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3,815 results on '"Rivas, Manuel A."'

1. Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking

Author

Menestrel, Thomas Le and Rivas, Manuel

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Statistics - Applications, Statistics - Computation
Abstract

Docking is a crucial component in drug discovery aimed at predicting the binding conformation and affinity between small molecules and target proteins. ML-based docking has recently emerged as a prominent approach, outpacing traditional methods like DOCK and AutoDock Vina in handling the growing scale and complexity of molecular libraries. However, the availability of comprehensive and user-friendly datasets for training and benchmarking ML-based docking algorithms remains limited. We introduce Smiles2Dock, an open large-scale multi-task dataset for molecular docking. We created a framework combining P2Rank and AutoDock Vina to dock 1.7 million ligands from the ChEMBL database against 15 AlphaFold proteins, giving us more than 25 million protein-ligand binding scores. The dataset leverages a wide range of high-accuracy AlphaFold protein models, encompasses a diverse set of biologically relevant compounds and enables researchers to benchmark all major approaches for ML-based docking such as Graph, Transformer and CNN-based methods. We also introduce a novel Transformer-based architecture for docking scores prediction and set it as an initial benchmark for our dataset. Our dataset and code are publicly available to support the development of novel ML-based methods for molecular docking to advance scientific research in this field.

Published
2024

2. Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank

Author

Menestrel, Thomas Le, Craig, Erin, Tibshirani, Robert, Hastie, Trevor, and Rivas, Manuel

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Applications, Statistics - Computation
Abstract

Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data. We evaluate the performance of Group-LASSO INTERaction-NET (glinternet) and pretrained lasso in disease prediction focusing on diverse ancestries in the UK Biobank. Models were trained on data from White British and other ancestries and validated across a cohort of over 96,000 individuals for 8 diseases. Out of 96 models trained, we report 16 with statistically significant incremental predictive performance in terms of ROC-AUC scores (p-value < 0.05), found for diabetes, arthritis, gall stones, cystitis, asthma and osteoarthritis. For the interaction and pretrained models that outperformed the baseline, the PRS score was the primary driver behind prediction. Our findings indicate that both interaction terms and pre-training can enhance prediction accuracy but for a limited set of diseases and moderate improvements in accuracy

Published
2024

3. Pretraining and the Lasso

Author

Craig, Erin, Pilanci, Mert, Menestrel, Thomas Le, Narasimhan, Balasubramanian, Rivas, Manuel, Gullaksen, Stein-Erik, Dehghannasiri, Roozbeh, Salzman, Julia, Taylor, Jonathan, and Tibshirani, Robert

Subjects
Statistics - Methodology
Abstract

Pretraining is a popular and powerful paradigm in machine learning to pass information from one model to another. As an example, suppose one has a modest-sized dataset of images of cats and dogs, and plans to fit a deep neural network to classify them from the pixel features. With pretraining, we start with a neural network trained on a large corpus of images, consisting of not just cats and dogs but hundreds of other image types. Then we fix all of the network weights except for the top layer(s) (which makes the final classification) and train (or "fine tune") those weights on our dataset. This often results in dramatically better performance than the network trained solely on our smaller dataset. In this paper, we ask the question "Can pretraining help the lasso?". We develop a framework for the lasso in which a model is fit to a large dataset, and then fine-tuned using a smaller dataset. This latter dataset can be a subset of the original dataset, or it can be a dataset with a different but related outcome. This framework has a wide variety of applications, including stratified models, multinomial responses, multi-response models, conditional average treatment estimation and even gradient boosting. In the stratified model setting, the pretrained lasso pipeline estimates the coefficients common to all groups at the first stage, and then estimates the group-specific coefficients at the second "fine-tuning" stage. We show that under appropriate assumptions, the support recovery rate of the common coefficients is superior to that of the usual lasso trained only on individual groups. This separate identification of common and individual coefficients can also be useful for scientific understanding.

Published
2024

4. Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy

Author

Ollila, Hanna M, Sharon, Eilon, Lin, Ling, Sinnott-Armstrong, Nasa, Ambati, Aditya, Yogeshwar, Selina M, Hillary, Ryan P, Jolanki, Otto, Faraco, Juliette, Einen, Mali, Luo, Guo, Zhang, Jing, Han, Fang, Yan, Han, Dong, Xiao Song, Li, Jing, Zhang, Jun, Hong, Seung-Chul, Kim, Tae Won, Dauvilliers, Yves, Barateau, Lucie, Lammers, Gert Jan, Fronczek, Rolf, Mayer, Geert, Santamaria, Joan, Arnulf, Isabelle, Knudsen-Heier, Stine, Bredahl, May Kristin Lyamouri, Thorsby, Per Medbøe, Plazzi, Giuseppe, Pizza, Fabio, Moresco, Monica, Crowe, Catherine, Van den Eeden, Stephen K, Lecendreux, Michel, Bourgin, Patrice, Kanbayashi, Takashi, Martínez-Orozco, Francisco J, Peraita-Adrados, Rosa, Benetó, Antonio, Montplaisir, Jacques, Desautels, Alex, Huang, Yu-Shu, Jennum, Poul, Nevsimalova, Sona, Kemlink, David, Iranzo, Alex, Overeem, Sebastiaan, Wierzbicka, Aleksandra, Geisler, Peter, Sonka, Karel, Honda, Makoto, Högl, Birgit, Stefani, Ambra, Coelho, Fernando Morgadinho, Mantovani, Vilma, Feketeova, Eva, Wadelius, Mia, Eriksson, Niclas, Smedje, Hans, Hallberg, Pär, Hesla, Per Egil, Rye, David, Pelin, Zerrin, Ferini-Strambi, Luigi, Bassetti, Claudio L, Mathis, Johannes, Khatami, Ramin, Aran, Adi, Nampoothiri, Sheela, Olsson, Tomas, Kockum, Ingrid, Partinen, Markku, Perola, Markus, Kornum, Birgitte R, Rueger, Sina, Winkelmann, Juliane, Miyagawa, Taku, Toyoda, Hiromi, Khor, Seik-Soon, Shimada, Mihoko, Tokunaga, Katsushi, Rivas, Manuel, Pritchard, Jonathan K, Risch, Neil, Kutalik, Zoltan, O’Hara, Ruth, Hallmayer, Joachim, Ye, Chun Jimmie, and Mignot, Emmanuel J

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Biological Sciences, Prevention, Vaccine Related, Influenza, Autoimmune Disease, Immunization, Pneumonia & Influenza, Emerging Infectious Diseases, Infectious Diseases, 2.1 Biological and endogenous factors, Aetiology, Infection, Inflammatory and immune system, Good Health and Well Being, Humans, Autoimmunity, Influenza, Human, Influenza A Virus, H1N1 Subtype, Autoimmune Diseases, Influenza Vaccines, Narcolepsy, FinnGen
Abstract

Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.

Published
2023

5. Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry

Author

Casas-Rojo, José-Manuel, Ventura, Paula Sol, Antón Santos, Juan Miguel, de Latierro, Aitor Ortiz, Arévalo-Lorido, José Carlos, Mauri, Marc, Rubio-Rivas, Manuel, González-Vega, Rocío, Giner-Galvañ, Vicente, Otero Perpiñá, Bárbara, Fonseca-Aizpuru, Eva, Muiño, Antonio, Del Corral-Beamonte, Esther, Gómez-Huelgas, Ricardo, Arnalich-Fernández, Francisco, Llorente Barrio, Mónica, Sancha-Lloret, Aresio, Rábago Lorite, Isabel, Loureiro-Amigo, José, Pintos-Martínez, Santiago, García-Sardón, Eva, Montaño-Martínez, Adrián, Rojano-Rivero, María Gloria, Ramos-Rincón, José-Manuel, and López-Escobar, Alejandro

Published
2023
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6. Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy

Author

Parikh, Victoria N, Ioannidis, Alexander G, Jimenez-Morales, David, Gorzynski, John E, De Jong, Hannah N, Liu, Xiran, Roque, Jonasel, Cepeda-Espinoza, Victoria P, Osoegawa, Kazutoyo, Hughes, Chris, Sutton, Shirley C, Youlton, Nathan, Joshi, Ruchi, Amar, David, Tanigawa, Yosuke, Russo, Douglas, Wong, Justin, Lauzon, Jessie T, Edelson, Jacob, Mas Montserrat, Daniel, Kwon, Yongchan, Rubinacci, Simone, Delaneau, Olivier, Cappello, Lorenzo, Kim, Jaehee, Shoura, Massa J, Raja, Archana N, Watson, Nathaniel, Hammond, Nathan, Spiteri, Elizabeth, Mallempati, Kalyan C, Montero-Martín, Gonzalo, Christle, Jeffrey, Kim, Jennifer, Kirillova, Anna, Seo, Kinya, Huang, Yong, Zhao, Chunli, Moreno-Grau, Sonia, Hershman, Steven G, Dalton, Karen P, Zhen, Jimmy, Kamm, Jack, Bhatt, Karan D, Isakova, Alina, Morri, Maurizio, Ranganath, Thanmayi, Blish, Catherine A, Rogers, Angela J, Nadeau, Kari, Yang, Samuel, Blomkalns, Andra, O’Hara, Ruth, Neff, Norma F, DeBoever, Christopher, Szalma, Sándor, Wheeler, Matthew T, Gates, Christian M, Farh, Kyle, Schroth, Gary P, Febbo, Phil, deSouza, Francis, Cornejo, Omar E, Fernandez-Vina, Marcelo, Kistler, Amy, Palacios, Julia A, Pinsky, Benjamin A, Bustamante, Carlos D, Rivas, Manuel A, and Ashley, Euan A

Subjects
Biological Sciences, Genetics, Pneumonia & Influenza, Lung, Prevention, Human Genome, Clinical Research, Infectious Diseases, Infection, Good Health and Well Being, COVID-19, Genome, Viral, Genome-Wide Association Study, Humans, Pandemics, SARS-CoV-2
Abstract

The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk.

