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5. Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets

Author

Xu, Xiaoguang, Khunsriraksakul, Chachrit, Eales, James M., Rubin, Sebastien, Scannali, David, Saluja, Sushant, Talavera, David, Markus, Havell, Wang, Lida, Drzal, Maciej, Maan, Akhlaq, Lay, Abigail C., Prestes, Priscilla R., Regan, Jeniece, Diwadkar, Avantika R., Denniff, Matthew, Rempega, Grzegorz, Ryszawy, Jakub, Król, Robert, Dormer, John P., Szulinska, Monika, Walczak, Marta, Antczak, Andrzej, Matías-García, Pamela R., Waldenberger, Melanie, Woolf, Adrian S., Keavney, Bernard, Zukowska-Szczechowska, Ewa, Wystrychowski, Wojciech, Zywiec, Joanna, Bogdanski, Pawel, Danser, A. H. Jan, Samani, Nilesh J., Guzik, Tomasz J., Morris, Andrew P., Liu, Dajiang J., Charchar, Fadi J., and Tomaszewski, Maciej

Published
2024
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6. Bregman Divergence-Based Data Integration with Application to Polygenic Risk Score (PRS) Heterogeneity Adjustment

Author

Li, Qinmengge, Patrick, Matthew T., Zhang, Haihan, Khunsriraksakul, Chachrit, Stuart, Philip E., Gudjonsson, Johann E., Nair, Rajan, Elder, James T., Liu, Dajiang J., Kang, Jian, Tsoi, Lam C., and He, Kevin

Subjects
Statistics - Methodology, Mathematics - Statistics Theory
Abstract

Polygenic risk scores (PRS) have recently received much attention for genetics risk prediction. While successful for the Caucasian population, the PRS based on the minority population suffer from small sample sizes, high dimensionality and low signal-to-noise ratios, exacerbating already severe health disparities. Due to population heterogeneity, direct trans-ethnic prediction by utilizing the Caucasian model for the minority population also has limited performance. In addition, due to data privacy, the individual genotype data is not accessible for either the Caucasian population or the minority population. To address these challenges, we propose a Bregman divergence-based estimation procedure to measure and optimally balance the information from different populations. The proposed method only requires the use of encrypted summary statistics and improves the PRS performance for ethnic minority groups by incorporating additional information. We provide the asymptotic consistency and weak oracle property for the proposed method. Simulations and real data analyses also show its advantages in prediction and variable selection., Comment: 35 pages, 6 figures

Published
2022

7. Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Author

Chen, Fang, Wang, Xingyan, Jang, Seon-Kyeong, Quach, Bryan C, Weissenkampen, J Dylan, Khunsriraksakul, Chachrit, Yang, Lina, Sauteraud, Renan, Albert, Christine M, Allred, Nicholette DD, Arnett, Donna K, Ashley-Koch, Allison E, Barnes, Kathleen C, Barr, R Graham, Becker, Diane M, Bielak, Lawrence F, Bis, Joshua C, Blangero, John, Boorgula, Meher Preethi, Chasman, Daniel I, Chavan, Sameer, Chen, Yii-Der I, Chuang, Lee-Ming, Correa, Adolfo, Curran, Joanne E, David, Sean P, Fuentes, Lisa de las, Deka, Ranjan, Duggirala, Ravindranath, Faul, Jessica D, Garrett, Melanie E, Gharib, Sina A, Guo, Xiuqing, Hall, Michael E, Hawley, Nicola L, He, Jiang, Hobbs, Brian D, Hokanson, John E, Hsiung, Chao A, Hwang, Shih-Jen, Hyde, Thomas M, Irvin, Marguerite R, Jaffe, Andrew E, Johnson, Eric O, Kaplan, Robert, Kardia, Sharon LR, Kaufman, Joel D, Kelly, Tanika N, Kleinman, Joel E, Kooperberg, Charles, Lee, I-Te, Levy, Daniel, Lutz, Sharon M, Manichaikul, Ani W, Martin, Lisa W, Marx, Olivia, McGarvey, Stephen T, Minster, Ryan L, Moll, Matthew, Moussa, Karine A, Naseri, Take, North, Kari E, Oelsner, Elizabeth C, Peralta, Juan M, Peyser, Patricia A, Psaty, Bruce M, Rafaels, Nicholas, Raffield, Laura M, Reupena, Muagututi’a Sefuiva, Rich, Stephen S, Rotter, Jerome I, Schwartz, David A, Shadyab, Aladdin H, Sheu, Wayne H-H, Sims, Mario, Smith, Jennifer A, Sun, Xiao, Taylor, Kent D, Telen, Marilyn J, Watson, Harold, Weeks, Daniel E, Weir, David R, Yanek, Lisa R, Young, Kendra A, Young, Kristin L, Zhao, Wei, Hancock, Dana B, Jiang, Bibo, Vrieze, Scott, and Liu, Dajiang J

Subjects
Genetics, Tobacco, Drug Abuse (NIDA only), Tobacco Smoke and Health, Substance Misuse, Brain Disorders, Human Genome, Good Health and Well Being, Humans, Transcriptome, Drug Repositioning, Genome-Wide Association Study, Tobacco Use, Biology, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Published
2023

