Search

Your search keyword '"Barnes, Michael R"' showing total 373 results
Start Over Author "Barnes, Michael R" Search Limiters Full Text
373 results on '"Barnes, Michael R"'

1. bursty_dynamics: A Python Package for Exploring the Temporal Properties of Longitudinal Data

Author

Angdembe, Alisha, Iqbal, Wasim A, Hamad, Rebeen Ali, Casement, John, Consortium, AI-Multiply, Missier, Paolo, Reynolds, Nick, Henkin, Rafael, and Barnes, Michael R

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Understanding the temporal properties of longitudinal data is critical for identifying trends, predicting future events, and making informed decisions in any field where temporal data is analysed, including health and epidemiology, finance, geosciences, and social sciences. Traditional time-series analysis techniques often fail to capture the complexity of irregular temporal patterns present in such data. To address this gap, we introduce bursty_dynamics, a Python package that enables the quantification of bursty dynamics through the calculation of the Burstiness Parameter (BP) and Memory Coefficient (MC). In temporal data, BP and MC provide insights into the irregularity and temporal dependencies within event sequences, shedding light on complex patterns of disease aetiology, human behaviour, or other information diffusion over time. An event train detection method is also implemented to identify clustered events occurring within a specified time interval, allowing for more focused analysis with reduced noise. With built-in visualisation tools, bursty_dynamics provides an accessible yet powerful platform for researchers to explore and interpret the temporal dynamics of longitudinal data. This paper outlines the core functionalities of the package, demonstrates its applications in diverse research domains, and discusses the advantages of using BP, MC, and event train detection for enhanced temporal data analysis.

Published
2024

2. Going Beyond H&E and Oncology: How Do Histopathology Foundation Models Perform for Multi-stain IHC and Immunology?

Author

Gallagher-Syed, Amaya, Pontarini, Elena, Lewis, Myles J., Barnes, Michael R., and Slabaugh, Gregory

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Quantitative Biology - Quantitative Methods, Quantitative Biology - Tissues and Organs
Abstract

This study evaluates the generalisation capabilities of state-of-the-art histopathology foundation models on out-of-distribution multi-stain autoimmune Immunohistochemistry datasets. We compare 13 feature extractor models, including ImageNet-pretrained networks, and histopathology foundation models trained on both public and proprietary data, on Rheumatoid Arthritis subtyping and Sjogren's Disease detection tasks. Using a simple Attention-Based Multiple Instance Learning classifier, we assess the transferability of learned representations from cancer H&E images to autoimmune IHC images. Contrary to expectations, histopathology-pretrained models did not significantly outperform ImageNet-pretrained models. Furthermore, there was evidence of both autoimmune feature misinterpretation and biased feature importance. Our findings highlight the challenges in transferring knowledge from cancer to autoimmune histopathology and emphasise the need for careful evaluation of AI models across diverse histopathological tasks. The code to run this benchmark is available at https://github.com/AmayaGS/ImmunoHistoBench., Comment: Accepted at Workshop on Advancements In Medical Foundation Models (NeurIPS 2024)

Published
2024

3. Automated segmentation of rheumatoid arthritis immunohistochemistry stained synovial tissue

Author

Gallagher-Syed, Amaya, Khan, Abbas, Rivellese, Felice, Pitzalis, Costantino, Lewis, Myles J., Slabaugh, Gregory, and Barnes, Michael R.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Rheumatoid Arthritis (RA) is a chronic, autoimmune disease which primarily affects the joint's synovial tissue. It is a highly heterogeneous disease, with wide cellular and molecular variability observed in synovial tissues. Over the last two decades, the methods available for their study have advanced considerably. In particular, Immunohistochemistry stains are well suited to highlighting the functional organisation of samples. Yet, analysis of IHC-stained synovial tissue samples is still overwhelmingly done manually and semi-quantitatively by expert pathologists. This is because in addition to the fragmented nature of IHC stained synovial tissue, there exist wide variations in intensity and colour, strong clinical centre batch effect, as well as the presence of many undesirable artefacts present in gigapixel Whole Slide Images (WSIs), such as water droplets, pen annotation, folded tissue, blurriness, etc. There is therefore a strong need for a robust, repeatable automated tissue segmentation algorithm which can cope with this variability and provide support to imaging pipelines. We train a UNET on a hand-curated, heterogeneous real-world multi-centre clinical dataset R4RA, which contains multiple types of IHC staining. The model obtains a DICE score of 0.865 and successfully segments different types of IHC staining, as well as dealing with variance in colours, intensity and common WSIs artefacts from the different clinical centres. It can be used as the first step in an automated image analysis pipeline for synovial tissue samples stained with IHC, increasing speed, reproducibility and robustness.

Published
2023

6. Statin-induced LDL cholesterol response and type 2 diabetes: a bidirectional two-sample Mendelian randomization study.

