S.H. (Sun Ha), Coresh, J. (Josef), Tsai, F.-J. (Fuu-Jen), Longo, D.L. (Dan L.), Chen, Y.-T. (Yuan-Tsong), Felix, J.F. (Janine), Yang, Q. (Qiong), Psaty, B.M. (Bruce), Boerwinkle, E.A. (Eric), Becker, L.C. (Lewis C.), Mook-Kanamori, D.O. (Dennis), Wilson, J.F. (James), Gudnason, V. (Vilmundur), O'Donnell, C.J. (Christopher J.), Dehghan, A. (Abbas), Cupples, L.A. (Adrienne), Nalls, M.A. (Michael), Morris, A.P. (Andrew), Okada, Y. (Yukinori), Reiner, A.P. (Alexander P.), Zon, L.I. (Leonard), and Ganesh, S.K. (Santhi)

Abstract

Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPM

Published
2017
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6. Comparison of HapMap and 1000 genomes reference panels in a large-scale genome-wide association study

Author

Vries, P.S. (Paul) de, Sabater-Lleal, M. (Maria), Chasman, D.I. (Daniel), Trompet, S. (Stella), Ahluwalia, T.S. (Tarunveer Singh), Teumer, A. (Alexander), Kleber, M.E. (Marcus), Chen, M.-H. (Ming-Huei), Wang, J.J. (Jie Jin), Attia, J. (John), Marioni, R.E. (Riccardo), Steri, M. (Maristella), Weng, L.-C. (Lu-Chen), Pool, R. (Reńe), Grossmann, V. (Vera), Brody, J.A. (Jennifer A.), Venturini, C. (Cristina), Tanaka, T. (Toshiko), Rose, L.M. (Lynda), Oldmeadow, C. (Christopher), Mazur, J. (Johanna), Basu, S. (Saonli), Frånberg, M. (Mattias), Yang, Q. (Qiong), Ligthart, S. (Symen), Hottenga, J.J. (Jouke Jan), Rumley, A. (Ann), Mulas, A. (Antonella), Craen, A.J. (Anton) de, Grotevendt, A. (Anne), Taylor, K.D. (Kent D.), Delgado, G., Kifley, A. (Annette), Lopez, L.M. (Lorna), Berentzen, T.L. (Tina L.), Mangino, M. (Massimo), Bandinelli, S. (Stefania), Morrison, A.C. (Alanna C.), Hamsten, A. (Anders), Tofler, G.H. (Geoffrey), Maat, M.P.M. (Moniek) de, Draisma, G. (Gerrit), Lowe, G.D. (Gordon D.), Zoledziewska, M. (Magdalena), Sattar, N. (Naveed), Lackner, K.J. (Karl J.), Völker, U. (Uwe), McKnight, B. (Barbara), Huang, J. (Jian), Holliday, E.G. (Elizabeth), McEvoy, M.A. (Mark A.), Starr, J.M. (John), Hysi, P.G. (Pirro), Hernandez, D.G. (Dena), Guan, W. (Weihua), Rivadeneira Ramirez, F. (Fernando), McArdle, W.L. (Wendy), Slagboom, P.E. (Eline), Zeller, T. (Tanja), Psaty, B.M. (Bruce), Uitterlinden, A.G. (André), Geus, E.J.C. (Eco) de, Stott, D.J. (David J.), Binder, H. (Harald), Hofman, A. (Albert), Franco, O.H. (Oscar), Rotter, J.I. (Jerome I.), Ferrucci, L. (Luigi), Spector, T.D. (Tim D.), Deary, I.J. (Ian), März, W. (Winfried), Greinacher, A. (Andreas), Wild, P.S. (Philipp