8. Rare genetic variants explain missing heritability in smoking.

Author

Jang, Seon-Kyeong, Evans, Luke, Fialkowski, Allison, Arnett, Donna K, Ashley-Koch, Allison E, Barnes, Kathleen C, Becker, Diane M, Bis, Joshua C, Blangero, John, Bleecker, Eugene R, Boorgula, Meher Preethi, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Jenkins, Brenda W Campbell, Carson, April P, Chavan, Sameer, Cupples, L Adrienne, Custer, Brian, Damrauer, Scott M, David, Sean P, de Andrade, Mariza, Dinardo, Carla L, Fingerlin, Tasha E, Fornage, Myriam, Freedman, Barry I, Garrett, Melanie E, Gharib, Sina A, Glahn, David C, Haessler, Jeffrey, Heckbert, Susan R, Hokanson, John E, Hou, Lifang, Hwang, Shih-Jen, Hyman, Matthew C, Judy, Renae, Justice, Anne E, Kaplan, Robert C, Kardia, Sharon LR, Kelly, Shannon, Kim, Wonji, Kooperberg, Charles, Levy, Daniel, Lloyd-Jones, Donald M, Loos, Ruth JF, Manichaikul, Ani W, Gladwin, Mark T, Martin, Lisa Warsinger, Nouraie, Mehdi, Melander, Olle, Meyers, Deborah A, Montgomery, Courtney G, North, Kari E, Oelsner, Elizabeth C, Palmer, Nicholette D, Payton, Marinelle, Peljto, Anna L, Peyser, Patricia A, Preuss, Michael, Psaty, Bruce M, Qiao, Dandi, Rader, Daniel J, Rafaels, Nicholas, Redline, Susan, Reed, Robert M, Reiner, Alexander P, Rich, Stephen S, Rotter, Jerome I, Schwartz, David A, Shadyab, Aladdin H, Silverman, Edwin K, Smith, Nicholas L, Smith, J Gustav, Smith, Albert V, Smith, Jennifer A, Tang, Weihong, Taylor, Kent D, Telen, Marilyn J, Vasan, Ramachandran S, Gordeuk, Victor R, Wang, Zhe, Wiggins, Kerri L, Yanek, Lisa R, Yang, Ivana V, Young, Kendra A, Young, Kristin L, Zhang, Yingze, Liu, Dajiang J, Keller, Matthew C, and Vrieze, Scott

Subjects
Smoking, Gene Frequency, Phenotype, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Tobacco, Genetics, Tobacco Smoke and Health, Human Genome, Cancer
Abstract

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Published
2022

10. HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning

Author

Cai, Xinjun, Shang, Jiaxing, Hao, Fei, Liu, Dajiang, and Zheng, Linjiang

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Social and Information Networks, 68T07, I.2.6
Abstract

Many real-world data can be represented as heterogeneous graphs with different types of nodes and connections. Heterogeneous graph neural network model aims to embed nodes or subgraphs into low-dimensional vector space for various downstream tasks such as node classification, link prediction, etc. Although several models were proposed recently, they either only aggregate information from the same type of neighbors, or just indiscriminately treat homogeneous and heterogeneous neighbors in the same way. Based on these observations, we propose a new heterogeneous graph neural network model named HMSG to comprehensively capture structural, semantic and attribute information from both homogeneous and heterogeneous neighbors. Specifically, we first decompose the heterogeneous graph into multiple metapath-based homogeneous and heterogeneous subgraphs, and each subgraph associates specific semantic and structural information. Then message aggregation methods are applied to each subgraph independently, so that information can be learned in a more targeted and efficient manner. Through a type-specific attribute transformation, node attributes can also be transferred among different types of nodes. Finally, we fuse information from subgraphs together to get the complete representation. Extensive experiments on several datasets for node classification, node clustering and link prediction tasks show that HMSG achieves the best performance in all evaluation metrics than state-of-the-art baselines., Comment: 12 pages, 3 figures, 6 tables

Published
2021

11. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction

Author

Karlsson Linnér, Richard, Mallard, Travis T, Barr, Peter B, Sanchez-Roige, Sandra, Madole, James W, Driver, Morgan N, Poore, Holly E, de Vlaming, Ronald, Grotzinger, Andrew D, Tielbeek, Jorim J, Johnson, Emma C, Liu, Mengzhen, Rosenthal, Sara Brin, Ideker, Trey, Zhou, Hang, Kember, Rachel L, Pasman, Joëlle A, Verweij, Karin JH, Liu, Dajiang J, Vrieze, Scott, Kranzler, Henry R, Gelernter, Joel, Harris, Kathleen Mullan, Tucker-Drob, Elliot M, Waldman, Irwin D, Palmer, Abraham A, Harden, K Paige, Koellinger, Philipp D, and Dick, Danielle M

Subjects
Biological Psychology, Psychology, Opioids, Social Determinants of Health, Mental Illness, Genetics, Mental Health, Behavioral and Social Science, Basic Behavioral and Social Science, Brain Disorders, Human Genome, Drug Abuse (NIDA only), Neurosciences, Substance Misuse, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Attention Deficit Disorder with Hyperactivity, Behavior, Addictive, Behavioral Symptoms, Computational Biology, Crime, Genetic Association Studies, Genome-Wide Association Study, HIV Infections, Humans, Meta-Analysis as Topic, Multifactorial Inheritance, Multivariate Analysis, Opioid-Related Disorders, Reproducibility of Results, Self-Control, Suicide, Unemployment, COGA Collaborators, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract

Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates.

Published
2021

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

Author

Saunders, Gretchen R. B., 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., Silva, Lilian Fernandes, 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 L. R., Keller, Matthew C., Kelly, Tanika N., Kooperberg, Charles, Korhonen, Tellervo, Kraft, Peter, Krauter, Kenneth, Kuusisto, Johanna, Laakso, Markku, Lasky-Su, Jessica, Lee, Wen-Jane, Lee, James J., Levy, Daniel, Li, Liming, Li, Kevin, Li, Yuqing, Lin, Kuang, Lind, Penelope A., Liu, Chunyu, Lloyd-Jones, Donald M., Lutz, Sharon M., Ma, Jiantao, Mägi, Reedik, Manichaikul, Ani, Martin, Nicholas G., Mathur, Ravi, Matoba, Nana, McArdle, Patrick F., McGue, Matt, McQueen, Matthew B., Medland, Sarah E., Metspalu, Andres, Meyers, Deborah A., Millwood, Iona Y., Mitchell, Braxton D., Mohlke, Karen L., Moll, Matthew, Montasser, May E., Morrison, Alanna C., Mulas, Antonella, Nielsen, Jonas B., North, Kari E., Oelsner, Elizabeth C., Okada, Yukinori, Orrù, Valeria, Palmer, Nicholette D., Palviainen, Teemu, Pandit, Anita, Park, S. Lani, Peters, Ulrike, Peters, Annette, Peyser, Patricia A., Polderman, Tinca J. C., Rafaels, Nicholas, Redline, Susan, Reed, Robert M., Reiner, Alex P., Rice, John P., Rich, Stephen S., Richmond, Nicole E., Roan, Carol, Rotter, Jerome I., Rueschman, Michael N., Runarsdottir, Valgerdur, Saccone, Nancy L., Schwartz, David A., Shadyab, Aladdin H., Shi, Jingchunzi, Shringarpure, Suyash S., Sicinski, Kamil, Skogholt, Anne Heidi, Smith, Jennifer A., Smith, Nicholas L., Sotoodehnia, Nona, Stallings, Michael C., Stefansson, Hreinn, Stefansson, Kari, Stitzel, Jerry A., Sun, Xiao, Syed, Moin, Tal-Singer, Ruth, Taylor, Amy E., Taylor, Kent D., Telen, Marilyn J., Thai, Khanh K., Tiwari, Hemant, Turman, Constance, Tyrfingsson, Thorarinn, Wall, Tamara L., Walters, Robin G., Weir, David R., Weiss, Scott T., White, Wendy B., Whitfield, John B., Wiggins, Kerri L., Willemsen, Gonneke, Willer, Cristen J., Winsvold, Bendik S., Xu, Huichun, Yanek, Lisa R., Yin, Jie, Young, Kristin L., Young, Kendra A., Yu, Bing, Zhao, Wei, Zhou, Wei, Zöllner, Sebastian, Zuccolo, Luisa, Batini, Chiara, Bergen, Andrew W., Bierut, Laura J., David, Sean P., Gagliano Taliun, Sarah A., Hancock, Dana B., Jiang, Bibo, Munafò, Marcus R., Thorgeirsson, Thorgeir E., Liu, Dajiang J., and Vrieze, Scott

