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1. Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning

Author

Yuan, Hongyi, Liu, Suqi, Cho, Kelly, Liao, Katherine, Pereira, Alexandre, and Cai, Tianxi

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Statistics - Applications
Abstract

We introduce GENomic Encoding REpresentation with Language Model (GENEREL), a framework designed to bridge genetic and biomedical knowledge bases. What sets GENEREL apart is its ability to fine-tune language models to infuse biological knowledge behind clinical concepts such as diseases and medications. This fine-tuning enables the model to capture complex biomedical relationships more effectively, enriching the understanding of how genomic data connects to clinical outcomes. By constructing a unified embedding space for biomedical concepts and a wide range of common SNPs from sources such as patient-level data, biomedical knowledge graphs, and GWAS summaries, GENEREL aligns the embeddings of SNPs and clinical concepts through multi-task contrastive learning. This allows the model to adapt to diverse natural language representations of biomedical concepts while bypassing the limitations of traditional code mapping systems across different data sources. Our experiments demonstrate GENEREL's ability to effectively capture the nuanced relationships between SNPs and clinical concepts. GENEREL also emerges to discern the degree of relatedness, potentially allowing for a more refined identification of concepts. This pioneering approach in constructing a unified embedding system for both SNPs and biomedical concepts enhances the potential for data integration and discovery in biomedical research., Comment: 15 pages, 2 figures, 5 tables

Published
2024

2. Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population

Author

Chandrashekar, Mayanka, Goethert, Ian, Haque, Md Inzamam Ul, McMahon, Benjamin, Dhaubhadel, Sayera, Knight, Kathryn, Erdos, Joseph, Reagan, Donna, Taylor, Caroline, Kuzmak, Peter, Gaziano, John Michael, McAllister, Eileen, Costa, Lauren, Ho, Yuk-Lam, Cho, Kelly, Tamang, Suzanne, Fodeh-Jarad, Samah, Ovchinnikova, Olga S., Justice, Amy C., Hinkle, Jacob, and Danciu, Ioana

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. Materials and Methods: We used a DenseNet121 model pretrained MIMIC-CXR dataset for deep learning-based multilabel classification using ground truth labels from radiology reports extracted using the CheXpert and CheXbert Labeler. We compared the performance of the 14 chest X-ray labels on the MIMIC-CXR and Veterans Healthcare Administration chest X-ray dataset (VA-CXR). The VA-CXR dataset comprises over 259k chest X-ray images spanning between the years 2010 and 2022. Results: The validation of ground truth and the assessment of multi-label classification performance across various NLP extraction tools revealed that the VA-CXR dataset exhibited lower disagreement rates than the MIMIC-CXR datasets. Additionally, there were notable differences in AUC scores between models utilizing CheXpert and CheXbert. When evaluating multi-label classification performance across different datasets, minimal domain shift was observed in unseen datasets, except for the label "Enlarged Cardiomediastinum." The study year's subgroup analyses exhibited the most significant variations in multi-label classification model performance. These findings underscore the importance of considering domain shifts in chest X-ray classification tasks, particularly concerning study years. Conclusion: Our study reveals the significant impact of domain shift and demographic factors on chest X-ray classification, emphasizing the need for improved transfer learning and equitable model development. Addressing these challenges is crucial for advancing medical imaging and enhancing patient care.

Published
2024

3. Quantifying the Impact of Biobanks and Cohort Studies

Author

Dorantes-Gilardi, Rodrigo, Ivey, Kerry, Costa, Lauren, Matty, Rachael, Cho, Kelly, Gaziano, John Michael, and Barabási, Albert-László

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Biobanks advance biomedical and clinical research by collecting and offering data and biological samples for numerous studies. However, the impact of these repositories varies greatly due to differences in their purpose, scope, governance, and data collected. Here, we computationally identified 2,663 biobanks and their textual mentions in 228,761 scientific articles, 16,210 grants, 15,469 patents, 1,769 clinical trials, and 9,468 public policy documents, helping characterize the academic communities that utilize and support them. We found a strong concentration of biobank-related research on a few diseases, where 20\% of publications focus on obesity, Alzheimer's disease, breast cancer, and diabetes. Moreover, collaboration, rather than citation count, shapes the community's recognition of a biobank. We show that, on average, 41.1\% of articles miss to reference any of the biobank's reference papers and 59.6\% include a biobank member as a co-author. Using a generalized linear model, we identified the key factors that contribute to the impact of a biobank, finding that an impactful biobank tends to be more open to external researchers, and that quality data -- especially linked medical records -- as opposed to large data, correlates with a higher impact in science, innovation, and disease. The collected data and findings are accessible through an open-access web application intended to inform strategies to expand access and maximize the value of these valuable resources., Comment: 14 pages, 5 figures

Published
2024

4. Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program

Author

Verma, Anurag, Huffman, Jennifer E, Rodriguez, Alex, Conery, Mitchell, Liu, Molei, Ho, Yuk-Lam, Kim, Youngdae, Heise, David A, Guare, Lindsay, Panickan, Vidul Ayakulangara, Garcon, Helene, Linares, Franciel, Costa, Lauren, Goethert, Ian, Tipton, Ryan, Honerlaw, Jacqueline, Davies, Laura, Whitbourne, Stacey, Cohen, Jeremy, Posner, Daniel C, Sangar, Rahul, Murray, Michael, Wang, Xuan, Dochtermann, Daniel R, Devineni, Poornima, Shi, Yunling, Nandi, Tarak Nath, Assimes, Themistocles L, Brunette, Charles A, Carroll, Robert J, Clifford, Royce, Duvall, Scott, Gelernter, Joel, Hung, Adriana, Iyengar, Sudha K, Joseph, Jacob, Kember, Rachel, Kranzler, Henry, Kripke, Colleen M, Levey, Daniel, Luoh, Shiuh-Wen, Merritt, Victoria C, Overstreet, Cassie, Deak, Joseph D, Grant, Struan FA, Polimanti, Renato, Roussos, Panos, Shakt, Gabrielle, Sun, Yan V, Tsao, Noah, Venkatesh, Sanan, Voloudakis, Georgios, Justice, Amy, Begoli, Edmon, Ramoni, Rachel, Tourassi, Georgia, Pyarajan, Saiju, Tsao, Philip, O'Donnell, Christopher J, Muralidhar, Sumitra, Moser, Jennifer, Casas, Juan P, Bick, Alexander G, Zhou, Wei, Cai, Tianxi, Voight, Benjamin F, Cho, Kelly, Gaziano, J Michael, Madduri, Ravi K, Damrauer, Scott, and Liao, Katherine P

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, 2.1 Biological and endogenous factors, Mental health, Humans, Male, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Longitudinal Studies, Polymorphism, Single Nucleotide, Quantitative Trait Loci, United States, United States Department of Veterans Affairs, Veterans, Female, General Science & Technology
Abstract

One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.

Published
2024

5. VISION: Toward a Standardized Process for Radiology Image Management at the National Level

Author

Knight, Kathryn, Danciu, Ioana, Ovchinnikova, Olga, Hinkle, Jacob, Shekar, Mayanka Chandra, Mukherjee, Debangshu, McAllister, Eileen, Rizy, Caitlin, Cho, Kelly, Justice, Amy C., Erdos, Joseph, Kuzmak, Peter, Costa, Lauren, Ho, Yuk-Lam, Madipadga, Reddy, Tamang, Suzanne, and Goethert, Ian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The compilation and analysis of radiological images poses numerous challenges for researchers. The sheer volume of data as well as the computational needs of algorithms capable of operating on images are extensive. Additionally, the assembly of these images alone is difficult, as these exams may differ widely in terms of clinical context, structured annotation available for model training, modality, and patient identifiers. In this paper, we describe our experiences and challenges in establishing a trusted collection of radiology images linked to the United States Department of Veterans Affairs (VA) electronic health record database. We also discuss implications in making this repository research-ready for medical investigators. Key insights include uncovering the specific procedures required for transferring images from a clinical to a research-ready environment, as well as roadblocks and bottlenecks in this process that may hinder future efforts at automation.

