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2,630 results on '"Chen Zhengming"'

1. Learning Discrete Latent Variable Structures with Tensor Rank Conditions

Author

Chen, Zhengming, Cai, Ruichu, Xie, Feng, Qiao, Jie, Wu, Anpeng, Li, Zijian, Hao, Zhifeng, and Zhang, Kun

Subjects
Computer Science - Machine Learning
Abstract

Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures. To achieve this, we explore a tensor rank condition on contingency tables for an observed variable set $\mathbf{X}_p$, showing that the rank is determined by the minimum support of a specific conditional set (not necessary in $\mathbf{X}_p$) that d-separates all variables in $\mathbf{X}_p$. By this, one can locate the latent variable through probing the rank on different observed variables set, and further identify the latent causal structure under some structure assumptions. We present the corresponding identification algorithm and conduct simulated experiments to verify the effectiveness of our method. In general, our results elegantly extend the identification boundary for causal discovery with discrete latent variables and expand the application scope of causal discovery with latent variables.

Published
2024

2. HateDebias: On the Diversity and Variability of Hate Speech Debiasing

Author

Lin, Nankai, Wu, Hongyan, Chen, Zhengming, Li, Zijian, Wang, Lianxi, Jiang, Shengyi, Zhou, Dong, and Yang, Aimin

Subjects
Computer Science - Computation and Language
Abstract

Hate speech on social media is ubiquitous but urgently controlled. Without detecting and mitigating the biases brought by hate speech, different types of ethical problems. While a number of datasets have been proposed to address the problem of hate speech detection, these datasets seldom consider the diversity and variability of bias, making it far from real-world scenarios. To fill this gap, we propose a benchmark, named HateDebias, to analyze the model ability of hate speech detection under continuous, changing environments. Specifically, to meet the diversity of biases, we collect existing hate speech detection datasets with different types of biases. To further meet the variability (i.e., the changing of bias attributes in datasets), we reorganize datasets to follow the continuous learning setting. We evaluate the detection accuracy of models trained on the datasets with a single type of bias with the performance on the HateDebias, where a significant performance drop is observed. To provide a potential direction for debiasing, we further propose a debiasing framework based on continuous learning and bias information regularization, as well as the memory replay strategies to ensure the debiasing ability of the model. Experiment results on the proposed benchmark show that the aforementioned method can improve several baselines with a distinguished margin, highlighting its effectiveness in real-world applications.

Published
2024

3. Automating the Selection of Proxy Variables of Unmeasured Confounders

Author

Xie, Feng, Chen, Zhengming, Luo, Shanshan, Miao, Wang, Cai, Ruichu, and Geng, Zhi

Subjects
Computer Science - Machine Learning, Statistics - Methodology
Abstract

Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational data. One difficulty inhibiting the practical use is finding valid proxy variables of unobserved confounding to a target causal effect of interest. These proxy variables are typically justified by background knowledge. In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables. To be more specific, we first extend the existing proxy variable estimator, originally addressing a single unmeasured confounder, to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. Subsequently, we present two different sets of precise identifiability conditions for selecting valid proxy variables of unmeasured confounders, based on the second-order statistics and higher-order statistics of the data, respectively. Moreover, we propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects. Theoretical analysis demonstrates the correctness of our proposed algorithms. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach.

Published
2024

4. Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis

Author

Qiao, Jie, Xiang, Yu, Chen, Zhengming, Cai, Ruichu, and Hao, Zhifeng

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology, and discovering causal structure among count data is a crucial task in various scientific and industrial scenarios. One of the most common characteristics of count data is the inherent branching structure described by a binomial thinning operator and an independent Poisson distribution that captures both branching and noise. For instance, in a population count scenario, mortality and immigration contribute to the count, where survival follows a Bernoulli distribution, and immigration follows a Poisson distribution. However, causal discovery from such data is challenging due to the non-identifiability issue: a single causal pair is Markov equivalent, i.e., $X\rightarrow Y$ and $Y\rightarrow X$ are distributed equivalent. Fortunately, in this work, we found that the causal order from $X$ to its child $Y$ is identifiable if $X$ is a root vertex and has at least two directed paths to $Y$, or the ancestor of $X$ with the most directed path to $X$ has a directed path to $Y$ without passing $X$. Specifically, we propose a Poisson Branching Structure Causal Model (PB-SCM) and perform a path analysis on PB-SCM using high-order cumulants. Theoretical results establish the connection between the path and cumulant and demonstrate that the path information can be obtained from the cumulant. With the path information, causal order is identifiable under some graphical conditions. A practical algorithm for learning causal structure under PB-SCM is proposed and the experiments demonstrate and verify the effectiveness of the proposed method., Comment: Accepted by AAAI-2024

Published
2024

5. The 1% gift to humanity: The Human Genome Project II

Author

Liu, Weibin, Li, Yan, Patrinos, George P., Xu, Shuhua, Thong, Meow-Keong, Chen, Zhengming, Crawley, Francis P., Li, Liming, Ekmekci, Perihan Elif, Drmanac, Radoje, Cheong, Weiyang, Benamouzig, Robert, Nguyen, Quan, Volchkov, Pavel, Reichardt, Juergen K. V., Carninci, Piero, Majumder, Partha, Jin, Xin, Church, George, Wang, Jian, and Xu, Xun

Published
2024
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6. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations

Author

Argentieri, M. Austin, Xiao, Sihao, Bennett, Derrick, Winchester, Laura, Nevado-Holgado, Alejo J., Ghose, Upamanyu, Albukhari, Ashwag, Yao, Pang, Mazidi, Mohsen, Lv, Jun, Millwood, Iona, Fry, Hannah, Rodosthenous, Rodosthenis S., Partanen, Jukka, Zheng, Zhili, Kurki, Mitja, Daly, Mark J., Palotie, Aarno, Adams, Cassandra J., Li, Liming, Clarke, Robert, Amin, Najaf, Chen, Zhengming, and van Duijn, Cornelia M.

Published
2024
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8. When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting

Author

Li, Zijian, Cai, Ruichu, Yang, Zhenhui, Huang, Haiqin, Chen, Guangyi, Shen, Yifan, Chen, Zhengming, Song, Xiangchen, and Zhang, Kun

Subjects
Computer Science - Machine Learning
Abstract

Temporal distribution shifts are ubiquitous in time series data. One of the most popular methods assumes that the temporal distribution shift occurs uniformly to disentangle the stationary and nonstationary dependencies. But this assumption is difficult to meet, as we do not know when the distribution shifts occur. To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur. Beyond that, we further disentangle the stationary and nonstationary latent states via sufficient observation assumption to learn how the latent states change. Specifically, we formalize the causal process with environment-irrelated stationary and environment-related nonstationary variables. Under mild conditions, we show that latent environments and stationary/nonstationary variables are identifiable. Based on these theories, we devise the IDEA model, which incorporates an autoregressive hidden Markov model to estimate latent environments and modular prior networks to identify latent states. The IDEA model outperforms several latest nonstationary forecasting methods on various benchmark datasets, highlighting its advantages in real-world scenarios.