Published
2022

7. Thrombosis risk in single- and double-heterozygous carriers of factor V Leiden and prothrombin G20210A in FinnGen and the UK Biobank

Author

Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Viollet, Coralie, Petrovski, Slavé, Chen, Chia-Yen, John, Sally, Okafo, George, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Pendergrass, Rion, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Mälarstig, Anders, Vlahiotis, Anna, Klinger, Katherine, Chatelain, Clement, Gossel, Matthias, Estrada, Karol, Graham, Robert, Waterworth, Dawn, O´Donnell, Chris, Renaud, Nicole, Mäkelä, Tomi P., Kaprio, Jaakko, Ruddock, Minna, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Raivio, Taneli, Tikkanen, Jani, Serpi, Raisa, Kristiansson, Kati, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Ding, Zhihao, Jung, Marc, Tuoken, Hanati, Biswas, Shameek, Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Hu, Xinli, Hedman, Åsa, Obeidat, Ma´en, Chung, Jonathan, Zierer, Jonas, Niemi, Mari, Ripatti, Samuli, Schleutker, Johanna, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Runz, Heiko, Lahdenperä, Sanni, Bowers, Natalie, Teng, Edmond, Xu, Fanli, Addis, Laura, Eicher, John, Li, Qingqin S., He, Karen, Khramtsova, Ekaterina, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Lu, Tim, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Lertratanakul, Apinya, Hochfeld, Marla, Gordillo, Jorge Esparza, Farias, Fabiana, Bing, Nan, Laitinen, Tarja, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Chen, Hubert, Betts, Joanna, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Niiranen, Teemu, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Elliott, Amanda, Reeve, Mary Pat, Ruotsalainen, Sanni, Paul, Dirk, Chu, Audrey, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Meretoja, Tuomo, Joensuu, Heikki, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Auvinen, Päivi, Elenius, Klaus, Pitkänen, Esa, Popovic, Relja, Fabre, Margarete, Schutzman, Jennifer, Kulkarni, Diptee, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A., Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Liu, Mengzhen, Loomis, Stephanie, Strauss, Erich, Chen, Hao, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Choy, David, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Laivuori, Hannele, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Kumar, Janet, Hovatta, Iiris, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Rahikkala, Elisa, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della Briotta Parolo, Pietro, Zhou, Wei, Maasha, Mutaamba, Lemmelä, Susanna, Rivas, Manuel, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Heyne, Henrike, Rämö, Joel, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Hyvärinen, Kati, Ritari, Jarmo, Pylkäs, Katri, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Kallio, Lila, Wahlfors, Tiina, Punkka, Eero, Siltanen, Sanna, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Cooper, Helen, Öller, Denise, Leinonen, Rasko, Palin, Henna, Linna, Malla-Maria, Kanai, Masahiro, Zheng, Zhili, Lahtela, L. Elisa, Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Dada, Oluwaseun Alexander, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Donner, Kati, Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Luo, Shuang, Padmanabhuni, Shanmukha Sampath, Niemi, Marianna, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Tammerluoto, Jyrki, Smith, Sarah, Southerington, Tom, Lehto, Petri, Ryu, Justine, Rämö, Joel T., Jurgens, Sean J., Sanna-Cherchi, Simone, Bauer, Kenneth A., Haj, Amelia, Choi, Seung Hoan, Ellinor, Patrick T., and Bendapudi, Pavan K.

Published
2024
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9. Mortality reduction in older COVID-19-patients hospitalized in Spain during the second pandemic wave from the SEMI-COVID-19 Registry

Author

Casas-Rojo, José-Manuel, Antón-Santos, Juan-Miguel, Millán-Núñez-Cortés, Jesús, Gómez-Huelgas, Ricardo, Ramos-Rincón, José-Manuel, Rubio-Rivas, Manuel, Corrales-González, Miguel-Ángel, Fernández-Madera-Martínez, Maria-Rosa, Beato-Pérez, José-Luis, Arnalich-Fernández, Francisco, Gállego-Lezaun, Cristina, Pérez-Martínez, Pablo, Molinos-Castro, Sonia, Tung-Chen, Yale, Madrazo, Manuel, Méndez-Bailón, Manuel, Monge-Monge, Daniel, García-García, Gema-María, García-Fenoll, Rosa, Gilabert, Noemí, Fuerte-Martínez, Rebeca, Contreras-Sánchez, Marta, Rhyman, Nicolás, Peris-García, Jorge, and Lumbreras-Bermejo, Carlos

Published
2023
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10. Cannabinoid receptor 1 antagonist genistein attenuates marijuana-induced vascular inflammation.

Author

Wei, Tzu-Tang, Chandy, Mark, Nishiga, Masataka, Zhang, Angela, Kumar, Kaavya Krishna, Thomas, Dilip, Manhas, Amit, Rhee, Siyeon, Justesen, Johanne Marie, Chen, Ian Y, Wo, Hung-Ta, Khanamiri, Saereh, Yang, Johnson Y, Seidl, Frederick J, Burns, Noah Z, Liu, Chun, Sayed, Nazish, Shie, Jiun-Jie, Yeh, Chih-Fan, Yang, Kai-Chien, Lau, Edward, Lynch, Kara L, Rivas, Manuel, Kobilka, Brian K, and Wu, Joseph C

Subjects
Endothelial Cells, Animals, Mice, Cannabis, Cardiovascular Diseases, Inflammation, Genistein, Receptors, Cannabinoid, Receptor, Cannabinoid, CB1, Analgesics, Hallucinogens, Cannabinoid Receptor Agonists, Dronabinol, Billy Martin tetrad, G protein-coupled receptor, GPCR, UK Biobank, atherosclerosis, cardiovascular disease, human-induced pluripotent stem cell, in silico drug screening, in vivo ligand binding, marijuana, Substance Misuse, Cannabinoid Research, Heart Disease, Neurosciences, Drug Abuse (NIDA only), Prevention, Cardiovascular, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Epidemiological studies reveal that marijuana increases the risk of cardiovascular disease (CVD); however, little is known about the mechanism. Δ9-tetrahydrocannabinol (Δ9-THC), the psychoactive component of marijuana, binds to cannabinoid receptor 1 (CB1/CNR1) in the vasculature and is implicated in CVD. A UK Biobank analysis found that cannabis was an risk factor for CVD. We found that marijuana smoking activated inflammatory cytokines implicated in CVD. In silico virtual screening identified genistein, a soybean isoflavone, as a putative CB1 antagonist. Human-induced pluripotent stem cell-derived endothelial cells were used to model Δ9-THC-induced inflammation and oxidative stress via NF-κB signaling. Knockdown of the CB1 receptor with siRNA, CRISPR interference, and genistein attenuated the effects of Δ9-THC. In mice, genistein blocked Δ9-THC-induced endothelial dysfunction in wire myograph, reduced atherosclerotic plaque, and had minimal penetration of the central nervous system. Genistein is a CB1 antagonist that attenuates Δ9-THC-induced atherosclerosis.

Published
2022

12. High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases

Author

Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Paul, Dirk, Petrovski, Slavé, Runz, Heiko, John, Sally, Okafo, George, Lawless, Nathan, Salminen-Mankonen, Heli, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Mälarstig, Anders, Klinger, Katherine, Chatelain, Clement, Gossel, Matthias, Estrada, Karol, Graham, Robert, Yang, Robert, ODonnell, Chris, Mäkelä, Tomi P., Kaprio, Jaakko, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Pitkäranta, Anne, Raivio, Taneli, Tikkanen, Jani, Serpi, Raisa, Laitinen, Tarja, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Pakkanen, Raimo, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Chen, Chia-Yen, Ding, Zhihao, Jung, Marc, Biswas, Shameek, Pendergrass, Rion, Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Hu, Xinli, Hedman, Åsa, Rivas, Manuel, Waterworth, Dawn, Renaud, Nicole, Obeidat, Maen, Ripatti, Samuli, Schleutker, Johanna, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Abbasi, Ali, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Lahdenperä, Sanni, Bowers, Natalie, Teng, Edmond, Xu, Fanli, Addis, Laura, Eicher, John, Li, Qingqin S., He, Karen, Khramtsova, Ekaterina, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Lu, Tim, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Lertratanakul, Apinya, Viollet, Coralie, Hochfeld, Marla, Gordillo, Jorge Esparza, Farias, Fabiana, Bing, Nan, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Chen, Hubert, Betts, Joanna, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Niiranen, Teemu, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Elliott, Amanda, Reeve, Mary Pat, Ruotsalainen, Sanni, Chu, Audrey, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Meretoja, Tuomo, Joensuu, Heikki, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Auvinen, Päivi, Elenius, Klaus, Pitkänen, Esa, Popovic, Relja, Fabre, Margarete, Schutzman, Jennifer, Kulkarni, Diptee, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A., Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Liu, Mengzhen, Loomis, Stephanie, Strauss, Erich, Chen, Hao, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Choy, David, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Laivuori, Hannele, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Kumar, Janet, Hovatta, Iiris, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Als, Thomas Damm, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Rahikkala, Elisa, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della Briotta Parolo, Pietro, Zhou, Wei, Maasha, Mutaamba, Lemmelä, Susanna, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Heyne, Henrike, Rämö, Joel, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Kristiansson, Kati, Hyvärinen, Kati, Ritari, Jarmo, Pylkäs, Katri, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Kallio, Lila, Wahlfors, Tiina, Punkka, Eero, Siltanen, Sanna, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Cooper, Helen, Öller, Denise, Leinonen, Rasko, Palin, Henna, Linna, Malla-Maria, Kanai, Masahiro, Zheng, Zhili, Lahtela, L. Elisa, Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Dada, Oluwaseun Alexander, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Donner, Kati, Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Pärn, Kalle, Kals, Mart, Luo, Shuang, Padmanabhuni, Shanmukha Sampath, Niemi, Marianna, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Tammerluoto, Jyrki, Smith, Sarah, Southerington, Tom, Lehto, Petri, Youssef, Omar, Zidi-Mouaffak, Yossra H.S., and Tamlander, Max

Published
2024
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13. Validation of the RIM Score-COVID in the Spanish SEMI-COVID-19 Registry

Author

Ramos-Rincón, José-Manuel, Ventura, Paula Sol, Casas-Rojo, José-Manuel, Mauri, Marc, Bermejo, Carlos Lumbreras, de Latierro, Aitor Ortiz, Rubio-Rivas, Manuel, Mérida-Rodrigo, Luis, Pérez-Casado, Lucia, Barrientos-Guerrero, María, Giner-Galvañ, Vicente, Gallego-Lezaun, Cristina, Milián, Almudena Hernández, Manzano, Luis, Blázquez-Encinar, Julio César, Solís-Marquínez, Marta Nataya, García, Marcos Guzmán, Lobo-García, Julia, Valente, Victoria Achával-Rodríguez, Roig-Martí, Celia, León-Téllez, Marta, Tellería-Gómez, Pablo, González-Juárez, María Jesús, Gómez-Huelgas, Ricardo, and López-Escobar, Alejandro

Published
2023
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14. Efficient computation and analysis of distributional Shapley values