Published
2024

6. Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy.

Author

Wang, Xuan, Liu, Molei, Nogues, Isabelle-Emmanuella, Chen, Tony, Xiong, Xin, Bonzel, Clara-Lea, Zhang, Harrison, Hong, Chuan, Xia, Yin, Dahal, Kumar, Costa, Lauren, Cui, Jing, Gaziano, J, Kim, Seoyoung, Ho, Yuk-Lam, Cho, Kelly, Cai, Tianxi, and Liao, Katherine

Subjects
Humans, Genome-Wide Association Study, Diabetes Mellitus, Type 2, Phenotype, Genetic Association Studies, Receptors, Interleukin-6, Polymorphism, Single Nucleotide
Abstract

The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10-3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10-131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype-phenotype screens in diverse populations.

Published
2024

7. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits

Author

Keaton, Jacob M., Kamali, Zoha, Xie, Tian, Vaez, Ahmad, Williams, Ariel, Goleva, Slavina B., Ani, Alireza, Evangelou, Evangelos, Hellwege, Jacklyn N., Yengo, Loic, Young, William J., Traylor, Matthew, Giri, Ayush, Zheng, Zhili, Zeng, Jian, Chasman, Daniel I., Morris, Andrew P., Caulfield, Mark J., Hwang, Shih-Jen, Kooner, Jaspal S., Conen, David, Attia, John R., Morrison, Alanna C., Loos, Ruth J. F., Kristiansson, Kati, Schmidt, Reinhold, Hicks, Andrew A., Pramstaller, Peter P., Nelson, Christopher P., Samani, Nilesh J., Risch, Lorenz, Gyllensten, Ulf, Melander, Olle, Riese, Harriette, Wilson, James F., Campbell, Harry, Rich, Stephen S., Psaty, Bruce M., Lu, Yingchang, Rotter, Jerome I., Guo, Xiuqing, Rice, Kenneth M., Vollenweider, Peter, Sundström, Johan, Langenberg, Claudia, Tobin, Martin D., Giedraitis, Vilmantas, Luan, Jian’an, Tuomilehto, Jaakko, Kutalik, Zoltan, Ripatti, Samuli, Salomaa, Veikko, Girotto, Giorgia, Trompet, Stella, Jukema, J. Wouter, van der Harst, Pim, Ridker, Paul M., Giulianini, Franco, Vitart, Veronique, Goel, Anuj, Watkins, Hugh, Harris, Sarah E., Deary, Ian J., van der Most, Peter J., Oldehinkel, Albertine J., Keavney, Bernard D., Hayward, Caroline, Campbell, Archie, Boehnke, Michael, Scott, Laura J., Boutin, Thibaud, Mamasoula, Chrysovalanto, Järvelin, Marjo-Riitta, Peters, Annette, Gieger, Christian, Lakatta, Edward G., Cucca, Francesco, Hui, Jennie, Knekt, Paul, Enroth, Stefan, De Borst, Martin H., Polašek, Ozren, Concas, Maria Pina, Catamo, Eulalia, Cocca, Massimiliano, Li-Gao, Ruifang, Hofer, Edith, Schmidt, Helena, Spedicati, Beatrice, Waldenberger, Melanie, Strachan, David P., Laan, Maris, Teumer, Alexander, Dörr, Marcus, Gudnason, Vilmundur, Cook, James P., Ruggiero, Daniela, Kolcic, Ivana, Boerwinkle, Eric, Traglia, Michela, Lehtimäki, Terho, Raitakari, Olli T., Johnson, Andrew D., Newton-Cheh, Christopher, Brown, Morris J., Dominiczak, Anna F., Sever, Peter J., Poulter, Neil, Chambers, John C., Elosua, Roberto, Siscovick, David, Esko, Tõnu, Metspalu, Andres, Strawbridge, Rona J., Laakso, Markku, Hamsten, Anders, Hottenga, Jouke-Jan, de Geus, Eco, Morris, Andrew D., Palmer, Colin N. A., Nolte, Ilja M., Milaneschi, Yuri, Marten, Jonathan, Wright, Alan, Zeggini, Eleftheria, Howson, Joanna M. M., O’Donnell, Christopher J., Spector, Tim, Nalls, Mike A., Simonsick, Eleanor M., Liu, Yongmei, van Duijn, Cornelia M., Butterworth, Adam S., Danesh, John N., Menni, Cristina, Wareham, Nicholas J., Khaw, Kay-Tee, Sun, Yan V., Wilson, Peter W. F., Cho, Kelly, Visscher, Peter M., Denny, Joshua C., Levy, Daniel, Edwards, Todd L., Munroe, Patricia B., Snieder, Harold, and Warren, Helen R.

Published
2024
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8. High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning

Author

Dhaubhadel, Sayera, Ganguly, Kumkum, Ribeiro, Ruy M, Cohn, Judith D, Hyman, James M, Hengartner, Nicolas W, Kolade, Beauty, Singley, Anna, Bhattacharya, Tanmoy, Finley, Patrick, Levin, Drew, Thelen, Haedi, Cho, Kelly, Costa, Lauren, Ho, Yuk-Lam, Justice, Amy C, Pestian, John, Santel, Daniel, Zamora-Resendiz, Rafael, Crivelli, Silvia, Tamang, Suzanne, Martins, Susana, Trafton, Jodie, Oslin, David W, Beckham, Jean C, Kimbrel, Nathan A, and McMahon, Benjamin H

Subjects
Clinical and Health Psychology, Health Sciences, Psychology, Behavioral and Social Science, Suicide, Mental Health, Mental health, Good Health and Well Being, Humans, Veterans, Retrospective Studies, Carcinoma, Renal Cell, Cross-Sectional Studies, Prospective Studies, Suicide, Attempted, Kidney Neoplasms, Machine Learning, Million Veteran Program Suicide Exemplar Work Group
Abstract

We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of [Formula: see text] 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics.

Published
2024

9. Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants.