Author

Smit, Roelof AJ, Trompet, Stella, Leong, Aaron, Goodarzi, Mark O, Postmus, Iris, Warren, Helen, Theusch, Elizabeth, Barnes, Michael R, Arsenault, Benoit J, Li, Xiaohui, Feng, QiPing, Chasman, Daniel I, Cupples, L Adrienne, Hitman, Graham A, Krauss, Ronald M, Psaty, Bruce M, Rotter, Jerome I, Cessie, Saskia le, Stein, C Michael, Jukema, J Wouter, and GIST consortium

Subjects
GIST consortium, Humans, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Female, Male, Cholesterol, LDL, Genetic Variation, Genome-Wide Association Study, Mendelian Randomization Analysis, Atherosclerosis, Clinical Research, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Pharmacology and Pharmaceutical Sciences, Pharmacology & Pharmacy
Abstract

It remains unclear whether the increased risk of new-onset type 2 diabetes (T2D) seen in statin users is due to low LDL-C concentrations, or due to the statin-induced proportional change in LDL-C. In addition, genetic instruments have not been proposed before to examine whether liability to T2D might cause greater proportional statin-induced LDL-C lowering. Using summary-level statistics from the Genomic Investigation of Statin Therapy (GIST, nmax = 40,914) and DIAGRAM (nmax = 159,208) consortia, we found a positive genetic correlation between LDL-C statin response and T2D using LD score regression (rgenetic = 0.36, s.e. = 0.13). However, mendelian randomization analyses did not provide support for statin response having a causal effect on T2D risk (OR 1.00 (95% CI: 0.97, 1.03) per 10% increase in statin response), nor that liability to T2D has a causal effect on statin-induced LDL-C response (0.20% increase in response (95% CI: -0.40, 0.80) per doubling of odds of liability to T2D). Although we found no evidence to suggest that proportional statin response influences T2D risk, a definitive assessment should be made in populations comprised exclusively of statin users, as the presence of nonstatin users in the DIAGRAM dataset may have substantially diluted our effect estimate.

Published
2020

7. Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction.

Author

Ntalla, Ioanna, Weng, Lu-Chen, Cartwright, James H, Hall, Amelia Weber, Sveinbjornsson, Gardar, Tucker, Nathan R, Choi, Seung Hoan, Chaffin, Mark D, Roselli, Carolina, Barnes, Michael R, Mifsud, Borbala, Warren, Helen R, Hayward, Caroline, Marten, Jonathan, Cranley, James J, Concas, Maria Pina, Gasparini, Paolo, Boutin, Thibaud, Kolcic, Ivana, Polasek, Ozren, Rudan, Igor, Araujo, Nathalia M, Lima-Costa, Maria Fernanda, Ribeiro, Antonio Luiz P, Souza, Renan P, Tarazona-Santos, Eduardo, Giedraitis, Vilmantas, Ingelsson, Erik, Mahajan, Anubha, Morris, Andrew P, Del Greco M, Fabiola, Foco, Luisa, Gögele, Martin, Hicks, Andrew A, Cook, James P, Lind, Lars, Lindgren, Cecilia M, Sundström, Johan, Nelson, Christopher P, Riaz, Muhammad B, Samani, Nilesh J, Sinagra, Gianfranco, Ulivi, Sheila, Kähönen, Mika, Mishra, Pashupati P, Mononen, Nina, Nikus, Kjell, Caulfield, Mark J, Dominiczak, Anna, Padmanabhan, Sandosh, Montasser, May E, O'Connell, Jeff R, Ryan, Kathleen, Shuldiner, Alan R, Aeschbacher, Stefanie, Conen, David, Risch, Lorenz, Thériault, Sébastien, Hutri-Kähönen, Nina, Lehtimäki, Terho, Lyytikäinen, Leo-Pekka, Raitakari, Olli T, Barnes, Catriona LK, Campbell, Harry, Joshi, Peter K, Wilson, James F, Isaacs, Aaron, Kors, Jan A, van Duijn, Cornelia M, Huang, Paul L, Gudnason, Vilmundur, Harris, Tamara B, Launer, Lenore J, Smith, Albert V, Bottinger, Erwin P, Loos, Ruth JF, Nadkarni, Girish N, Preuss, Michael H, Correa, Adolfo, Mei, Hao, Wilson, James, Meitinger, Thomas, Müller-Nurasyid, Martina, Peters, Annette, Waldenberger, Melanie, Mangino, Massimo, Spector, Timothy D, Rienstra, Michiel, van de Vegte, Yordi J, van der Harst, Pim, Verweij, Niek, Kääb, Stefan, Schramm, Katharina, Sinner, Moritz F, Strauch, Konstantin, Cutler, Michael J, Fatkin, Diane, London, Barry, Olesen, Morten, and Roden, Dan M

Subjects
Humans, Cardiovascular Diseases, Genetic Predisposition to Disease, Electrocardiography, Gene Expression, Multifactorial Inheritance, Quantitative Trait Loci, Female, Male, Arrhythmias, Cardiac, Genetic Variation, Genome-Wide Association Study, Genetic Loci, Endophenotypes, Arrhythmias, Cardiac
Abstract