Published
2022
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16. ConCas: Cascade Popularity Prediction Based on Topic-Aware Graph Contrastive Learning

Author

Ling, Chen, Zhang, Xianren, Shang, Jiaxing, Liu, Dajiang, Li, Yong, Xie, Wu, Qiang, Baohua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Memmi, Gerard, editor, Yang, Baijian, editor, Kong, Linghe, editor, Zhang, Tianwei, editor, and Qiu, Meikang, editor

Published
2022
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17. CollaborateCas: Popularity Prediction of Information Cascades Based on Collaborative Graph Attention Networks

Author

Zhang, Xianren, Shang, Jiaxing, Jia, Xueqi, Liu, Dajiang, Hao, Fei, Zhang, Zhiqing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhattacharya, Arnab, editor, Lee Mong Li, Janice, editor, Agrawal, Divyakant, editor, Reddy, P. Krishna, editor, Mohania, Mukesh, editor, Mondal, Anirban, editor, Goyal, Vikram, editor, and Uday Kiran, Rage, editor

Published
2022
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21. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

Author

Mahajan, Anubha, Wessel, Jennifer, Willems, Sara M, Zhao, Wei, Robertson, Neil R, Chu, Audrey Y, Gan, Wei, Kitajima, Hidetoshi, Taliun, Daniel, Rayner, N William, Guo, Xiuqing, Lu, Yingchang, Li, Man, Jensen, Richard A, Hu, Yao, Huo, Shaofeng, Lohman, Kurt K, Zhang, Weihua, Cook, James P, Prins, Bram Peter, Flannick, Jason, Grarup, Niels, Trubetskoy, Vassily Vladimirovich, Kravic, Jasmina, Kim, Young Jin, Rybin, Denis V, Yaghootkar, Hanieh, Müller-Nurasyid, Martina, Meidtner, Karina, Li-Gao, Ruifang, Varga, Tibor V, Marten, Jonathan, Li, Jin, Smith, Albert Vernon, An, Ping, Ligthart, Symen, Gustafsson, Stefan, Malerba, Giovanni, Demirkan, Ayse, Tajes, Juan Fernandez, Steinthorsdottir, Valgerdur, Wuttke, Matthias, Lecoeur, Cécile, Preuss, Michael, Bielak, Lawrence F, Graff, Marielisa, Highland, Heather M, Justice, Anne E, Liu, Dajiang J, Marouli, Eirini, Peloso, Gina Marie, Warren, Helen R, ExomeBP Consortium, MAGIC Consortium, GIANT Consortium, Afaq, Saima, Afzal, Shoaib, Ahlqvist, Emma, Almgren, Peter, Amin, Najaf, Bang, Lia B, Bertoni, Alain G, Bombieri, Cristina, Bork-Jensen, Jette, Brandslund, Ivan, Brody, Jennifer A, Burtt, Noël P, Canouil, Mickaël, Chen, Yii-Der Ida, Cho, Yoon Shin, Christensen, Cramer, Eastwood, Sophie V, Eckardt, Kai-Uwe, Fischer, Krista, Gambaro, Giovanni, Giedraitis, Vilmantas, Grove, Megan L, de Haan, Hugoline G, Hackinger, Sophie, Hai, Yang, Han, Sohee, Tybjærg-Hansen, Anne, Hivert, Marie-France, Isomaa, Bo, Jäger, Susanne, Jørgensen, Marit E, Jørgensen, Torben, Käräjämäki, Annemari, Kim, Bong-Jo, Kim, Sung Soo, Koistinen, Heikki A, Kovacs, Peter, Kriebel, Jennifer, Kronenberg, Florian, Läll, Kristi, Lange, Leslie A, Lee, Jung-Jin, Lehne, Benjamin, Li, Huaixing, and Lin, Keng-Hung

Subjects
ExomeBP Consortium, MAGIC Consortium, GIANT Consortium, Humans, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Chromosome Mapping, Alleles, European Continental Ancestry Group, Female, Male, Genetic Variation, Genome-Wide Association Study, Whole Exome Sequencing, Diabetes Mellitus, Type 2, Developmental Biology, Biological Sciences, Medical and Health Sciences
Abstract

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P

Published
2018

24. A Deep Sequence-to-Sequence Method for Aircraft Landing Speed Prediction Based on QAR Data

Author

Kang, Zongwei, Shang, Jiaxing, Feng, Yong, Zheng, Linjiang, Liu, Dajiang, Qiang, Baohua, Wei, Ran, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, Zhisheng, editor, Beek, Wouter, editor, Wang, Hua, editor, Zhou, Rui, editor, and Zhang, Yanchun, editor