S.), Cucca, F. (Francesco), Boomsma, D.I. (Dorret), Watkins, H. (Hugh), Tang, W. (Weihong), Ridker, P.M. (Paul), Jukema, J.W., Scott, R.J. (Rodney J.), Mitchell, P. (Paul), Hansen, T. (T.), O'Donnell, C.J. (Christopher J.), Smith, N.L. (Nicholas L.), Strachan, D.P. (David P.), Dehghan, A. (Abbas), Vries, P.S. (Paul) de, Sabater-Lleal, M. (Maria), Chasman, D.I. (Daniel), Trompet, S. (Stella), Ahluwalia, T.S. (Tarunveer Singh), Teumer, A. (Alexander), Kleber, M.E. (Marcus), Chen, M.-H. (Ming-Huei), Wang, J.J. (Jie Jin), Attia, J. (John), Marioni, R.E. (Riccardo), Steri, M. (Maristella), Weng, L.-C. (Lu-Chen), Pool, R. (Reńe), Grossmann, V. (Vera), Brody, J.A. (Jennifer A.), Venturini, C. (Cristina), Tanaka, T. (Toshiko), Rose, L.M. (Lynda), Oldmeadow, C. (Christopher), Mazur, J. (Johanna), Basu, S. (Saonli), Frånberg, M. (Mattias), Yang, Q. (Qiong), Ligthart, S. (Symen), Hottenga, J.J. (Jouke Jan), Rumley, A. (Ann), Mulas, A. (Antonella), Craen, A.J. (Anton) de, Grotevendt, A. (Anne), Taylor, K.D. (Kent D.), Delgado, G., Kifley, A. (Annette), Lopez, L.M. (Lorna), Berentzen, T.L. (Tina L.), Mangino, M. (Massimo), Bandinelli, S. (Stefania), Morrison, A.C. (Alanna C.), Hamsten, A. (Anders), Tofler, G.H. (Geoffrey), Maat, M.P.M. (Moniek) de, Draisma, G. (Gerrit), Lowe, G.D. (Gordon D.), Zoledziewska, M. (Magdalena), Sattar, N. (Naveed), Lackner, K.J. (Karl J.), Völker, U. (Uwe), McKnight, B. (Barbara), Huang, J. (Jian), Holliday, E.G. (Elizabeth), McEvoy, M.A. (Mark A.), Starr, J.M. (John), Hysi, P.G. (Pirro), Hernandez, D.G. (Dena), Guan, W. (Weihua), Rivadeneira Ramirez, F. (Fernando), McArdle, W.L. (Wendy), Slagboom, P.E. (Eline), Zeller, T. (Tanja), Psaty, B.M. (Bruce), Uitterlinden, A.G. (André), Geus, E.J.C. (Eco) de, Stott, D.J. (David J.), Binder, H. (Harald), Hofman, A. (Albert), Franco, O.H. (Oscar), Rotter, J.I. (Jerome I.), Ferrucci, L. (Luigi), Spector, T.D. (Tim D.), Deary, I.J. (Ian), März, W. (Winfried), Greinacher, A. (Andreas), Wild, P.S. (Philipp S.), Cucca, F. (Francesco), Boomsma, D.I. (Dorret), Watkins, H. (Hugh), Tang, W. (Weihong), Ridker, P.M. (Paul), Jukema, J.W., Scott, R.J. (Rodney J.), Mitchell, P. (Paul), Hansen, T. (T.), O'Donnell, C.J. (Christopher J.), Smith, N.L. (Nicholas L.), Strachan, D.P. (David P.), and Dehghan, A. (Abbas)

Abstract

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

Published
2017
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7. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author