Author

Wang, Anqi, Shen, Jiayi, Rodriguez, Alex, Saunders, Edward, Chen, Fei, Janivara, Rohini, Darst, Burcu, Sheng, Xin, Xu, Yili, Chou, Alisha, Benlloch, Sara, Dadaev, Tokhir, Brook, Mark, Plym, Anna, Sahimi, Ali, Hoffman, Thomas, Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Laisk, Triin, Figuerêdo, Jéssica, Muir, Kenneth, Ito, Shuji, Liu, Xiaoxi, Uchio, Yuji, Kubo, Michiaki, Kamatani, Yoichiro, Lophatananon, Artitaya, Wan, Peggy, Andrews, Caroline, Lori, Adriana, Choudhury, Parichoy, Schleutker, Johanna, Tammela, Teuvo, Sipeky, Csilla, Auvinen, Anssi, Giles, Graham, Southey, Melissa, MacInnis, Robert, Cybulski, Cezary, Wokolorczyk, Dominika, Lubinski, Jan, Rentsch, Christopher, Cho, Kelly, Mcmahon, Benjamin, Neal, David, Donovan, Jenny, Hamdy, Freddie, Martin, Richard, Nordestgaard, Borge, Nielsen, Sune, Weischer, Maren, Bojesen, Stig, Røder, Andreas, Stroomberg, Hein, Batra, Jyotsna, Chambers, Suzanne, Horvath, Lisa, Clements, Judith, Tilly, Wayne, Risbridger, Gail, Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordstrom, Tobias, Pashayan, Nora, Dunning, Alison, Ghoussaini, Maya, Travis, Ruth, Key, Tim, Riboli, Elio, Park, Jong, Sellers, Thomas, Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie, Cook, Michael, Mucci, Lorelei, Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David, Penney, Kathryn, Turman, Constance, Tangen, Catherine, Goodman, Phyllis, Thompson, Ian, Hamilton, Robert, Fleshner, Neil, Finelli, Antonio, Parent, Marie-Élise, Stanford, Janet, Ostrander, Elaine, Koutros, Stella, Beane Freeman, Laura, Stampfer, Meir, Wolk, Alicja, and Håkansson, Niclas

Subjects
Humans, Male, Black People, Genetic Predisposition to Disease, Genome-Wide Association Study, Hispanic or Latino, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Risk Factors, White People, Asian People
Abstract

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.

Published
2023

10. LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records

Author

Wen, Jun, Hou, Jue, Bonzel, Clara-Lea, Zhao, Yihan, Castro, Victor M., Gainer, Vivian S., Weisenfeld, Dana, Cai, Tianrun, Ho, Yuk-Lam, Panickan, Vidul A., Costa, Lauren, Hong, Chuan, Gaziano, J. Michael, Liao, Katherine P., Lu, Junwei, Cho, Kelly, and Cai, Tianxi

Subjects
Computer Science - Artificial Intelligence
Abstract

Electronic health record (EHR) data are increasingly used to support real-world evidence (RWE) studies. Yet its ability to generate reliable RWE is limited by the lack of readily available precise information on the timing of clinical events such as the onset time of heart failure. We propose a LAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data. By leveraging the pre-trained semantic embedding vectors from large-scale EHR data as prior knowledge, LATTE selects predictive EHR features in a concept re-weighting module by mining their relationship to the target event and compresses their information into longitudinal visit embeddings through a visit attention learning network. LATTE employs a recurrent neural network to capture the sequential dependency between the target event and visit embeddings before/after it. To improve label efficiency, LATTE constructs highly informative longitudinal silver-standard labels from large-scale unlabeled patients to perform unsupervised pre-training and semi-supervised joint training. Finally, LATTE enhances cross-site portability via contrastive representation learning. LATTE is evaluated on three analyses: the onset of type-2 diabetes, heart failure, and the onset and relapses of multiple sclerosis. We use various evaluation metrics present in the literature including the $ABC_{gain}$, the proportion of reduction in the area between the observed event indicator and the predicted cumulative incidences in reference to the prediction per incident prevalence. LATTE consistently achieves substantial improvement over benchmark methods such as SAMGEP and RETAIN in all settings., Comment: ERHs data

Published
2023

11. Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium

Author

Moal, Bertrand, Orieux, Arthur, Ferté, Thomas, Neuraz, Antoine, Brat, Gabriel A, Avillach, Paul, Bonzel, Clara-Lea, Cai, Tianxi, Cho, Kelly, Cossin, Sébastien, Griffier, Romain, Hanauer, David A, Haverkamp, Christian, Ho, Yuk-Lam, Hong, Chuan, Hutch, Meghan R, Klann, Jeffrey G, Le, Trang T, Loh, Ne Hooi Will, Luo, Yuan, Makoudjou, Adeline, Morris, Michele, Mowery, Danielle L, Olson, Karen L, Patel, Lav P, Samayamuthu, Malarkodi J, Vidorreta, Fernando J Sanz, Schriver, Emily R, Schubert, Petra, Verdy, Guillaume, Visweswaran, Shyam, Wang, Xuan, Weber, Griffin M, Xia, Zongqi, Yuan, William, Zhang, Harrison G, Zöller, Daniela, Kohane, Isaac S, EHR, The Consortium for Clinical Characterization of COVID-19 by, Boyer, Alexandre, and Jouhet, Vianney

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Health Sciences, Infectious Diseases, Acute Respiratory Distress Syndrome, Rare Diseases, Lung, Clinical Research, Emerging Infectious Diseases, Prevention, Patient Safety, Aetiology, 2.4 Surveillance and distribution, Good Health and Well Being, Humans, Young Adult, Aged, Adolescent, Adult, Middle Aged, COVID-19, SARS-CoV-2, Cohort Studies, Retrospective Studies, Electronic Health Records, Respiratory Distress Syndrome, Obesity, Consortium for Clinical Characterization of COVID-19 by EHR, General Science & Technology
Abstract

PurposeIn young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population.MethodsA retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS.ResultsAmong the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%).ConclusionTrough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Published
2023

12. Mild-to-Moderate Kidney Dysfunction and Cardiovascular Disease: Observational and Mendelian Randomization Analyses.

Author

Gaziano, Liam, Sun, Luanluan, Arnold, Matthew, Bell, Steven, Cho, Kelly, Kaptoge, Stephen, Song, Rebecca, Burgess, Stephen, Posner, Daniel, Mosconi, Katja, Robinson-Cohen, Cassianne, Mason, Amy, Bolton, Thomas, Tao, Ran, Allara, Elias, Schubert, Petra, Chen, Lingyan, Staley, James, Staplin, Natalie, Altay, Servet, Amiano, Pilar, Arndt, Volker, Ärnlöv, Johan, Barr, Elizabeth, Björkelund, Cecilia, Boer, Jolanda, Brenner, Hermann, Casiglia, Edoardo, Chiodini, Paolo, Cooper, Jackie, Coresh, Josef, Cushman, Mary, Dankner, Rachel, Davidson, Karina, de Jongh, Renate, Donfrancesco, Chiara, Engström, Gunnar, Freisling, Heinz, de la Cámara, Agustín, Gudnason, Vilmundur, Hankey, Graeme, Hansson, Per-Olof, Heath, Alicia, Hoorn, Ewout, Imano, Hironori, Jassal, Simerjot, Kaaks, Rudolf, Katzke, Verena, Kauhanen, Jussi, Kiechl, Stefan, Koenig, Wolfgang, Kronmal, Richard, Kyrø, Cecilie, Lawlor, Deborah, Ljungberg, Börje, MacDonald, Conor, Masala, Giovanna, Meisinger, Christa, Melander, Olle, Moreno Iribas, Conchi, Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Børge, Onland-Moret, Charlotte, Palmieri, Luigi, Petrova, Dafina, Garcia, Jose, Rosengren, Annika, Sacerdote, Carlotta, Sakurai, Masaru, Santiuste, Carmen, Schulze, Matthias, Sieri, Sabina, Sundström, Johan, Tikhonoff, Valérie, Tjønneland, Anne, Tong, Tammy, Tumino, Rosario, Tzoulaki, Ioanna, van der Schouw, Yvonne, Monique Verschuren, W, Völzke, Henry, Wallace, Robert, Wannamethee, S, Weiderpass, Elisabete, Willeit, Peter, Woodward, Mark, Yamagishi, Kazumasa, Zamora-Ros, Raul, Akwo, Elvis, Pyarajan, Saiju, Gagnon, David, Tsao, Philip, Muralidhar, Sumitra, Edwards, Todd, Damrauer, Scott, Joseph, Jacob, Pennells, Lisa, Wilson, Peter, and Harrison, Seamus