Published
2020
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28. Efficient dehydration and recovery of ionic liquid after lignocellulosic processing using pervaporation

Author

Sun, Jian, Shi, Jian, Murthy Konda, NVSN, Campos, Dan, Liu, Dajiang, Nemser, Stuart, Shamshina, Julia, Dutta, Tanmoy, Berton, Paula, Gurau, Gabriela, Rogers, Robin D, Simmons, Blake A, and Singh, Seema

Subjects
Biochemistry and Cell Biology, Biological Sciences, Industrial Biotechnology, Biomass pretreatment, Ionic liquid, Saccharification, Biofuels, Recycle, Pervaporation
Abstract

BackgroundBiomass pretreatment using certain ionic liquids (ILs) is very efficient, generally producing a substrate that is amenable to saccharification with fermentable sugar yields approaching theoretical limits. Although promising, several challenges must be addressed before an IL pretreatment technology can become commercially viable. One of the most significant challenges is the affordable and scalable recovery and recycle of the IL itself. Pervaporation (PV) is a highly selective and scalable membrane separation process for quantitatively recovering volatile solutes or solvents directly from non-volatile solvents that could prove more versatile for IL dehydration.ResultsWe evaluated a commercially available PV system for IL dehydration and recycling as part of an integrated IL pretreatment process using 1-ethyl-3-methylimidazolium acetate ([C2C1Im][OAc]) that has been proven to be very effective as a biomass pretreatment solvent. Separation factors as high as 1500 were observed. We demonstrate that >99.9 wt% [C2C1Im][OAc] can be recovered from aqueous solution (≤20 wt% IL) and recycled five times. A preliminary technoeconomic analysis validated the promising role of PV in improving overall biorefinery process economics, especially in the case where other IL recovery technologies might lead to significant losses.ConclusionsThese findings establish the foundation for further development of PV as an effective method of recovering and recycling ILs using a commercially viable process technology.

Published
2017

29. SC-CGRA: An Energy-Efficient CGRA Using Stochastic Computing

Author

Mou, Di, Wang, Bo, and Liu, Dajiang

Abstract

Stochastic Computing (SC) offers a promising computing paradigm for low-power and cost-effective applications, with the added advantage of high error tolerance. In parallel, Coarse-Grained Reconfigurable Arrays (CGRA) prove to be a highly promising platform for domain-specific applications due to their combination of energy efficiency and flexibility. Intuitively, introducing SC to CGRA would significantly reinforce the strengths of both paradigms. However, existing SC-based architectures often encounter inherent computation errors, while the stochastic number generators employed in SC result in exponentially growing latency, which is deemed unacceptable in CGRA. In this work, we propose an SC-based CGRA by replacing the exact multiplication in traditional CGRA with an SC-based multiplication. To improve the accuracy of SC and shorten the latency of Stochastic Number Generators (SNG), we introduce the leading zero shifting and comparator truncation, while keeping the length of bitstream fixed. In addition, due to the flexible interconnections among PEs, we propose a quality scaling strategy that combines neighbor PEs to achieve high-accuracy operations without switching costs like power-gating. Compared to the state-of-the-art approximate computing design of CGRA, our proposed CGRA can averagely achieve a 65.3% reduction in output error while having a 21.2% reduction in energy consumption and a noteworthy 28.37% area savings.

Published
2024
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31. Type 1 Diabetes in Acute Pancreatitis Consortium: From Concept to Reality

Author

Serrano, Jose, Laughlin, Maren R., Bellin, Melena D., Yadav, Dhiraj, Chinchilli, Vernon M., Andersen, Dana K., Greenbaum, Carla, Kozarek, Richard, Speake, Cate, Lord, Sandra, Irani, Shayan, Linsley, Peter, Kwok, William, Lacy-Hulbert, Adam, Hamerman, Jessica, Bahnson, Henry, Grubb, Brooke, Williams, Cassandra, Ackermann, Sarah, Varner, Kimberly, Goodarzi, Mark O., Pandol, Stephen J., Buxbaum, James, Diniz, Marcio, Goodman, Marc, Hines, O. Joe, Jeon, Christie, Korc, Murray, Li, Debiao, Petrov, Maxim, Pisegna, Joseph, Van Eyk, Jennifer, Vege, Santhi Swaroop, Fogel, Evan, Evans-Molina, Carmella, Tirkes, Temel, Sherman, Stuart, Easler, Jeffrey, Saeed, Zed, Sims, Emily, Oram, Richard, Weedon, Michael, Singh, Vikesh, Sun, Zhaoli, Afghani, Elham, Kalyani, Rita, Jacobs, Michael, Zaheer, Atif, Zhu, Heng, Akshintala, Venkata S., Papachristou, Georgios I., Hart, Phil A., Conwell, Darwin L., Bradley, David, Dungan, Kathleen, Shah, Zarine, Krebs, Zoe, Hazelett, Samantha, Forsmark, Christopher E., Casu, Anna, Hughes, Steven, Pratley, Richard E., Thompson, Martha Campbell, Wasserfall, Clive, Grajo, Joseph, Yazici, Cemal, Layden, Brian, Danielson, Kirstie, Boulay, Brian, Villa, Edward, Mutlu, Ece, Xia, Yinglin, Green, Stefan, Tussing-Humphreys, Lisa, Prabhakar, Bellur, Gaba, Ron, Ratia, Kiira, Daviglus, Martha, Spaggiari, Mario, Grippo, Paul J., Setiawan, Veronica, Keswani, Rajesh, Komanduri, Srinadh, Aadam, Aziz, Bellin, Melena, Trikudanathan, Guru, Fife, Brian, Freeman, Martin, Harindhanavudhi, Tasma, Moran, Antoinette, Spilseth, Benjamin, Hodges, James, Yadav, Dhiraj, Toledo, Frederico G. S., Dasyam, Anil, Phillips, Anna E., Kang, Chae-Ryon, Yechoor, Vijay, Saloman, Jami, Becker, Dorothy, Reynolds, Shari, Hall, Kristin, Stello, Kim, Mays, Melanie, Saul, Melissa, Park, Walter G., Basina, Marina, Habtezion, Aida, Poullos, Peter, Meyer, Everett, Chinchilli, Vernon M., Kong, Lan, Liu, Dajiang, Maranki, Jennifer, McCormick, Jennifer, Pichardo-Lowden, Ariana, Siddiqui, Ayesha, Raja-Khan, Nazia, Wang, Ming, Zhan, Xiang, Bottino, Rita, Faulkner, Georgia, Baab, Kendall Thomas, Dyer, Anne-Marie, Guzman, Jennifer, Holmes, Beth, Baron, Rose, Merchlinski, Aimee, Valencia-Moulton, Paula, Broach, James, and Bradley, David

