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Your search keyword '"Wang, Xiaoliang"' showing total 2,256 results
Start Over Author "Wang, Xiaoliang" Publication Type Academic Journals
2,256 results on '"Wang, Xiaoliang"'

22. Genetic Susceptibility to Nonalcoholic Fatty Liver Disease and Risk for Pancreatic Cancer: Mendelian Randomization.

Author

Canzian, Federico, Du, Mengmeng, Gallinger, Steven, Giles, Graham, Goodman, Phyllis, Haiman, Christopher, Kogevinas, Manolis, Kooperberg, Charles, LeMarchand, Loic, Neale, Rachel, Visvanathan, Kala, White, Emily, Albanes, Demetrius, Andreotti, Gabriella, Babic, Ana, Berndt, Sonja, Brais, Lauren, Brennan, Paul, Buring, Julie, Rabe, Kari, Bamlet, William, Chanock, Stephen, Fuchs, Charles, Gaziano, J, Giovannucci, Edward, Hackert, Thilo, Hassan, Manal, Katzke, Verena, Kurtz, Robert, Lee, I-Min, Malats, Núria, Murphy, Neil, Oberg, Ann, Orlow, Irene, Porta, Miquel, Real, Francisco, Rothman, Nathaniel, Sesso, Howard, Silverman, Debra, Thompson, Ian, Wactawski-Wende, Jean, Wang, Xiaoliang, Wentzensen, Nicolas, Yu, Herbert, Zeleniuch-Jacquotte, Anne, Yu, Kai, Wolpin, Brian, Duell, Eric, Li, Donghui, Hung, Rayjean, Perdomo, Sandra, McCullough, Marjorie, Freedman, Neal, Patel, Alpa, Peters, Ulrike, Riboli, Elio, Sund, Malin, Tjønneland, Anne, Zhong, Jun, Van Den Eeden, Stephen, Kraft, Peter, Risch, Harvey, Amundadottir, Laufey, Klein, Alison, Stolzenberg-Solomon, Rachael, Antwi, Samuel, King, Sontoria, Veliginti, Swathi, Brouwers, Martijn, Ren, Zhewen, Zheng, Wei, Setiawan, Veronica, Wilkens, Lynne, Shu, Xiao-Ou, Arslan, Alan, Beane Freeman, Laura, and Bracci, Paige

Subjects
Humans, Non-alcoholic Fatty Liver Disease, Genetic Predisposition to Disease, Genome-Wide Association Study, Mendelian Randomization Analysis, Pancreatic Neoplasms, Obesity, Polymorphism, Single Nucleotide
Abstract

BACKGROUND: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. METHODS: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. RESULTS: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88-1.22; MR-Egger OR, 0.89; 95% CI, 0.65-1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90-1.27; MR-Egger OR, 0.93; 95% CI, 0.67-1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. CONCLUSIONS: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. IMPACT: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.

Published
2023

23. Voltage-gated potassium channels (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

Author

Attali, Bernard, Chandy, K George, Giese, M Hunter, Grissmer, Stephan, Gutman, George A, Jan, Lily Y, Lazdunski, Michel, Mckinnon, David, Nerbonne, Jeanne, Pardo, Luis A, Robertson, Gail A, Rudy, Bernardo, Sanguinetti, Michael C, Stühmer, Walter, Trimmer, James S, and Wang, Xiaoliang

Abstract

The 6TM family of K channels comprises the voltage-gated KV subfamilies, the EAG subfamily (which includes hERG channels), the Ca2+-activated Slo subfamily (actually with 7TM, termed BK) and the Ca2+-activated SK subfamily. These channels possess a pore-forming α subunit that comprise tetramers of identical subunits (homomeric) or of different subunits (heteromeric). Heteromeric channels can only be formed within subfamilies (e.g. Kv1.1 with Kv1.2; Kv7.2 with Kv7.3). The pharmacology largely reflects the subunit composition of the functional channel.