Subjects
cardiovascular diseases, coronary disease, kidney diseases, stroke, Humans, Mendelian Randomization Analysis, Prospective Studies, Cardiovascular Diseases, Coronary Disease, Risk Factors, Diabetes Mellitus, Stroke, Kidney
Abstract

BACKGROUND: End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke. METHODS: Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition-Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.8 million person-years of follow-up. Using a genetic risk score of 218 variants for estimated glomerular filtration rate (eGFR), we conducted Mendelian randomization analyses involving 413 718 participants (25 917 CHD and 8622 strokes) in EPIC-CVD, Million Veteran Program, and UK Biobank. RESULTS: There were U-shaped observational associations of creatinine-based eGFR with CHD and stroke, with higher risk in participants with eGFR values 105 mL·min-1·1.73 m-2, compared with those with eGFR between 60 and 105 mL·min-1·1.73 m-2. Mendelian randomization analyses for CHD showed an association among participants with eGFR 105 mL·min-1·1.73 m-2. Results were not materially different after adjustment for factors associated with the eGFR genetic risk score, such as lipoprotein(a), triglycerides, hemoglobin A1c, and blood pressure. Mendelian randomization results for stroke were nonsignificant but broadly similar to those for CHD. CONCLUSIONS: In people without manifest cardiovascular disease or diabetes, mild-to-moderate kidney dysfunction is causally related to risk of CHD, highlighting the potential value of preventive approaches that preserve and modulate kidney function.

Published
2022

13. A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality

Author

Verma, Anurag, Minnier, Jessica, Wan, Emily S, Huffman, Jennifer E, Gao, Lina, Joseph, Jacob, Ho, Yuk-Lam, Wu, Wen-Chih, Cho, Kelly, Gorman, Bryan R, Rajeevan, Nallakkandi, Pyarajan, Saiju, Garcon, Helene, Meigs, James B, Sun, Yan V, Reaven, Peter D, McGeary, John E, Suzuki, Ayako, Gelernter, Joel, Lynch, Julie A, Petersen, Jeffrey M, Zekavat, Seyedeh Maryam, Natarajan, Pradeep, Dalal, Sharvari, Jhala, Darshana N, Arjomandi, Mehrdad, Gatsby, Elise, Lynch, Kristine E, Bonomo, Robert A, Freiberg, Matthew, Pathak, Gita A, Zhou, Jin J, Donskey, Curtis J, Madduri, Ravi K, Wells, Quinn S, Huang, Rose DL, Polimanti, Renato, Chang, Kyong-Mi, Liao, Katherine P, Tsao, Philip S, Wilson, Peter WF, Hung, Adriana M, O’Donnell, Christopher J, Gaziano, John M, Hauger, Richard L, Iyengar, Sudha K, Luoh, Shiuh-Wen, and Initiative, the Million Veteran Program COVID-19 Science

Subjects
Lung, Rare Diseases, Genetics, Autoimmune Disease, Aetiology, 2.1 Biological and endogenous factors, Respiratory, Good Health and Well Being, Humans, COVID-19, Mucin-5B, Polymorphism, Genetic, Idiopathic Pulmonary Fibrosis, Genotype, Hospitalization, Genetic Predisposition to Disease, coronavirus disease 2019, severe acute respiratory syndrome coronavirus 2, idiopathic pulmonary fibrosis, electronic health records, genetic association, Million Veteran Program COVID-19 Science Initiative, Medical and Health Sciences, Respiratory System
Abstract

Rationale: A common MUC5B gene polymorphism, rs35705950-T, is associated with idiopathic pulmonary fibrosis (IPF), but its role in severe acute respiratory syndrome coronavirus 2 infection and disease severity is unclear. Objectives: To assess whether rs35705950-T confers differential risk for clinical outcomes associated with coronavirus disease (COVID-19) infection among participants in the Million Veteran Program (MVP). Methods: The MUC5B rs35705950-T allele was directly genotyped among MVP participants; clinical events and comorbidities were extracted from the electronic health records. Associations between the incidence or severity of COVID-19 and rs35705950-T were analyzed within each ancestry group in the MVP followed by transancestry meta-analysis. Replication and joint meta-analysis were conducted using summary statistics from the COVID-19 Host Genetics Initiative (HGI). Sensitivity analyses with adjustment for additional covariates (body mass index, Charlson comorbidity index, smoking, asbestosis, rheumatoid arthritis with interstitial lung disease, and IPF) and associations with post-COVID-19 pneumonia were performed in MVP subjects. Measurements and Main Results: The rs35705950-T allele was associated with fewer COVID-19 hospitalizations in transancestry meta-analyses within the MVP (Ncases = 4,325; Ncontrols = 507,640; OR = 0.89 [0.82-0.97]; P = 6.86 × 10-3) and joint meta-analyses with the HGI (Ncases = 13,320; Ncontrols = 1,508,841; OR, 0.90 [0.86-0.95]; P = 8.99 × 10-5). The rs35705950-T allele was not associated with reduced COVID-19 positivity in transancestry meta-analysis within the MVP (Ncases = 19,168/Ncontrols = 492,854; OR, 0.98 [0.95-1.01]; P = 0.06) but was nominally significant (P

Published
2022

15. Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Author

Han, Larry, Hou, Jue, Cho, Kelly, Duan, Rui, and Cai, Tianxi

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Applications
Abstract

Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level heterogeneity in the distribution of covariates through density ratio weighting. To safely incorporate source sites and avoid negative transfer, we introduce an adaptive weighting procedure via a penalized regression, which achieves both consistency and optimal efficiency. Our strategy is communication-efficient and privacy-preserving, allowing participating sites to share summary statistics only once with other sites. We conduct both theoretical and numerical evaluations of FACE and apply it to conduct a comparative effectiveness study of BNT162b2 (Pfizer) and mRNA-1273 (Moderna) vaccines on COVID-19 outcomes in U.S. veterans using electronic health records from five VA regional sites. We show that compared to traditional methods, FACE meaningfully increases the precision of treatment effect estimates, with reductions in standard errors ranging from $26\%$ to $67\%$., Comment: 59 pages