Published
2022
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32. Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices

Author

Chen, Tony, primary, Pham, Giang, additional, Fox, Louis, additional, Zhang, Jingning, additional, Byun, Jinyoung, additional, Han, Younghun, additional, Saunders, Gretchen RB, additional, Liu, Dajiang, additional, Bray, Michael J, additional, Ramsey, Alex T, additional, McKay, James, additional, Bierut, Laura J, additional, Amos, Christopher Ian, additional, Hung, Rayjean J., additional, Lin, Xihong, additional, Zhang, Haoyu, additional, and Chen, Li-Shiun, additional

Published
2024
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34. Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals

Author

Eicher, John D, Chami, Nathalie, Kacprowski, Tim, Nomura, Akihiro, Chen, Ming-Huei, Yanek, Lisa R, Tajuddin, Salman M, Schick, Ursula M, Slater, Andrew J, Pankratz, Nathan, Polfus, Linda, Schurmann, Claudia, Giri, Ayush, Brody, Jennifer A, Lange, Leslie A, Manichaikul, Ani, Hill, W David, Pazoki, Raha, Elliot, Paul, Evangelou, Evangelos, Tzoulaki, Ioanna, Gao, He, Vergnaud, Anne-Claire, Mathias, Rasika A, Becker, Diane M, Becker, Lewis C, Burt, Amber, Crosslin, David R, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Hernesniemi, Jussi, Kähönen, Mika, Raitoharju, Emma, Mononen, Nina, Raitakari, Olli T, Lehtimäki, Terho, Cushman, Mary, Zakai, Neil A, Nickerson, Deborah A, Raffield, Laura M, Quarells, Rakale, Willer, Cristen J, Peloso, Gina M, Abecasis, Goncalo R, Liu, Dajiang J, Consortium, Global Lipids Genetics, Deloukas, Panos, Samani, Nilesh J, Schunkert, Heribert, Erdmann, Jeanette, Consortium, CARDIoGRAM Exome, Consortium, Myocardial Infarction Genetics, Fornage, Myriam, Richard, Melissa, Tardif, Jean-Claude, Rioux, John D, Dube, Marie-Pierre, de Denus, Simon, Lu, Yingchang, Bottinger, Erwin P, Loos, Ruth JF, Smith, Albert Vernon, Harris, Tamara B, Launer, Lenore J, Gudnason, Vilmundur, Edwards, Digna R Velez, Torstenson, Eric S, Liu, Yongmei, Tracy, Russell P, Rotter, Jerome I, Rich, Stephen S, Highland, Heather M, Boerwinkle, Eric, Li, Jin, Lange, Ethan, Wilson, James G, Mihailov, Evelin, Mägi, Reedik, Hirschhorn, Joel, Metspalu, Andres, Esko, Tõnu, Vacchi-Suzzi, Caterina, Nalls, Mike A, Zonderman, Alan B, Evans, Michele K, Engström, Gunnar, Orho-Melander, Marju, Melander, Olle, O’Donoghue, Michelle L, Waterworth, Dawn M, Wallentin, Lars, White, Harvey D, Floyd, James S, Bartz, Traci M, Rice, Kenneth M, Psaty, Bruce M, Starr, JM, Liewald, David CM, Hayward, Caroline, and Deary, Ian J

Subjects
Genetics, Cardiovascular, Hematology, 1.1 Normal biological development and functioning, Underpinning research, Blood, Blood Platelets, Exome, Female, Genetic Variation, Genome-Wide Association Study, Humans, Male, Mean Platelet Volume, Platelet Count, Global Lipids Genetics Consortium, CARDIoGRAM Exome Consortium, Myocardial Infarction Genetics Consortium, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.

Published
2016

35. Study on Key Parameters of Hard Roof Cutting and Pressure Release and Roadway Retaining in Medium Thick Coal Seam

Author

LIU Dajiang, XU Xuhui, ZHU Hengzhong, OUYANG Feng

Subjects
medium thick coal seam, roof cutting and pressure release, key parts, reinforcing anchor cable, key parameters, Mining engineering. Metallurgy, TN1-997
Abstract

Based on the engineering geological conditions of the hard roof of the medium thick coal seam in Dadougou Coal Mine, in order to implement the non-pillar mining technology of roof cutting and pressure release, the key parameters of roof cutting and roadway retaining are studied and optimized. By combining theoretical analysis with numerical simulation, the influence of cutting roof height and cutting angle on the stress distribution and displacement of surrounding rock along gob side entry is analyzed. In view of the key position of surrounding rock deformation in the roadway non-pillar mining technology, the design parameters of reinforced anchor cable support were designed reasonably. The results show that the reasonable cutting height is 9 m and the reasonable cutting angle is 15 °. The non-pillar mining technology of roof cutting and pressure release is implemented in the field, and the working resistance of the hydraulic support in the working face is monitored. The results show that the effect of gangue retaining and roadway retaining is good, and the effect of pressure relief is obvious.

Published
2020
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36. TaSer (TabAnno and SeqMiner): a toolset for annotating and querying next-generation sequence data

Author

Zhan, Xiaowei and Liu, Dajiang J.