Published
2024

9. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Author

Suzuki, Ken, Hatzikotoulas, Konstantinos, Southam, Lorraine, Taylor, Henry, Yin, Xianyong, Lorenz, Kim, Mandla, Ravi, Huerta-Chagoya, Alicia, Melloni, Giorgio, Kanoni, Stavroula, Rayner, Nigel, Bocher, Ozvan, Arruda, Ana, Sonehara, Kyuto, Namba, Shinichi, Lee, Simon, Preuss, Michael, Petty, Lauren, Schroeder, Philip, Vanderwerff, Brett, Kals, Mart, Bragg, Fiona, Lin, Kuang, Guo, Xiuqing, Zhang, Weihua, Yao, Jie, Kim, Young, Graff, Mariaelisa, Takeuchi, Fumihiko, Nano, Jana, Lamri, Amel, Nakatochi, Masahiro, Moon, Sanghoon, Scott, Robert, Cook, James, Lee, Jung-Jin, Pan, Ian, Taliun, Daniel, Parra, Esteban, Chai, Jin-Fang, Bielak, Lawrence, Tabara, Yasuharu, Hai, Yang, Thorleifsson, Gudmar, Grarup, Niels, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloé, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Kwak, Soo-Heon, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Nongmaithem, Suraj, Noordam, Raymond, Lim, Victor, Tam, Claudia, Joo, Yoonjung, Chen, Chien-Hsiun, Raffield, Laura, Prins, Bram, Nicolas, Aude, Yanek, Lisa, Chen, Guanjie, Brody, Jennifer, Kabagambe, Edmond, An, Ping, Xiang, Anny, Choi, Hyeok, Cade, Brian, Tan, Jingyi, Broadaway, K, Williamson, Alice, Kamali, Zoha, Cui, Jinrui, Thangam, Manonanthini, Adair, Linda, Adeyemo, Adebowale, Aguilar-Salinas, Carlos, Ahluwalia, Tarunveer, Anand, Sonia, Bertoni, Alain, Bork-Jensen, Jette, Brandslund, Ivan, Buchanan, Thomas, Burant, Charles, Butterworth, Adam, Canouil, Mickaël, Chan, Juliana, Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, and Cushman, Mary

Subjects
Humans, Adipocytes, Chromatin, Coronary Artery Disease, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Disease Progression, Endothelial Cells, Enteroendocrine Cells, Epigenomics, Genetic Predisposition to Disease, Genome-Wide Association Study, Islets of Langerhans, Multifactorial Inheritance, Peripheral Arterial Disease, Single-Cell Analysis
Abstract

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P

Published
2024

10. Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model

Author

Qiao, Jie, Chen, Zhengming, Yu, Jianhua, Cai, Ruichu, and Hao, Zhifeng

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Methodology
Abstract

Missing data are an unavoidable complication frequently encountered in many causal discovery tasks. When a missing process depends on the missing values themselves (known as self-masking missingness), the recovery of the joint distribution becomes unattainable, and detecting the presence of such self-masking missingness remains a perplexing challenge. Consequently, due to the inability to reconstruct the original distribution and to discern the underlying missingness mechanism, simply applying existing causal discovery methods would lead to wrong conclusions. In this work, we found that the recent advances additive noise model has the potential for learning causal structure under the existence of the self-masking missingness. With this observation, we aim to investigate the identification problem of learning causal structure from missing data under an additive noise model with different missingness mechanisms, where the `no self-masking missingness' assumption can be eliminated appropriately. Specifically, we first elegantly extend the scope of identifiability of causal skeleton to the case with weak self-masking missingness (i.e., no other variable could be the cause of self-masking indicators except itself). We further provide the sufficient and necessary identification conditions of the causal direction under additive noise model and show that the causal structure can be identified up to an IN-equivalent pattern. We finally propose a practical algorithm based on the above theoretical results on learning the causal skeleton and causal direction. Extensive experiments on synthetic and real data demonstrate the efficiency and effectiveness of the proposed algorithms., Comment: Accepted by AAAI-2024

Published
2023

11. Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Author

Kentistou, Katherine A., Kaisinger, Lena R., Stankovic, Stasa, Vaudel, Marc, Mendes de Oliveira, Edson, Messina, Andrea, Walters, Robin G., Liu, Xiaoxi, Busch, Alexander S., Helgason, Hannes, Thompson, Deborah J., Santoni, Federico, Petricek, Konstantin M., Zouaghi, Yassine, Huang-Doran, Isabel, Gudbjartsson, Daniel F., Bratland, Eirik, Lin, Kuang, Gardner, Eugene J., Zhao, Yajie, Jia, Raina Y., Terao, Chikashi, Riggan, Marjorie J., Bolla, Manjeet K., Yazdanpanah, Mojgan, Yazdanpanah, Nahid, Bradfield, Jonathan P., Broer, Linda, Campbell, Archie, Chasman, Daniel I., Cousminer, Diana L., Franceschini, Nora, Franke, Lude H., Girotto, Giorgia, He, Chunyan, Järvelin, Marjo-Riitta, Joshi, Peter K., Kamatani, Yoichiro, Karlsson, Robert, Luan, Jian’an, Lunetta, Kathryn L., Mägi, Reedik, Mangino, Massimo, Medland, Sarah E., Meisinger, Christa, Noordam, Raymond, Nutile, Teresa, Concas, Maria Pina, Polašek, Ozren, Porcu, Eleonora, Ring, Susan M., Sala, Cinzia, Smith, Albert V., Tanaka, Toshiko, van der Most, Peter J., Vitart, Veronique, Wang, Carol A., Willemsen, Gonneke, Zygmunt, Marek, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antoniou, Antonis C., Auer, Paul L., Barnes, Catriona L. K., Beckmann, Matthias W., Berrington de Gonzalez, Amy, Bogdanova, Natalia V., Bojesen, Stig E., Brenner, Hermann, Buring, Julie E., Canzian, Federico, Chang-Claude, Jenny, Couch, Fergus J., Cox, Angela, Crisponi, Laura, Czene, Kamila, Daly, Mary B., Demerath, Ellen W., Dennis, Joe, Devilee, Peter, De Vivo, Immaculata, Dörk, Thilo, Dunning, Alison M., Dwek, Miriam, Eriksson, Johan G., Fasching, Peter A., Fernandez-Rhodes, Lindsay, Ferreli, Liana, Fletcher, Olivia, Gago-Dominguez, Manuela, García-Closas, Montserrat, García-Sáenz, José A., González-Neira, Anna, Grallert, Harald, Guénel, Pascal, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hakonarson, Hakon, Hart, Roger J., Hickey, Martha, Hooning, Maartje J., Hoppe, Reiner, Hopper, John L., Hottenga, Jouke-Jan, Hu, Frank B., Huebner, Hanna, Hunter, David J., Jernström, Helena, John, Esther M., Karasik, David, Khusnutdinova, Elza K., Kristensen, Vessela N., Lacey, James V., Lambrechts, Diether, Launer, Lenore J., Lind, Penelope A., Lindblom, Annika, Magnusson, Patrik K. E., Mannermaa, Arto, McCarthy, Mark I., Meitinger, Thomas, Menni, Cristina, Michailidou, Kyriaki, Millwood, Iona Y., Milne, Roger L., Montgomery, Grant W., Nevanlinna, Heli, Nolte, Ilja M., Nyholt, Dale R., Obi, Nadia, O’Brien, Katie M., Offit, Kenneth, Oldehinkel, Albertine J., Ostrowski, Sisse R., Palotie, Aarno, Pedersen, Ole B., Peters, Annette, Pianigiani, Giulia, Plaseska-Karanfilska, Dijana, Pouta, Anneli, Pozarickij, Alfred, Radice, Paolo, Rennert, Gad, Rosendaal, Frits R., Ruggiero, Daniela, Saloustros, Emmanouil, Sandler, Dale P., Schipf, Sabine, Schmidt, Carsten O., Schmidt, Marjanka K., Small, Kerrin, Spedicati, Beatrice, Stampfer, Meir, Stone, Jennifer, Tamimi, Rulla M., Teras, Lauren R., Tikkanen, Emmi, Turman, Constance, Vachon, Celine M., Wang, Qin, Winqvist, Robert, Wolk, Alicja, Zemel, Babette S., Zheng, Wei, van Dijk, Ko W., Alizadeh, Behrooz Z., Bandinelli, Stefania, Boerwinkle, Eric, Boomsma, Dorret I., Ciullo, Marina, Chenevix-Trench, Georgia, Cucca, Francesco, Esko, Tõnu, Gieger, Christian, Grant, Struan F. A., Gudnason, Vilmundur, Hayward, Caroline, Kolčić, Ivana, Kraft, Peter, Lawlor, Deborah A., Martin, Nicholas G., Nøhr, Ellen A., Pedersen, Nancy L., Pennell, Craig E., Ridker, Paul M., Robino, Antonietta, Snieder, Harold, Sovio, Ulla, Spector, Tim D., Stöckl, Doris, Sudlow, Cathie, Timpson, Nic J., Toniolo, Daniela, Uitterlinden, André, Ulivi, Sheila, Völzke, Henry, Wareham, Nicholas J., Widen, Elisabeth, Wilson, James F., Pharoah, Paul D. P., Li, Liming, Easton, Douglas F., Njølstad, Pål R., Sulem, Patrick, Murabito, Joanne M., Murray, Anna, Manousaki, Despoina, Juul, Anders, Erikstrup, Christian, Stefansson, Kari, Horikoshi, Momoko, Chen, Zhengming, Farooqi, I. Sadaf, Pitteloud, Nelly, Johansson, Stefan, Day, Felix R., Perry, John R. B., and Ong, Ken K.