Published
2021

16. A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation

Author

Vujkovic, Marijana, Ramdas, Shweta, Lorenz, Kim M, Guo, Xiuqing, Darlay, Rebecca, Cordell, Heather J, He, Jing, Gindin, Yevgeniy, Chung, Chuhan, Myers, Robert P, Schneider, Carolin V, Park, Joseph, Lee, Kyung Min, Serper, Marina, Carr, Rotonya M, Kaplan, David E, Haas, Mary E, MacLean, Matthew T, Witschey, Walter R, Zhu, Xiang, Tcheandjieu, Catherine, Kember, Rachel L, Kranzler, Henry R, Verma, Anurag, Giri, Ayush, Klarin, Derek M, Sun, Yan V, Huang, Jie, Huffman, Jennifer E, Creasy, Kate Townsend, Hand, Nicholas J, Liu, Ching-Ti, Long, Michelle T, Yao, Jie, Budoff, Matthew, Tan, Jingyi, Li, Xiaohui, Lin, Henry J, Chen, Yii-Der Ida, Taylor, Kent D, Chang, Ruey-Kang, Krauss, Ronald M, Vilarinho, Silvia, Brancale, Joseph, Nielsen, Jonas B, Locke, Adam E, Jones, Marcus B, Verweij, Niek, Baras, Aris, Reddy, K Rajender, Neuschwander-Tetri, Brent A, Schwimmer, Jeffrey B, Sanyal, Arun J, Chalasani, Naga, Ryan, Kathleen A, Mitchell, Braxton D, Gill, Dipender, Wells, Andrew D, Manduchi, Elisabetta, Saiman, Yedidya, Mahmud, Nadim, Miller, Donald R, Reaven, Peter D, Phillips, Lawrence S, Muralidhar, Sumitra, DuVall, Scott L, Lee, Jennifer S, Assimes, Themistocles L, Pyarajan, Saiju, Cho, Kelly, Edwards, Todd L, Damrauer, Scott M, Wilson, Peter W, Gaziano, J Michael, O’Donnell, Christopher J, Khera, Amit V, Grant, Struan FA, Brown, Christopher D, Tsao, Philip S, Saleheen, Danish, Lotta, Luca A, Bastarache, Lisa, Anstee, Quentin M, Daly, Ann K, Meigs, James B, Rotter, Jerome I, Lynch, Julie A, Rader, Daniel J, Voight, Benjamin F, and Chang, Kyong-Mi

Subjects
Genetics, Liver Disease, Human Genome, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Aetiology, 2.1 Biological and endogenous factors, Alanine Transaminase, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Genome-Wide Association Study, Humans, Intracellular Signaling Peptides and Proteins, Lipase, Membrane Proteins, Non-alcoholic Fatty Liver Disease, Polymorphism, Single Nucleotide, Protein Serine-Threonine Kinases, Regeneron Genetics Center, Geisinger-Regeneron DiscovEHR Collaboration, EPoS Consortium, VA Million Veteran Program, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic elevation of alanine aminotransferase (cALT) levels without other liver diseases, we performed a multiancestry genome-wide association study (GWAS) in the Million Veteran Program (MVP) including 90,408 cALT cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance, including 25 without prior NAFLD or alanine aminotransferase associations, with one additional locus identified in European American-only and two in African American-only analyses (P

Published
2022

17. Semi-supervised Approach to Event Time Annotation Using Longitudinal Electronic Health Records

Author

Liang, Liang, Hou, Jue, Uno, Hajime, Cho, Kelly, Ma, Yanyuan, and Cai, Tianxi

Subjects
Statistics - Methodology
Abstract

Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to develop models for personalized prediction of risk or treatment response. Efficiently deriving prediction models using real world data, however, faces practical and methodological challenges. Precise information on important clinical outcomes such as time to cancer progression are not readily available in these databases. The true clinical event times typically cannot be approximated well based on simple extracts of billing or procedure codes. Whereas, annotating event times manually is time and resource prohibitive. In this paper, we propose a two-step semi-supervised multi-modal automated time annotation (MATA) method leveraging multi-dimensional longitudinal EHR encounter records. In step I, we employ a functional principal component analysis approach to estimate the underlying intensity functions based on observed point processes from the unlabeled patients. In step II, we fit a penalized proportional odds model to the event time outcomes with features derived in step I in the labeled data where the non-parametric baseline function is approximated using B-splines. Under regularity conditions, the resulting estimator of the feature effect vector is shown as root-$n$ consistent. We demonstrate the superiority of our approach relative to existing approaches through simulations and a real data example on annotating lung cancer recurrence in an EHR cohort of lung cancer patients from Veteran Health Administration.

Published
2021

19. Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure

Author

Rasooly, Danielle, Peloso, Gina M., Pereira, Alexandre C., Dashti, Hesam, Giambartolomei, Claudia, Wheeler, Eleanor, Aung, Nay, Ferolito, Brian R., Pietzner, Maik, Farber-Eger, Eric H., Wells, Quinn Stanton, Kosik, Nicole M., Gaziano, Liam, Posner, Daniel C., Bento, A. Patrícia, Hui, Qin, Liu, Chang, Aragam, Krishna, Wang, Zeyuan, Charest, Brian, Huffman, Jennifer E., Wilson, Peter W. F., Phillips, Lawrence S., Whittaker, John, Munroe, Patricia B., Petersen, Steffen E., Cho, Kelly, Leach, Andrew R., Magariños, María Paula, Gaziano, John Michael, Langenberg, Claudia, Sun, Yan V., Joseph, Jacob, and Casas, Juan P.

Published
2023
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20. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

Author

Patel, Aniruddh P., Wang, Minxian, Ruan, Yunfeng, Koyama, Satoshi, Clarke, Shoa L., Yang, Xiong, Tcheandjieu, Catherine, Agrawal, Saaket, Fahed, Akl C., Ellinor, Patrick T., Tsao, Philip S., Sun, Yan V., Cho, Kelly, Wilson, Peter W. F., Assimes, Themistocles L., van Heel, David A., Butterworth, Adam S., Aragam, Krishna G., Natarajan, Pradeep, and Khera, Amit V.

Published
2023
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21. Potential pitfalls in the use of real-world data for studying long COVID

Author

Zhang, Harrison G., Honerlaw, Jacqueline P., Maripuri, Monika, Samayamuthu, Malarkodi Jebathilagam, Beaulieu-Jones, Brendin R., Baig, Huma S., L’Yi, Sehi, Ho, Yuk-Lam, Morris, Michele, Panickan, Vidul Ayakulangara, Wang, Xuan, Weber, Griffin M., Liao, Katherine P., Visweswaran, Shyam, Tan, Bryce W. Q., Yuan, William, Gehlenborg, Nils, Muralidhar, Sumitra, Ramoni, Rachel B., Kohane, Isaac S., Xia, Zongqi, Cho, Kelly, Cai, Tianxi, and Brat, Gabriel A.

Published
2023
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23. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Author

Weber, Griffin M, Hong, Chuan, Xia, Zongqi, Palmer, Nathan P, Avillach, Paul, L’Yi, Sehi, Keller, Mark S, Murphy, Shawn N, Gutiérrez-Sacristán, Alba, Bonzel, Clara-Lea, Serret-Larmande, Arnaud, Neuraz, Antoine, Omenn, Gilbert S, Visweswaran, Shyam, Klann, Jeffrey G, South, Andrew M, Loh, Ne Hooi Will, Cannataro, Mario, Beaulieu-Jones, Brett K, Bellazzi, Riccardo, Agapito, Giuseppe, Alessiani, Mario, Aronow, Bruce J, Bell, Douglas S, Benoit, Vincent, Bourgeois, Florence T, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, DuVall, Scott L, Barrio, Noelia García, Hanauer, David A, Ho, Yuk-Lam, Holmes, John H, Issitt, Richard W, Liu, Molei, Luo, Yuan, Lynch, Kristine E, Maidlow, Sarah E, Malovini, Alberto, Mandl, Kenneth D, Mao, Chengsheng, Matheny, Michael E, Moore, Jason H, Morris, Jeffrey S, Morris, Michele, Mowery, Danielle L, Ngiam, Kee Yuan, Patel, Lav P, Pedrera-Jimenez, Miguel, Ramoni, Rachel B, Schriver, Emily R, Schubert, Petra, Balazote, Pablo Serrano, Spiridou, Anastasia, Tan, Amelia LM, Tan, Byorn WL, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico M, Wang, Xuan, Kohane, Isaac S, Cai, Tianxi, and Brat, Gabriel A

Subjects
Health Services and Systems, Health Sciences, Basic Behavioral and Social Science, Behavioral and Social Science, Good Health and Well Being, Consortium for Clinical Characterization of COVID-19 by EHR, Health services and systems
Abstract

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Published
2022

24. 19-year-old Walmart employee found dead inside walk-in oven

Author

Cho, Kelly Kasulis

Subjects
Walmart Inc., Discount stores, General interest, News, opinion and commentary
Abstract

Byline: Kelly Kasulis Cho Police in eastern Canada are investigating after a 19-year-old woman was found dead inside a large walk-in oven at a Walmart in Halifax, Nova Scotia. The [...]