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

Published
2020

8. A national initiative in data science for health: an evaluation of the UK Farr Institute

Author

Hemingway, H, Lyons, R, Li, Q, Buchan, I, Ainsworth, J, Pell, J, Morris, A, Barnes, Michael R, Bedford, Helen, Bennie, Marion, Blandford, Ann, Briggs, Andy, Brocklehurst, Peter, Brophy, Sinead, Brown, Gavin, Burton, Paul, Butler, Christopher, Capewell, Simon, Carpenter, James, Carroll, John, Cassell, Jackie A, Castillo, Fortunato, Catchpole, Mike, Caulfield, Mark, Colhoun, Helen, Coveney, Peter, Cunningham-Burley, Sarah, Custovic, Adnan, Deanfield, John, Denaxas, Spiros, Dennis, Michael, Dezateux, Carol, Dibben, Chris, Diggle, Peter, Dixon, Will, Dunn, Graham, Emam, Khaled El, Fone, David, Ford, David, Ford, Ian, Frank, John, Freemantle, Nick, Gabbe, Belinda, Gallacher, John, Gibson, Martin, Gilbert, Ruth, Gissler, Mika, Goble, Carol, Goldberg, Andy, Gravenor, Mike, Gunnell, David, Hannaford, Phil, Hayward, Andrew, Hickman, Matthew, Hingorani, Aroon, Holm, Soren, Holman, Cashel, John, Gareth, John, Ann, Jones, Kerina, Kalra, Dipak, Laurie, Graeme, Lewis, Shon, Lloyd, Keith, Lowe, Sarah, McCowan, Colin, Macleod, John, Martin, Richard M, Moore, Anthony, Moore, Laurence, Nazareth, Irwin, Nenadic, Goran, Paranjothy, Shantini, Parmar, Max, Pebody, Richard, Petersen, Steffen, Petersen, Irene, Pillay, Deenan, Preen, David, Pickett, Kate, Pritchard-Jones, Kathy, Przulj, Natasa, Renehan, Andrew, Roberts, Stephen, Robson, John, Rodgers, Sarah, Rossor, Martin, Russell, Ian, Shawe-Taylor, John, Sheikh, Aziz, Siebert, Stefan, Snooks, Helen, Sperrin, Matthew, Stephenson, Judith, Sullivan, Frank, Taylor, Chris, Taylor, Paul, Timmis, Adam, and Ward, Hester JT

Subjects
Information and Computing Sciences, Library and Information Studies, Health Sciences, Generic health relevance, Health sciences, Human society, Information and computing sciences
Abstract

ObjectiveTo evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK.Design and outcome measuresWe evaluated evidence of six domains characterising a new field of science:defining central scientific challenges,demonstrating how the central challenges might be solved,creating novel interactions among groups of scientists,training new types of experts,re-organising universities,demonstrating impacts in society.We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools.SettingFour UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018.Results1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (>70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the final 30 months). 4. Training expanded substantially with 3 new masters courses, training >400 people at masters, short-course and leadership level and 48 PhD students. 5. Universities reorganised with 4/5 Centres established 27 new faculty (tenured) positions, 3 new university institutes. 6. Emerging evidence of impacts included: > 3200 citations for the 10 most cited papers and Farr research informed eight practice-changing clinical guidelines and policies relevant to the health of millions of UK citizens.ConclusionThe Farr Institute played a major role in establishing and growing the field of data science for health in the UK, with some initial evidence of benefits for health and healthcare. The Farr Institute has now expanded into Health Data Research (HDR) UK but key challenges remain including, how to network such activities internationally.

Published
2020

9. Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial

Author

Rivellese, Felice, Surace, Anna E. A., Goldmann, Katriona, Sciacca, Elisabetta, Çubuk, Cankut, Giorli, Giovanni, John, Christopher R., Nerviani, Alessandra, Fossati-Jimack, Liliane, Thorborn, Georgina, Ahmed, Manzoor, Prediletto, Edoardo, Church, Sarah E., Hudson, Briana M., Warren, Sarah E., McKeigue, Paul M., Humby, Frances, Bombardieri, Michele, Barnes, Michael R., Lewis, Myles J., and Pitzalis, Costantino

Published
2022
Full Text
View/download PDF

12. Meta-analysis of genome-wide association studies of HDL cholesterol response to statins

Author

Postmus, Iris, Warren, Helen R, Trompet, Stella, Arsenault, Benoit J, Avery, Christy L, Bis, Joshua C, Chasman, Daniel I, de Keyser, Catherine E, Deshmukh, Harshal A, Evans, Daniel S, Feng, QiPing, Li, Xiaohui, Smit, Roelof AJ, Smith, Albert V, Sun, Fangui, Taylor, Kent D, Arnold, Alice M, Barnes, Michael R, Barratt, Bryan J, Betteridge, John, Boekholdt, S Matthijs, Boerwinkle, Eric, Buckley, Brendan M, Chen, Y-D Ida, de Craen, Anton JM, Cummings, Steven R, Denny, Joshua C, Dubé, Marie Pierre, Durrington, Paul N, Eiriksdottir, Gudny, Ford, Ian, Guo, Xiuqing, Harris, Tamara B, Heckbert, Susan R, Hofman, Albert, Hovingh, G Kees, Kastelein, John JP, Launer, Leonore J, Liu, Ching-Ti, Liu, Yongmei, Lumley, Thomas, McKeigue, Paul M, Munroe, Patricia B, Neil, Andrew, Nickerson, Deborah A, Nyberg, Fredrik, O'Brien, Eoin, O'Donnell, Christopher J, Post, Wendy, Poulter, Neil, Vasan, Ramachandran S, Rice, Kenneth, Rich, Stephen S, Rivadeneira, Fernando, Sattar, Naveed, Sever, Peter, Shaw-Hawkins, Sue, Shields, Denis C, Slagboom, P Eline, Smith, Nicholas L, Smith, Joshua D, Sotoodehnia, Nona, Stanton, Alice, Stott, David J, Stricker, Bruno H, Stürmer, Til, Uitterlinden, André G, Wei, Wei-Qi, Westendorp, Rudi GJ, Whitsel, Eric A, Wiggins, Kerri L, Wilke, Russell A, Ballantyne, Christie M, Colhoun, Helen M, Cupples, L Adrienne, Franco, Oscar H, Gudnason, Vilmundur, Hitman, Graham, Palmer, Colin NA, Psaty, Bruce M, Ridker, Paul M, Stafford, Jeanette M, Stein, Charles M, Tardif, Jean-Claude, Caulfield, Mark J, Jukema, J Wouter, Rotter, Jerome I, and Krauss, Ronald M

