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135 results on '"Huang, W.Y."'

3. Genome-wide Association Study of Bladder Cancer Reveals New Biological and Translational Insights.

Author

Koutros, S., Kiemeney, L.A., Pal Choudhury, P., Milne, R.L., Lopez de Maturana, E., Ye, Y., Joseph, V., Florez-Vargas, O., Dyrskjøt, L., Figueroa, J., Dutta, D., Giles, G.G., Hildebrandt, M.A.T., Offit, K., Kogevinas, M., Weiderpass, E., McCullough, M.L., Freedman, N.D., Albanes, D., Kooperberg, C., Cortessis, V.K., Karagas, M.R., Johnson, A., Schwenn, M.R., Baris, D., Furberg, H., Bajorin, D.F., Cussenot, O., Cancel-Tassin, G., Benhamou, S., Kraft, P., Porru, S., Carta, A., Bishop, T., Southey, M.C., Matullo, G., Fletcher, T., Kumar, R., Taylor, J.A., Lamy, P., Prip, F., Kalisz, M., Weinstein, S.J., Hengstler, J.G., Selinski, S., Harland, M., Teo, M., Kiltie, A.E., Tardón, A., Serra, C., Carrato, A., García-Closas, R., Lloreta, J., Schned, A., Lenz, P., Riboli, E., Brennan, P., Tjønneland, A., Otto, T., Ovsiannikov, D., Volkert, F., Vermeulen, S.H., Aben, K.K.H., Galesloot, T.E., Turman, C., Vivo, I. De, Giovannucci, E., Hunter, D.J., Hohensee, C., Hunt, R., Patel, A.V., Huang, W.Y., Thorleifsson, G., Gago-Dominguez, M., Amiano, P., Golka, K., Stern, M.C., Yan, W., Liu, J., Li, S.A., Katta, S., Hutchinson, A., Hicks, B., Wheeler, W.A., Purdue, M.P., McGlynn, K.A., Kitahara, C.M., Haiman, C.A., Greene, M.H., Rafnar, T., Chatterjee, N., Chanock, S.J., Wu, X., Real, F.X., Silverman, D.T., Garcia-Closas, M., Stefansson, K., Prokunina-Olsson, L., Malats, N., Rothman, N., Koutros, S., Kiemeney, L.A., Pal Choudhury, P., Milne, R.L., Lopez de Maturana, E., Ye, Y., Joseph, V., Florez-Vargas, O., Dyrskjøt, L., Figueroa, J., Dutta, D., Giles, G.G., Hildebrandt, M.A.T., Offit, K., Kogevinas, M., Weiderpass, E., McCullough, M.L., Freedman, N.D., Albanes, D., Kooperberg, C., Cortessis, V.K., Karagas, M.R., Johnson, A., Schwenn, M.R., Baris, D., Furberg, H., Bajorin, D.F., Cussenot, O., Cancel-Tassin, G., Benhamou, S., Kraft, P., Porru, S., Carta, A., Bishop, T., Southey, M.C., Matullo, G., Fletcher, T., Kumar, R., Taylor, J.A., Lamy, P., Prip, F., Kalisz, M., Weinstein, S.J., Hengstler, J.G., Selinski, S., Harland, M., Teo, M., Kiltie, A.E., Tardón, A., Serra, C., Carrato, A., García-Closas, R., Lloreta, J., Schned, A., Lenz, P., Riboli, E., Brennan, P., Tjønneland, A., Otto, T., Ovsiannikov, D., Volkert, F., Vermeulen, S.H., Aben, K.K.H., Galesloot, T.E., Turman, C., Vivo, I. De, Giovannucci, E., Hunter, D.J., Hohensee, C., Hunt, R., Patel, A.V., Huang, W.Y., Thorleifsson, G., Gago-Dominguez, M., Amiano, P., Golka, K., Stern, M.C., Yan, W., Liu, J., Li, S.A., Katta, S., Hutchinson, A., Hicks, B., Wheeler, W.A., Purdue, M.P., McGlynn, K.A., Kitahara, C.M., Haiman, C.A., Greene, M.H., Rafnar, T., Chatterjee, N., Chanock, S.J., Wu, X., Real, F.X., Silverman, D.T., Garcia-Closas, M., Stefansson, K., Prokunina-Olsson, L., Malats, N., and Rothman, N.

Abstract

01 juli 2023, Item does not contain fulltext, BACKGROUND: Genomic regions identified by genome-wide association studies (GWAS) for bladder cancer risk provide new insights into etiology. OBJECTIVE: To identify new susceptibility variants for bladder cancer in a meta-analysis of new and existing genome-wide genotype data. DESIGN, SETTING, AND PARTICIPANTS: Data from 32 studies that includes 13,790 bladder cancer cases and 343,502 controls of European ancestry were used for meta-analysis. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: Log-additive associations of genetic variants were assessed using logistic regression models. A fixed-effects model was used for meta-analysis of the results. Stratified analyses were conducted to evaluate effect modification by sex and smoking status. A polygenic risk score (PRS) was generated on the basis of known and novel susceptibility variants and tested for interaction with smoking. RESULTS AND LIMITATIONS: Multiple novel bladder cancer susceptibility loci (6p.22.3, 7q36.3, 8q21.13, 9p21.3, 10q22.1, 19q13.33) as well as improved signals in three known regions (4p16.3, 5p15.33, 11p15.5) were identified, bringing the number of independent markers at genome-wide significance (p < 5 × 10(-8)) to 24. The 4p16.3 (FGFR3/TACC3) locus was associated with a stronger risk for women than for men (p-interaction = 0.002). Bladder cancer risk was increased by interactions between smoking status and genetic variants at 8p22 (NAT2; multiplicative p value for interaction [p(M-I)] = 0.004), 8q21.13 (PAG1; p(M-I) = 0.01), and 9p21.3 (LOC107987026/MTAP/CDKN2A; p(M-I) = 0.02). The PRS based on the 24 independent GWAS markers (odds ratio per standard deviation increase 1.49, 95% confidence interval 1.44-1.53), which also showed comparable results in two prospective cohorts (UK Biobank, PLCO trial), revealed an approximately fourfold difference in the lifetime risk of bladder cancer according to the PRS (e.g., 1st vs 10th decile) for both smokers and nonsmokers. CONCLUSIONS: We report novel loci ass