Author

Kwon, Yongchan, Rivas, Manuel A., and Zou, James

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

Distributional data Shapley value (DShapley) has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. DShapley develops the foundational game theory concept of Shapley values into a statistical framework and can be applied to identify data points that are useful (or harmful) to a learning algorithm. Estimating DShapley is computationally expensive, however, and this can be a major challenge to using it in practice. Moreover, there has been little mathematical analyses of how this value depends on data characteristics. In this paper, we derive the first analytic expressions for DShapley for the canonical problems of linear regression, binary classification, and non-parametric density estimation. These analytic forms provide new algorithms to estimate DShapley that are several orders of magnitude faster than previous state-of-the-art methods. Furthermore, our formulas are directly interpretable and provide quantitative insights into how the value varies for different types of data. We demonstrate the practical efficacy of our approach on multiple real and synthetic datasets., Comment: 24 pages, accepted for AISTATS 2021

Published
2020

15. The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections

Author

Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Paul, Dirk, Petrovski, Slavé, Runz, Heiko, John, Sally, Okafo, George, Lawless, Nathan, Salminen-Mankonen, Heli, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Mälarstig, Anders, Klinger, Katherine, Chatelain, Clement, Gossel, Matthias, Estrada, Karol, Graham, Robert, Yang, Robert, O´Donnell, Chris, Mäkelä, Tomi P., Kaprio, Jaakko, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Pitkäranta, Anne, Raivio, Taneli, Tikkanen, Jani, Serpi, Raisa, Laitinen, Tarja, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Pakkanen, Raimo, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Chen, Chia-Yen, Ding, Zhihao, Jung, Marc, Biswas, Shameek, Pendergrass, Rion, Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Hu, Xinli, Hedman, Åsa, Rivas, Manuel, Waterworth, Dawn, Renaud, Nicole, Obeidat, Ma´en, Ripatti, Samuli, Schleutker, Johanna, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Abbasi, Ali, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Lahdenperä, Sanni, Bowers, Natalie, Teng, Edmond, Xu, Fanli, Addis, Laura, Eicher, John, Li, Qingqin S., He, Karen, Khramtsova, Ekaterina, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Lu, Tim, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Lertratanakul, Apinya, Viollet, Coralie, Hochfeld, Marla, Gordillo, Jorge Esparza, Farias, Fabiana, Bing, Nan, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Chen, Hubert, Betts, Joanna, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Niiranen, Teemu, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Elliott, Amanda, Reeve, Mary Pat, Ruotsalainen, Sanni, Chu, Audrey, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Meretoja, Tuomo, Joensuu, Heikki, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Auvinen, Päivi, Elenius, Klaus, Pitkänen, Esa, Popovic, Relja, Fabre, Margarete, Schutzman, Jennifer, Kulkarni, Diptee, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A., Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Liu, Mengzhen, Loomis, Stephanie, Strauss, Erich, Chen, Hao, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Choy, David, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Laivuori, Hannele, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Kumar, Janet, Hovatta, Iiris, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Als, Thomas Damm, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Rahikkala, Elisa, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della Briotta Parolo, Pietro, Zhou, Wei, Maasha, Mutaamba, Lemmelä, Susanna, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Heyne, Henrike, Rämö, Joel, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Kristiansson, Kati, Hyvärinen, Kati, Ritari, Jarmo, Pylkäs, Katri, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Kallio, Lila, Wahlfors, Tiina, Punkka, Eero, Siltanen, Sanna, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Leinonen, Rasko, Palin, Henna, Linna, Malla-Maria, Kanai, Masahiro, Zheng, Zhili, Lahtela, L. Elisa, Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Dada, Oluwaseun Alexander, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Donner, Kati, Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Pärn, Kalle, Kals, Mart, Luo, Shuang, Padmanabhuni, Shanmukha Sampath, Niemi, Marianna, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Tammerluoto, Jyrki, Smith, Sarah, Southerington, Tom, Lehto, Petri, Jones, Samuel E., Maisha, Fahrisa I., Strausz, Satu J., Lammi, Vilma, Cade, Brian E., Tervi, Anniina, Helaakoski, Viola, Broberg, Martin E., Lane, Jacqueline M., Redline, Susan, Saxena, Richa, and Ollila, Hanna M.

Published
2023
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18. Large-scale sequencing identifies multiple genes and rare variants associated with Crohn’s disease susceptibility

Author

Sazonovs, Aleksejs, Stevens, Christine R., Venkataraman, Guhan R., Yuan, Kai, Avila, Brandon, Abreu, Maria T., Ahmad, Tariq, Allez, Matthieu, Ananthakrishnan, Ashwin N., Atzmon, Gil, Baras, Aris, Barrett, Jeffrey C., Barzilai, Nir, Beaugerie, Laurent, Beecham, Ashley, Bernstein, Charles N., Bitton, Alain, Bokemeyer, Bernd, Chan, Andrew, Chung, Daniel, Cleynen, Isabelle, Cosnes, Jacques, Cutler, David J., Daly, Allan, Damas, Oriana M., Datta, Lisa W., Dawany, Noor, Devoto, Marcella, Dodge, Sheila, Ellinghaus, Eva, Fachal, Laura, Farkkila, Martti, Faubion, William, Ferreira, Manuel, Franchimont, Denis, Gabriel, Stacey B., Ge, Tian, Georges, Michel, Gettler, Kyle, Giri, Mamta, Glaser, Benjamin, Goerg, Siegfried, Goyette, Philippe, Graham, Daniel, Hämäläinen, Eija, Haritunians, Talin, Heap, Graham A., Hiltunen, Mikko, Hoeppner, Marc, Horowitz, Julie E., Irving, Peter, Iyer, Vivek, Jalas, Chaim, Kelsen, Judith, Khalili, Hamed, Kirschner, Barbara S., Kontula, Kimmo, Koskela, Jukka T., Kugathasan, Subra, Kupcinskas, Juozas, Lamb, Christopher A., Laudes, Matthias, Lévesque, Chloé, Levine, Adam P., Lewis, James D., Liefferinckx, Claire, Loescher, Britt-Sabina, Louis, Edouard, Mansfield, John, May, Sandra, McCauley, Jacob L., Mengesha, Emebet, Mni, Myriam, Moayyedi, Paul, Moran, Christopher J., Newberry, Rodney D., O’Charoen, Sirimon, Okou, David T., Oldenburg, Bas, Ostrer, Harry, Palotie, Aarno, Paquette, Jean, Pekow, Joel, Peter, Inga, Pierik, Marieke J., Ponsioen, Cyriel Y., Pontikos, Nikolas, Prescott, Natalie, Pulver, Ann E., Rahmouni, Souad, Rice, Daniel L., Saavalainen, Päivi, Sands, Bruce, Sartor, R. Balfour, Schiff, Elena R., Schreiber, Stefan, Schumm, L. Philip, Segal, Anthony W., Seksik, Philippe, Shawky, Rasha, Sheikh, Shehzad Z., Silverberg, Mark S., Simmons, Alison, Skeiceviciene, Jurgita, Sokol, Harry, Solomonson, Matthew, Somineni, Hari, Sun, Dylan, Targan, Stephan, Turner, Dan, Uhlig, Holm H., van der Meulen, Andrea E., Vermeire, Séverine, Verstockt, Sare, Voskuil, Michiel D., Winter, Harland S., Young, Justine, Duerr, Richard H., Franke, Andre, Brant, Steven R., Cho, Judy, Weersma, Rinse K., Parkes, Miles, Xavier, Ramnik J., Rivas, Manuel A., Rioux, John D., McGovern, Dermot P. B., Huang, Hailiang, Anderson, Carl A., and Daly, Mark J.

Published
2022
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19. Psychiatric disorders and cognitive impairment following COVID-19: a comprehensive review and its implications for smart healthcare design

Author

Azimi, Tayebe, primary, Koochekian, Amirhossein, additional, Marateb, Hamid Reza, additional, Shirzadi, Mehdi, additional, Rojas-Martínez, Mónica, additional, Alonso, Joan F, additional, Matarranz, Alejandro Bachiller, additional, Mansourian, Marjan, additional, Rubio Rivas, Manuel, additional, Lafuente, Sergio Romero, additional, and Mañanas, Miquel Angel, additional

Published
2022
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21. Assessing the impact of long-term inhaled corticosteroid therapy on patients with COVID-19 and coexisting chronic lung disease: A multicenter retrospective cohort study

Author

Pina Belmonte, Adela, primary, Madrazo, Manuel, additional, Piles, Laura, additional, Rubio-Rivas, Manuel, additional, de Jorge Huerta, Lucía, additional, Gómez Antúnez, María, additional, López Caleya, Juan Francisco, additional, Arnalich Fernández, Francisco, additional, Gericó-Aseguinolaza, Martin, additional, Pesqueira Fontan, Paula Maria, additional, Rhyman, Nicolás, additional, Prieto Dehesa, Marina, additional, Romero Cabrera, Juan Luis, additional, García García, Gema María, additional, García-Casasola, Gonzalo, additional, Labirua-Iturburu Ruiz, Ane, additional, Carrasco-Sánchez, Francisco Javier, additional, Martínez Hernández, Sara, additional, Pascual Pérez, Maria de los Reyes, additional, López Castro, José, additional, Serrano Carrillo de Albornoz, José Luis, additional, Varona, José F., additional, Gómez-Huelgas, Ricardo, additional, Antón-Santos, Juan-Miguel, additional, and Lumbreras-Bermejo, Carlos, additional

Published
2024
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22. High heritability of ascending aortic diameter and trans-ancestry prediction of thoracic aortic disease

Author

Tcheandjieu, Catherine, Xiao, Ke, Tejeda, Helio, Lynch, Julie A., Ruotsalainen, Sanni, Bellomo, Tiffany, Palnati, Madhuri, Judy, Renae, Klarin, Derek, Kember, Rachel L., Verma, Shefali, Palotie, Aarno, Daly, Mark, Ritchie, Marylyn, Rader, Daniel J., Rivas, Manuel A., Assimes, Themistocles, Tsao, Philip, Damrauer, Scott, and Priest, James R.