Subjects
Atherosclerosis, Genetics, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Stroke, Good Health and Well Being, Cholesterol Ester Transfer Proteins, Cholesterol, HDL, Female, Genome-Wide Association Study, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Male, Pharmacogenomic Variants, Polymorphism, Single Nucleotide, Treatment Outcome, White People, Genome-wide association study, HDL-cholesterol, Statins, pharmacogenetics, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

BackgroundIn addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Inter-individual variation in HDL-C response to statins may be partially explained by genetic variation.Methods and resultsWe performed a meta-analysis of genome-wide association studies (GWAS) to identify variants with an effect on statin-induced high density lipoprotein cholesterol (HDL-C) changes. The 123 most promising signals with p

Published
2016

13. Clinical Impact of Antibodies against Ustekinumab in Psoriasis: An Observational, Cross-Sectional, Multicenter Study

Author

Barker, Jonathan, Benham, Marilyn, Burden, David, Evans, Ian, Griffiths, Christopher, Hussain, Sagair, Kirby, Brian, Lawson, Linda, Mason, Kayleigh, McElhone, Kathleen, Murphy, Ruth, Ormerod, Anthony, Owen, Caroline, Reynolds, Nick, Smith, Catherine, Warren, Richard, Barker, Jonathan N.W.N., Barnes, Michael R., Burden, A. David, DiMeglio, Paola, Emsley, Richard, Evans, Andrea, Griffiths, Christopher E.M., Payne, Katherine, Reynolds, Nick J., Smith, Catherine H., Stocken, Deborah, Warren, Richard B., Loeff, Floris C., Tsakok, Teresa, Dijk, Lisanne, Hart, Margreet H., Duckworth, Michael, Baudry, David, Russell, Alice, Dand, Nick, van Leeuwen, Astrid, de Vries, Annick, Bloem, Karien, Wolbink, Gerrit Jan, and Rispens, Theo

Published
2020
Full Text
View/download PDF

14. Rooted in risk: genetic predisposition for low-density lipoprotein cholesterol level associates with diminished low-density lipoprotein cholesterol response to statin treatment

Author

Smit, Roelof AJ, Postmus, Iris, Trompet, Stella, Barnes, Michael R, Warren, Helen, Arsenault, Benoit J, Chasman, Daniel I, Cupples, L Adrienne, Hitman, Graham A, Krauss, Ronald M, Li, Xiaohui, Psaty, Bruce M, Stein, Charles M, Rotter, Jerome I, and Jukema, J Wouter

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Heart Disease, Cardiovascular, Atherosclerosis, Human Genome, Prevention, Aetiology, 2.1 Biological and endogenous factors, Cholesterol, LDL, Genetic Predisposition to Disease, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Pharmacogenetics, Polymorphism, Single Nucleotide, Triglycerides, MR-Egger, pharmacogenetics, statin therapy, MR–Egger, Medical and Health Sciences, Pharmacology & Pharmacy, Pharmacology and pharmaceutical sciences
Abstract

AimsTo utilize previously reported lead SNPs for low-density lipoprotein cholesterol (LDL-c) levels to find additional loci of importance to statin response, and examine whether genetic predisposition to LDL-c levels associates with differential statin response.MethodsWe investigated effects on statin response of 59 LDL-c SNPs, by combining summary level statistics from the Global Lipids Genetics and Genomic Investigation of Statin Therapy consortia.ResultsLead SNPs for APOE, SORT1 and NPC1L1 were associated with a decreased LDL-c response to statin treatment, as was overall genetic predisposition for increased LDL-c levels as quantified with 59 SNPs, with a 5.4% smaller statin response per standard deviation increase in genetically raised LDL-c levels.ConclusionGenetic predisposition for increased LDL-c level may decrease efficacy of statin therapy.

Published
2016

15. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals.

