Browsing by Author "Chen, Fang"

Now showing 1 - 3 of 3
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    Data Related to Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use
    (2019-01-16) Liu, Mengzhen; Jiang, Yu; Wedow, Robbee; Li, Yue; Brazel, David M; Chen, Fang; Datta, Gargi; Davila-Velderrain, Jose; McGuire, Daniel; Tian, Chao; Zhan, Xiaowei; 23andMe Research Team; HUNT All-In Psychiatry; Choquet, Hélène; Docherty, Anna R; Faul, Jessica D; Foerster, Johanna R; Fritsche, Lars G; Gabrielsen, Maiken Elvestad; Gordon, Scott D; Haessler, Jeffrey; Hottenga, Jouke-Jan; Huang, Hongyan; Jang, Seon-Kyeong; Jansen, Philip R; Ling, Yueh; Mägi, Reedik; Matoba, Nana; McMahon, George; Mulas, Antonella; Orrù, Valeria; Palviainen, Teemu; Pandit, Anita; Reginsson, Gunnar W, Skogholt, Anne Heidi; Smith, Jennifer A; Taylor, Amy E; Turman, Constance; Willemsen, Gonneke; Young, Hannah; Young, Kendra A; Zajac, Gregory J M; Zhao, Wei; Zhou, Wei; Bjornsdottir, Gyda; Boardman, Jason D; Boehnke, Michael; Boomsma, Dorret I; Chen, Chu; Cucca, Francesco; Davies, Gareth E; Eaton, Charles B; Ehringer, Marissa A; Esko, Tõnu; Fiorillo, Edoardo; Gillespie, Nathan A; Gudbjartsson, Daniel F; Haller, Toomas; Harris, Kathleen Mullan; Heath, Andrew C; Hewitt, John K; Hickie, Ian B; Hokanson, John E; Hopfer, Christian J; Hunter, David J; Iacono, William G; Johnson, Eric O; Kamatani, Yoichiro; Kardia, Sharon L. R; Keller, Matthew C; Kellis, Manolis; Kooperberg, Charles; Kraft, Peter; Krauter, Kenneth S; Laakso, Markku; Lind, Penelope A; Loukola, Anu; Lutz, Sharon M; Madden, Pamela A F; Martin, Nicholas G; McGue, Matt; McQueen, Matthew B; Medland, Sarah E; Metspalu, Andres; Mohlke, Karen L; Nielsen, Jonas B; Okada, Yukinori; Peters, Ulrike; Polderman, Tinca J C; Posthuma, Danielle; Reiner, Alexander P; Rice, John P; Rimm, Eric; Rose, Richard J; Runarsdottir, Valgerdur; Stallings, Michael C; Stančáková, Alena; Stefansson, Hreinn; Thai, Khanh K; Tindle, Hilary A; Tyrfingsson, Thorarinn; Wall, Tamara L; Weir, David R; Weisner, Constance; Whitfield, John B; Winsvold, Bendik Slagsvold; Yin, Jie; Zuccolo, Luisa; Bierut, Laura J; Hveem, Kristian; Lee, James J; Munafò, Marcus R; Saccone, Nancy L; Willer, Cristen J; Cornelis, Marilyn C; David, Sean P; Hinds, David A; Jorgenson, Eric; Kaprio, Jaakko; Stitzel, Jerry A; Stefansson, Kari; Thorgeirsson, Thorgeir E; Abecasis, Gonçalo; Liu Dajiang J; Vrieze Scott; liu00282@umn.edu; Liu, Mengzhen
    We conducted a meta-analysis of over 30 genome wide association studies (GWAS) in over 1.2 million participants with European ancestry on nicotine and substance use. Specifically, we targeted different stages and kinds of substance use from initiation (smoking initiation and age of regular smoking initiation) to regular use (drinks per week and cigarettes per day) to cessation (smoking cessation). The GWAS included have all been imputed to Haplotype Reference Consortium, 1000 Genomes or a combination including more specific reference panels. The studies are then meta-analyzed using sample size, allele frequencies and the imputation quality score as weight. Here we present the final set of filtered meta-analysis summary statistics as presented in the paper (https://doi.org/10.1038/s41588-018-0307-5) excluding 23andMe. As per requirement and to ease dissemination of our results for other scientific endeavors, we are sharing our results here to facilitate downloading.
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    Data related to Genetic diversity fuels gene discovery for tobacco and alcohol use
    (2022-10-13) Saunders, Gretchen R B; Wang, Xingyan; Chen, Fang; Jang, Seon-Kyeong; Liu, Mengzhen; Wang, Chen; Liu, Dajiang J; Vrieze, Scott; saund247@umn.edu; Saunders, Gretchen R B
    We conducted a meta-analysis of 60 genome wide association studies (GWAS) in up to 3.4 million participants from four major ancestries on nicotine and substance use. Specifically, we targeted different stages and kinds of substance use from initiation (smoking initiation and age of regular smoking initiation) to regular use (drinks per week and cigarettes per day) to cessation (smoking cessation). Here we present the final set of filtered meta-analysis summary statistics and polygenic risk score weights excluding 23andMe. As per requirement and to ease dissemination of our results for other scientific endeavors, we are sharing our results here to facilitate downloading.
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    Developing an Intelligent Decision Support System for the Proactive Implementation of Traffic Safety Strategies
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, 2013-03) Chen, Hongyi; Chen, Fang; Anderson, Chris
    The growing number of traffic safety strategies, including the Intelligent Transportation Systems (ITS) and lowcost proactive safety improvement (LCPSI), call for an integrated approach to optimize resource allocation systematically and proactively. While most of the currently used standard methods such as the six-step method that identify and eliminate hazardous locations serve their purpose well, they represent a reactive approach that seeks improvement after crashes happen. In this project, a decision support system with Geographic Information System (GIS) interface is developed to proactively optimize the resource allocation of traffic safety improvement strategies. With its optimization function, the decision support system is able to suggest a systematically optimized implementation plan together with the associated cost once the concerned areas and possible countermeasures are selected. It proactively improves the overall traffic safety by implementing the most effective safety strategies that meet the budget to decrease the total number of crashes to the maximum degree. The GIS interface of the decision support system enables the users to select concerned areas directly from the map and calculates certain inputs automatically from parameters related to the geometric design and traffic control features. An associated database is also designed to support the system so that as more data are input into the system, the calibration factors and crash modification functions used to calculate the expected number of crashes will be continuously updated and refined.