Published
2024
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13. Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables

Author

Xie, Feng, Huang, Biwei, Chen, Zhengming, Cai, Ruichu, Glymour, Clark, Geng, Zhi, and Zhang, Kun

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Methodology
Abstract

We investigate the task of learning causal structure in the presence of latent variables, including locating latent variables and determining their quantity, and identifying causal relationships among both latent and observed variables. To this end, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables. Specifically, for two observed random vectors $\bf{Y}$ and $\bf{Z}$, GIN holds if and only if $\omega^{\intercal}\mathbf{Y}$ and $\mathbf{Z}$ are independent, where $\omega$ is a non-zero parameter vector determined by the cross-covariance between $\mathbf{Y}$ and $\mathbf{Z}$. We then give necessary and sufficient graphical criteria of the GIN condition in linear non-Gaussian acyclic models. Roughly speaking, GIN implies the existence of a set $\mathcal{S}$ such that $\mathcal{S}$ is causally earlier (w.r.t. the causal ordering) than $\mathbf{Y}$, and that every active (collider-free) path between $\mathbf{Y}$ and $\mathbf{Z}$ must contain a node from $\mathcal{S}$. Interestingly, we find that the independent noise condition (i.e., if there is no confounder, causes are independent of the residual derived from regressing the effect on the causes) can be seen as a special case of GIN. With such a connection between GIN and latent causal structures, we further leverage the proposed GIN condition, together with a well-designed search procedure, to efficiently estimate Linear, Non-Gaussian Latent Hierarchical Models (LiNGLaHs), where latent confounders may also be causally related and may even follow a hierarchical structure. We show that the causal structure of a LiNGLaH is identifiable in light of GIN conditions. Experimental results show the effectiveness of the proposed method.

Published
2023

14. Proteo-genomic analyses in relatively lean Chinese adults identify proteins and pathways that affect general and central adiposity levels

Author

Iona, Andri, Yao, Pang, Pozarickij, Alfred, Kartsonaki, Christiana, Said, Saredo, Wright, Neil, Lin, Kuang, Millwood, Iona, Fry, Hannah, Mazidi, Mohsen, Wang, Baihan, Chen, Yiping, Du, Huaidong, Yang, Ling, Avery, Daniel, Schmidt, Dan, Sun, Dianjianyi, Pei, Pei, Lv, Jun, Yu, Canqing, Hill, Michael, Chen, Junshi, Bragg, Fiona, Bennett, Derrick, Walters, Robin, Li, Liming, Clarke, Robert, and Chen, Zhengming

Published
2024
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15. Strengthening China’s National Essential Public Health Services Package for hypertension and diabetes care: protocol for an interrupted time series study with mixed-methods process evaluation and health economic evaluation

Author

Xiong, Shangzhi, Jiang, Wei, Zhang, Xinyi, Wang, Yongchen, Hu, Chi, Bao, Mingjia, Li, Fan, Yang, Jiajuan, Hou, Huinan, Peng, Nan, Wang, Qiujun, Jiang, Rui, Wang, Jin’ge, Liu, Tingzhuo, Ye, Pengpeng, Ma, Yanqiuzi, Li, Bingqin, Chen, Zhengming, Li, Qiang, Du, Xin, Lung, Thomas, Si, Lei, Mao, Limin, Peiris, David, and Tian, Maoyi

Published
2024
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16. Genome-wide DNA methylation profiling in blood reveals epigenetic signature of incident acute coronary syndrome

Author

Long, Pinpin, Si, Jiahui, Zhu, Ziwei, Jiang, Yi, Wang, Yufei, Jiang, Qin, Li, Wending, Xu, Xuedan, You, Yutong, Qu, Minghan, Wang, Huihui, Mo, Tingting, Liu, Kang, Jiang, Jing, Wang, Qiuhong, Yu, Canqing, Guo, Yu, Millwood, Iona Y., Walters, Robin G., He, Ximiao, Yuan, Yu, Wang, Hao, Zhang, Xiaomin, He, Meian, Guo, Huan, Chen, Zhengming, Li, Liming, Lv, Jun, Wang, Chaolong, and Wu, Tangchun

Published
2024
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17. Methods and participant characteristics in the Cancer Risk in Vegetarians Consortium: a cross-sectional analysis across 11 prospective studies

Author

Dunneram, Yashvee, Lee, Jia Yi, Watling, Cody Z., Fraser, Gary E., Miles, Fayth, Prabhakaran, Dorairaj, Shridhar, Krithiga, Kondal, Dimple, Mohan, Viswanathan, Ali, Mohammed K., Narayan, Kabayam M. Venkat, Tandon, Nikhil, Tong, Tammy Y. N., Chiu, Tina H. T., Lin, Ming-Nan, Lin, Chin-Lon, Yang, Hsin-Chou, Liang, Yu-Jen, Greenwood, Darren C., Du, Huaidong, Chen, Zhengming, Yu, Canqing, Kakkoura, Maria G., Reeves, Gillian K., Papier, Keren, Floud, Sarah, Sinha, Rashmi, Liao, Linda M., Loftfield, Erikka, Cade, Janet E., Key, Timothy J., and Perez-Cornago, Aurora

Published
2024
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18. Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian randomisation in 100,000 Chinese adults

Author

Pozarickij, Alfred, Gan, Wei, Lin, Kuang, Clarke, Robert, Fairhurst-Hunter, Zammy, Koido, Masaru, Kanai, Masahiro, Okada, Yukinori, Kamatani, Yoichiro, Bennett, Derrick, Du, Huaidong, Chen, Yiping, Yang, Ling, Avery, Daniel, Guo, Yu, Yu, Min, Yu, Canqing, Schmidt Valle, Dan, Lv, Jun, Chen, Junshi, Peto, Richard, Collins, Rory, Li, Liming, Chen, Zhengming, Millwood, Iona Y., and Walters, Robin G.

Published
2024
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19. HIV-1 subtype A1, D, and recombinant proviral genome landscapes during long-term suppressive therapy

Author

Lee, Guinevere Q., Khadka, Pragya, Gowanlock, Sarah N., Copertino, Jr, Dennis C., Duncan, Maggie C., Omondi, F. Harrison, Kinloch, Natalie N., Kasule, Jingo, Kityamuweesi, Taddeo, Buule, Paul, Jamiru, Samiri, Tomusange, Stephen, Anok, Aggrey, Chen, Zhengming, Jones, R. Brad, Galiwango, Ronald M., Reynolds, Steven J., Quinn, Thomas C., Brumme, Zabrina L., Redd, Andrew D., and Prodger, Jessica L.