Author

Ehret, Georg B, Ferreira, Teresa, Chasman, Daniel I, Jackson, Anne U, Schmidt, Ellen M, Johnson, Toby, Thorleifsson, Gudmar, Luan, Jian'an, Donnelly, Lousie A, Kanoni, Stavroula, Petersen, Ann-Kristin, Pihur, Vasyl, Strawbridge, Rona J, Shungin, Dmitry, Hughes, Maria F, Meirelles, Osorio, Kaakinen, Marika, Bouatia-Naji, Nabila, Kristiansson, Kati, Shah, Sonia, Kleber, Marcus E, Guo, Xiuqing, Lyytikäinen, Leo-Pekka, Fava, Cristiano, Eriksson, Niclas, Nolte, Ilja M, Magnusson, Patrik K, Salfati, Elias L, Rallidis, Loukianos S, Theusch, Elizabeth, Smith, Andrew JP, Folkersen, Lasse, Witkowska, Kate, Pers, Tune H, Joehanes, Roby, Kim, Stuart K, Lataniotis, Lazaros, Jansen, Rick, Johnson, Andrew D, Warren, Helen, Kim, Young Jin, Zhao, Wei, Wu, Ying, Tayo, Bamidele O, Bochud, Murielle, CHARGE-EchoGen consortium, CHARGE-HF consortium, Wellcome Trust Case Control Consortium, Absher, Devin, Adair, Linda S, Amin, Najaf, Arking, Dan E, Axelsson, Tomas, Baldassarre, Damiano, Balkau, Beverley, Bandinelli, Stefania, Barnes, Michael R, Barroso, Inês, Bevan, Stephen, Bis, Joshua C, Bjornsdottir, Gyda, Boehnke, Michael, Boerwinkle, Eric, Bonnycastle, Lori L, Boomsma, Dorret I, Bornstein, Stefan R, Brown, Morris J, Burnier, Michel, Cabrera, Claudia P, Chambers, John C, Chang, I-Shou, Cheng, Ching-Yu, Chines, Peter S, Chung, Ren-Hua, Collins, Francis S, Connell, John M, Döring, Angela, Dallongeville, Jean, Danesh, John, de Faire, Ulf, Delgado, Graciela, Dominiczak, Anna F, Doney, Alex SF, Drenos, Fotios, Edkins, Sarah, Eicher, John D, Elosua, Roberto, Enroth, Stefan, Erdmann, Jeanette, Eriksson, Per, Esko, Tonu, Evangelou, Evangelos, Evans, Alun, Fall, Tove, Farrall, Martin, Felix, Janine F, Ferrières, Jean, Ferrucci, Luigi, Fornage, Myriam, and Forrester, Terrence

Subjects
CHARGE-EchoGen consortium, CHARGE-HF consortium, Wellcome Trust Case Control Consortium, Cells, Cultured, Humans, Hypertension, Microarray Analysis, Blood Pressure, Polymorphism, Single Nucleotide, African Continental Ancestry Group, Asian Continental Ancestry Group, Genome-Wide Association Study, Cells, Cultured, Polymorphism, Single Nucleotide, Developmental Biology, Biological Sciences, Medical and Health Sciences
Abstract

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

Published
2016

16. Author Correction: Enhanced NF-κB signaling in type-2 dendritic cells at baseline predicts non-response to adalimumab in psoriasis

Author

Andres-Ejarque, Rosa, Ale, Hira Bahadur, Grys, Katarzyna, Tosi, Isabella, Solanky, Shane, Ainali, Chrysanthi, Catak, Zeynep, Sreeneebus, Hemawtee, Saklatvala, Jake, Dand, Nick, de Rinaldis, Emanuele, Chapman, Anna, Nestle, Frank O., Barnes, Michael R., Warren, Richard B., Reynolds, Nick J., Griffiths, Christopher E. M., Barker, Jonathan N., Smith, Catherine H., and Di Meglio, Paola

Published
2021
Full Text
View/download PDF

17. Enhanced NF-κB signaling in type-2 dendritic cells at baseline predicts non-response to adalimumab in psoriasis

Author

Andres-Ejarque, Rosa, Ale, Hira Bahadur, Grys, Katarzyna, Tosi, Isabella, Solanky, Shane, Ainali, Chrysanthi, Catak, Zeynep, Sreeneebus, Hemawtee, Saklatvala, Jake, Dand, Nick, de Rinaldis, Emanuele, Chapman, Anna, Nestle, Frank O., Barnes, Michael R., Warren, Richard B., Reynolds, Nick J., Griffiths, Christopher E. M., Barker, Jonathan N., Smith, Catherine H., and Di Meglio, Paola