Subjects
Statistics - Computation, Quantitative Biology - Genomics
Abstract

Summary: We develop TaSer (TabAnno and SeqMiner), a toolkit for annotating and querying next generation sequence (NGS) dataset in tab-delimited files. TabAnno is a powerful and efficient command-line tool designed to pre-process sequence data, annotate variations and generate an indexed feature-enriched project file that can integrate multiple sources of information. Using the project file generated by TabAnno, complex queries to the sequence dataset can be performed using SeqMiner, an R-package designed to efficiently access large datasets. Extracted information can be conveniently viewed and analyzed by tools in R. TaSer is optimized and computationally more efficient than software using database systems. It enables annotating and querying NGS dataset using moderate computing resource. Availability and implementation: TabAnno can be downloaded from github (zhanxw.github.io/anno/). SeqMiner is distributed on CRAN (cran.r-project.org/web/packages/seqminer). Contact: X.Z. (zhanxw@umich.edu) D.J.L (dajiang@umich.edu)

Published
2013

37. Meta-Analysis of Gene Level Association Tests

Author

Liu, Dajiang J., Peloso, Gina M., Zhan, Xiaowei, Holmen, Oddgeir, Zawistowski, Matthew, Feng, Shuang, Nikpay, Majid, Auer, Paul L., Goel, Anuj, Zhang, He, Peters, Ulrike, Farrall, Martin, Orho-Melander, Marju, Kooperberg, Charles, McPherson, Ruth, Watkins, Hugh, Willer, Cristen J., Hveem, Kristian, Melander, Olle, Kathiresan, Sekar, and Abecasis, Gonçalo R.

Subjects
Statistics - Methodology
Abstract

The vast majority of connections between complex disease and common genetic variants were identified through meta-analysis, a powerful approach that enables large samples sizes while protecting against common artifacts due to population structure, repeated small sample analyses, and/or limitations with sharing individual level data. As the focus of genetic association studies shifts to rare variants, genes and other functional units are becoming the unit of analysis. Here, we propose and evaluate new approaches for meta-analysis of rare variant association. We show that our approach retains useful features of single variant meta-analytic approaches and demonstrate its utility in a study of blood lipid levels in ~18,500 individuals genotyped with exome arrays.

Published
2013

39. Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use

Author

Surendran, Praveen, Young, Robin, Barnes, Daniel R., Nielsen, Sune Fallgaard, Rasheed, Asif, Samuel, Maria, Zhao, Wei, Kontto, Jukka, Perola, Markus, Caslake, Muriel, de Craen, Anton J.M., Trompet, Stella, Uria-Nickelsen, Maria, Malarstig, Anders, Reily, Dermot F., Hoek, Maarten, Vogt, Thomas, Jukema, J. Wouter, Sattar, Naveed, Ford, Ian, Packard, Chris J., Alam, Dewan S., Majumder, Abdulla al Shafi, Di Angelantonio, Emanuele, Chowdhury, Rajiv, Amouyel, Philippe, Arveiler, Dominique, Blankenberg, Stefan, Ferrières, Jean, Kee, Frank, Kuulasmaa, Kari, Müller-Nurasyid, Martina, Veronesi, Giovanni, Virtamo, Jarmo, EPIC-CVD Consortium, Frossard, Philippe, Nordestgaard, Børge Grønne, Saleheen, Danish, Danesh, John, Butterworth, Adam S., Howson, Joanna M.M., Erzurumluoglu, A. Mesut, Jackson, Victoria E., Melbourne, Carl A., Varga, Tibor V., Warren, Helen R., Tragante, Vinicius, Tachmazidou, Ioanna, Harris, Sarah E., Evangelou, Evangelos, Marten, Jonathan, Zhang, Weihua, Altmaier, Elisabeth, Luan, Jian’an, Langenberg, Claudia, Scott, Robert A., Yaghootkar, Hanieh, Stirrups, Kathleen, Kanoni, Stavroula, Marouli, Eirini, Karpe, Fredrik, Dominiczak, Anna F., Sever, Peter, Poulter, Neil, Rolandsson, Olov, Baumbach, Clemens, Afaq, Saima, Chambers, John C., Kooner, Jaspal S., Wareham, Nicholas J., Renström, Frida, Hallmans, Göran, Marioni, Riccardo E., Corley, Janie, Starr, John M., Verweij, Niek, de Boer, Rudolf A., van der Meer, Peter, Yavas, Ersin, Vaartjes, Ilonca, Bots, Michiel L., Asselbergs, Folkert W., Grabe, Hans J., Völzke, Henry, Nauck, Matthias, Weiss, Stefan, Pharoah, Paul D.P., Dunning, Alison M., Dennis, Joe G., Thompson, Deborah J., Michailidou, Kyriaki, Easton, Douglas F., Antoniou, Antonis C., Tyrrell, Jessica, Mihailov, Evelin, Samani, Nilesh J., Zhou, Kaixin, Neville, Matthew J., Metspalu, Andres, Palmer, Colin N.A., Hall, Ian P., Strachan, David P., Deary, Ian J., Frayling, Tim M., Hayward, Caroline, van der Harst, Pim, Zeggini, Eleftheria, Understanding Society Scientific Group, Munroe, Patricia B., Jansson, Jan-Håkan, Franks, Paul W., Deloukas, Panos, Caulfield, Mark J., Wain, Louise V., Tobin, Martin D., Brazel, David M., Jiang, Yu, Hughey, Jordan M., Turcot, Valérie, Zhan, Xiaowei, Gong, Jian, Batini, Chiara, Weissenkampen, J. Dylan, Liu, MengZhen, Bertelsen, Sarah, Chou, Yi-Ling, Faul, Jessica D., Haessler, Jeff, Hammerschlag, Anke R., Hsu, Chris, Kapoor, Manav, Lai, Dongbing, Le, Nhung, de Leeuw, Christiaan A., Loukola, Anu, Mangino, Massimo, Pistis, Giorgio, Qaiser, Beenish, Rohde, Rebecca, Shao, Yaming, Stringham, Heather, Wetherill, Leah, Agrawal, Arpana, Bierut, Laura, Chen, Chu, Eaton, Charles B., Goate, Alison, Haiman, Christopher, Heath, Andrew, Iacono, William G., Martin, Nicholas G., Polderman, Tinca J., Reiner, Alex, Rice, John, Schlessinger, David, Scholte, H. Steven, Smith, Jennifer A., Tardif, Jean-Claude, Tindle, Hilary A., van der Leij, Andries R., Boehnke, Michael, Chang-Claude, Jenny, Cucca, Francesco, David, Sean P., Foroud, Tatiana, Kardia, Sharon L.R., Kooperberg, Charles, Laakso, Markku, Lettre, Guillaume, Madden, Pamela, McGue, Matt, North, Kari, Posthuma, Danielle, Spector, Timothy, Stram, Daniel, Weir, David R., Kaprio, Jaakko, Abecasis, Gonçalo R., Liu, Dajiang J., and Vrieze, Scott

Published
2019
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40. Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.