Published
2024
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26. Genome-wide characterization of circulating metabolic biomarkers

Author

Karjalainen, Minna K., Karthikeyan, Savita, Oliver-Williams, Clare, Sliz, Eeva, Allara, Elias, Fung, Wing Tung, Surendran, Praveen, Zhang, Weihua, Jousilahti, Pekka, Kristiansson, Kati, Salomaa, Veikko, Goodwin, Matt, Hughes, David A., Boehnke, Michael, Fernandes Silva, Lilian, Yin, Xianyong, Mahajan, Anubha, Neville, Matt J., van Zuydam, Natalie R., de Mutsert, Renée, Li-Gao, Ruifang, Mook-Kanamori, Dennis O., Demirkan, Ayse, Liu, Jun, Noordam, Raymond, Trompet, Stella, Chen, Zhengming, Kartsonaki, Christiana, Li, Liming, Lin, Kuang, Hagenbeek, Fiona A., Hottenga, Jouke Jan, Pool, René, Ikram, M. Arfan, van Meurs, Joyce, Haller, Toomas, Milaneschi, Yuri, Kähönen, Mika, Mishra, Pashupati P., Joshi, Peter K., Macdonald-Dunlop, Erin, Mangino, Massimo, Zierer, Jonas, Acar, Ilhan E., Hoyng, Carel B., Lechanteur, Yara T. E., Franke, Lude, Kurilshikov, Alexander, Zhernakova, Alexandra, Beekman, Marian, van den Akker, Erik B., Kolcic, Ivana, Polasek, Ozren, Rudan, Igor, Gieger, Christian, Waldenberger, Melanie, Asselbergs, Folkert W., Hayward, Caroline, Fu, Jingyuan, den Hollander, Anneke I., Menni, Cristina, Spector, Tim D., Wilson, James F., Lehtimäki, Terho, Raitakari, Olli T., Penninx, Brenda W. J. H., Esko, Tonu, Walters, Robin G., Jukema, J. Wouter, Sattar, Naveed, Ghanbari, Mohsen, Willems van Dijk, Ko, Karpe, Fredrik, McCarthy, Mark I., Laakso, Markku, Järvelin, Marjo-Riitta, Timpson, Nicholas J., Perola, Markus, Kooner, Jaspal S., Chambers, John C., van Duijn, Cornelia, Slagboom, P. Eline, Boomsma, Dorret I., Danesh, John, Ala-Korpela, Mika, Butterworth, Adam S., and Kettunen, Johannes

Published
2024
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27. Epsilon toxin-producing Clostridium perfringens colonize the MS gut and epsilon toxin overcomes immune privilege

Author

Ma, Yinghua, Sannino, David, Linden, Jennifer R, Haigh, Sylvia, Zhao, Baohua, Grigg, John B, Zumbo, Paul, Dündar, Friederike, Butler, Daniel J, Profaci, Caterina P, Telesford, Kiel M, Winokur, Paige N, Rumah, Kareem R, Gauthier, Susan A, Fischetti, Vincent A, McClane, Bruce A, Uzal, Francisco A, Zexter, Lily, Mazzucco, Michael, Rudick, Richard, Danko, David, Balmuth, Evan, Nealon, Nancy, Perumal, Jai, Kaunzner, Ulrike W, Brito, Ilana L, Chen, Zhengming, Xiang, Jenny Z, Betel, Doron, Daneman, Richard, Sonnenberg, Gregory F, Mason, Christopher E, and Vartanian, Timothy

Subjects
Multiple Sclerosis, Neurosciences, Brain Disorders, Neurodegenerative, Autoimmune Disease, 2.1 Biological and endogenous factors, Aetiology, Neurological, Animals, Humans, Clostridium perfringens, Gastrointestinal Microbiome, Immune Privilege, Lymphocytes, Encephalomyelitis, Autoimmune, Experimental, Autoimmunity, Demyelinating disorders, Microbiology, Multiple sclerosis, Toxins/drugs/xenobiotics, Medical and Health Sciences, Immunology
Abstract

Multiple sclerosis (MS) is a complex disease of the CNS thought to require an environmental trigger. Gut dysbiosis is common in MS, but specific causative species are unknown. To address this knowledge gap, we used sensitive and quantitative PCR detection to show that people with MS were more likely to harbor and show a greater abundance of epsilon toxin-producing (ETX-producing) strains of C. perfringens within their gut microbiomes compared with individuals who are healthy controls (HCs). Isolates derived from patients with MS produced functional ETX and had a genetic architecture typical of highly conjugative plasmids. In the active immunization model of experimental autoimmune encephalomyelitis (EAE), where pertussis toxin (PTX) is used to overcome CNS immune privilege, ETX can substitute for PTX. In contrast to PTX-induced EAE, where inflammatory demyelination is largely restricted to the spinal cord, ETX-induced EAE caused demyelination in the corpus callosum, thalamus, cerebellum, brainstem, and spinal cord, more akin to the neuroanatomical lesion distribution seen in MS. CNS endothelial cell transcriptional profiles revealed ETX-induced genes that are known to play a role in overcoming CNS immune privilege. Together, these findings suggest that ETX-producing C. perfringens strains are biologically plausible pathogens in MS that trigger inflammatory demyelination in the context of circulating myelin autoreactive lymphocytes.

Published
2023

28. Biomarkers and cardiovascular outcomes in chimeric antigen receptor T-cell therapy recipients.

Author

Mahmood, Syed S, Riedell, Peter A, Feldman, Stephanie, George, Gina, Sansoterra, Stephen A, Althaus, Thomas, Rehman, Mahin, Mead, Elena, Liu, Jennifer E, Devereux, Richard B, Weinsaft, Jonathan W, Kim, Jiwon, Balkan, Lauren, Barbar, Tarek, Lee Chuy, Katherine, Harchandani, Bhisham, Perales, Miguel-Angel, Geyer, Mark B, Park, Jae H, Palomba, M Lia, Shouval, Roni, Tomas, Ana A, Shah, Gunjan L, Yang, Eric H, Gaut, Daria L, Rothberg, Michael V, Horn, Evelyn M, Leonard, John P, Van Besien, Koen, Frigault, Matthew J, Chen, Zhengming, Mehrotra, Bhoomi, Neilan, Tomas G, and Steingart, Richard M

Abstract

Chimeric antigen receptor T-cell therapy (CAR-T) harnesses a patient's immune system to target cancer. There are sparse existing data characterizing death outcomes after CAR-T-related cardiotoxicity. This study examines the association between CAR-T-related severe cardiovascular events (SCE) and mortality.From a multi-centre registry of 202 patients receiving anti-CD19 CAR-T, covariates including standard baseline cardiovascular and cancer parameters and biomarkers were collected. Severe cardiovascular events were defined as a composite of heart failure, cardiogenic shock, or myocardial infarction. Thirty-three patients experienced SCE, and 108 patients died during a median follow-up of 297 (interquartile range 104-647) days. Those that did and did not die after CAR-T were similar in age, sex, and prior anthracycline use. Those who died had higher peak interleukin (IL)-6 and ferritin levels after CAR-T infusion, and those who experienced SCE had higher peak IL-6, C-reactive protein (CRP), ferritin, and troponin levels. The day-100 and 1-year Kaplan-Meier overall mortality estimates were 18% and 43%, respectively, while the non-relapse mortality (NRM) cumulative incidence rates were 3.5% and 6.7%, respectively. In a Cox model, SCE occurrence following CAR-T was independently associated with increased overall mortality risk [hazard ratio (HR) 2.8, 95% confidence interval (CI) 1.6-4.7] after adjusting for age, cancer type and burden, anthracycline use, cytokine release syndrome grade ≥ 2, pre-existing heart failure, hypertension, and African American ancestry; SCEs were independently associated with increased NRM (HR 3.5, 95% CI 1.4-8.8) after adjusting for cancer burden.Chimeric antigen receptor T-cell therapy recipients who experience SCE have higher overall mortality and NRM and higher peak levels of IL-6, CRP, ferritin, and troponin.