Published
2023

24. Voltage-gated potassium channels (Kv) in GtoPdb v.2021.3

Author

Attali, Bernard, Chandy, K George, Giese, M Hunter, Grissmer, Stephan, Gutman, George A, Jan, Lily Y, Lazdunski, Michel, Mckinnon, David, Nerbonne, Jeanne, Pardo, Luis A, Robertson, Gail A, Rudy, Bernardo, Sanguinetti, Michael C, Stühmer, Walter, Trimmer, James S, and Wang, Xiaoliang

Abstract

The 6TM family of K channels comprises the voltage-gated KV subfamilies, the EAG subfamily (which includes hERG channels), the Ca2+-activated Slo subfamily (actually with 7TM, termed BK) and the Ca2+-activated SK subfamily. These channels possess a pore-forming α subunit that comprise tetramers of identical subunits (homomeric) or of different subunits (heteromeric). Heteromeric channels can only be formed within subfamilies (e.g. Kv1.1 with Kv1.2; Kv7.2 with Kv7.3). The pharmacology largely reflects the subunit composition of the functional channel.

Published
2023

29. A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

Author

Middha, Pooja, Wang, Xiaoliang, Behrens, Sabine, Bolla, Manjeet K, Wang, Qin, Dennis, Joe, Michailidou, Kyriaki, Ahearn, Thomas U, Andrulis, Irene L, Anton-Culver, Hoda, Arndt, Volker, Aronson, Kristan J, Auer, Paul L, Augustinsson, Annelie, Baert, Thaïs, Freeman, Laura E Beane, Becher, Heiko, Beckmann, Matthias W, Benitez, Javier, Bojesen, Stig E, Brauch, Hiltrud, Brenner, Hermann, Brooks-Wilson, Angela, Campa, Daniele, Canzian, Federico, Carracedo, Angel, Castelao, Jose E, Chanock, Stephen J, Chenevix-Trench, Georgia, Cordina-Duverger, Emilie, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Dossus, Laure, Dugué, Pierre-Antoine, Eliassen, A Heather, Eriksson, Mikael, Evans, D Gareth, Fasching, Peter A, Figueroa, Jonine D, Fletcher, Olivia, Flyger, Henrik, Gabrielson, Marike, Gago-Dominguez, Manuela, Giles, Graham G, González-Neira, Anna, Grassmann, Felix, Grundy, Anne, Guénel, Pascal, Haiman, Christopher A, Håkansson, Niclas, Hall, Per, Hamann, Ute, Hankinson, Susan E, Harkness, Elaine F, Holleczek, Bernd, Hoppe, Reiner, Hopper, John L, Houlston, Richard S, Howell, Anthony, Hunter, David J, Ingvar, Christian, Isaksson, Karolin, Jernström, Helena, John, Esther M, Jones, Michael E, Kaaks, Rudolf, Keeman, Renske, Kitahara, Cari M, Ko, Yon-Dschun, Koutros, Stella, Kurian, Allison W, Lacey, James V, Lambrechts, Diether, Larson, Nicole L, Larsson, Susanna, Le Marchand, Loic, Lejbkowicz, Flavio, Li, Shuai, Linet, Martha, Lissowska, Jolanta, Martinez, Maria Elena, Maurer, Tabea, Mulligan, Anna Marie, Mulot, Claire, Murphy, Rachel A, Newman, William G, Nielsen, Sune F, Nordestgaard, Børge G, Norman, Aaron, O’Brien, Katie M, Olson, Janet E, Patel, Alpa V, Prentice, Ross, Rees-Punia, Erika, Rennert, Gad, Rhenius, Valerie, Ruddy, Kathryn J, and Sandler, Dale P

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Breast Cancer, Cancer, Estrogen, Prevention, Women's Health, Human Genome, Genetics, Aging, Cancer Genomics, 2.1 Biological and endogenous factors, Adult, Female, Humans, Gene-Environment Interaction, Genetic Predisposition to Disease, Breast Neoplasms, Bayes Theorem, Genome-Wide Association Study, Risk Factors, Polymorphism, Single Nucleotide, Case-Control Studies, Breast cancer, Gene-environment interactions, Genetic epidemiology, European ancestry, CTS Consortium, ABCTB Investigators, kConFab Investigators, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

BackgroundGenome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.MethodsAnalyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.ResultsAssuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability

Published
2023

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