Published
2024

25. What to know about U.S. military support for Israel after a year of war

Author

Taylor, Adam, Sands, Leo, Cho, Kelly Kasulis, and Suliman, Adela

Subjects
Government regulation, Military assistance, American -- Laws, regulations and rules, Israel-Hamas War, 2023-
Abstract

Byline: Adam Taylor, Leo Sands, Kelly Kasulis Cho, Adela Suliman The United States has provided Israel with billions of dollars' worth of security assistance and weapons over the past year [...]

Published
2024

26. Chinese Americans face discrimination amid U.S-China tensions, survey shows

Author

Cho, Kelly Kasulis

Subjects
Race discrimination -- Surveys, Chinese Americans -- Surveys, General interest, News, opinion and commentary
Abstract

Byline: Kelly Kasulis Cho Roughly two-thirds of Chinese Americans say they experience discrimination regularly, according to a new survey published Wednesday. A study published by the National Opinion Research Center [...]

Published
2024

27. FAA investigates after cabin pressure malfunction injures Delta passengers

Author

Cho, Kelly Kasulis

Subjects
Company legal issue, Airlines -- Investigations, Aircraft accidents -- Investigations, Delta Air Lines Inc. -- Investigations, United States. Federal Aviation Administration -- Powers and duties
Abstract

Byline: Kelly Kasulis Cho The Federal Aviation Administration is investigating after pressurization issues on a Delta Air Lines flight forced the plane to turn around and caused injuries. Delta Flight [...]

Published
2024

28. YouTuber MrBeast, Amazon sued by show contestants for alleged abuses

Author

Cho, Kelly Kasulis

Subjects
Company legal issue, Online information service, Salary, Company personnel management, Beast Games (Television program) -- Cases, Online information services -- Cases -- Human resource management, Reality television programs -- Cases, Online services -- Cases -- Human resource management, Information services -- Cases -- Human resource management, Wages -- Cases, Amazon.com Inc. -- Cases -- Human resource management
Abstract

Byline: Kelly Kasulis Cho MrBeast, a 26-year-old multimillionaire YouTuber who is estimated by Forbes to be the platform's highest earner, is being sued alongside Amazon by contestants of his reality [...]

Published
2024

29. Israel expands war goals on Lebanon border as U.S. tries to calm tensions

Author

Chason, Rachel, Soroka, Lior, Cho, Kelly Kasulis, and Hassan, Jennifer

Subjects
Hezbollah -- Military aspects, Market trend/market analysis, Prime ministers -- Powers and duties, Israel-Arab conflicts -- Forecasts and trends, Generals -- Powers and duties, Israel-Hamas War, 2023-
Abstract

Byline: Rachel Chason, Lior Soroka, Kelly Kasulis Cho, Jennifer Hassan JERUSALEM - Top Israeli officials who met with a senior White House adviser said that military action will be needed [...]

Published
2024

30. China frees U.S. pastor after nearly two decades, State Department says

Author

Cho, Kelly Kasulis

Subjects
Company legal issue, Ministers (Clergy) -- Safety and security measures -- Cases, Fraud -- Cases, United States. Department of State -- Powers and duties
Abstract

Byline: Kelly Kasulis Cho California pastor David Lin has been released from prison in China after nearly two decades and has returned to the United States, the State Department said [...]

Published
2024

31. How Harris and Trump answered foreign policy questions during the debate

Author

Pannett, Rachel, Masih, Niha, and Cho, Kelly Kasulis

Subjects
Campaign debates, Presidential candidates -- Political activity -- Foreign policy, General interest, News, opinion and commentary
Abstract

Byline: Rachel Pannett, Niha Masih, Kelly Kasulis Cho Vice President Kamala Harris accused former president Donald Trump of taking a soft tack with Xi Jinping, the leader of China, and [...]

Published
2024

32. Israeli strike kills 19 in Gaza humanitarian area, health officials say

Author

Ebel, Francesca, Cho, Kelly Kasulis, Harb, Hajar, Hassan, Jennifer, and Balousha, Hazem

Subjects
Israel. Defense Forces -- Powers and duties, Gaza Strip -- Military aspects, Bombing, Aerial -- Casualties, Civilian casualties -- Reports, General interest, News, opinion and commentary
Abstract

Byline: Francesca Ebel, Kelly Kasulis Cho, Hajar Harb, Jennifer Hassan, Hazem Balousha JERUSALEM - An Israeli strike Tuesday on Mawasi, a coastal tent encampment in southern Gaza that Israeli forces [...]

Published
2024

33. What to know about a Kentucky manhunt after 5 people were shot on I-75

Author

Brasch, Ben, Javaid, Maham, Kaur, Anumita, and Cho, Kelly Kasulis

Subjects
Searches and seizures, Criminal suspects -- Investigations, Company legal issue, General interest, News, opinion and commentary
Abstract

Byline: Ben Brasch, Maham Javaid, Anumita Kaur, Kelly Kasulis Cho The search for a man accused of opening fire on Interstate 75 in Kentucky over the weekend, injuring five people, [...]

Published
2024

35. Whole-genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles

Author

Huffman, Jennifer E., Nicholas, Jayna, Hahn, Julie, Heath, Adam S., Raffield, Laura M., Yanek, Lisa R., Brody, Jennifer A., Thibord, Florian, Almasy, Laura, Bartz, Traci M., Bielak, Lawrence F., Bowler, Russell P., Carrasquilla, Germán D., Chasman, Daniel I., Chen, Ming-Huei, Emmert, David B., Ghanbari, Mohsen, Haessler, Jeffrey, Hottenga, Jouke-Jan, Kleber, Marcus E., Le, Ngoc-Quynh, Lee, Jiwon, Lewis, Joshua P., Li-Gao, Ruifang, Luan, Jian'an, Malmberg, Anni, Mangino, Massimo, Marioni, Riccardo E., Martinez-Perez, Angel, Pankratz, Nathan, Polasek, Ozren, Richmond, Anne, Rodriguez, Benjamin A. T., Rotter, Jerome I., Steri, Maristella, Suchon, Pierre, Trompet, Stella, Weiss, Stefan, Zare, Marjan, Auer, Paul, Cho, Michael H., Christofidou, Paraskevi, Davies, Gail, de Geus, Eco, Deleuze, Jean-François, Delgado, Graciela E., Ekunwe, Lynette, Faraday, Nauder, Gögele, Martin, Greinacher, Andreas, Gao, He, Howard, Tom, Joshi, Peter K., Kilpeläinen, Tuomas O., Lahti, Jari, Linneberg, Allan, Naitza, Silvia, Noordam, Raymond, Paüls-Vergés, Ferran, Rich, Stephen S., Rosendaal, Frits R., Rudan, Igor, Ryan, Kathleen A., Souto, Juan Carlos, van Rooij, Frank J. A., Wang, Heming, Zhao, Wei, Becker, Lewis C., Beswick, Andrew, Brown, Michael R., Cade, Brian E., Campbell, Harry, Cho, Kelly, Crapo, James D., Curran, Joanne E., de Maat, Moniek P. M., Doyle, Margaret, Elliott, Paul, Floyd, James S., Fuchsberger, Christian, Grarup, Niels, Guo, Xiuqing, Harris, Sarah E., Hou, Lifang, Kolcic, Ivana, Kooperberg, Charles, Menni, Cristina, Nauck, Matthias, O'Connell, Jeffrey R., Orrù, Valeria, Psaty, Bruce M., Räikkönen, Katri, Smith, Jennifer A., Soria, Jose Manuel, Stott, David J., van Hylckama Vlieg, Astrid, Watkins, Hugh, Willemsen, Gonneke, Wilson, Peter W. F., Ben-Shlomo, Yoav, Blangero, John, Boomsma, Dorret, Cox, Simon R., Dehghan, Abbas, Eriksson, Johan G., Fiorillo, Edoardo, Fornage, Myriam, Hansen, Torben, Hayward, Caroline, Ikram, M. Arfan, Jukema, J. Wouter, Kardia, Sharon L. R., Lange, Leslie A., März, Winfried, Mathias, Rasika A., Mitchell, Braxton D., Mook-Kanamori, Dennis O., Morange, Pierre-Emmanuel, Pedersen, Oluf, Pramstaller, Peter P., Redline, Susan, Reiner, Alexander, Ridker, Paul M., Silverman, Edwin K., Spector, Tim D., Völker, Uwe, Wareham, Nicholas J., Wilson, James F., Yao, Jie, Trégouët, David-Alexandre, Johnson, Andrew D., Wolberg, Alisa S., de Vries, Paul S., Sabater-Lleal, Maria, Morrison, Alanna C., and Smith, Nicholas L.