Published
2022
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23. WHO Ordinal Scale and Inflammation Risk Categories in COVID-19. Comparative Study of the Severity Scales

Author

Rubio-Rivas, Manuel, Mora-Luján, José María, Formiga, Francesc, Arévalo-Cañas, Coral, Lebrón Ramos, Juan Manuel, Villalba García, María Victoria, Fonseca Aizpuru, Eva Mª, Díez-Manglano, Jesús, Arnalich Fernández, Francisco, Romero Cabrera, Juan Luis, García García, Gema María, Pesqueira Fontan, Paula M., Vargas Núñez, Juan Antonio, Freire Castro, Santiago Jesús, Loureiro Amigo, José, Pascual Pérez, Maria de los Reyes, Alcalá Pedrajas, José N., Encinas-Sánchez, Daniel, Mella Pérez, Carmen, Ena, Javier, Gracia Gutiérrez, Anyuli, Esteban Giner, María José, Varona, José F., Millán Núñez-Cortés, Jesús, and Casas-Rojo, José-Manuel

Published
2022
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24. Clusters of inflammation in COVID-19: descriptive analysis and prognosis on more than 15,000 patients from the Spanish SEMI-COVID-19 Registry

Author

Rubio-Rivas, Manuel, Mora-Luján, José María, Formiga, Francesc, Corrales González, Miguel Ángel, García Andreu, María del Mar, Moreno-Torres, Víctor, García García, Gema María, Alcalá Pedrajas, José N, Boixeda, Ramon, Pérez-Lluna, Leticia, Cortés-Rodríguez, Begoña, Mella-Pérez, Carmen, Navas Alcántara, María de la Sierra, López Reboiro, Manuel Lorenzo, Alfaro-Lara, Verónica, Pérez-Martín, Santiago, Martín-Oterino, José Ángel, Gracia Gutiérrez, Anyuli, Martín-Urda Díez-Canseco, Anabel, Comas Casanova, Pere, Pérez García, Cristina, Varona, José F, Gómez-Huelgas, Ricardo, Antón-Santos, Juan-Miguel, and Lumbreras-Bermejo, Carlos

Published
2022
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25. DeepTag: inferring all-cause diagnoses from clinical notes in under-resourced medical domain

Author

Nie, Allen, Zehnder, Ashley, Page, Rodney L., Pineda, Arturo L., Rivas, Manuel A., Bustamante, Carlos D., and Zou, James

Subjects
Computer Science - Computation and Language
Abstract

Large scale veterinary clinical records can become a powerful resource for patient care and research. However, clinicians lack the time and resource to annotate patient records with standard medical diagnostic codes and most veterinary visits are captured in free text notes. The lack of standard coding makes it challenging to use the clinical data to improve patient care. It is also a major impediment to cross-species translational research, which relies on the ability to accurately identify patient cohorts with specific diagnostic criteria in humans and animals. In order to reduce the coding burden for veterinary clinical practice and aid translational research, we have developed a deep learning algorithm, DeepTag, which automatically infers diagnostic codes from veterinary free text notes. DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts. DeepTag extends multi-task LSTM with an improved hierarchical objective that captures the semantic structures between diseases. To foster human-machine collaboration, DeepTag also learns to abstain in examples when it is uncertain and defers them to human experts, resulting in improved performance. DeepTag accurately infers disease codes from free text even in challenging cross-hospital settings where the text comes from different clinical settings than the ones used for training. It enables automated disease annotation across a broad range of clinical diagnoses with minimal pre-processing. The technical framework in this work can be applied in other medical domains that currently lack medical coding resources., Comment: 17 pages, 6 figures. Updated the text for clarity

Published
2018

26. Risk of Midlife Stroke After Adverse Pregnancy Outcomes: The FinnGen Study

Author

Miller, Eliza C., Kauko, Anni, Tom, Sarah E., Laivuori, Hannele, Niiranen, Teemu, Bello, Natalie A., Palotie, Aarno, Daly, Mark, Riley-Gills, Bridget, Jacob, Howard, Paul, Dirk, Matakidou, Athena, Platt, Adam, Runz, Heiko, John, Sally, Okafo, George, Lawless, Nathan, Salminen-Mankonen, Heli, Plenge, Robert, Maranville, Joseph, McCarthy, Mark, Hunkapiller, Julie, Ehm, Margaret G., Auro, Kirsi, Longerich, Simonne, Fox, Caroline, Mälarstig, Anders, Klinger, Katherine, Raipal, Deepak, Green, Eric, Graham, Robert, Yang, Robert, Donnell, Chris O´, Mäkelä, Tomi P., Kaprio, Jaakko, Virolainen, Petri, Hakanen, Antti, Kilpi, Terhi, Perola, Markus, Partanen, Jukka, Pitkäranta, Anne, Raivio, Taneli, Serpi, Raisa, Laitinen, Tarja, Kosma, Veli-Matti, Laukkanen, Jari, Hautalahti, Marco, Tuovila, Outi, Pakkanen, Raimo, Waring, Jeffrey, Riley-Gillis, Bridget, Rahimov, Fedik, Tachmazidou, Ioanna, Chen, Chia-Yen, Runz, Heiko, Ding, Zhihao, Jung, Marc, Biswas, Shameek, Pendergrass, Rion, Hunkapiller, Julie, Ehm, Margaret G., Pulford, David, Raghavan, Neha, Huertas-Vazquez, Adriana, Sul, Jae-Hoon, Mälarstig, Anders, Hu, Xinli, Klinger, Katherine, Graham, Robert, Green, Eric, Mozaffari, Sahar, Waterworth, Dawn, Renaud, Nicole, Obeidat, Ma´en, Ripatti, Samuli, Schleutker, Johanna, Perola, Markus, Arvas, Mikko, Carpén, Olli, Hinttala, Reetta, Kettunen, Johannes, Mannermaa, Arto, Aalto-Setälä, Katriina, Kähönen, Mika, Laukkanen, Jari, Mäkelä, Johanna, Kälviäinen, Reetta, Julkunen, Valtteri, Soininen, Hilkka, Remes, Anne, Hiltunen, Mikko, Peltola, Jukka, Raivio, Minna, Tienari, Pentti, Rinne, Juha, Kallionpää, Roosa, Partanen, Juulia, Abbasi, Ali, Ziemann, Adam, Smaoui, Nizar, Lehtonen, Anne, Eaton, Susan, Runz, Heiko, Lahdenperä, Sanni, Biswas, Shameek, Hunkapiller, Julie, Bowers, Natalie, Teng, Edmond, Pendergrass, Rion, Xu, Fanli, Pulford, David, Auro, Kirsi, Addis, Laura, Eicher, John, Li, Qingqin S, He, Karen, Khramtsova, Ekaterina, Raghavan, Neha, Färkkilä, Martti, Koskela, Jukka, Pikkarainen, Sampsa, Jussila, Airi, Kaukinen, Katri, Blomster, Timo, Kiviniemi, Mikko, Voutilainen, Markku, Daly, Mark, Abbasi, Ali, Waring, Jeffrey, Smaoui, Nizar, Rahimov, Fedik, Lehtonen, Anne, Lu, Tim, Bowers, Natalie, Pendergrass, Rion, McCarthy, Linda, Hart, Amy, Guan, Meijian, Miller, Jason, Kalpala, Kirsi, Miller, Melissa, Hu, Xinli, Eklund, Kari, Palomäki, Antti, Isomäki, Pia, Pirilä, Laura, Kaipiainen-Seppänen, Oili, Huhtakangas, Johanna, Mars, Nina, Abbasi, Ali, Waring, Jeffrey, Rahimov, Fedik, Lertratanakul, Apinya, Smaoui, Nizar, Lehtonen, Anne, Hochfeld, Marla, Bowers, Natalie, Pendergrass, Rion, Esparza Gordillo, Jorge, Auro, Kirsi, Waterworth, Dawn, Farias, Fabiana, Kalpala, Kirsi, Bing, Nan, Hu, Xinli, Laitinen, Tarja, Pelkonen, Margit, Kauppi, Paula, Kankaanranta, Hannu, Harju, Terttu, Lahesmaa, Riitta, Smaoui, Nizar, Lassi, Glenda, Eaton, Susan, Chen, Hubert, Pendergrass, Rion, Bowers, Natalie, Betts, Joanna, Auro, Kirsi, Mishra, Rajashree, Mouded, Majd, Ngo, Debby, Vaura, Felix, Salomaa, Veikko, Metsärinne, Kaj, Aittokallio, Jenni, Kähönen, Mika, Hernesniemi, Jussi, Gordin, Daniel, Sinisalo, Juha, Taskinen, Marja-Riitta, Tuomi, Tiinamaija, Hiltunen, Timo, Laukkanen, Jari, Elliott, Amanda, Pat Reeve, Mary, Ruotsalainen, Sanni, Challis, Benjamin, Paul, Dirk, Hunkapiller, Julie, Bowers, Natalie, Pendergrass, Rion, Chu, Audrey, Auro, Kirsi, Reilly, Dermot, Mendelson, Mike, Parkkinen, Jaakko, Miller, Melissa, Meretoja, Tuomo, Joensuu, Heikki, Carpén, Olli, Mattson, Johanna, Salminen, Eveliina, Auranen, Annika, Karihtala, Peeter, Elenius, Päivi Auvinen Klaus, Schleutker, Johanna, Pitkänen, Esa, Mars, Nina, Daly, Mark, Popovic, Relja, Waring, Jeffrey, Riley-Gillis, Bridget, Lehtonen, Anne, Schutzman, Jennifer, Hunkapiller, Julie, Bowers, Natalie, Pendergrass, Rion, Kulkarni, Diptee, Auro, Kirsi, Porello, Alessandro, Loboda, Andrey, Lehtonen, Heli, McDonough, Stefan, Vuoti, Sauli, Kaarniranta, Kai, Turunen, Joni A, Ollila, Terhi, Uusitalo, Hannu, Karjalainen, Juha, Pitkänen, Esa, Liu, Mengzhen, Runz, Heiko, Loomis, Stephanie, Strauss, Erich, Bowers, Natalie, Chen, Hao, Pendergrass, Rion, Tasanen, Kaisa, Huilaja, Laura, Hannula-Jouppi, Katariina, Salmi, Teea, Peltonen, Sirkku, Koulu, Leena, Smaoui, Nizar, Rahimov, Fedik, Lehtonen, Anne, Choy, David, Pendergrass, Rion, Waterworth, Dawn, Kalpala, Kirsi, Wu, Ying, Pussinen, Pirkko, Salminen, Aino, Salo, Tuula, Rice, David, Nieminen, Pekka, Palotie, Ulla, Siponen, Maria, Suominen, Liisa, Mäntylä, Päivi, Gursoy, Ulvi, Anttonen, Vuokko, Sipilä, Kirsi, Pendergrass, Rion, Kurra, Venla, Kotaniemi-Talonen, Laura, Heikinheimo, Oskari, Kalliala, Ilkka, Aaltonen, Lauri, Jokimaa, Varpu, Kettunen, Johannes, Vääräsmäki, Marja, Uimari, Outi, Morin-Papunen, Laure, Niinimäki, Maarit, Piltonen, Terhi, Kivinen, Katja, Widen, Elisabeth, Tukiainen, Taru, Pat Reeve, Mary, Daly, Mark, Välimäki, Niko, Laakkonen, Eija, Tyrmi, Jaakko, Silven, Heidi, Sliz, Eeva, Arffman, Riikka, Savukoski, Susanna, Laisk, Triin, Pujol, Natalia, Liu, Mengzhen, Riley-Gillis, Bridget, Pendergrass, Rion, Kumar, Janet, Auro, Kirsi, Hovatta, Iiris, Chen, Chia-Yen, Isometsä, Erkki, Ollila, Hanna, Suvisaari, Jaana, Damm Als, Thomas, Mäkitie, Antti, Bizaki-Vallaskangas, Argyro, Toppila-Salmi, Sanna, Willberg, Tytti, Saarentaus, Elmo, Aarnisalo, Antti, Salminen, Eveliina, Rahikkala, Elisa, Kettunen, Johannes, Aittomäki, Kristiina, Åberg, Fredrik, Kurki, Mitja, Ripatti, Samuli, Daly, Mark, Karjalainen, Juha, Havulinna, Aki, Mehtonen, Juha, Palta, Priit, Hassan, Shabbeer, Della, Pietro, Parolo, Briotta, Zhou, Wei, Maasha, Mutaamba, Hassan, Shabbeer, Lemmelä, Susanna, Rivas, Manuel, Niemi, Mari E., Palotie, Aarno, Liu, Aoxing, Lehisto, Arto, Ganna, Andrea, Llorens, Vincent, Tukiainen, Taru, Pat Reeve, Mary, Heyne, Henrike, Mars, Nina, Rämö, Joel, Saarentaus, Elmo, Ollila, Hanna, Rodosthenous, Rodos, Strausz, Satu, Palotie, Tuula, Palin, Kimmo, Garcia-Tabuenca, Javier, Siirtola, Harri, Kiiskinen, Tuomo, Lee, Jiwoo, Tsuo, Kristin, Elliott, Amanda, Kristiansson, Kati, Arvas, Mikko, Hyvärinen, Kati, Ritari, Jarmo, Carpén, Olli, Kettunen, Johannes, Pylkäs, Katri, Sliz, Eeva, Karjalainen, Minna, Mantere, Tuomo, Kangasniemi, Eeva, Heikkinen, Sami, Mannermaa, Arto, Laakkonen, Eija, Pitkänen, Nina, Lessard, Samuel, Chatelain, Clément, Terho, Perttu, Soini, Sirpa, Partanen, Jukka, Punkka, Eero, Serpi, Raisa, Siltanen, Sanna, Kosma, Veli-Matti, Kuopio, Teijo, Jalanko, Anu, Shen, Huei-Yi, Kajanne, Risto, Aavikko, Mervi, Palin, Henna, Linna, Malla-Maria, Kurki, Mitja, Karjalainen, Juha, Della, Pietro, Parolo, Briotta, Lehisto, Arto, Mehtonen, Juha, Zhou, Wei, Kanai, Masahiro, Maasha, Mutaamba, Havulinna, Aki, Lemmelä, Susanna, Kiiskinen, Tuomo, Elisa Lahtela, L., Kaunisto, Mari, Kilpeläinen, Elina, Sipilä, Timo P., Alexander Dada, Oluwaseun, Ghazal, Awaisa, Kytölä, Anastasia, Weldatsadik, Rigbe, Ruotsalainen, Sanni, Donner, Kati, Sipilä, Timo P., Loukola, Anu, Laiho, Päivi, Sistonen, Tuuli, Kaiharju, Essi, Laukkanen, Markku, Järvensivu, Elina, Lähteenmäki, Sini, Männikkö, Lotta, Wong, Regis, Toivola, Auli, Brunfeldt, Minna, Mattsson, Hannele, Kristiansson, Kati, Lemmelä, Susanna, Koskelainen, Sami, Hiekkalinna, Tero, Paajanen, Teemu, Palta, Priit, Pärn, Kalle, Kals, Mart, Luo, Shuang, Laitinen, Tarja, Pat Reeve, Mary, Sampath Padmanabhuni, Shanmukha, Niemi, Marianna, Siirtola, Harri, Gracia-Tabuenca, Javier, Helminen, Mika, Luukkaala, Tiina, Vähätalo, Iida, Pitkänen, Jyrki, Hautalahti, Marco, Mäkelä, Johanna, Smith, Sarah, and Southerington, Tom