Published
2023

30. Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference

Author

Meng, Xiangrui, Navoly, Georgina, Giannakopoulou, Olga, Levey, Daniel F., Koller, Dora, Pathak, Gita A., Koen, Nastassja, Lin, Kuang, Adams, Mark J., Rentería, Miguel E., Feng, Yanzhe, Gaziano, J. Michael, Stein, Dan J., Zar, Heather J., Campbell, Megan L., van Heel, David A., Trivedi, Bhavi, Finer, Sarah, McQuillin, Andrew, Bass, Nick, Chundru, V. Kartik, Martin, Hilary C., Huang, Qin Qin, Valkovskaya, Maria, Chu, Chia-Yi, Kanjira, Susan, Kuo, Po-Hsiu, Chen, Hsi-Chung, Tsai, Shih-Jen, Liu, Yu-Li, Kendler, Kenneth S., Peterson, Roseann E., Cai, Na, Fang, Yu, Sen, Srijan, Scott, Laura J., Burmeister, Margit, Loos, Ruth J. F., Preuss, Michael H., Actkins, Ky’Era V., Davis, Lea K., Uddin, Monica, Wani, Agaz H., Wildman, Derek E., Aiello, Allison E., Ursano, Robert J., Kessler, Ronald C., Kanai, Masahiro, Okada, Yukinori, Sakaue, Saori, Rabinowitz, Jill A., Maher, Brion S., Uhl, George, Eaton, William, Cruz-Fuentes, Carlos S., Martinez-Levy, Gabriela A., Campos, Adrian I., Millwood, Iona Y., Chen, Zhengming, Li, Liming, Wassertheil-Smoller, Sylvia, Jiang, Yunxuan, Tian, Chao, Martin, Nicholas G., Mitchell, Brittany L., Byrne, Enda M., Awasthi, Swapnil, Coleman, Jonathan R. I., Ripke, Stephan, Sofer, Tamar, Walters, Robin G., McIntosh, Andrew M., Polimanti, Renato, Dunn, Erin C., Stein, Murray B., Gelernter, Joel, Lewis, Cathryn M., and Kuchenbaecker, Karoline

Published
2024
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31. Publisher Correction: Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Author

Kentistou, Katherine A., Kaisinger, Lena R., Stankovic, Stasa, Vaudel, Marc, Mendes de Oliveira, Edson, Messina, Andrea, Walters, Robin G., Liu, Xiaoxi, Busch, Alexander S., Helgason, Hannes, Thompson, Deborah J., Santoni, Federico, Petricek, Konstantin M., Zouaghi, Yassine, Huang-Doran, Isabel, Gudbjartsson, Daniel F., Bratland, Eirik, Lin, Kuang, Gardner, Eugene J., Zhao, Yajie, Jia, Raina Y., Terao, Chikashi, Riggan, Marjorie J., Bolla, Manjeet K., Yazdanpanah, Mojgan, Yazdanpanah, Nahid, Bradfield, Jonathan P., Broer, Linda, Campbell, Archie, Chasman, Daniel I., Cousminer, Diana L., Franceschini, Nora, Franke, Lude H., Girotto, Giorgia, He, Chunyan, Järvelin, Marjo-Riitta, Joshi, Peter K., Kamatani, Yoichiro, Karlsson, Robert, Luan, Jian’an, Lunetta, Kathryn L., Mägi, Reedik, Mangino, Massimo, Medland, Sarah E., Meisinger, Christa, Noordam, Raymond, Nutile, Teresa, Concas, Maria Pina, Polašek, Ozren, Porcu, Eleonora, Ring, Susan M., Sala, Cinzia, Smith, Albert V., Tanaka, Toshiko, van der Most, Peter J., Vitart, Veronique, Wang, Carol A., Willemsen, Gonneke, Zygmunt, Marek, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antoniou, Antonis C., Auer, Paul L., Barnes, Catriona L. K., Beckmann, Matthias W., Berrington de Gonzalez, Amy, Bogdanova, Natalia V., Bojesen, Stig E., Brenner, Hermann, Buring, Julie E., Canzian, Federico, Chang-Claude, Jenny, Couch, Fergus J., Cox, Angela, Crisponi, Laura, Czene, Kamila, Daly, Mary B., Demerath, Ellen W., Dennis, Joe, Devilee, Peter, De Vivo, Immaculata, Dörk, Thilo, Dunning, Alison M., Dwek, Miriam, Eriksson, Johan G., Fasching, Peter A., Fernandez-Rhodes, Lindsay, Ferreli, Liana, Fletcher, Olivia, Gago-Dominguez, Manuela, García-Closas, Montserrat, García-Sáenz, José A., González-Neira, Anna, Grallert, Harald, Guénel, Pascal, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hakonarson, Hakon, Hart, Roger J., Hickey, Martha, Hooning, Maartje J., Hoppe, Reiner, Hopper, John L., Hottenga, Jouke-Jan, Hu, Frank B., Huebner, Hanna, Hunter, David J., Jernström, Helena, John, Esther M., Karasik, David, Khusnutdinova, Elza K., Kristensen, Vessela N., Lacey, James V., Lambrechts, Diether, Launer, Lenore J., Lind, Penelope A., Lindblom, Annika, Magnusson, Patrik K. E., Mannermaa, Arto, McCarthy, Mark I., Meitinger, Thomas, Menni, Cristina, Michailidou, Kyriaki, Millwood, Iona Y., Milne, Roger L., Montgomery, Grant W., Nevanlinna, Heli, Nolte, Ilja M., Nyholt, Dale R., Obi, Nadia, O’Brien, Katie M., Offit, Kenneth, Oldehinkel, Albertine J., Ostrowski, Sisse R., Palotie, Aarno, Pedersen, Ole B., Peters, Annette, Pianigiani, Giulia, Plaseska-Karanfilska, Dijana, Pouta, Anneli, Pozarickij, Alfred, Radice, Paolo, Rennert, Gad, Rosendaal, Frits R., Ruggiero, Daniela, Saloustros, Emmanouil, Sandler, Dale P., Schipf, Sabine, Schmidt, Carsten O., Schmidt, Marjanka K., Small, Kerrin, Spedicati, Beatrice, Stampfer, Meir, Stone, Jennifer, Tamimi, Rulla M., Teras, Lauren R., Tikkanen, Emmi, Turman, Constance, Vachon, Celine M., Wang, Qin, Winqvist, Robert, Wolk, Alicja, Zemel, Babette S., Zheng, Wei, van Dijk, Ko W., Alizadeh, Behrooz Z., Bandinelli, Stefania, Boerwinkle, Eric, Boomsma, Dorret I., Ciullo, Marina, Chenevix-Trench, Georgia, Cucca, Francesco, Esko, Tõnu, Gieger, Christian, Grant, Struan F. A., Gudnason, Vilmundur, Hayward, Caroline, Kolčić, Ivana, Kraft, Peter, Lawlor, Deborah A., Martin, Nicholas G., Nøhr, Ellen A., Pedersen, Nancy L., Pennell, Craig E., Ridker, Paul M., Robino, Antonietta, Snieder, Harold, Sovio, Ulla, Spector, Tim D., Stöckl, Doris, Sudlow, Cathie, Timpson, Nic J., Toniolo, Daniela, Uitterlinden, André, Ulivi, Sheila, Völzke, Henry, Wareham, Nicholas J., Widen, Elisabeth, Wilson, James F., Pharoah, Paul D. P., Li, Liming, Easton, Douglas F., Njølstad, Pål R., Sulem, Patrick, Murabito, Joanne M., Murray, Anna, Manousaki, Despoina, Juul, Anders, Erikstrup, Christian, Stefansson, Kari, Horikoshi, Momoko, Chen, Zhengming, Farooqi, I. Sadaf, Pitteloud, Nelly, Johansson, Stefan, Day, Felix R., Perry, John R. B., and Ong, Ken K.

Published
2024
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32. BTG1 mutation yields supercompetitive B cells primed for malignant transformation.