Vries, PS, Sabater-Lleal, M, Chasman, DI, Trompet, S, Ahluwalia, TS, Teumer, A, Kleber, ME, Chen, MH, Wang, JJ, Attia, JR, Marioni, RE, Steri, M, Weng, LC, Pool, R, Grossmann, V, Brody, JA, Venturini, C, Tanaka, T, Rose, LM, Oldmeadow, C, Mazur, J, Basu, S, Franberg, M, Yang, Q, Ligthart, Symen, Hottenga, JJ, Rumley, A, Mulas, A, de Craen, AJM, Grotevendt, A, Taylor, KD, Delgado, GE, Kifley, A, Lopez, LM, Berentzen, TL, Mangino, M, Bandinelli, S, Morrison, AC, Hamsten, A, Tofler, G, de Maat, Moniek, Draisma, HHM, Lowe, GD, Zoledziewska, M, Sattar, N, Lackner, KJ, Volker, U, McKnight, B, Huang, J, Holliday, EG, McEvoy, M A, Starr, JM, Hysi, PG, Hernandez, DG, Guan, WH, Rivadeneira, Fernando, McArdle, WL, Slagboom, PE (Eline), Zeller, T, Psaty, BM, Uitterlinden, André, de Geus, EJC, Stott, DJ, Binder, H, Hofman, Bert, Franco Duran, OH, Rotter, JI, Ferrucci, L, Spector, TD, Deary, IJ, Marz, W, Greinacher, A, Wild, PS, Cucca, F, Boomsma, DI, Watkins, H, Tang, WH, Ridker, PM, Jukema, JW, Scott, RJ, Mitchell, P, Hansen, T, O'Donnell, CJ, Smith, NL, Strachan, DP, Dehghan, Abbas, Vries, PS, Sabater-Lleal, M, Chasman, DI, Trompet, S, Ahluwalia, TS, Teumer, A, Kleber, ME, Chen, MH, Wang, JJ, Attia, JR, Marioni, RE, Steri, M, Weng, LC, Pool, R, Grossmann, V, Brody, JA, Venturini, C, Tanaka, T, Rose, LM, Oldmeadow, C, Mazur, J, Basu, S, Franberg, M, Yang, Q, Ligthart, Symen, Hottenga, JJ, Rumley, A, Mulas, A, de Craen, AJM, Grotevendt, A, Taylor, KD, Delgado, GE, Kifley, A, Lopez, LM, Berentzen, TL, Mangino, M, Bandinelli, S, Morrison, AC, Hamsten, A, Tofler, G, de Maat, Moniek, Draisma, HHM, Lowe, GD, Zoledziewska, M, Sattar, N, Lackner, KJ, Volker, U, McKnight, B, Huang, J, Holliday, EG, McEvoy, M A, Starr, JM, Hysi, PG, Hernandez, DG, Guan, WH, Rivadeneira, Fernando, McArdle, WL, Slagboom, PE (Eline), Zeller, T, Psaty, BM, Uitterlinden, André, de Geus, EJC, Stott, DJ, Binder, H, Hofman, Bert, Franco Duran, OH, Rotter, JI, Ferrucci, L, Spector, TD, Deary, IJ, Marz, W, Greinacher, A, Wild, PS, Cucca, F, Boomsma, DI, Watkins, H, Tang, WH, Ridker, PM, Jukema, JW, Scott, RJ, Mitchell, P, Hansen, T, O'Donnell, CJ, Smith, NL, Strachan, DP, and Dehghan, Abbas