Published
2023
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27. Squalamine blocks tumor-associated angiogenesis and growth of human breast cancer cells with or without HER-2/neu overexpression.

Author

Márquez-Garbán, Diana C, Gorrín-Rivas, Manuel, Chen, Hsiao-Wang, Sterling, Colin, Elashoff, David, Hamilton, Nalo, and Pietras, Richard J

Subjects
Animals, Humans, Mice, Breast Neoplasms, Cholestanols, Receptor, erbB-2, Angiogenesis Inhibitors, Vascular Endothelial Growth Factor A, Xenograft Model Antitumor Assays, Signal Transduction, Cell Proliferation, Gene Expression Regulation, Neoplastic, Phosphorylation, Female, Focal Adhesion Kinase 1, Human Umbilical Vein Endothelial Cells, MCF-7 Cells, Trastuzumab, Breast cancer, MCF-7, Squalamine, Tumor-associated angiogenesis, VEGF, Receptor, ErbB-2, Cancer, Breast Cancer, Oncology & Carcinogenesis, Oncology and Carcinogenesis
Abstract

Angiogenesis is critical for breast cancer progression. Overexpression of HER-2/neu receptors occur in 25-30% of breast cancers, and treatment with trastuzumab inhibits HER-2-overexpressing tumor growth. Notably, HER-2-mediated signaling enhances vascular endothelial growth factor (VEGF) secretion to increase tumor-associated angiogenesis. Squalamine (aminosterol compound) suppresses VEGF-induced activation of kinases in vascular endothelial cells and inhibits tumor-associated angiogenesis. We assessed antitumor effects of squalamine either alone or with trastuzumab in nude mice bearing breast tumor xenografts without (MCF-7) or with HER2-overexpression (MCF-7/HER-2). Squalamine alone inhibited progression of MCF-7 tumors lacking HER2 overexpression, and squalamine combined with trastuzumab elicited marked inhibition of MCF-7/HER2 growth exceeding that of trastuzumab alone. MCF-7/HER-2 cells secrete higher levels of VEGF than MCF-7 cells, but squalamine elicited no growth inhibition of either MCF-7/HER-2 or MCF-7 cells in vitro. However, squalamine did stop growth of human umbilical vein endothelial cells (HUVECs) and reduced VEGF-induced endothelial tube-like formations in vitro. These effects correlated with blockade of focal adhesion kinase phosphorylation and stress fiber assembly in HUVECs. Thus, squalamine effectively inhibits growth of breast cancers with or without HER-2-overexpression, an effect due in part to blockade of tumor-associated angiogenesis.

Published
2019

28. Opportunities and challenges for transcriptome-wide association studies

Author

Wainberg, Michael, Sinnott-Armstrong, Nasa, Mancuso, Nicholas, Barbeira, Alvaro N, Knowles, David A, Golan, David, Ermel, Raili, Ruusalepp, Arno, Quertermous, Thomas, Hao, Ke, Björkegren, Johan LM, Im, Hae Kyung, Pasaniuc, Bogdan, Rivas, Manuel A, and Kundaje, Anshul

Subjects
Genetics, Human Genome, Prevention, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Crohn Disease, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Lipoproteins, LDL, Quantitative Trait Loci, Schizophrenia, Transcriptome, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn's disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.

Published
2019

29. Standardized incidence ratios and risk factors for cancer in patients with systemic sclerosis: Data from the Spanish Scleroderma Registry (RESCLE)

Author

Carbonell, Cristina, Marcos, Miguel, Guillén-del-Castillo, Alfredo, Rubio-Rivas, Manuel, Argibay, Ana, Marín-Ballvé, Adela, Rodríguez-Pintó, Ignasi, Baldà-Masmiquel, Maria, Callejas-Moraga, Eduardo, Colunga, Dolores, Sáez-Comet, Luis, González-Echávarri, Cristina, Ortego-Centeno, Norberto, Marí-Alfonso, Begoña, Vargas-Hitos, José-Antonio, Todolí-Parra, José-Antonio, Trapiella, Luis, Herranz-Marín, María-Teresa, Freire, Mayka, Castro-Salomó, Antoni, Perales-Fraile, Isabel, Madroñero-Vuelta, Ana-Belén, Sánchez-García, María-Esther, Ruiz-Muñoz, Manuel, González-García, Andrés, Sánchez-Redondo, Jorge, de-la-Red-Bellvis, Gloria, Fernández-Luque, Alejandra, Muela-Molinero, Alberto, Lledó, Gema-María, Tolosa-Vilella, Carles, Fonollosa-Pla, Vicent, Chamorro, Antonio-Javier, and Simeón-Aznar, Carmen-Pilar

Published
2022
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30. Integration of rare expression outlier-associated variants improves polygenic risk prediction

Author

Smail, Craig, Ferraro, Nicole M., Hui, Qin, Durrant, Matthew G., Aguirre, Matthew, Tanigawa, Yosuke, Keever-Keigher, Marissa R., Rao, Abhiram S., Justesen, Johanne M., Li, Xin, Gloudemans, Michael J., Assimes, Themistocles L., Kooperberg, Charles, Reiner, Alexander P., Huang, Jie, O'Donnell, Christopher J., Sun, Yan V., Rivas, Manuel A., and Montgomery, Stephen B.