Published
2024
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37. African ancestry GWAS of dementia in a large military cohort identifies significant risk loci

Author

Sherva, Richard, Zhang, Rui, Sahelijo, Nathan, Jun, Gyungah, Anglin, Tori, Chanfreau, Catherine, Cho, Kelly, Fonda, Jennifer R., Gaziano, J. Michael, Harrington, Kelly M., Ho, Yuk-Lam, Kremen, William S., Litkowski, Elizabeth, Lynch, Julie, Neale, Zoe, Roussos, Panos, Marra, David, Mez, Jesse, Miller, Mark W., Salat, David H., Tsuang, Debby, Wolf, Erika, Zeng, Qing, Panizzon, Matthew S., Merritt, Victoria C., Farrer, Lindsay A., Hauger, Richard L., and Logue, Mark W.

Published
2023
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38. Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints

Author

Liu, Molei, Xia, Yin, Cho, Kelly, and Cai, Tianxi

Subjects
Statistics - Methodology
Abstract

Identifying informative predictors in a high dimensional regression model is a critical step for association analysis and predictive modeling. Signal detection in the high dimensional setting often fails due to the limited sample size. One approach to improve power is through meta-analyzing multiple studies on the same scientific question. However, integrative analysis of high dimensional data from multiple studies is challenging in the presence of between study heterogeneity. The challenge is even more pronounced with additional data sharing constraints under which only summary data but not individual level data can be shared across different sites. In this paper, we propose a novel data shielding integrative large-scale testing (DSILT) approach to signal detection by allowing between study heterogeneity and not requiring sharing of individual level data. Assuming the underlying high dimensional regression models of the data differ across studies yet share similar support, the DSILT approach incorporates proper integrative estimation and debiasing procedures to construct test statistics for the overall effects of specific covariates. We also develop a multiple testing procedure to identify significant effects while controlling for false discovery rate (FDR) and false discovery proportion (FDP). Theoretical comparisons of the DSILT procedure with the ideal individual--level meta--analysis (ILMA) approach and other distributed inference methods are investigated. Simulation studies demonstrate that the DSILT procedure performs well in both false discovery control and attaining power. The proposed method is applied to a real example on detecting interaction effect of the genetic variants for statins and obesity on the risk for Type 2 Diabetes.

Published
2020

39. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium

Author

Aaron, James R., Adam, Atif, Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., Angoulvant, François, Anthony, Li LLJ., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Panickan, Vidul Ayakulangara, Azevedo, Paula S., Badenes, Rafael, Balshi, James, Batugo, Ashley, Beaulieu-Jones, Brendin R., Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Cattelan, Anna Maria, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, García-Barrio, Noelia, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Goh, Rachel SJ., González, Tomás González, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Guzzi, Pietro H., Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Honerlaw, Jacqueline P., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kainth, Mundeep K., Kate, Kernan F., Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Kernan, Kate F., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Makwana, Simran, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Maripuri, Monika, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mazzotti, Diego R., Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku FM., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Ashley C., Pfaff, Emily R., Pillion, Danielle, Pizzimenti, Sara, Priya, Tanu, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Romero-Garcia, Nekane, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin A., Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Sperotto, Francesca, Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia LM., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma MS., Torti, Carlo, Trecarichi, Enrico M., Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zachariasse, Joany M., Zahner, Janet J., Zambelli, Alberto, Zhang, Harrison G., Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Li, Xiudi, Rofeberg, Valerie N., Elias, Matthew D., Laird-Gion, Jessica, and Newburger, Jane W.

Published
2023
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40. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Author

Aaron, James R., Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., François Angoulvant, Anthony, Li L.L.J., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Azevedo, Paula S., Balshi, James, Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Le, Trang T., Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku F.M., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Emily R., Pfaff, Ashley C., Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin, Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia L.M., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma M.S., Torti, Carlo, Trecarichi, Enrico M., Tseng, Yi-Ju, Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison G., Zo¨ller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Mesa, Rebecca, and Verdy, Guillame

Published
2023
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43. Abstract 16950: The Genetic Basis of Atrial Fibrillation in a Large-Scale Multi-Ancestry Sample

Author

Weng, Lu-Chen, Roselli, Carolina, Rämö, Joel, Treu, Timothy M, Gunn, Sophia, Wang, Xin, Sun, Yan, Pirruccello, James, Nauffal, Victor, Bapat, Aneesh, Choi, Seung H, Jurgens, Sean J, Gudbjartsson, Daniel, Holm, Hilma, Stefánsson, Kári, Surakka, Ida, Willer, Cristen, Lunetta, Kathryn, Gaziano, Michael, Cho, Kelly, Wilson, Peter, Ellinor, Patrick T, and Lubitz, Steven A

Published
2023
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48. Genetic Inhibition of APOL1 Pore-Forming Function Prevents APOL1-Mediated Kidney Disease

