Browsing by Author "Zheng J"

Now showing 1 - 2 of 2
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    Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations.
    (Springer Nature, 2023-10-02) Thomas M; Su Y-R; Rosenthal EA; Sakoda LC; Schmit SL; Timofeeva MN; Chen Z; Fernandez-Rozadilla C; Law PJ; Murphy N; Carreras-Torres R; Diez-Obrero V; van Duijnhoven FJB; Jiang S; Shin A; Wolk A; Phipps AI; Burnett-Hartman A; Gsur A; Chan AT; Zauber AG; Wu AH; Lindblom A; Um CY; Tangen CM; Gignoux C; Newton C; Haiman CA; Qu C; Bishop DT; Buchanan DD; Crosslin DR; Conti DV; Kim D-H; Hauser E; White E; Siegel E; Schumacher FR; Rennert G; Giles GG; Hampel H; Brenner H; Oze I; Oh JH; Lee JK; Schneider JL; Chang-Claude J; Kim J; Huyghe JR; Zheng J; Hampe J; Greenson J; Hopper JL; Palmer JR; Visvanathan K; Matsuo K; Matsuda K; Jung KJ; Li L; Le Marchand L; Vodickova L; Bujanda L; Gunter MJ; Matejcic M; Jenkins MA; Slattery ML; D'Amato M; Wang M; Hoffmeister M; Woods MO; Kim M; Song M; Iwasaki M; Du M; Udaltsova N; Sawada N; Vodicka P; Campbell PT; Newcomb PA; Cai Q; Pearlman R; Pai RK; Schoen RE; Steinfelder RS; Haile RW; Vandenputtelaar R; Prentice RL; Küry S; Castellví-Bel S; Tsugane S; Berndt SI; Lee SC; Brezina S; Weinstein SJ; Chanock SJ; Jee SH; Kweon S-S; Vadaparampil S; Harrison TA; Yamaji T; Keku TO; Vymetalkova V; Arndt V; Jia W-H; Shu X-O; Lin Y; Ahn Y-O; Stadler ZK; Van Guelpen B; Ulrich CM; Platz EA; Potter JD; Li CI; Meester R; Moreno V; Figueiredo JC; Casey G; Lansdorp Vogelaar I; Dunlop MG; Gruber SB; Hayes RB; Pharoah PDP; Houlston RS; Jarvik GP; Tomlinson IP; Zheng W; Corley DA; Peters U; Hsu L
    Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.
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    Integrating pH into the metabolic theory of ecology to predict bacterial diversity in soil
    (National Academy of Sciences, 2023-01-17) Luan L; Jiang Y; Dini-Andreote F; Crowther TW; Li P; Bahram M; Zheng J; Xu Q; Zhang X-X; Sun B; Brown J
    Microorganisms play essential roles in soil ecosystem functioning and maintenance, but methods are currently lacking for quantitative assessments of the mechanisms underlying microbial diversity patterns observed across disparate systems and scales. Here we established a quantitative model to incorporate pH into metabolic theory to capture and explain some of the unexplained variation in the relationship between temperature and soil bacterial diversity. We then tested and validated our newly developed models across multiple scales of ecological organization. At the species level, we modeled the diversification rate of the model bacterium Pseudomonas fluorescens evolving under laboratory media gradients varying in temperature and pH. At the community level, we modeled patterns of bacterial communities in paddy soils across a continental scale, which included natural gradients of pH and temperature. Last, we further extended our model at a global scale by integrating a meta-analysis comprising 870 soils collected worldwide from a wide range of ecosystems. Our results were robust in consistently predicting the distributional patterns of bacterial diversity across soil temperature and pH gradients-with model variation explaining from 7 to 66% of the variation in bacterial diversity, depending on the scale and system complexity. Together, our study represents a nexus point for the integration of soil bacterial diversity and quantitative models with the potential to be used at distinct spatiotemporal scales. By mechanistically representing pH into metabolic theory, our study enhances our capacity to explain and predict the patterns of bacterial diversity and functioning under current or future climate change scenarios.