Published
2021
Full Text
View/download PDF

19. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways

Author

O'Dushlaine, Colm, Rossin, Lizzy, Lee, Phil H, Duncan, Laramie, Parikshak, Neelroop N, Newhouse, Stephen, Ripke, Stephan, Neale, Benjamin M, Purcell, Shaun M, Posthuma, Danielle, Nurnberger, John I, Lee, S Hong, Faraone, Stephen V, Perlis, Roy H, Mowry, Bryan J, Thapar, Anita, Goddard, Michael E, Witte, John S, Absher, Devin, Agartz, Ingrid, Akil, Huda, Amin, Farooq, Andreassen, Ole A, Anjorin, Adebayo, Anney, Richard, Anttila, Verneri, Arking, Dan E, Asherson, Philip, Azevedo, Maria H, Backlund, Lena, Badner, Judith A, Bailey, Anthony J, Banaschewski, Tobias, Barchas, Jack D, Barnes, Michael R, Barrett, Thomas B, Bass, Nicholas, Battaglia, Agatino, Bauer, Michael, Bayes, Monica, Bellivier, Frank, Bergen, Sarah E, Berrettini, Wade, Betancur, Catalina, Bettecken, Thomas, Biederman, Joseph, Binder, Elisabeth B, Black, Donald W, Blackwood, Douglas HR, Bloss, Cinnamon S, Boehnke, Michael, Boomsma, Dorret I, Breuer, Rene, Bruggeman, Richard, Cormican, Paul, Buccola, Nancy G, Buitelaar, Jan K, Bunney, William E, Buxbaum, Joseph D, Byerley, William F, Byrne, Enda M, Caesar, Sian, Cahn, Wiepke, Cantor, Rita M, Casas, Miguel, Chakravarti, Aravinda, Chambert, Kimberly, Choudhury, Khalid, Cichon, Sven, Mattheisen, Manuel, Cloninger, C Robert, Collier, David A, Cook, Edwin H, Coon, Hilary, Cormand, Bru, Corvin, Aiden, Coryell, William H, Craig, David W, Craig, Ian W, Crosbie, Jennifer, Cuccaro, Michael L, Curtis, David, Czamara, Darina, Datta, Susmita, Dawson, Geraldine, Day, Richard, De Geus, Eco J, Degenhardt, Franziska, Djurovic, Srdjan, Donohoe, Gary J, Doyle, Alysa E, Duan, Jubao, Dudbridge, Frank, Duketis, Eftichia, Ebstein, Richard P, Edenberg, Howard J, Elia, Josephine, Ennis, Sean, Etain, Bruno, and Fanous, Ayman

Subjects
Human Genome, Brain Disorders, Serious Mental Illness, Schizophrenia, Genetics, Depression, Mental Health, Aetiology, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Brain, Databases, Genetic, Genetic Predisposition to Disease, Genome-Wide Association Study, Histones, Humans, Mental Disorders, Signal Transduction, Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium, Neurosciences, Psychology, Cognitive Sciences, Neurology & Neurosurgery
Abstract

Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders.

Published
2015

21. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins.

Author

Postmus, Iris, Trompet, Stella, Deshmukh, Harshal A, Barnes, Michael R, Li, Xiaohui, Warren, Helen R, Chasman, Daniel I, Zhou, Kaixin, Arsenault, Benoit J, Donnelly, Louise A, Wiggins, Kerri L, Avery, Christy L, Griffin, Paula, Feng, QiPing, Taylor, Kent D, Li, Guo, Evans, Daniel S, Smith, Albert V, de Keyser, Catherine E, Johnson, Andrew D, de Craen, Anton JM, Stott, David J, Buckley, Brendan M, Ford, Ian, Westendorp, Rudi GJ, Slagboom, P Eline, Sattar, Naveed, Munroe, Patricia B, Sever, Peter, Poulter, Neil, Stanton, Alice, Shields, Denis C, O'Brien, Eoin, Shaw-Hawkins, Sue, Chen, Y-D Ida, Nickerson, Deborah A, Smith, Joshua D, Dubé, Marie Pierre, Boekholdt, S Matthijs, Hovingh, G Kees, Kastelein, John JP, McKeigue, Paul M, Betteridge, John, Neil, Andrew, Durrington, Paul N, Doney, Alex, Carr, Fiona, Morris, Andrew, McCarthy, Mark I, Groop, Leif, Ahlqvist, Emma, Welcome Trust Case Control Consortium, Bis, Joshua C, Rice, Kenneth, Smith, Nicholas L, Lumley, Thomas, Whitsel, Eric A, Stürmer, Til, Boerwinkle, Eric, Ngwa, Julius S, O'Donnell, Christopher J, Vasan, Ramachandran S, Wei, Wei-Qi, Wilke, Russell A, Liu, Ching-Ti, Sun, Fangui, Guo, Xiuqing, Heckbert, Susan R, Post, Wendy, Sotoodehnia, Nona, Arnold, Alice M, Stafford, Jeanette M, Ding, Jingzhong, Herrington, David M, Kritchevsky, Stephen B, Eiriksdottir, Gudny, Launer, Leonore J, Harris, Tamara B, Chu, Audrey Y, Giulianini, Franco, MacFadyen, Jean G, Barratt, Bryan J, Nyberg, Fredrik, Stricker, Bruno H, Uitterlinden, André G, Hofman, Albert, Rivadeneira, Fernando, Emilsson, Valur, Franco, Oscar H, Ridker, Paul M, Gudnason, Vilmundur, Liu, Yongmei, Denny, Joshua C, Ballantyne, Christie M, Rotter, Jerome I, Adrienne Cupples, L, Psaty, Bruce M, Palmer, Colin NA, Tardif, Jean-Claude, and Colhoun, Helen M

Subjects
Welcome Trust Case Control Consortium, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Pharmacogenetics, Polymorphism, Single Nucleotide, Cholesterol, LDL, Genome-Wide Association Study, Cholesterol, LDL, Polymorphism, Single Nucleotide
Abstract

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.