Published
2022
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31. Does admission acetylsalicylic acid uptake in hospitalized COVID-19 patients have a protective role? Data from the Spanish SEMI-COVID-19 Registry

Author

Formiga, Francesc, Rubio-Rivas, Manuel, Mora-Luján, José María, Escudero, Samara Campos, Martinez, Rosa Fernandez Madera, Mendez-Bailón, Manuel, Campo, Pedro Durán-del, Pérez, Andrea Riaño, García-Sánchez, Francisco-Javier, Alcalá-Pedrajas, José Nicolás, Hernández, Sergio Arnedo, Milian, Almudena Hernández, Díez, Ana Latorre, Sánchez, Ricardo Gil, Boixeda, Ramon, Vicente, Julio, Cortes, Begoña, Pérez, Carmen Mella, Espartero, María Esther Guisado, Castro, José López, Suárez, Santiago Rodríguez, Varona, Jose F., Gomez-Huelgas, Ricardo, and Ramos-Rincón, Jose Manuel

Published
2022
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32. Impact of interstitial lung disease on the survival of systemic sclerosis with pulmonary arterial hypertension

Author

Guillén-Del-Castillo, Alfredo, Meseguer, Manuel López, Fonollosa-Pla, Vicent, Giménez, Berta Sáez, Colunga-Argüelles, Dolores, Revilla-López, Eva, Rubio-Rivas, Manuel, Ropero, Maria Jose Cristo, Argibay, Ana, Barberá, Joan Albert, Salas, Xavier Pla, Meñaca, Amaya Martínez, Vuelta, Ana Belén Madroñero, Padrón, Antonio Lara, Comet, Luis Sáez, Morera, Juan Antonio Domingo, González-Echávarri, Cristina, Mombiela, Teresa, Ortego-Centeno, Norberto, González, Manuela Marín, Tolosa-Vilella, Carles, Blanco, Isabel, Subías, Pilar Escribano, and Simeón-Aznar, Carmen Pilar

Published
2022
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33. Author Correction: Impact of interstitial lung disease on the survival of systemic sclerosis with pulmonary arterial hypertension

Author

Guillén-Del-Castillo, Alfredo, Meseguer, Manuel López, Fonollosa-Pla, Vicent, Giménez, Berta Sáez, Colunga-Argüelles, Dolores, Revilla-López, Eva, Rubio-Rivas, Manuel, Ropero, Maria Jose Cristo, Argibay, Ana, Barberá, Joan Albert, Salas, Xavier Pla, Meñaca, Amaya Martínez, Vuelta, Ana Belén Madroñero, Padrón, Antonio Lara, Comet, Luis Sáez, Morera, Juan Antonio Domingo, González-Echávarri, Cristina, Mombiela, Teresa, Ortego-Centeno, Norberto, González, Manuela Marín, Tolosa-Vilella, Carles, Blanco, Isabel, Subías, Pilar Escribano, and Simeón-Aznar, Carmen Pilar

Published
2022
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34. Influence of chronic use of corticosteroids and calcineurin inhibitors on COVID-19 clinical outcomes: analysis of a nationwide registry

Author

Calderón-Parra, Jorge, Cuervas-Mons, Valentín, Moreno-Torres, Victor, Rubio-Rivas, Manuel, Blas, Paloma Agudo-de, Pinilla-Llorente, Blanca, Helguera-Amezua, Cristina, Jiménez-García, Nicolás, Pesqueira-Fontan, Paula-María, Méndez-Bailón, Manuel, Artero, Arturo, Gilabert, Noemí, Ibánez-Estéllez, Fátima, Freire-Castro, Santiago-Jesús, Lumbreras-Bermejo, Carlos, and Antón-Santos, Juan-Miguel

Published
2022
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35. A Physical Framework to Study the Effect of Magnetic Fields on the Spike-Time Coding.

Author

Rivas, Manuel and Martinez-Garcia, Marina

Subjects
*MAGNETIC field effects, *ELECTROMAGNETIC fields, *MAGNETIC fields, *NEURAL codes, *MEMBRANE proteins
Abstract

A temporal neural code reliant on the pattern of spike times rather than spike rates offers a feasible mechanism for encoding information from weak periodic external stimuli, such as static or extremely low-frequency electromagnetic fields. Our model focuses on the influence of magnetic fields on neurotransmitter dynamics near the neuron membrane. Neurotransmitter binding to specific receptor sites on membrane proteins can regulate biochemical reactions. The duration a neurotransmitter spends in the bonded state serves as a metric for the magnetic field's capacity as a chemical regulator. By initiating a physical analysis of ligand-receptor binding, utilizing the alpha function for synaptic conductance, and employing a modified version of Bell's law, we quantified the impact of magnetic fields on the bond half-life time and, consequently, on postsynaptic spike timing. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE).

Author

Mohammadi, Hamed, Marateb, Hamid Reza, Momenzadeh, Mohammadreza, Wolkewitz, Martin, and Rubio-Rivas, Manuel

Subjects
COVID-19 pandemic, HEALTH information systems, LENGTH of stay in hospitals, COVID-19, LYMPHOCYTE count
Abstract

This study aims to develop and apply multistate models to estimate, forecast, and manage hospital length of stay during the COVID-19 epidemic without using any external packages. Data from Bellvitge University Hospital in Barcelona, Spain, were analyzed, involving 2285 hospitalized COVID-19 patients with moderate to severe conditions. The implemented multistate model includes transition probabilities and risk rates calculated from transitions between defined states, such as admission, ICU transfer, discharge, and death. In addition to examining key factors like age and gender, diabetes, lymphocyte count, comorbidity burden, symptom duration, and different COVID-19 waves were analyzed. Based on the model, patients hospitalized stay an average of 11.90 days before discharge, 2.84 days before moving to the ICU, or 34.21 days before death. ICU patients remain for about 24.08 days, with subsequent stays of 124.30 days before discharge and 35.44 days before death. These results highlight hospital stays' varying durations and trajectories, providing critical insights into patient flow and healthcare resource utilization. Additionally, it can predict ICU peak loads for specific subgroups, aiding in preparedness. Future work will integrate the developed code into the hospital's Health Information System (HIS) following ISO 13606 EHR standards and implement recursive methods to enhance the model's efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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38. The Use of Corticosteroids or Tocilizumab in COVID-19 Based on Inflammatory Markers

Author

Rubio-Rivas, Manuel, Mora-Luján, José M., Montero, Abelardo, Aguilar García, Josefa Andrea, Méndez Bailón, Manuel, Fernández Cruz, Ana, Oriol, Isabel, Teigell-Muñoz, Francisco-Javier, Dendariena Borque, Beatriz, De la Peña Fernández, Andrés, Fernández González, Raquel, Gil Sánchez, Ricardo, Fernández Fernández, Javier, Catalán, Marta, Cortés-Rodríguez, Begoña, Mella Pérez, Carmen, Montero Rivas, Lorena, Suárez Fuentetaja, Rebeca, Ternero Vega, Jara Eloísa, Ena, Javier, Martin-Urda Díez-Canseco, Anabel, Pérez García, Cristina, Varona, José F., Casas-Rojo, José Manuel, and Millán Núñez-Cortés, Jesús

Published
2022
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39. Insights into the genetic epidemiology of Crohns and rare diseases in the Ashkenazi Jewish population.

Author

Rivas, Manuel, Avila, Brandon, Koskela, Jukka, Huang, Hailiang, Stevens, Christine, Pirinen, Matti, Haritunians, Talin, Neale, Benjamin, Kurki, Mitja, Ganna, Andrea, Graham, Daniel, Glaser, Benjamin, Peter, Inga, Atzmon, Gil, Barzilai, Nir, Levine, Adam, Schiff, Elena, Pontikos, Nikolas, Weisburd, Ben, Lek, Monkol, Karczewski, Konrad, Bloom, Jonathan, Minikel, Eric, Petersen, Britt-Sabina, Beaugerie, Laurent, Seksik, Philippe, Cosnes, Jacques, Schreiber, Stefan, Bokemeyer, Bernd, Bethge, Johannes, Heap, Graham, Ahmad, Tariq, Plagnol, Vincent, Segal, Anthony, Targan, Stephan, Turner, Dan, Saavalainen, Paivi, Farkkila, Martti, Kontula, Kimmo, Palotie, Aarno, Brant, Steven, Duerr, Richard, Silverberg, Mark, Rioux, John, Weersma, Rinse, Franke, Andre, Jostins, Luke, Anderson, Carl, Barrett, Jeffrey, MacArthur, Daniel, Jalas, Chaim, Sokol, Harry, Xavier, Ramnik, Pulver, Ann, Cho, Judy, McGovern, Dermot, and Daly, Mark

Subjects
Algorithms, Crohn Disease, Genetic Predisposition to Disease, Genetics, Population, Genome-Wide Association Study, Haplotypes, Humans, Jews, Models, Genetic, Molecular Epidemiology, Polymorphism, Single Nucleotide, Rare Diseases
Abstract

As part of a broader collaborative network of exome sequencing studies, we developed a jointly called data set of 5,685 Ashkenazi Jewish exomes. We make publicly available a resource of site and allele frequencies, which should serve as a reference for medical genetics in the Ashkenazim (hosted in part at https://ibd.broadinstitute.org, also available in gnomAD at http://gnomad.broadinstitute.org). We estimate that 34% of protein-coding alleles present in the Ashkenazi Jewish population at frequencies greater than 0.2% are significantly more frequent (mean 15-fold) than their maximum frequency observed in other reference populations. Arising via a well-described founder effect approximately 30 generations ago, this catalog of enriched alleles can contribute to differences in genetic risk and overall prevalence of diseases between populations. As validation we document 148 AJ enriched protein-altering alleles that overlap with pathogenic ClinVar alleles (table available at https://github.com/macarthur-lab/clinvar/blob/master/output/clinvar.tsv), including those that account for 10-100 fold differences in prevalence between AJ and non-AJ populations of some rare diseases, especially recessive conditions, including Gaucher disease (GBA, p.Asn409Ser, 8-fold enrichment); Canavan disease (ASPA, p.Glu285Ala, 12-fold enrichment); and Tay-Sachs disease (HEXA, c.1421+1G>C, 27-fold enrichment; p.Tyr427IlefsTer5, 12-fold enrichment). We next sought to use this catalog, of well-established relevance to Mendelian disease, to explore Crohns disease, a common disease with an estimated two to four-fold excess prevalence in AJ. We specifically attempt to evaluate whether strong acting rare alleles, particularly protein-truncating or otherwise large effect-size alleles, enriched by the same founder-effect, contribute excess genetic risk to Crohns disease in AJ, and find that ten rare genetic risk factors in NOD2 and LRRK2 are enriched in AJ (p < 0.005), including several novel contributing alleles, show evidence of association to CD. Independently, we find that genomewide common variant risk defined by GWAS shows a strong difference between AJ and non-AJ European control population samples (0.97 s.d. higher, p

Published
2018

40. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Abstract

This corrects the article DOI: 10.1038/sdata.2017.179.