Author

Hung, Adriana M., Assimon, Victoria A., Chen, Hua-Chang, Yu, Zhihong, Vlasschaert, Caitlyn, Triozzi, Jefferson L., Chan, Helen, Wheless, Lee, Wilson, Otis, Shah, Shailja C., Mack, Taralynn, Thompson, Trevor, Matheny, Michael E., Chandrasekar, Saranya, Mozaffari, Sahar V., Chung, Cecilia P., Tsao, Philip, Susztak, Katalin, Siew, Edward D., Estrada, Karol, Gaziano, J. Michael, Graham, Robert R., Tao, Ran, Hoek, Maarten, Robinson-Cohen, Cassianne, Green, Eric M., Bick, Alexander G., Muralidhar, Sumitra, Moser, Jennifer, Deen, Jennifer E., Tsao, Philip S., Gaziano, J. Michael, Hauser, Elizabeth, Kilbourne, Amy, Luoh, Shiuh-Wen, Matheny, Michael, Oslin, Dave, Churby, Lori, Whitbourne, Stacey B., Brewer, Jessica V., Shayan, Shahpoor (Alex), Selva, Luis E., Pyarajan, Saiju, Cho, Kelly, DuVall, Scott L., Brophy, Mary T., Stephens, Brady, Connor, Todd, Argyres, Dean P., Assimes, Tim, Hung, Adriana, Kranzler, Henry, Aguayo, Samuel, Ahuja, Sunil, Alexander, Kathrina, Androulakis, Xiao M., Balasubramanian, Prakash, Ballas, Zuhair, Beckham, Jean, Bhushan, Sujata, Boyko, Edward, Cohen, David, Dellitalia, Louis, Faulk, L. Christine, Fayad, Joseph, Fujii, Daryl, Gappy, Saib, Gesek, Frank, Greco, Jennifer, Godschalk, Michael, Gress, Todd W., Gupta, Samir, Gutierrez, Salvador, Harley, John, Hammer, Kimberly, Hamner, Mark, Hurley, Robin, Iruvanti, Pran, Jacono, Frank, Jhala, Darshana, Kinlay, Scott, Klein, Jon, Landry, Michael, Liang, Peter, Liangpunsakul, Suthat, Lichy, Jack, Mahan, C. Scott, Marrache, Ronnie, Mastorides, Stephen, Mates, Elisabeth, Mattocks, Kristin, Meyer, Paul, Moorman, Jonathan, Morgan, Timothy, Murdoch, Maureen, Norton, James, Okusaga, Olaoluwa, Oursler, Kris Ann, Palacio, Ana, Poon, Samuel, Potter, Emily, Rauchman, Michael, Servatius, Richard, Sharma, Satish, Smith, River, Sriram, Peruvemba, Strollo, Patrick, Jr., Tandon, Neeraj, Tsao, Philip, Villareal, Gerardo, Wallbom, Agnes, Walsh, Jessica, Wells, John, Whittle, Jeffrey, Whooley, Mary, Williams, Allison E., Wilson, Peter, Xu, Junzhe, and Yeh, Shing Shing

Published
2023
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50. Stroke genetics informs drug discovery and risk prediction across ancestries

Author

Mishra, Aniket, Malik, Rainer, Hachiya, Tsuyoshi, Jürgenson, Tuuli, Namba, Shinichi, Posner, Daniel C., Kamanu, Frederick K., Koido, Masaru, Le Grand, Quentin, Shi, Mingyang, He, Yunye, Georgakis, Marios K., Caro, Ilana, Krebs, Kristi, Liaw, Yi-Ching, Vaura, Felix C., Lin, Kuang, Winsvold, Bendik Slagsvold, Srinivasasainagendra, Vinodh, Parodi, Livia, Bae, Hee-Joon, Chauhan, Ganesh, Chong, Michael R., Tomppo, Liisa, Akinyemi, Rufus, Roshchupkin, Gennady V., Habib, Naomi, Jee, Yon Ho, Thomassen, Jesper Qvist, Abedi, Vida, Cárcel-Márquez, Jara, Nygaard, Marianne, Leonard, Hampton L., Yang, Chaojie, Yonova-Doing, Ekaterina, Knol, Maria J., Lewis, Adam J., Judy, Renae L., Ago, Tetsuro, Amouyel, Philippe, Armstrong, Nicole D., Bakker, Mark K., Bartz, Traci M., Bennett, David A., Bis, Joshua C., Bordes, Constance, Børte, Sigrid, Cain, Anael, Ridker, Paul M., Cho, Kelly, Chen, Zhengming, Cruchaga, Carlos, Cole, John W., de Jager, Phil L., de Cid, Rafael, Endres, Matthias, Ferreira, Leslie E., Geerlings, Mirjam I., Gasca, Natalie C., Gudnason, Vilmundur, Hata, Jun, He, Jing, Heath, Alicia K., Ho, Yuk-Lam, Havulinna, Aki S., Hopewell, Jemma C., Hyacinth, Hyacinth I., Inouye, Michael, Jacob, Mina A., Jeon, Christina E., Jern, Christina, Kamouchi, Masahiro, Keene, Keith L., Kitazono, Takanari, Kittner, Steven J., Konuma, Takahiro, Kumar, Amit, Lacaze, Paul, Launer, Lenore J., Lee, Keon-Joo, Lepik, Kaido, Li, Jiang, Li, Liming, Manichaikul, Ani, Markus, Hugh S., Marston, Nicholas A., Meitinger, Thomas, Mitchell, Braxton D., Montellano, Felipe A., Morisaki, Takayuki, Mosley, Thomas H., Nalls, Mike A., Nordestgaard, Børge G., O’Donnell, Martin J., Okada, Yukinori, Onland-Moret, N. Charlotte, Ovbiagele, Bruce, Peters, Annette, Psaty, Bruce M., Rich, Stephen S., Rosand, Jonathan, Sabatine, Marc S., Sacco, Ralph L., Saleheen, Danish, Sandset, Else Charlotte, Salomaa, Veikko, Sargurupremraj, Muralidharan, Sasaki, Makoto, Satizabal, Claudia L., Schmidt, Carsten O., Shimizu, Atsushi, Smith, Nicholas L., Sloane, Kelly L., Sutoh, Yoichi, Sun, Yan V., Tanno, Kozo, Tiedt, Steffen, Tatlisumak, Turgut, Torres-Aguila, Nuria P., Tiwari, Hemant K., Trégouët, David-Alexandre, Trompet, Stella, Tuladhar, Anil Man, Tybjærg-Hansen, Anne, van Vugt, Marion, Vibo, Riina, Verma, Shefali S., Wiggins, Kerri L., Wennberg, Patrik, Woo, Daniel, Wilson, Peter W. F., Xu, Huichun, Yang, Qiong, Yoon, Kyungheon, Millwood, Iona Y., Gieger, Christian, Ninomiya, Toshiharu, Grabe, Hans J., Jukema, J. Wouter, Rissanen, Ina L., Strbian, Daniel, Kim, Young Jin, Chen, Pei-Hsin, Mayerhofer, Ernst, Howson, Joanna M. M., Irvin, Marguerite R., Adams, Hieab, Wassertheil-Smoller, Sylvia, Christensen, Kaare, Ikram, Mohammad A., Rundek, Tatjana, Worrall, Bradford B., Lathrop, G. Mark, Riaz, Moeen, Simonsick, Eleanor M., Kõrv, Janika, França, Paulo H. C., Zand, Ramin, Prasad, Kameshwar, Frikke-Schmidt, Ruth, de Leeuw, Frank-Erik, Liman, Thomas, Haeusler, Karl Georg, Ruigrok, Ynte M., Heuschmann, Peter Ulrich, Longstreth, W. T., Jung, Keum Ji, Bastarache, Lisa, Paré, Guillaume, Damrauer, Scott M., Chasman, Daniel I., Rotter, Jerome I., Anderson, Christopher D., Zwart, John-Anker, Niiranen, Teemu J., Fornage, Myriam, Liaw, Yung-Po, Seshadri, Sudha, Fernández-Cadenas, Israel, Walters, Robin G., Ruff, Christian T., Owolabi, Mayowa O., Huffman, Jennifer E., Milani, Lili, Kamatani, Yoichiro, Dichgans, Martin, and Debette, Stephanie

Published
2022
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