Published
2017

8. A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration

Author

Vries, P.S. (Paul) de, Chasman, D.I. (Daniel), Sabater-Lleal, M. (Maria), Chen, M.-H. (Ming-Huei), Huffman, J.E. (Jennifer E.), Steri, M. (Maristella), Tang, W. (Weihong), Teumer, A. (Alexander), Marioni, R.E. (Riccardo), Grossmann, V. (Vera), Hottenga, J.J. (Jouke Jan), Trompet, S. (Stella), Müller-Nurasyid, M. (Martina), Zhao, J.H. (Jing Hua), Brody, J.A. (Jennifer A.), Kleber, M.E. (Marcus), Guo, X. (Xiuqing), Wang, J.J. (Jie Jin), Auer, P. (Paul), Attia, J. (John), Yanek, L.R. (Lisa), Ahluwalia, T.S. (Tarunveer Singh), Lahti, J. (Jari), Venturini, C. (Cristina), Tanaka, T. (Toshiko), Bielak, L.F. (Lawrence F.), Joshi, P.K. (Peter), Rocanin-Arjo, A. (Ares), Kolcic, I. (Ivana), Navarro, P. (Pau), Rose, L.M. (Lynda), Oldmeadow, C. (Christopher), Riess, H. (Helene), Mazur, J. (Johanna), Basu, S. (Saonli), Goel, A. (Anuj), Yang, Q. (Qiong), Ghanbari, M. (Mohsen), Willemsen, G. (Gonneke), Rumley, A. (Ann), Fiorillo, E. (Edoardo), Craen, A.J. (Anton) de, Grotevendt, A. (Anne), Scott, R.A. (Robert), Taylor, K.D. (Kent D.), Delgado, G.E. (Graciela E.), Yao, J. (Jie), Kifley, A. (Annette), Kooperberg, C. (Charles), Qayyum, Q. (Rehan), Lopez, L. (Lornam), Berentzen, T.L. (Tina L.), Räikkönen, K. (Katri), Mangino, M. (Massimo), Bandinelli, S. (Stefania), Peyser, P.A. (Patricia A.), Wild, S. (Sarah), Tregouet, D.-A. (David-Alexandre), Wright, A.F. (Alan), Marten, J. (Jonathan), Zemunik, T. (Tatijana), Morrison, A.C. (Alanna), Sennblad, B. (Bengt), Tofler, G.H. (Geoffrey), Maat, M.P.M. (Moniek) de, Geus, E.J.C. (Eco) de, Lowe, G.D. (Gordon D.), Zoledziewska, M. (Magdalena), Sattar, N. (Naveed), Binder, H. (Harald), Völker, U. (Uwe), Waldenberger, M. (Melanie), Khaw, K.-T. (Kay-Tee), McKnight, B. (Barbara), Huang, J. (Jian), Jenny, N.S. (Nancy), Holliday, E.G. (Elizabeth), Qi, L. (Lihong), Mcevoy, M.G. (Mark G.), Becker, D.M. (Diane), Starr, J.M. (John), Sarin, A.-P., Hysi, P.G. (Pirro), Hernandez, D.G. (Dena), Jhun, M.A. (Min A.), Campbell, H. (Harry), Hamsten, A. (Anders), Sarin, F. (Fernando), McArdle, W.L. (Wendy), Slagboom, P.E. (Eline), Zeller, T. (Tanja), Koenig, W. (Wolfgang), Psaty, B. (Brucem), Haritunians, T. (Talin), Liu, J. (Jingmin), Palotie, A. (Aarno), Uitterlinden, A.G. (André), Stott, D.J. (David J.), Hofman, A. (Albert), Franco, O.H. (Oscar), Polasek, O. (Ozren), Rudan, I. (Igor), Morange, P.-E. (P.), Wilson, J.F. (James F.), Kardia, S.L. (Sharon L.r), Ferrucci, L. (Luigi), Spector, T.D. (Timothy), Eriksson, J.G. (Johan G.), Hansen, T. (Torben), Deary, I.J. (Ian), Becker, L.C. (Lewis), Scott, R.J. (Rodney), Mitchell, P. (Paul), März, W. (Winfried), Wareham, N.J. (Nick J.), Peters, A. (Annette), Greinacher, A. (Andreas), Wild, P.S. (Philipp S.), Jukema, J.W. (Jan Wouter), Boomsma, D.I. (Dorret), Hayward, C. (Caroline), Cucca, F. (Francesco), Tracy, R.P. (Russell), Watkins, H. (Hugh), Reiner, A.P. (Alex P.), Folsom, A.R. (Aaron), Ridker, P.M. (Paul), O'Donnell, C.J. (Christopher J.), Smith, N.L. (Nicholas L.), Strachan, D.P. (David P.), Dehghan, A. (Abbas), Vries, P.S. (Paul) de, Chasman, D.I. (Daniel), Sabater-Lleal, M. (Maria), Chen, M.