Author

Lida Wang, Khunsriraksakul, Chachrit, Markus, Havell, Dieyi Chen, Fan Zhang, Fang Chen, Xiaowei Zhan, Carrel, Laura, Liu, Dajiang. J., and Jiang, Bibo

Abstract

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Identification of a rare coding variant in complement 3 associated with age-related macular degeneration.

Author

Zhan, Xiaowei, Larson, David, Wang, Chaolong, Koboldt, Daniel, Sergeev, Yuri, Fulton, Robert, Fulton, Lucinda, Fronick, Catrina, Branham, Kari, Bragg-Gresham, Jennifer, Jun, Goo, Hu, Youna, Kang, Hyun, Liu, Dajiang, Othman, Mohammad, Brooks, Matthew, Ratnapriya, Rinki, Boleda, Alexis, Grassmann, Felix, von Strachwitz, Claudia, Olson, Lana, Buitendijk, Gabriëlle, Hofman, Albert, van Duijn, Cornelia, Cipriani, Valentina, Shahid, Humma, Jiang, Yingda, Conley, Yvette, Morgan, Denise, Kim, Ivana, Johnson, Matthew, Cantsilieris, Stuart, Richardson, Andrea, Guymer, Robyn, Luo, Hongrong, Ouyang, Hong, Licht, Christoph, Pluthero, Fred, Zhang, Mindy, Zhang, Kang, Baird, Paul, Blangero, John, Klein, Michael, Farrer, Lindsay, DeAngelis, Margaret, Weeks, Daniel, Yates, John, Klaver, Caroline, Pericak-Vance, Margaret, Haines, Jonathan, Weber, Bernhard, Wilson, Richard, Heckenlively, John, Chew, Emily, Stambolian, Dwight, Mardis, Elaine, Swaroop, Anand, Abecasis, Goncalo, Gorin, Michael, and Moore, Anthony

Subjects
Aging, Complement C3, Complement Factor H, Complement Pathway, Alternative, Gene Frequency, Genetic Variation, Genotype, Macular Degeneration, Polymorphism, Single Nucleotide
Abstract

Macular degeneration is a common cause of blindness in the elderly. To identify rare coding variants associated with a large increase in risk of age-related macular degeneration (AMD), we sequenced 2,335 cases and 789 controls in 10 candidate loci (57 genes). To increase power, we augmented our control set with ancestry-matched exome-sequenced controls. An analysis of coding variation in 2,268 AMD cases and 2,268 ancestry-matched controls identified 2 large-effect rare variants: previously described p.Arg1210Cys encoded in the CFH gene (case frequency (fcase) = 0.51%; control frequency (fcontrol) = 0.02%; odds ratio (OR) = 23.11) and newly identified p.Lys155Gln encoded in the C3 gene (fcase = 1.06%; fcontrol = 0.39%; OR = 2.68). The variants suggest decreased inhibition of C3 by complement factor H, resulting in increased activation of the alternative complement pathway, as a key component of disease biology.