Published
2014

22. Pathway and network-based analysis of genome-wide association studies in multiple sclerosis

Author

Baranzini, Sergio E, Galwey, Nicholas W, Wang, Joanne, Khankhanian, Pouya, Lindberg, Raija, Pelletier, Daniel, Wu, Wen, Uitdehaag, Bernard MJ, Kappos, Ludwig, Consortium, GeneMSA, Polman, Chris H, Matthews, Paul M, Hauser, Stephen L, Gibson, Rachel A, Oksenberg, Jorge R, and Barnes, Michael R

Subjects
Multiple Sclerosis, Prevention, Brain Disorders, Neurosciences, Autoimmune Disease, Genetics, Neurodegenerative, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Neurological, Female, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Polymorphism, Single Nucleotide, Signal Transduction, GeneMSA Consortium, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Genome-wide association studies (GWAS) testing several hundred thousand SNPs have been performed in multiple sclerosis (MS) and other complex diseases. Typically, the number of markers in which the evidence for association exceeds the genome-wide significance threshold is very small, and markers that do not exceed this threshold are generally neglected. Classical statistical analysis of these datasets in MS revealed genes with known immunological functions. However, many of the markers showing modest association may represent false negatives. We hypothesize that certain combinations of genes flagged by these markers can be identified if they belong to a common biological pathway. Here we conduct a pathway-oriented analysis of two GWAS in MS that takes into account all SNPs with nominal evidence of association (P < 0.05). Gene-wise P-values were superimposed on a human protein interaction network and searches were conducted to identify sub-networks containing a higher proportion of genes associated with MS than expected by chance. These sub-networks, and others generated at random as a control, were categorized for membership of biological pathways. GWAS from eight other diseases were analyzed to assess the specificity of the pathways identified. In the MS datasets, we identified sub-networks of genes from several immunological pathways including cell adhesion, communication and signaling. Remarkably, neural pathways, namely axon-guidance and synaptic potentiation, were also over-represented in MS. In addition to the immunological pathways previously identified, we report here for the first time the potential involvement of neural pathways in MS susceptibility.

Published
2009

25. Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients

Author

Taylor, John C., Bongartz, Tim, Massey, Jonathan, Mifsud, Borbala, Spiliopoulou, Athina, Scott, Ian C., Wang, Jianmei, Morgan, Michael, Plant, Darren, Colombo, Marco, Orchard, Peter, Twigg, Sarah, McInnes, Iain B., Porter, Duncan, Freeston, Jane E., Nam, Jackie L., Cordell, Heather J., Isaacs, John D., Strathdee, Jenna L., Arnett, Donna, de Hair, Maria J. H., Tak, Paul P., Aslibekyan, Stella, van Vollenhoven, Ronald F., Padyukov, Leonid, Bridges, S. Louis, Pitzalis, Costantino, Cope, Andrew P., Verstappen, Suzanne M. M., Emery, Paul, Barnes, Michael R., Agakov, Felix, McKeigue, Paul, Mushiroda, Taisei, Kubo, Michiaki, Weinshilboum, Richard, Barton, Anne, Morgan, Ann W., Barrett, Jennifer H., on behalf of the MATURA, and PAMERA, and Consortia

Published
2018
Full Text
View/download PDF

27. Investigating Collaborative Data Practices: a Case Study on Artificial Intelligence for Healthcare Research

Author

Henkin, Rafael, Remfry, Elizabeth, Reynolds, Duncan J., Clinch, Megan, Barnes, Michael R., Henkin, Rafael, Remfry, Elizabeth, Reynolds, Duncan J., Clinch, Megan, and Barnes, Michael R.

Abstract

Developing artificial intelligence (AI) tools for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other disciplines together. In this paper, we explore the collaborative data practices of research consortia tasked with applying AI tools to understand and manage multiple long-term conditions in the UK. Through an inductive thematic analysis of 13 semi-structured interviews with participants of these consortia, we aimed to understand how collaboration happens based on the tools used, communication processes and settings, as well as the conditions and obstacles for collaborative work. Our findings reveal the adaptation of tools that are used for sharing knowledge and the tailoring of information based on the audience, particularly those from a clinical or patient perspective. Limitations on the ability to do this were also found to be imposed by the use of electronic healthcare records and access to datasets. We identified meetings as the key setting for facilitating exchanges between disciplines and allowing for the blending and creation of knowledge. Finally, we bring to light the conditions needed to facilitate collaboration and discuss how some of the challenges may be navigated in future work., Comment: 17 pages

Published
2023

28. Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development

Author

Aung, Nay, Vargas, Jose D., Yang, Chaojie, Cabrera, Claudia P., Warren, Helen R., Fung, Kenneth, Tzanis, Evan, Barnes, Michael R., Rotter, Jerome I., Taylor, Kent D., Manichaikul, Ani W., Lima, Joao A.C., Bluemke, David A., Piechnik, Stefan K., Neubauer, Stefan, Munroe, Patricia B., and Petersen, Steffen E.