-H. (Ming-Huei), Huffman, J.E. (Jennifer E.), Steri, M. (Maristella), Tang, W. (Weihong), Teumer, A. (Alexander), Marioni, R.E. (Riccardo), Grossmann, V. (Vera), Hottenga, J.J. (Jouke Jan), Trompet, S. (Stella), Müller-Nurasyid, M. (Martina), Zhao, J.H. (Jing Hua), Brody, J.A. (Jennifer A.), Kleber, M.E. (Marcus), Guo, X. (Xiuqing), Wang, J.J. (Jie Jin), Auer, P. (Paul), Attia, J. (John), Yanek, L.R. (Lisa), Ahluwalia, T.S. (Tarunveer Singh), Lahti, J. (Jari), Venturini, C. (Cristina), Tanaka, T. (Toshiko), Bielak, L.F. (Lawrence F.), Joshi, P.K. (Peter), Rocanin-Arjo, A. (Ares), Kolcic, I. (Ivana), Navarro, P. (Pau), Rose, L.M. (Lynda), Oldmeadow, C. (Christopher), Riess, H. (Helene), Mazur, J. (Johanna), Basu, S. (Saonli), Goel, A. (Anuj), Yang, Q. (Qiong), Ghanbari, M. (Mohsen), Willemsen, G. (Gonneke), Rumley, A. (Ann), Fiorillo, E. (Edoardo), Craen, A.J. (Anton) de, Grotevendt, A. (Anne), Scott, R.A. (Robert), Taylor, K.D. (Kent D.), Delgado, G.E. (Graciela E.), Yao, J. (Jie), Kifley, A. (Annette), Kooperberg, C. (Charles), Qayyum, Q. (Rehan), Lopez, L. (Lornam), Berentzen, T.L. (Tina L.), Räikkönen, K. (Katri), Mangino, M. (Massimo), Bandinelli, S. (Stefania), Peyser, P.A. (Patricia A.), Wild, S. (Sarah), Tregouet, D.-A. (David-Alexandre), Wright, A.F. (Alan), Marten, J. (Jonathan), Zemunik, T. (Tatijana), Morrison, A.C. (Alanna), Sennblad, B. (Bengt), Tofler, G.H. (Geoffrey), Maat, M.P.M. (Moniek) de, Geus, E.J.C. (Eco) de, Lowe, G.D. (Gordon D.), Zoledziewska, M. (Magdalena), Sattar, N. (Naveed), Binder, H. (Harald), Völker, U. (Uwe), Waldenberger, M. (Melanie), Khaw, K.-T. (Kay-Tee), McKnight, B. (Barbara), Huang, J. (Jian), Jenny, N.S. (Nancy), Holliday, E.G. (Elizabeth), Qi, L. (Lihong), Mcevoy, M.G. (Mark G.), Becker, D.M. (Diane), Starr, J.M. (John), Sarin, A.-P., Hysi, P.G. (Pirro), Hernandez, D.G. (Dena), Jhun, M.A. (Min A.), Campbell, H. (Harry), Hamsten, A. (Anders), Sarin, F. (Fernando), McArdle, W.L. (Wendy), Slagboom, P.E. (Eline), Zeller, T. (Tanja), Koenig, W. (Wolfgang), Psaty, B. (Brucem), Haritunians, T. (Talin), Liu, J. (Jingmin), Palotie, A. (Aarno), Uitterlinden, A.G. (André), Stott, D.J. (David J.), Hofman, A. (Albert), Franco, O.H. (Oscar), Polasek, O. (Ozren), Rudan, I. (Igor), Morange, P.-E. (P.), Wilson, J.F. (James F.), Kardia, S.L. (Sharon L.r), Ferrucci, L. (Luigi), Spector, T.D. (Timothy), Eriksson, J.G. (Johan G.), Hansen, T. (Torben), Deary, I.J. (Ian), Becker, L.C. (Lewis), Scott, R.J. (Rodney), Mitchell, P. (Paul), März, W. (Winfried), Wareham, N.J. (Nick J.), Peters, A. (Annette), Greinacher, A. (Andreas), Wild, P.S. (Philipp S.), Jukema, J.W. (Jan Wouter), Boomsma, D.I. (Dorret), Hayward, C. (Caroline), Cucca, F. (Francesco), Tracy, R.P. (Russell), Watkins, H. (Hugh), Reiner, A.P. (Alex P.), Folsom, A.R. (Aaron), Ridker, P.M. (Paul), O'Donnell, C.J. (Christopher J.), Smith, N.L. (Nicholas L.), Strachan, D.P. (David P.), and Dehghan, A. (Abbas)