Published
2013

45. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Author

Justice, Anne E., Karaderi, Tugce, Highland, Heather M., Young, Kristin L., Graff, Mariaelisa, Lu, Yingchang, Turcot, Valérie, Auer, Paul L., Fine, Rebecca S., Guo, Xiuqing, Schurmann, Claudia, Lempradl, Adelheid, Marouli, Eirini, Mahajan, Anubha, Winkler, Thomas W., Locke, Adam E., Medina-Gomez, Carolina, Esko, Tõnu, Vedantam, Sailaja, Giri, Ayush, Lo, Ken Sin, Alfred, Tamuno, Mudgal, Poorva, Ng, Maggie C. Y., Heard-Costa, Nancy L., Feitosa, Mary F., Manning, Alisa K., Willems, Sara M., Sivapalaratnam, Suthesh, Abecasis, Goncalo, Alam, Dewan S., Allison, Matthew, Amouyel, Philippe, Arzumanyan, Zorayr, Balkau, Beverley, Bastarache, Lisa, Bergmann, Sven, Bielak, Lawrence F., Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A., Bork-Jensen, Jette, Bottinger, Erwin P., Bowden, Donald W., Brandslund, Ivan, Broer, Linda, Burt, Amber A., Butterworth, Adam S., Caulfield, Mark J., Cesana, Giancarlo, Chambers, John C., Chasman, Daniel I., Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y., Collins, Francis S., Cook, James P., Cox, Amanda J., Crosslin, David S., Danesh, John, de Bakker, Paul I. W., Denus, Simon de, Mutsert, Renée de, Dedoussis, George, Demerath, Ellen W., Dennis, Joe G., Denny, Josh C., Di Angelantonio, Emanuele, Dörr, Marcus, Drenos, Fotios, Dubé, Marie-Pierre, Dunning, Alison M., Easton, Douglas F., Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, Feng, Shuang, Ferrannini, Ele, Ferrieres, Jean, Florez, Jose C., Fornage, Myriam, Fox, Caroline S., Franks, Paul W., Friedrich, Nele, Gan, Wei, Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Girotto, Giorgia, Gorski, Mathias, Grallert, Harald, Grarup, Niels, Grove, Megan L., Gustafsson, Stefan, Haessler, Jeff, Hansen, Torben, Hattersley, Andrew T., Hayward, Caroline, Heid, Iris M., Holmen, Oddgeir L., Hovingh, G. Kees, Howson, Joanna M. M., Hu, Yao, Hung, Yi-Jen, Hveem, Kristian, Ikram, M. Arfan, Ingelsson, Erik, Jackson, Anne U., Jarvik, Gail P., Jia, Yucheng, Jørgensen, Torben, Jousilahti, Pekka, Justesen, Johanne M., Kahali, Bratati, Karaleftheri, Maria, Kardia, Sharon L. R., Karpe, Fredrik, Kee, Frank, Kitajima, Hidetoshi, Komulainen, Pirjo, Kooner, Jaspal S., Kovacs, Peter, Krämer, Bernhard K., Kuulasmaa, Kari, Kuusisto, Johanna, Laakso, Markku, Lakka, Timo A., Lamparter, David, Lange, Leslie A., Langenberg, Claudia, Larson, Eric B., Lee, Nanette R., Lee, Wen-Jane, Lehtimäki, Terho, Lewis, Cora E., Li, Huaixing, Li, Jin, Li-Gao, Ruifang, Lin, Li-An, Lin, Xu, Lind, Lars, Lindström, Jaana, Linneberg, Allan, Liu, Ching-Ti, Liu, Dajiang J., Luan, Jian’an, Lyytikäinen, Leo-Pekka, MacGregor, Stuart, Mägi, Reedik, Männistö, Satu, Marenne, Gaëlle, Marten, Jonathan, Masca, Nicholas G. D., McCarthy, Mark I., Meidtner, Karina, Mihailov, Evelin, Moilanen, Leena, Moitry, Marie, Mook-Kanamori, Dennis O., Morgan, Anna, Morris, Andrew P., Müller-Nurasyid, Martina, Munroe, Patricia B., Narisu, Narisu, Nelson, Christopher P., Neville, Matt, Ntalla, Ioanna, O’Connell, Jeffrey R., Owen, Katharine R., Pedersen, Oluf, Peloso, Gina M., Pennell, Craig E., Perola, Markus, Perry, James A., Perry, John R. B., Pers, Tune H., Ewing, Ailith, Polasek, Ozren, Raitakari, Olli T., Rasheed, Asif, Raulerson, Chelsea K., Rauramaa, Rainer, Reilly, Dermot F., Reiner, Alex P., Ridker, Paul M., Rivas, Manuel A., Robertson, Neil R., Robino, Antonietta, Rudan, Igor, Ruth, Katherine S., Saleheen, Danish, Salomaa, Veikko, Samani, Nilesh J., Schreiner, Pamela J., Schulze, Matthias B., Scott, Robert A., Segura-Lepe, Marcelo, Sim, Xueling, Slater, Andrew J., Small, Kerrin S., Smith, Blair H., Smith, Jennifer A., Southam, Lorraine, Spector, Timothy D., Speliotes, Elizabeth K., Stefansson, Kari, Steinthorsdottir, Valgerdur, Stirrups, Kathleen E., Strauch, Konstantin, Stringham, Heather M., Stumvoll, Michael, Sun, Liang, Surendran, Praveen, Swart, Karin M. A., Tardif, Jean-Claude, Taylor, Kent D., Teumer, Alexander, Thompson, Deborah J., Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, Thuesen, Betina H., Tönjes, Anke, Torres, Mina, Tsafantakis, Emmanouil, Tuomilehto, Jaakko, Uitterlinden, André G., Uusitupa, Matti, van Duijn, Cornelia M., Vanhala, Mauno, Varma, Rohit, Vermeulen, Sita H., Vestergaard, Henrik, Vitart, Veronique, Vogt, Thomas F., Vuckovic, Dragana, Wagenknecht, Lynne E., Walker, Mark, Wallentin, Lars, Wang, Feijie, Wang, Carol A., Wang, Shuai, Wareham, Nicholas J., Warren, Helen R., Waterworth, Dawn M., Wessel, Jennifer, White, Harvey D., Willer, Cristen J., Wilson, James G., Wood, Andrew R., Wu, Ying, Yaghootkar, Hanieh, Yao, Jie, Yerges-Armstrong, Laura M., Young, Robin, Zeggini, Eleftheria, Zhan, Xiaowei, Zhang, Weihua, Zhao, Jing Hua, Zhao, Wei, Zheng, He, Zhou, Wei, Zillikens, M. Carola, Rivadeneira, Fernando, Borecki, Ingrid B., Pospisilik, J. Andrew, Deloukas, Panos, Frayling, Timothy M., Lettre, Guillaume, Mohlke, Karen L., Rotter, Jerome I., Kutalik, Zoltán, Hirschhorn, Joel N., Cupples, L. Adrienne, Loos, Ruth J. F., North, Kari E., and Lindgren, Cecilia M.

Published
2019
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46. The pivotal role of the X-chromosome in the genetic architecture of the human brain

Author

Jiang, Zhiwen, primary, Sullivan, Patrick F, additional, Li, Tengfei, additional, Zhao, Bingxin, additional, Wang, Xifeng, additional, Luo, Tianyou, additional, Huang, Shuai, additional, Guan, Peter Y, additional, Chen, Jie, additional, Yang, Yue, additional, Stein, Jason L, additional, Li, Yun, additional, Liu, Dajiang, additional, Sun, Lei, additional, and Zhu, Hongtu, additional

Published
2023
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50. Experimental study of the direct shear characteristics of cement grout under constant normal loading and stiffness boundary conditions.

Author

Liu, Dajiang, Jing, Hongwen, Meng, Bo, Gao, Yuan, and Yu, Zixuan

Subjects
*GROUT (Mortar), *LIFE cycles (Biology), *MINING engineering, *ACOUSTIC emission, *SHEAR strength, *GROUTING, *CEMENT composites
Abstract

Grouting cable is one of the most critical technologies in mine support. However, there are few studies on the effect of post-peak residual strength of cement grout under direct shear testing, which is of great significance for evaluating the bearing capacity of grouting cables in the whole life cycle. Hence, in this study, we carried out a systematic direct shear test to reveal the shear characteristics of cement grout under both constant normal loading (CNL) and constant normal stiffness (CNS) boundary conditions. The test results show that, under the same initial normal stress, the peak shear strength of the cement grout obtained under the CNS boundary conditions is greater than that under the CNL boundary conditions. The high initial normal force reduces the effect of boundary conditions on the post-peak mechanical shear properties of the cement specimens. Moreover, under CNS boundary conditions, the acoustic emission activity of the sample has an obvious signal before the peak shear strength, accompanied by multiple large ringing counts, indicating that the internal damage of the cement grout starts before the peak intensity. This work enhances the understanding of cement grouting fracture characteristics in mining support engineering. [ABSTRACT FROM AUTHOR]

Published
2024
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