Published
2018

41. Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees

Author

Jun, Goo, Manning, Alisa, Almeida, Marcio, Zawistowski, Matthew, Wood, Andrew R, Teslovich, Tanya M, Fuchsberger, Christian, Feng, Shuang, Cingolani, Pablo, Gaulton, Kyle J, Dyer, Thomas, Blackwell, Thomas W, Chen, Han, Chines, Peter S, Choi, Sungkyoung, Churchhouse, Claire, Fontanillas, Pierre, King, Ryan, Lee, SungYoung, Lincoln, Stephen E, Trubetskoy, Vasily, DePristo, Mark, Fingerlin, Tasha, Grossman, Robert, Grundstad, Jason, Heath, Alison, Kim, Jayoun, Kim, Young Jin, Laramie, Jason, Lee, Jaehoon, Li, Heng, Liu, Xuanyao, Livne, Oren, Locke, Adam E, Maller, Julian, Mazur, Alexander, Morris, Andrew P, Pollin, Toni I, Ragona, Derek, Reich, David, Rivas, Manuel A, Scott, Laura J, Sim, Xueling, Tearle, Rick G, Teo, Yik Ying, Williams, Amy L, Zöllner, Sebastian, Curran, Joanne E, Peralta, Juan, Akolkar, Beena, Bell, Graeme I, Burtt, Noël P, Cox, Nancy J, Florez, Jose C, Hanis, Craig L, McKeon, Catherine, Mohlke, Karen L, Seielstad, Mark, Wilson, James G, Atzmon, Gil, Below, Jennifer E, Dupuis, Josée, Nicolae, Dan L, Lehman, Donna, Park, Taesung, Won, Sungho, Sladek, Robert, Altshuler, David, McCarthy, Mark I, Duggirala, Ravindranath, Boehnke, Michael, Frayling, Timothy M, Abecasis, Gonçalo R, and Blangero, John

Subjects
Diabetes, Genetics, Human Genome, Clinical Research, Obesity, Genetic Testing, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Diabetes Mellitus, Type 2, Family Health, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Male, Mexican Americans, Pedigree, Phenotype, Quantitative Trait Loci, Whole Genome Sequencing, genetics, sequencing, type 2 diabetes, eQTL, rare variants
Abstract

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

Published
2018

42. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Author

Flannick, Jason, Fuchsberger, Christian, Mahajan, Anubha, Teslovich, Tanya M, Agarwala, Vineeta, Gaulton, Kyle J, Caulkins, Lizz, Koesterer, Ryan, Ma, Clement, Moutsianas, Loukas, McCarthy, Davis J, Rivas, Manuel A, Perry, John RB, Sim, Xueling, Blackwell, Thomas W, Robertson, Neil R, Rayner, N William, Cingolani, Pablo, Locke, Adam E, Tajes, Juan Fernandez, Highland, Heather M, Dupuis, Josee, Chines, Peter S, Lindgren, Cecilia M, Hartl, Christopher, Jackson, Anne U, Chen, Han, Huyghe, Jeroen R, van de Bunt, Martijn, Pearson, Richard D, Kumar, Ashish, Müller-Nurasyid, Martina, Grarup, Niels, Stringham, Heather M, Gamazon, Eric R, Lee, Jaehoon, Chen, Yuhui, Scott, Robert A, Below, Jennifer E, Chen, Peng, Huang, Jinyan, Go, Min Jin, Stitzel, Michael L, Pasko, Dorota, Parker, Stephen CJ, Varga, Tibor V, Green, Todd, Beer, Nicola L, Day-Williams, Aaron G, Ferreira, Teresa, Fingerlin, Tasha, Horikoshi, Momoko, Hu, Cheng, Huh, Iksoo, Ikram, Mohammad Kamran, Kim, Bong-Jo, Kim, Yongkang, Kim, Young Jin, Kwon, Min-Seok, Lee, Juyoung, Lee, Selyeong, Lin, Keng-Han, Maxwell, Taylor J, Nagai, Yoshihiko, Wang, Xu, Welch, Ryan P, Yoon, Joon, Zhang, Weihua, Barzilai, Nir, Voight, Benjamin F, Han, Bok-Ghee, Jenkinson, Christopher P, Kuulasmaa, Teemu, Kuusisto, Johanna, Manning, Alisa, Ng, Maggie CY, Palmer, Nicholette D, Balkau, Beverley, Stančáková, Alena, Abboud, Hanna E, Boeing, Heiner, Giedraitis, Vilmantas, Prabhakaran, Dorairaj, Gottesman, Omri, Scott, James, Carey, Jason, Kwan, Phoenix, Grant, George, Smith, Joshua D, Neale, Benjamin M, Purcell, Shaun, Butterworth, Adam S, Howson, Joanna MM, Lee, Heung Man, Lu, Yingchang, Kwak, Soo-Heon, Zhao, Wei, Danesh, John, and Lam, Vincent KL

Subjects
Humans, Diabetes Mellitus, Type 2, European Continental Ancestry Group, Genetic Variation, Diabetes, Human Genome, Genetics, 2.1 Biological and endogenous factors, Metabolic and endocrine
Abstract

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

Published
2017

43. Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model

Author

Abrego-Vaca, Luis F., Andreu-Arnanz, Ana, Arce-García, Octavio A., Bajo-González, Marta, Borque-Sanz, Pablo, Cózar-Llistó, Alberto, Del Hoyo-Cuenda, Beatriz, Gamboa-Osorio, Alejandra, García-Sánchez, Isabel, López-Cisneros, Óscar A., Merino-Ortiz, Borja, Riera-González, Elisa, Rey-García, Jimena, Sánchez-Díaz, Cristina, Starita-Fajardo, Grisell, Suárez-Carantoña, Cecilia, Zhilina, Svetlana Zhilina, Martínez-Lacalzada, Miguel, Viteri-Noël, Adrián, Manzano, Luis, Fabregate, Martin, Rubio-Rivas, Manuel, Luis García, Sara, Arnalich-Fernández, Francisco, Beato-Pérez, José Luis, Vargas-Núñez, Juan Antonio, Calvo-Manuel, Elpidio, Espiño-Álvarez, Alexia Constanza, Freire-Castro, Santiago J., Loureiro-Amigo, Jose, Pesqueira Fontan, Paula Maria, Pina, Adela, Álvarez Suárez, Ana María, Silva-Asiain, Andrea, García-López, Beatriz, Luque del Pino, Jairo, Sanz-Cánovas, Jaime, Chazarra-Pérez, Paloma, García-García, Gema María, Núñez-Cortés, Jesús Millán, Casas-Rojo, José Manuel, and Gómez-Huelgas, Ricardo

Published
2021
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45. Comprehensive Inherited Risk Estimation for Risk-Based Breast Cancer Screening in Women