Abstract

Genome-wide association studies have previously identified 23 genetic loci associated with circulating fibrinogen concentration. These studies used HapMap imputation and did not examine the X-chromosome. 1000 Genomes imputation provides better coverage of uncommon variants, and includes indels.We conducted a genome-wide association analysis of 34 studies imputed to the 1000 Genomes Project reference panel and including ~120 000 participants of European ancestry (95 806 participants with data on the X-chromosome). Approximately 10.7 million single-nucleotide polymorphisms and 1.2 million indelswere examined.We identified 41 genome-wide significant fibrinogen loci; of which, 18were newly identified. Therewere no genome-wide significant signals on the X-chromosome. The lead variants of five significant loci were indels. We further identified six additional independent signals, including three rare variants, at two previously characterized loci: FGB and IRF1. Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration.

Published
2016
Full Text
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9. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes.

Author

Haferlach, T, Haferlach, T, Nagata, Y, Grossmann, V, Okuno, Y, Bacher, U, Nagae, G, Schnittger, S, Sanada, M, Kon, A, Alpermann, T, Yoshida, K, Roller, A, Nadarajah, N, Shiraishi, Y, Shiozawa, Y, Chiba, K, Tanaka, H, Koeffler, HP, Klein, H-U, Dugas, M, Aburatani, H, Kohlmann, A, Miyano, S, Haferlach, C, Kern, W, Ogawa, S, Haferlach, T, Haferlach, T, Nagata, Y, Grossmann, V, Okuno, Y, Bacher, U, Nagae, G, Schnittger, S, Sanada, M, Kon, A, Alpermann, T, Yoshida, K, Roller, A, Nadarajah, N, Shiraishi, Y, Shiozawa, Y, Chiba, K, Tanaka, H, Koeffler, HP, Klein, H-U, Dugas, M, Aburatani, H, Kohlmann, A, Miyano, S, Haferlach, C, Kern, W, and Ogawa, S

Abstract

High-throughput DNA sequencing significantly contributed to diagnosis and prognostication in patients with myelodysplastic syndromes (MDS). We determined the biological and prognostic significance of genetic aberrations in MDS. In total, 944 patients with various MDS subtypes were screened for known/putative mutations/deletions in 104 genes using targeted deep sequencing and array-based genomic hybridization. In total, 845/944 patients (89.5%) harbored at least one mutation (median, 3 per patient; range, 0-12). Forty-seven genes were significantly mutated with TET2, SF3B1, ASXL1, SRSF2, DNMT3A, and RUNX1 mutated in >10% of cases. Many mutations were associated with higher risk groups and/or blast elevation. Survival was investigated in 875 patients. By univariate analysis, 25/48 genes (resulting from 47 genes tested significantly plus PRPF8) affected survival (P<0.05). The status of 14 genes combined with conventional factors revealed a novel prognostic model ('Model-1') separating patients into four risk groups ('low', 'intermediate', 'high', 'very high risk') with 3-year survival of 95.2, 69.3, 32.8, and 5.3% (P<0.001). Subsequently, a 'gene-only model' ('Model-2') was constructed based on 14 genes also yielding four significant risk groups (P<0.001). Both models were reproducible in the validation cohort (n=175 patients; P<0.001 each). Thus, large-scale genetic and molecular profiling of multiple target genes is invaluable for subclassification and prognostication in MDS patients.

Published
2014

10. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes.