Author

Mlynarczyk, Coraline, Teater, Matt, Pae, Juhee, Chin, Christopher, Wang, Ling, Arulraj, Theinmozhi, Barisic, Darko, Papin, Antonin, Hoehn, Kenneth, Kots, Ekaterina, Ersching, Jonatan, Bandyopadhyay, Arnab, Barin, Ersilia, Poh, Hui, Evans, Chiara, Chadburn, Amy, Chen, Zhengming, Shen, Hao, Isles, Hannah, Pelzer, Benedikt, Tsialta, Ioanna, Doane, Ashley, Rehman, Muhammad, Melnick, Jonah, Morgan, Wyatt, Nguyen, Diu, Elemento, Olivier, Kharas, Michael, Jaffrey, Samie, Scott, David, Khelashvili, George, Meyer-Hermann, Michael, Victora, Gabriel, Melnick, Ari, and Geng, Huimin

Subjects
Animals, Humans, Mice, Antibody Affinity, B-Lymphocytes, Germinal Center, Mutation, Neoplasm Proteins, Lymphoma, Large B-Cell, Diffuse, Cell Transformation, Neoplastic, Selection, Genetic
Abstract

Multicellular life requires altruistic cooperation between cells. The adaptive immune system is a notable exception, wherein germinal center B cells compete vigorously for limiting positive selection signals. Studying primary human lymphomas and developing new mouse models, we found that mutations affecting BTG1 disrupt a critical immune gatekeeper mechanism that strictly limits B cell fitness during antibody affinity maturation. This mechanism converted germinal center B cells into supercompetitors that rapidly outstrip their normal counterparts. This effect was conferred by a small shift in MYC protein induction kinetics but resulted in aggressive invasive lymphomas, which in humans are linked to dire clinical outcomes. Our findings reveal a delicate evolutionary trade-off between natural selection of B cells to provide immunity and potentially dangerous features that recall the more competitive nature of unicellular organisms.

Published
2023

33. Genetic diversity fuels gene discovery for tobacco and alcohol use

Author

Saunders, Gretchen RB, Wang, Xingyan, Chen, Fang, Jang, Seon-Kyeong, Liu, Mengzhen, Wang, Chen, Gao, Shuang, Jiang, Yu, Khunsriraksakul, Chachrit, Otto, Jacqueline M, Addison, Clifton, Akiyama, Masato, Albert, Christine M, Aliev, Fazil, Alonso, Alvaro, Arnett, Donna K, Ashley-Koch, Allison E, Ashrani, Aneel A, Barnes, Kathleen C, Barr, R Graham, Bartz, Traci M, Becker, Diane M, Bielak, Lawrence F, Benjamin, Emelia J, Bis, Joshua C, Bjornsdottir, Gyda, Blangero, John, Bleecker, Eugene R, Boardman, Jason D, Boerwinkle, Eric, Boomsma, Dorret I, Boorgula, Meher Preethi, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Chasman, Daniel I, Chavan, Sameer, Chen, Yii-Der Ida, Chen, Zhengming, Cheng, Iona, Cho, Michael H, Choquet, Hélène, Cole, John W, Cornelis, Marilyn C, Cucca, Francesco, Curran, Joanne E, de Andrade, Mariza, Dick, Danielle M, Docherty, Anna R, Duggirala, Ravindranath, Eaton, Charles B, Ehringer, Marissa A, Esko, Tõnu, Faul, Jessica D, Fernandes Silva, Lilian, Fiorillo, Edoardo, Fornage, Myriam, Freedman, Barry I, Gabrielsen, Maiken E, Garrett, Melanie E, Gharib, Sina A, Gieger, Christian, Gillespie, Nathan, Glahn, David C, Gordon, Scott D, Gu, Charles C, Gu, Dongfeng, Gudbjartsson, Daniel F, Guo, Xiuqing, Haessler, Jeffrey, Hall, Michael E, Haller, Toomas, Harris, Kathleen Mullan, He, Jiang, Herd, Pamela, Hewitt, John K, Hickie, Ian, Hidalgo, Bertha, Hokanson, John E, Hopfer, Christian, Hottenga, JoukeJan, Hou, Lifang, Huang, Hongyan, Hung, Yi-Jen, Hunter, David J, Hveem, Kristian, Hwang, Shih-Jen, Hwu, Chii-Min, Iacono, William, Irvin, Marguerite R, Jee, Yon Ho, Johnson, Eric O, Joo, Yoonjung Y, Jorgenson, Eric, Justice, Anne E, Kamatani, Yoichiro, Kaplan, Robert C, Kaprio, Jaakko, Kardia, Sharon LR, and Keller, Matthew C

Subjects
Genetics, Alcoholism, Alcohol Use and Health, Prevention, Human Genome, Substance Misuse, Aetiology, 2.1 Biological and endogenous factors, Cancer, Good Health and Well Being, Humans, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Multifactorial Inheritance, Risk Factors, Tobacco Use, Alcohol Drinking, Transcriptome, Sample Size, Genetic Loci, Internationality, Europe, 23andMe Research Team, Biobank Japan Project, General Science & Technology
Abstract

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1-4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.

Published
2022

34. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Author

Zhou, Wei, Kanai, Masahiro, Wu, Kuan-Han H, Rasheed, Humaira, Tsuo, Kristin, Hirbo, Jibril B, Wang, Ying, Bhattacharya, Arjun, Zhao, Huiling, Namba, Shinichi, Surakka, Ida, Wolford, Brooke N, Faro, Valeria Lo, Lopera-Maya, Esteban A, Läll, Kristi, Favé, Marie-Julie, Partanen, Juulia J, Chapman, Sinéad B, Karjalainen, Juha, Kurki, Mitja, Maasha, Mutaamba, Brumpton, Ben M, Chavan, Sameer, Chen, Tzu-Ting, Daya, Michelle, Ding, Yi, Feng, Yen-Chen A, Guare, Lindsay A, Gignoux, Christopher R, Graham, Sarah E, Hornsby, Whitney E, Ingold, Nathan, Ismail, Said I, Johnson, Ruth, Laisk, Triin, Lin, Kuang, Lv, Jun, Millwood, Iona Y, Moreno-Grau, Sonia, Nam, Kisung, Palta, Priit, Pandit, Anita, Preuss, Michael H, Saad, Chadi, Setia-Verma, Shefali, Thorsteinsdottir, Unnur, Uzunovic, Jasmina, Verma, Anurag, Zawistowski, Matthew, Zhong, Xue, Afifi, Nahla, Al-Dabhani, Kawthar M, Thani, Asma Al, Bradford, Yuki, Campbell, Archie, Crooks, Kristy, de Bock, Geertruida H, Damrauer, Scott M, Douville, Nicholas J, Finer, Sarah, Fritsche, Lars G, Fthenou, Eleni, Gonzalez-Arroyo, Gilberto, Griffiths, Christopher J, Guo, Yu, Hunt, Karen A, Ioannidis, Alexander, Jansonius, Nomdo M, Konuma, Takahiro, Lee, Ming Ta Michael, Lopez-Pineda, Arturo, Matsuda, Yuta, Marioni, Riccardo E, Moatamed, Babak, Nava-Aguilar, Marco A, Numakura, Kensuke, Patil, Snehal, Rafaels, Nicholas, Richmond, Anne, Rojas-Muñoz, Agustin, Shortt, Jonathan A, Straub, Peter, Tao, Ran, Vanderwerff, Brett, Vernekar, Manvi, Veturi, Yogasudha, Barnes, Kathleen C, Boezen, Marike, Chen, Zhengming, Chen, Chia-Yen, Cho, Judy, Smith, George Davey, Finucane, Hilary K, Franke, Lude, Gamazon, Eric R, Ganna, Andrea, Gaunt, Tom R, Ge, Tian, Huang, Hailiang, and Huffman, Jennifer