Published
2023

17. Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels

Author

Jiang, X., O'Reilly, P.F., Aschard, H., Hsu, Y.H., Richards, J.B., Dupuis, J., Ingelsson, E., Karasik, D., Pilz, S., Berry, D., Kestenbaum, B., Zheng, J.S., Luan, J.N., Sofianopoulou, E., Streeten, E.A., Albanes, D., Lutsey, P.L., Yao, L., Tang, W.H., Econs, M.J., Wallaschofski, H., Volzke, H., Zhou, A., Power, C., McCarthy, M.I., Michos, E.D., Boerwinkle, E., Weinstein, S.J., Freedman, N.D., Huang, W.Y., Schoor, N.M. van, Velde, N. van der, Groot, L.C.P.G.M. de, Enneman, A., Cupples, L.A., Booth, S.L., Vasan, R.S., Liu, C.T., Zhou, Y.H., Ripatti, S., Ohlsson, C., Vandenput, L., Lorentzon, M., Eriksson, J.G., Shea, M.K., Houston, D.K., Kritchevsky, S.B., Liu, Y.M., Lohman, K.K., Ferrucci, L., Peacock, M., Gieger, C., Beekman, M., Slagboom, E., Deelen, J., Heemst, D. van, Kleber, M.E., Marz, W., Boer, I.H. de, Wood, A.C., Rotter, J.I., Rich, S.S., Robinson-Cohen, C., Heijer, M. den, Jarvelin, M.R., Cavadino, A., Joshi, P.K., Wilson, J.F., Hayward, C., Lind, L., Michaelsson, K., Trompet, S., Zillikens, M.C., Uitterlinden, A.G., Rivadeneira, F., Broer, L., Zgaga, L., Campbell, H., Theodoratou, E., Farrington, S.M., Timofeeva, M., Dunlop, M.G., Valdes, A.M., Tikkanen, E., Lehtimaki, T., Lyytikainen, L.P., Kahonen, M., Raitakari, O.T., Mikkila, V., Ikram, M.A., Sattar, N., Jukema, J.W., Wareham, N.J., Langenberg, C., Forouhi, N.G., Gundersen, T.E., Khaw, K.T., Butterworth, A.S., Danesh, J., Spector, T., Wang, T.J., Hypponen, E., Kraft, P., and Kiel, D.P.

Published
2018

18. Integrated plasma scenario analysis for the HL-2M tokamak

Author

Xue, L., primary, Garcia, J., additional, Zheng, G.Y., additional, Hoang, G.T., additional, Artaud, J.F., additional, Duan, X.R., additional, Li, J.X., additional, Giruzzi, G., additional, Zou, X.L., additional, Pan, W., additional, Zhang, J.H., additional, Huang, M., additional, Wei, H.L., additional, Bai, X.Y., additional, Ji, X.Q., additional, Song, X.M., additional, Wang, S., additional, Song, X., additional, Xue, M., additional, and Huang, W.Y., additional

Published
2019
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30. Enhancing the effectiveness of lean clustering in establishing benchmarks for automatic classification systems

Author

Hsia, T.C., Hsu, S.H., Huang, W.Y., and Wu, M.C.

Subjects
Clustering (Computers) -- Analysis, Information technology -- Usage, Server clustering, Information technology, Engineering and manufacturing industries
Abstract

Byline: T.C. Hsia, S.H. Hsu, W.Y. Huang, M.C. Wu The classification of workpieces according to their shape can enhance the efficiency of workpiece design and production. The lean clustering method employs only a small number of workpieces to perform the similarity comparison and the data obtained will be used to infer the similarity data of other workpieces in order to form a workpiece similarity matrix. With the aid of such a similarity matrix, one can perform the workpiece classification. To enhance the effectiveness of the lean clustering method and to extend its applicability, the study investigated this issue with regards to the following three aspects: 1. add the hypothesis of skew-to-left, centralised, and skew-to-right workpiece distribution patterns in addition to the uniform distribution of workpiece similarity; 2. beside the most commonly used max-min method, apply the Hamming method, interval average method, and weighting method to the workpiece similarity inference; 3. compare the effectiveness of rough classification and fine classification in lean clustering method. The results revealed that there is no significant difference among the four similarity distribution patterns and among the four similarity inference methods. However, the degree of consistency can reach up to 89% when the rough classification is used for workpiece classification. The authors, therefore, suggest that there is no need to test the similarity distribution in advance, in application of lean clustering method when the rough classification will be used for the classification task of a large number of workpieces so as to reduce the cost.

Published
2003

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