Author

Haferlach, T, Haferlach, T, Nagata, Y, Grossmann, V, Okuno, Y, Bacher, U, Nagae, G, Schnittger, S, Sanada, M, Kon, A, Alpermann, T, Yoshida, K, Roller, A, Nadarajah, N, Shiraishi, Y, Shiozawa, Y, Chiba, K, Tanaka, H, Koeffler, HP, Klein, H-U, Dugas, M, Aburatani, H, Kohlmann, A, Miyano, S, Haferlach, C, Kern, W, Ogawa, S, Haferlach, T, Haferlach, T, Nagata, Y, Grossmann, V, Okuno, Y, Bacher, U, Nagae, G, Schnittger, S, Sanada, M, Kon, A, Alpermann, T, Yoshida, K, Roller, A, Nadarajah, N, Shiraishi, Y, Shiozawa, Y, Chiba, K, Tanaka, H, Koeffler, HP, Klein, H-U, Dugas, M, Aburatani, H, Kohlmann, A, Miyano, S, Haferlach, C, Kern, W, and Ogawa, S

Abstract

High-throughput DNA sequencing significantly contributed to diagnosis and prognostication in patients with myelodysplastic syndromes (MDS). We determined the biological and prognostic significance of genetic aberrations in MDS. In total, 944 patients with various MDS subtypes were screened for known/putative mutations/deletions in 104 genes using targeted deep sequencing and array-based genomic hybridization. In total, 845/944 patients (89.5%) harbored at least one mutation (median, 3 per patient; range, 0-12). Forty-seven genes were significantly mutated with TET2, SF3B1, ASXL1, SRSF2, DNMT3A, and RUNX1 mutated in >10% of cases. Many mutations were associated with higher risk groups and/or blast elevation. Survival was investigated in 875 patients. By univariate analysis, 25/48 genes (resulting from 47 genes tested significantly plus PRPF8) affected survival (P<0.05). The status of 14 genes combined with conventional factors revealed a novel prognostic model ('Model-1') separating patients into four risk groups ('low', 'intermediate', 'high', 'very high risk') with 3-year survival of 95.2, 69.3, 32.8, and 5.3% (P<0.001). Subsequently, a 'gene-only model' ('Model-2') was constructed based on 14 genes also yielding four significant risk groups (P<0.001). Both models were reproducible in the validation cohort (n=175 patients; P<0.001 each). Thus, large-scale genetic and molecular profiling of multiple target genes is invaluable for subclassification and prognostication in MDS patients.

Published
2014

11. The Interlaboratory RObustness of Next-generation sequencing (IRON) study: a deep sequencing investigation of TET2, CBL and KRAS mutations by an international consortium involving 10 laboratories.

Author

Kohlmann, A., Klein, H.U., Weissmann, S., Bresolin, S., Chaplin, T., Cuppens, H., Haschke-Becher, E., Garicochea, B., Grossmann, V., Hanczaruk, B., Hebestreit, K., Gabriel, C., Iacobucci, I., Jansen, J.H., Kronnie, G. Te, Locht, L.T. van de, Martinelli, G., McGowan, K., Schweiger, M.R., Timmermann, B., Vandenberghe, P., Young, B.D., Dugas, M., Haferlach, T., Kohlmann, A., Klein, H.U., Weissmann, S., Bresolin, S., Chaplin, T., Cuppens, H., Haschke-Becher, E., Garicochea, B., Grossmann, V., Hanczaruk, B., Hebestreit, K., Gabriel, C., Iacobucci, I., Jansen, J.H., Kronnie, G. Te, Locht, L.T. van de, Martinelli, G., McGowan, K., Schweiger, M.R., Timmermann, B., Vandenberghe, P., Young, B.D., Dugas, M., and Haferlach, T.

Abstract

1 december 2011, Item does not contain fulltext, Massively parallel pyrosequencing allows sensitive deep sequencing to detect molecular aberrations. Thus far, data are limited on the technical performance in a clinical diagnostic setting. Here, we investigated as an international consortium the robustness, precision and reproducibility of amplicon next-generation deep sequencing across 10 laboratories in eight countries. In a cohort of 18 chronic myelomonocytic leukemia patients, mutational analyses were performed on TET2, a frequently mutated gene in myeloproliferative neoplasms. Additionally, hotspot regions of CBL and KRAS were investigated. The study was executed using GS FLX sequencing instruments and the small volume 454 Life Sciences Titanium emulsion PCR setup. We report a high concordance in mutation detection across all laboratories, including a robust detection of novel variants, which were undetected by standard Sanger sequencing. The sensitivity to detect low-level variants present with as low as 1-2% frequency, compared with the 20% threshold for Sanger-based sequencing is increased. Together with the output of high-quality long reads and fast run time, we demonstrate the utility of deep sequencing in clinical applications. In conclusion, this multicenter analysis demonstrated that amplicon-based deep sequencing is technically feasible, achieves high concordance across multiple laboratories and allows a broad and in-depth molecular characterization of cancer specimens with high diagnostic sensitivity.