Subjects
Human Genome, Genetics, Biotechnology, Generic health relevance, Good Health and Well Being, Biobank of the Americas, Biobank Japan Project, BioMe, BioVU, CanPath - Ontario Health Study, China Kadoorie Biobank Collaborative Group, Colorado Center for Personalized Medicine, deCODE Genetics, Estonian Biobank, FinnGen, Generation Scotland, Genes & Health Research Team, LifeLines, Mass General Brigham Biobank, Michigan Genomics Initiative, National Biobank of Korea, Penn Medicine BioBank, Qatar Biobank, QSkin Sun and Health Study, Taiwan Biobank, HUNT Study, UCLA ATLAS Community Health Initiative, Uganda Genome Resource, UK Biobank, GWAS, ancestry diversity, biobank, genetic association studies, meta-analysis, phenotype harmonization
Abstract

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Published
2022

38. Meat consumption and incident type 2 diabetes: an individual-participant federated meta-analysis of 1·97 million adults with 100 000 incident cases from 31 cohorts in 20 countries

Author

Li, Chunxiao, Bishop, Tom R P, Imamura, Fumiaki, Sharp, Stephen J, Pearce, Matthew, Brage, Soren, Ong, Ken K, Ahsan, Habibul, Bes-Rastrollo, Maira, Beulens, Joline W J, den Braver, Nicole, Byberg, Liisa, Canhada, Scheine, Chen, Zhengming, Chung, Hsin-Fang, Cortés-Valencia, Adrian, Djousse, Luc, Drouin-Chartier, Jean-Philippe, Du, Huaidong, Du, Shufa, Duncan, Bruce B, Gaziano, J Michael, Gordon-Larsen, Penny, Goto, Atsushi, Haghighatdoost, Fahimeh, Härkänen, Tommi, Hashemian, Maryam, Hu, Frank B, Ittermann, Till, Järvinen, Ritva, Kakkoura, Maria G, Neelakantan, Nithya, Knekt, Paul, Lajous, Martin, Li, Yanping, Magliano, Dianna J, Malekzadeh, Reza, Le Marchand, Loic, Marques-Vidal, Pedro, Martinez-Gonzalez, Miguel A, Maskarinec, Gertraud, Mishra, Gita D, Mohammadifard, Noushin, O'Donoghue, Gráinne, O'Gorman, Donal, Popkin, Barry, Poustchi, Hossein, Sarrafzadegan, Nizal, Sawada, Norie, Schmidt, Maria Inês, Shaw, Jonathan E, Soedamah-Muthu, Sabita, Stern, Dalia, Tong, Lin, van Dam, Rob M, Völzke, Henry, Willett, Walter C, Wolk, Alicja, Yu, Canqing, Forouhi, Nita G, and Wareham, Nicholas J

Published
2024
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39. Conventional and genetic associations of adiposity with 1463 proteins in relatively lean Chinese adults

Author

Yao, Pang, Iona, Andri, Kartsonaki, Christiana, Said, Saredo, Wright, Neil, Lin, Kuang, Pozarickij, Alfred, Millwood, Iona, Fry, Hannah, Mazidi, Mohsen, Chen, Yiping, Du, Huaidong, Bennett, Derrick, Avery, Daniel, Schmidt, Dan, Pei, Pei, Lv, Jun, Yu, Canqing, Hill, Michael, Chen, Junshi, Peto, Richard, Walters, Robin, Collins, Rory, Li, Liming, Clarke, Robert, and Chen, Zhengming

Published
2023
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40. Author Correction: New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

Author

Shrine, Nick, Guyatt, Anna L., Erzurumluoglu, A. Mesut, Jackson, Victoria E., Hobbs, Brian D., Melbourne, Carl A., Batini, Chiara, Fawcett, Katherine A., Song, Kijoung, Sakornsakolpat, Phuwanat, Li, Xingnan, Boxall, Ruth, Reeve, Nicola F., Obeidat, Ma’en, Zhao, Jing Hua, Wielscher, Matthias, Weiss, Stefan, Kentistou, Katherine A., Cook, James P., Sun, Benjamin B., Zhou, Jian, Hui, Jennie, Karrasch, Stefan, Imboden, Medea, Harris, Sarah E., Marten, Jonathan, Enroth, Stefan, Kerr, Shona M., Surakka, Ida, Vitart, Veronique, Lehtimäki, Terho, Allen, Richard J., Bakke, Per S., Beaty, Terri H., Bleecker, Eugene R., Bossé, Yohan, Brandsma, Corry-Anke, Chen, Zhengming, Crapo, James D., Danesh, John, DeMeo, Dawn L., Dudbridge, Frank, Ewert, Ralf, Gieger, Christian, Gulsvik, Amund, Hansell, Anna L., Hao, Ke, Hoffman, Joshua D., Hokanson, John E., Homuth, Georg, Joshi, Peter K., Joubert, Philippe, Langenberg, Claudia, Li, Xuan, Li, Liming, Lin, Kuang, Lind, Lars, Locantore, Nicholas, Luan, Jian’an, Mahajan, Anubha, Maranville, Joseph C., Murray, Alison, Nickle, David C., Packer, Richard, Parker, Margaret M., Paynton, Megan L., Porteous, David J., Prokopenko, Dmitry, Qiao, Dandi, Rawal, Rajesh, Runz, Heiko, Sayers, Ian, Sin, Don D., Smith, Blair H., Artigas, María Soler, Sparrow, David, Tal-Singer, Ruth, Timmers, Paul R. H. J., Van den Berge, Maarten, Whittaker, John C., Woodruff, Prescott G., Yerges-Armstrong, Laura M., Troyanskaya, Olga G., Raitakari, Olli T., Kähönen, Mika, Polašek, Ozren, Gyllensten, Ulf, Rudan, Igor, Deary, Ian J., Probst-Hensch, Nicole M., Schulz, Holger, James, Alan L., Wilson, James F., Stubbe, Beate, Zeggini, Eleftheria, Jarvelin, Marjo-Riitta, Wareham, Nick, Silverman, Edwin K., Hayward, Caroline, Morris, Andrew P., Butterworth, Adam S., Scott, Robert A., Walters, Robin G., Meyers, Deborah A., Cho, Michael H., Strachan, David P., Hall, Ian P., Tobin, Martin D., and Wain, Louise V.

Published
2024
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42. General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants

Author

Zhou, Bin, Bennett, James E, Wickham, Aidan P, Singleton, Rosie K, Mishra, Anu, Carrillo-Larco, Rodrigo M, Ikeda, Nayu, Jain, Lakshya, Barradas-Pires, Ana, Heap, Rachel A, Lhoste, Victor PF, Sheffer, Kate E, Phelps, Nowell H, Rayner, Archie W, Gregg, Edward W, Woodward, Mark, Stevens, Gretchen A, Iurilli, Maria LC, Danaei, Goodarz, Di Cesare, Mariachiara, Aguilar-Salinas, Carlos A, Ahmad, Noor Ani, Bovet, Pascal, Chen, Zhengming, Damasceno, Albertino, Filippi, Sarah L, Janszky, Imre, Kengne, Andre P, Khang, Young-Ho, Khunti, Kamlesh, Laxmaiah, Avula, Lim, Lee-Ling, Lissner, Lauren, Margozzini, Paula, Mbanya, Jean Claude N, McGarvey, Stephen T, Shaw, Jonathan E, Söderberg, Stefan, Soto-Mota, Luis Adrián, Wang, Junyang, Zaccardi, Francesco, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Aekplakorn, Wichai, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkhatib, Buthaina M, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amougou, Norbert, Andersen, Lars Bo, Anderssen, Sigmund A, Androutsos, Odysseas, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Anufrieva, Elena, Aounallah-Skhiri, Hajer, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Assah, Felix K, Assembekov, Batyrbek, Assunção, Maria Cecília F, Auvinen, Juha, Avdičová, Mária, Azad, Kishwar, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Bacopoulou, Flora, Bahijri, Suhad, Bajramovic, Izet, Balakrishna, Nagalla, Bamoshmoosh, Mohamed, Banach, Maciej, Bandosz, Piotr, Banegas, José R, Baran, Rafał, Barbagallo, Carlo M, Barbosa Filho, Valter, Barceló, Alberto, Baretić, Maja, Barnoya, Joaquin, Barrera, Lena, Barros, Aluisio JD, Barros, Mauro Virgílio Gomes, Basit, Abdul, Bastos, Joao Luiz, Batieha, Anwar M, Batista, Aline P, Batista, Rosangela L, Battakova, Zhamilya, Baur, Louise A, Bayauli, Pascal M, Bel-Serrat, Silvia, Belavendra, Antonisamy, Ben Romdhane, Habiba, Benedek, Theodora, Benedics, Judith, Benet, Mikhail, Benitez Rolandi, Gilda Estela, Benzeval, Michaela, Bere, Elling, Berger, Nicolas, Bergh, Ingunn Holden, Berkinbayev, Salim, Bernabe-Ortiz, Antonio, Bettiol, Heloísa, Beybey, Augustin F, Bezerra, Jorge, Bhagyalaxmi, Aroor, Bhargava, Santosh K, Bika Lele, Elysée Claude, Bikbov, Mukharram M, Bista, Bihungum, Bjelica, Dusko J, Bjerregaard, Peter, Bjertness, Espen, Bjertness, Marius B, Björkelund, Cecilia, Bloch, Katia V, Blokstra, Anneke, Bo, Simona, Bobak, Martin, Boddy, Lynne M, Boehm, 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Touloumi, Giota, Traissac, Pierre, Triantafyllou, Areti, Trivedi, Atul, Tshepo, Lechaba, Tsintavis, Panagiotis, Tuitele, John, Tuliakova, Azaliia M, Tulloch-Reid, Marshall K, Tullu, Fikru, Tuomainen, Tomi-Pekka, Turley, Maria L, Tzala, Evangelia, Tzotzas, Themistoklis, Tzourio, Christophe, Ueda, Peter, Ugel, Eunice, Ukoli, Flora AM, Usupova, Zhamyila, Uusitalo, Hannu MT, Uysal, Nalan, Valdivia, Gonzalo, Valvi, Damaskini, van Dam, Rob M, van den Born, Bert-Jan, Van der Heyden, Johan, van der Schouw, Yvonne T, Van Lippevelde, Wendy, Van Minh, Hoang, Van Schoor, Natasja M, van Valkengoed, Irene GM, Vanderschueren, Dirk, Vanuzzo, Diego, Varela-Moreiras, Gregorio, Vargas, Luz Nayibe, Vasan, Senthil K, Vasques, Daniel G, Vega, Tomas, Velasquez-Melendez, Gustavo, Velika, Biruta, Verdot, Charlotte, Verloigne, Maïté, Veronesi, Giovanni, Verschuren, WM Monique, Verstraeten, Roosmarijn, Viet, Lucie, Vik, Frøydis N, Vilar, Monica, Villalpando, Salvador, Vioque, Jesus, Virtanen, Jyrki K, Visser, Marjolein, Viswanathan, Bharathi, Vladulescu, Mihaela, Völzke, Henry, Voutilainen, Ari, Vrijheid, Martine, Wade, Alisha N, Wan Bebakar, Wan Mohamad, Wan Mohamud, Wan Nazaimoon, Wanderley Júnior, Rildo de Souza, Wang, Chongjian, Wang, Huijun, Wang, Ningli, Wang, Qian, Wang, Xiangjun, Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S Goya, Wareham, Nicholas, Wartha, Olivia, Weber, Adelheid, Webster-Kerr, Karen, Wedderkopp, Niels, Weghuber, Daniel, Wei, Wenbin, Westbury, Leo, Whincup, Peter H, Wickramasinghe, Kremlin, Widhalm, Kurt, Widyahening, Indah S, Więcek, Andrzej, Wilks, Rainford J, Willeit, Karin, Willeit, Peter, Williams, Julianne, Wilsgaard, Tom, Wojtyniak, Bogdan, Wong-McClure, Roy A, Wong, Andrew, Wong, Emily B, Wu, Frederick C, Wyszyńska, Justyna, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yan, Li, Yan, Weili, Yang, Yang, Yépez García, Martha, Yoosefi, Moein, Yoshihara, Akihiro, Younger-Coleman, Novie O, Yu, Yu-Ling, Yu, Yunjiang, Yusoff, Ahmad Faudzi, Zafiropulos, Vassilis, Zainuddin, Ahmad A, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zapata, Maria Elisa, Zaw, Ko Ko, Zdrojewski, Tomasz, Żegleń, Magdalena, Zejglicova, Kristyna, Zeljkovic Vrkic, Tajana, Zhang, Bing, Zhang, Zhen-Yu, Zhecheva, Yanitsa V, Zholdin, Bekbolat, Zimmet, Paul, Zins, Marie, Zuñiga Cisneros, Julio, Zuziak, Monika, and Ezzati, Majid

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2024
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43. Development and evaluation of a risk prediction tool for risk-adapted screening of colorectal cancer in China

Author

Hang, Dong, Sun, Dianjianyi, Du, Lingbin, Huang, Jianv, Li, Jiacong, Zhu, Chen, Wang, Le, He, Jingjing, Zhu, Xia, Zhu, Meng, Song, Ci, Dai, Juncheng, Yu, Canqing, Xu, Zekuan, Li, Ni, Ma, Hongxia, Jin, Guangfu, Yang, Ling, Chen, Yiping, Du, Huaidong, Cheng, Xiangdong, Chen, Zhengming, Lv, Jun, Hu, Zhibin, Li, Liming, and Shen, Hongbing

Published
2024
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44. One-size-fits-all versus risk-category-based screening interval strategies for cardiovascular disease prevention in Chinese adults: a prospective cohort study

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Li, Liming, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Barnard, Maxim, Bennett, Derrick, Belbasis, Lazaros, Boxall, Ruth, Chan, Ka Hung, Chen, Yiping, Clarke, Charlotte, Clarke, Johnathan, Du, Huaidong, Mohamed, Ahmed Edris, Fry, Hannah, Gilbert, Simon, Im, Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Lam, Hubert, Lin, Kuang, Liu, James, Mazidi, Mohsen, Millwood, Iona, Morris, Sam, Nie, Qunhua, Pozarickij, Alfred, Rahmati, Maryanm, Ryder, Paul, Said, Saredo, Schmidt, Dan, Stevens, Becky, Turnbull, Iain, Wang, Baihan, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Han, Xiao, Hou, Can, Xia, Qingmei, Liu, Chao, Pei, Pei, Sun, Dianjianyi, Yu, Canqing, Pan, Lang, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Duan, Haiping, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Sun, Wei, Yan, Shichun, Cui, Xiaoming, Wang, Chi, Wu, Zhenyuan, Li, Yanjie, Kang, Quan, Luo, Huiming, Ou, Tingting, Zheng, Xiangyang, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Fang Liu, Jian Su., Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Lin, Mei, Lu, Zhenzhen, Zhou, Lifang, Xie, Changping, Lan, Jian, Zhu, Tingping, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Chang, Xiaoyu, Yuan, Mingqiang, Wu, Xia, Jiang, Wei, Liu, Jiaqiu, Sun, Qiang, Chen, Faqing, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Kang, Kai, Feng, Shixian, Tian, Huizi, Fan, Lei, Li, XiaoLin, Sun, Huarong, He, Pan, Zhang, Xukui, Yu, Min, Hu, Ruying, Wang, Hao, Zhang, Xiaoyi, Cao, Yuan, Xie, Kaixu, Chen, Lingli, Shen, Dun, Li, Xiaojun, Jin, Donghui, Yin, Li, Liu, Huilin, Fu, Zhongxi, Xu, Xin, Zhang, Hao, Chen, Jianwei, Peng, Yuan, Zhang, Libo, Qu, Chan, Sun, Zhijia, Ma, Yu, and Pang, Yuanjie

Published
2024
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