Author

Mars, Nina, primary, Kerminen, Sini, additional, Tamlander, Max, additional, Pirinen, Matti, additional, Jakkula, Eveliina, additional, Aaltonen, Kirsimari, additional, Meretoja, Tuomo, additional, Heinävaara, Sirpa, additional, Widén, Elisabeth, additional, Ripatti, Samuli, additional, Palotie, Aarno, additional, Daly, Mark, additional, Riley-Gills, Bridget, additional, Jacob, Howard, additional, Paul, Dirk, additional, Matakidou, Athena, additional, Platt, Adam, additional, Runz, Heiko, additional, John, Sally, additional, Okafo, George, additional, Lawless, Nathan, additional, Plenge, Robert, additional, Maranville, Joseph, additional, McCarthy, Mark, additional, Hunkapiller, Julie, additional, Ehm, Margaret G., additional, Auro, Kirsi, additional, Longerich, Simonne, additional, Fox, Caroline, additional, Mälarstig, Anders, additional, Klinger, Katherine, additional, Raipal, Deepak, additional, Green, Eric, additional, Graham, Robert, additional, Yang, Robert, additional, O'Donnell, Chris, additional, Mäkelä, Tomi P., additional, Kaprio, Jaakko, additional, Virolainen, Petri, additional, Hakanen, Antti, additional, Kilpi, Terhi, additional, Perola, Markus, additional, Partanen, Jukka, additional, Pitkäranta, Anne, additional, Junttila, Juhani, additional, Serpi, Raisa, additional, Laitinen, Tarja, additional, Kosma, Veli-Matti, additional, Laukkanen, Jari, additional, Hautalahti, Marco, additional, Tuovila, Outi, additional, Pakkanen, Raimo, additional, Waring, Jeffrey, additional, Riley-Gillis, Bridget, additional, Rahimov, Fedik, additional, Tachmazidou, Ioanna, additional, Chen, Chia-Yen, additional, Ding, Zhihao, additional, Jung, Marc, additional, Biswas, Shameek, additional, Pendergrass, Rion, additional, Pulford, David, additional, Raghavan, Neha, additional, Huertas-Vazquez, Adriana, additional, Sul, Jae-Hoon, additional, Hu, Xinli, additional, Mozaffari, Sahar, additional, Waterworth, Dawn, additional, Renaud, Nicole, additional, Obeidat, Ma'en, additional, Schleutker, Johanna, additional, Arvas, Mikko, additional, Carpén, Olli, additional, Hinttala, Reetta, additional, Kettunen, Johannes, additional, Mannermaa, Arto, additional, Aalto-Setälä, Katriina, additional, Kähönen, Mika, additional, Mäkelä, Johanna, additional, Kälviäinen, Reetta, additional, Julkunen, Valtteri, additional, Soininen, Hilkka, additional, Remes, Anne, additional, Hiltunen, Mikko, additional, Peltola, Jukka, additional, Raivio, Minna, additional, Tienari, Pentti, additional, Rinne, Juha, additional, Kallionpää, Roosa, additional, Partanen, Juulia, additional, Abbasi, Ali, additional, Ziemann, Adam, additional, Smaoui, Nizar, additional, Lehtonen, Anne, additional, Eaton, Susan, additional, Lahdenperä, Sanni, additional, Bowers, Natalie, additional, Teng, Edmond, additional, Xu, Fanli, additional, Addis, Laura, additional, Eicher, John, additional, Li, Qingqin S, additional, He, Karen, additional, Khramtsova, Ekaterina, additional, Färkkilä, Martti, additional, Koskela, Jukka, additional, Pikkarainen, Sampsa, additional, Jussila, Airi, additional, Kaukinen, Katri, additional, Blomster, Timo, additional, Kiviniemi, Mikko, additional, Voutilainen, Markku, additional, Lu, Tim, additional, McCarthy, Linda, additional, Hart, Amy, additional, Guan, Meijian, additional, Miller, Jason, additional, Kalpala, Kirsi, additional, Miller, Melissa, additional, Eklund, Kari, additional, Palomäki, Antti, additional, Isomäki, Pia, additional, Pirilä, Laura, additional, Kaipiainen-Seppänen, Oili, additional, Huhtakangas, Johanna, additional, Mars, Nina, additional, Lertratanakul, Apinya, additional, Close, David, additional, Hochfeld, Marla, additional, Gordillo, Jorge Esparza, additional, Farias, Fabiana, additional, Bing, Nan, additional, Pelkonen, Margit, additional, Kauppi, Paula, additional, Kankaanranta, Hannu, additional, Harju, Terttu, additional, Lahesmaa, Riitta, additional, Mackay, Alex, additional, Lassi, Glenda, additional, Chen, Hubert, additional, Betts, Joanna, additional, Mishra, Rajashree, additional, Mouded, Majd, additional, Ngo, Debby, additional, Niiranen, Teemu, additional, Vaura, Felix, additional, Salomaa, Veikko, additional, Metsärinne, Kaj, additional, Aittokallio, Jenni, additional, Hernesniemi, Jussi, additional, Gordin, Daniel, additional, Sinisalo, Juha, additional, Taskinen, Marja-Riitta, additional, Tuomi, Tiinamaija, additional, Hiltunen, Timo, additional, Elliott, Amanda, additional, Reeve, Mary Pat, additional, Ruotsalainen, Sanni, additional, Challis, Benjamin, additional, Chu, Audrey, additional, Reilly, Dermot, additional, Mendelson, Mike, additional, Parkkinen, Jaakko, additional, Joensuu, Heikki, additional, Mattson, Johanna, additional, Salminen, Eveliina, additional, Auranen, Annika, additional, Karihtala, Peeter, additional, Auvinen, Päivi, additional, Elenius, Klaus, additional, Pitkänen, Esa, additional, Popovic, Relja, additional, Schutzman, Jennifer, additional, Kulkarni, Diptee, additional, Porello, Alessandro, additional, Loboda, Andrey, additional, Lehtonen, Heli, additional, McDonough, Stefan, additional, Vuoti, Sauli, additional, Kaarniranta, Kai, additional, Turunen, Joni A, additional, Ollila, Terhi, additional, Uusitalo, Hannu, additional, Karjalainen, Juha, additional, Liu, Mengzhen, additional, Loomis, Stephanie, additional, Strauss, Erich, additional, Chen, Hao, additional, Tasanen, Kaisa, additional, Huilaja, Laura, additional, Hannula-Jouppi, Katariina, additional, Salmi, Teea, additional, Peltonen, Sirkku, additional, Koulu, Leena, additional, Choy, David, additional, Wu, Ying, additional, Pussinen, Pirkko, additional, Salminen, Aino, additional, Salo, Tuula, additional, Rice, David, additional, Nieminen, Pekka, additional, Palotie, Ulla, additional, Siponen, Maria, additional, Suominen, Liisa, additional, Mäntylä, Päivi, additional, Gursoy, Ulvi, additional, Anttonen, Vuokko, additional, Sipilä, Kirsi, additional, Laivuori, Hannele, additional, Kurra, Venla, additional, Kotaniemi-Talonen, Laura, additional, Heikinheimo, Oskari, additional, Kalliala, Ilkka, additional, Aaltonen, Lauri, additional, Jokimaa, Varpu, additional, Vääräsmäki, Marja, additional, Uimari, Outi, additional, Morin-Papunen, Laure, additional, Niinimäki, Maarit, additional, Piltonen, Terhi, additional, Kivinen, Katja, additional, Widen, Elisabeth, additional, Tukiainen, Taru, additional, Välimäki, Niko, additional, Laakkonen, Eija, additional, Tyrmi, Jaakko, additional, Silven, Heidi, additional, Sliz, Eeva, additional, Arffman, Riikka, additional, Savukoski, Susanna, additional, Laisk, Triin, additional, Pujol, Natalia, additional, Kumar, Janet, additional, Hovatta, Iiris, additional, Isometsä, Erkki, additional, Veerapen, Kumar, additional, Ollila, Hanna, additional, Suvisaari, Jaana, additional, Als, Thomas Damm, additional, Mäkitie, Antti, additional, Bizaki-Vallaskangas, Argyro, additional, Toppila-Salmi, Sanna, additional, Willberg, Tytti, additional, Saarentaus, Elmo, additional, Aarnisalo, Antti, additional, Rahikkala, Elisa, additional, Aittomäki, Kristiina, additional, Åberg, Fredrik, additional, Kurki, Mitja, additional, Havulinna, Aki, additional, Mehtonen, Juha, additional, Palta, Priit, additional, Hassan, Shabbeer, additional, Della Briotta Parolo, Pietro, additional, Zhou, Wei, additional, Maasha, Mutaamba, additional, Lemmelä, Susanna, additional, Rivas, Manuel, additional, Niemi, Mari E., additional, Liu, Aoxing, additional, Lehisto, Arto, additional, Ganna, Andrea, additional, Llorens, Vincent, additional, Heyne, Henrike, additional, Rämö, Joel, additional, Rodosthenous, Rodos, additional, Strausz, Satu, additional, Palotie, Tuula, additional, Palin, Kimmo, additional, Garcia-Tabuenca, Javier, additional, Siirtola, Harri, additional, Kiiskinen, Tuomo, additional, Lee, Jiwoo, additional, Tsuo, Kristin, additional, Kristiansson, Kati, additional, Hyvärinen, Kati, additional, Ritari, Jarmo, additional, Pylkäs, Katri, additional, Karjalainen, Minna, additional, Mantere, Tuomo, additional, Kangasniemi, Eeva, additional, Heikkinen, Sami, additional, Pitkänen, Nina, additional, Lessard, Samuel, additional, Chatelain, Clément, additional, Terho, Perttu, additional, Soini, Sirpa, additional, Punkka, Eero, additional, Siltanen, Sanna, additional, Kuopio, Teijo, additional, Jalanko, Anu, additional, Shen, Huei-Yi, additional, Kajanne, Risto, additional, Aavikko, Mervi, additional, Kanai, Masahiro, additional, Lahtela, L. Elisa, additional, Kaunisto, Mari, additional, Kilpeläinen, Elina, additional, Sipilä, Timo P., additional, Dada, Oluwaseun Alexander, additional, Ghazal, Awaisa, additional, Kytölä, Anastasia, additional, Weldatsadik, Rigbe, additional, Donner, Kati, additional, Loukola, Anu, additional, Laiho, Päivi, additional, Sistonen, Tuuli, additional, Kaiharju, Essi, additional, Laukkanen, Markku, additional, Järvensivu, Elina, additional, Lähteenmäki, Sini, additional, Männikkö, Lotta, additional, Wong, Regis, additional, Toivola, Auli, additional, Brunfeldt, Minna, additional, Mattsson, Hannele, additional, Koskelainen, Sami, additional, Hiekkalinna, Tero, additional, Paajanen, Teemu, additional, Pärn, Kalle, additional, Kals, Mart, additional, Luo, Shuang, additional, Sinha, Vishal, additional, Niemi, Marianna, additional, Gracia-Tabuenca, Javier, additional, Helminen, Mika, additional, Luukkaala, Tiina, additional, Vähätalo, Iida, additional, Pitkänen, Jyrki, additional, Smith, Sarah, additional, and Southerington, Tom, additional

Published
2024
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48. Estimation of Admission D-dimer Cut-off Value to Predict Venous Thrombotic Events in Hospitalized COVID-19 Patients: Analysis of the SEMI-COVID-19 Registry

Author

García-Cervera, Carles, Giner-Galvañ, Vicente, Wikman-Jorgensen, Philip, Laureiro, Jaime, Rubio-Rivas, Manuel, Gurjian Arena, Anthony, Arnalich-Fernandez, Francisco, Beato Pérez, José Luis, Vargas Núñez, Juan Antonio, González Igual, Jesús Javier, Díez-Manglano, Jesús, Méndez Bailón, Manuel, García Blanco, María José, Freire Castro, Santiago J., Aranda Lobo, Judit, Manzano, Luis, Magallanes Gamboa, Jeffrey Oskar, Arribas Pérez, Luis, González Moraleja, Julio, Calderón Hernaiz, Ruth, García Alegría, Javier, González Noya, Amara, Gómez Huelgas, Ricardo, Lumbreras Bermejo, Carlos, and Antón Santos, Juan Miguel

Published
2021
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49. A regulatory variant at 3q21.1 confers an increased pleiotropic risk for hyperglycemia and altered bone mineral density

Author

Sinnott-Armstrong, Nasa, Sousa, Isabel S., Laber, Samantha, Rendina-Ruedy, Elizabeth, Nitter Dankel, Simon E., Ferreira, Teresa, Mellgren, Gunnar, Karasik, David, Rivas, Manuel, Pritchard, Jonathan, Guntur, Anyonya R., Cox, Roger D., Lindgren, Cecilia M., Hauner, Hans, Sallari, Richard, Rosen, Clifford J., Hsu, Yi-Hsiang, Lander, Eric S., Kiel, Douglas P., and Claussnitzer, Melina

Published
2021
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50. Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness.

Author

Willems, Sara M, Wright, Daniel J, Day, Felix R, Trajanoska, Katerina, Joshi, Peter K, Morris, John A, Matteini, Amy M, Garton, Fleur C, Grarup, Niels, Oskolkov, Nikolay, Thalamuthu, Anbupalam, Mangino, Massimo, Liu, Jun, Demirkan, Ayse, Lek, Monkol, Xu, Liwen, Wang, Guan, Oldmeadow, Christopher, Gaulton, Kyle J, Lotta, Luca A, Miyamoto-Mikami, Eri, Rivas, Manuel A, White, Tom, Loh, Po-Ru, Aadahl, Mette, Amin, Najaf, Attia, John R, Austin, Krista, Benyamin, Beben, Brage, Søren, Cheng, Yu-Ching, Cięszczyk, Paweł, Derave, Wim, Eriksson, Karl-Fredrik, Eynon, Nir, Linneberg, Allan, Lucia, Alejandro, Massidda, Myosotis, Mitchell, Braxton D, Miyachi, Motohiko, Murakami, Haruka, Padmanabhan, Sandosh, Pandey, Ashutosh, Papadimitriou, Ioannis, Rajpal, Deepak K, Sale, Craig, Schnurr, Theresia M, Sessa, Francesco, Shrine, Nick, Tobin, Martin D, Varley, Ian, Wain, Louise V, Wray, Naomi R, Lindgren, Cecilia M, MacArthur, Daniel G, Waterworth, Dawn M, McCarthy, Mark I, Pedersen, Oluf, Khaw, Kay-Tee, Kiel, Douglas P, GEFOS Any-Type of Fracture Consortium, Pitsiladis, Yannis, Fuku, Noriyuki, Franks, Paul W, North, Kathryn N, van Duijn, Cornelia M, Mather, Karen A, Hansen, Torben, Hansson, Ola, Spector, Tim, Murabito, Joanne M, Richards, J Brent, Rivadeneira, Fernando, Langenberg, Claudia, Perry, John RB, Wareham, Nick J, and Scott, Robert A

Subjects
GEFOS Any-Type of Fracture Consortium, Hand, Humans, Actins, Transforming Growth Factor alpha, Membrane Proteins, Neoplasm Proteins, Nuclear Proteins, Repressor Proteins, Hand Strength, Cohort Studies, Genetics, Population, Polymorphism, Single Nucleotide, Adult, Aged, Middle Aged, European Continental Ancestry Group, Female, Male, Genome-Wide Association Study, Genetic Loci, United Kingdom, Prevention, Genetics, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Musculoskeletal
Abstract

Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P

Published
2017

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