Published
2011

12. The Interlaboratory RObustness of Next-generation sequencing (IRON) study: a deep sequencing investigation of TET2, CBL and KRAS mutations by an international consortium involving 10 laboratories.

Author

Kohlmann, A., Klein, H.U., Weissmann, S., Bresolin, S., Chaplin, T., Cuppens, H., Haschke-Becher, E., Garicochea, B., Grossmann, V., Hanczaruk, B., Hebestreit, K., Gabriel, C., Iacobucci, I., Jansen, J.H., Kronnie, G. Te, Locht, L.T. van de, Martinelli, G., McGowan, K., Schweiger, M.R., Timmermann, B., Vandenberghe, P., Young, B.D., Dugas, M., Haferlach, T., Kohlmann, A., Klein, H.U., Weissmann, S., Bresolin, S., Chaplin, T., Cuppens, H., Haschke-Becher, E., Garicochea, B., Grossmann, V., Hanczaruk, B., Hebestreit, K., Gabriel, C., Iacobucci, I., Jansen, J.H., Kronnie, G. Te, Locht, L.T. van de, Martinelli, G., McGowan, K., Schweiger, M.R., Timmermann, B., Vandenberghe, P., Young, B.D., Dugas, M., and Haferlach, T.

Abstract

01 december 2011, Item does not contain fulltext, Massively parallel pyrosequencing allows sensitive deep sequencing to detect molecular aberrations. Thus far, data are limited on the technical performance in a clinical diagnostic setting. Here, we investigated as an international consortium the robustness, precision and reproducibility of amplicon next-generation deep sequencing across 10 laboratories in eight countries. In a cohort of 18 chronic myelomonocytic leukemia patients, mutational analyses were performed on TET2, a frequently mutated gene in myeloproliferative neoplasms. Additionally, hotspot regions of CBL and KRAS were investigated. The study was executed using GS FLX sequencing instruments and the small volume 454 Life Sciences Titanium emulsion PCR setup. We report a high concordance in mutation detection across all laboratories, including a robust detection of novel variants, which were undetected by standard Sanger sequencing. The sensitivity to detect low-level variants present with as low as 1-2% frequency, compared with the 20% threshold for Sanger-based sequencing is increased. Together with the output of high-quality long reads and fast run time, we demonstrate the utility of deep sequencing in clinical applications. In conclusion, this multicenter analysis demonstrated that amplicon-based deep sequencing is technically feasible, achieves high concordance across multiple laboratories and allows a broad and in-depth molecular characterization of cancer specimens with high diagnostic sensitivity.

Published
2011

16. The Tycho Catalogue

Author

Høg, E., Bässgen, G., Bastian, U., Egret, D., Fabricius, C., Grossmann, V., Halbwachs, J.L., Makarov, V.V., Perryman, M.C., Schwekendiek, P., Wagner, K., Wicenec, A., Høg, E., Bässgen, G., Bastian, U., Egret, D., Fabricius, C., Grossmann, V., Halbwachs, J.L., Makarov, V.V., Perryman, M.C., Schwekendiek, P., Wagner, K., and Wicenec, A.

Published
1997

22. The Tycho Catalogue

Author

Høg, E., Bässgen, G., Bastian, U., Egret, D., Fabricius, C., Grossmann, V., Halbwachs, J.L., Makarov, V.V., Perryman, M.C., Schwekendiek, P., Wagner, K., Wicenec, A., Høg, E., Bässgen, G., Bastian, U., Egret, D., Fabricius, C., Grossmann, V., Halbwachs, J.L., Makarov, V.V., Perryman, M.C., Schwekendiek, P., Wagner, K., and Wicenec, A.

Published
1997

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