Published
2019
Full Text
View/download PDF

29. Local delivery of nitric oxide prevents endothelial dysfunction in periodontitis

Author

Fernandes, Daniel, primary, Khambata, Rayomand S., additional, Massimo, Gianmichele, additional, Ruivo, Ernesto, additional, Gee, Lorna C., additional, Foster, Julie, additional, Goddard, Alison, additional, Curtis, Mike, additional, Barnes, Michael R., additional, Wade, William G., additional, Godec, Thomas, additional, Orlandi, Marco, additional, D’Aiuto, Francesco, additional, and Ahluwalia, Amrita, additional

Published
2023
Full Text
View/download PDF

30. Bayesian network analysis of panomic biological big data identifies the importance of triglyceride-rich LDL in atherosclerosis development

Author

Voros, Szilard, primary, Bansal, Aruna T., additional, Barnes, Michael R., additional, Narula, Jagat, additional, Maurovich-Horvat, Pal, additional, Vazquez, Gustavo, additional, Marvasty, Idean B., additional, Brown, Bradley O., additional, Voros, Isaac D., additional, Harris, William, additional, Voros, Viktor, additional, Dayspring, Thomas, additional, Neff, David, additional, Greenfield, Alex, additional, Furchtgott, Leon, additional, Church, Bruce, additional, Runge, Karl, additional, Khalil, Iya, additional, Hayete, Boris, additional, Lucero, Diego, additional, Remaley, Alan T., additional, and Newton, Roger S., additional

Published
2023
Full Text
View/download PDF

34. IGSF10 mutations dysregulate gonadotropin‐releasing hormone neuronal migration resulting in delayed puberty

Author

Howard, Sasha R, Guasti, Leonardo, Ruiz‐Babot, Gerard, Mancini, Alessandra, David, Alessia, Storr, Helen L, Metherell, Lousie A, Sternberg, Michael JE, Cabrera, Claudia P, Warren, Helen R, Barnes, Michael R, Quinton, Richard, de Roux, Nicolas, Young, Jacques, Guiochon‐Mantel, Anne, Wehkalampi, Karoliina, André, Valentina, Gothilf, Yoav, Cariboni, Anna, and Dunkel, Leo

Published
2016
Full Text
View/download PDF

35. Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods

Author

Abdulkareem, Musa, primary, Kenawy, Asmaa A., additional, Rauseo, Elisa, additional, Lee, Aaron M., additional, Sojoudi, Alireza, additional, Amir-Khalili, Alborz, additional, Lekadir, Karim, additional, Young, Alistair A., additional, Barnes, Michael R., additional, Barckow, Philipp, additional, Khanji, Mohammed Y., additional, Aung, Nay, additional, and Petersen, Steffen E., additional

Published
2022
Full Text
View/download PDF

36. RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients

Author

Isaacs, John D., Brockbank, Sarah, Pedersen, Ayako Wakatsuki, Hilkens, Catharien, Anderson, Amy, Stocks, Philip, Lendrem, Dennis, Tarn, Jessica, Smith, Graham R., Allen, Ben, Casement, John, Diboll, Julie, Harry, Rachel, Cooles, Faye A. H., Cope, Andrew P., Simpson, Gemma, Toward, Ruth, Noble, Hayley, Parke, Angela, Wu, Wing, Clarke, Fiona, Scott, David, Scott, Ian C., Galloway, James, Lempp, Heidi, Ibrahim, Fowzia, Schwank, Samana, Molyneux, Gemma, Lazarov, Tomi, Geissmann, Frederic, Goodyear, Carl S., McInnes, Iain B., Donnelly, Iona, Gilmour, Ashley, Virlan, Aysin Tulunay, Porter, Duncan, Ponchel, Frederique, Emery, Paul, El-Jawhari, Jehan, Parmar, Rekha, McDermott, Michael F., Fisher, Benjamin A., Young, Steve P., Jones, Philip, Raza, Karim, Filer, Andrew, Pitzalis, Costantino, Barnes, Michael R., Watson, David S., Henkin, Rafael, Thorborn, Georgina, Fossati-Jimack, Liliane, Kelly, Stephen, Humby, Frances, Bombardieri, Michele, Rana, Sharmila, Jia, Zhilong, Goldmann, Katriona, Lewis, Myles, Ng, Sandra, Barbosa-Silva, Adriano, Tzanis, Evan, Gallagher-Syed, Amaya, John, Christopher R., Ehrenstein, Michael R., Altobelli, Gioia, Martins, Sandra, Nguyen, Dao, Ali, Humayara, Ciurtin, Coziana, Buch, Maya, Symmons, Deborah, Worthington, Jane, Bruce, Ian N., Sergeant, Jamie C., Verstappen, Suzanne M. M., Stirling, Fiona, Hughes-Morley, Adwoa, Tom, Brian, Farewell, Vernon, Zhong, Yujie, Taylor, Peter C., Buckley, Christopher D., Keidel, Sarah, Cuff, Carolyn, Levesque, Marc, Long, Andrew, Liu, Zheng, Lipsky, Samantha, Harvey, Bohdan, Macoritto, Michael, Hong, Feng, Kaymakcalan, Sukru, Tsuji, Wayne, Sabin, Tony, Ward, Neil, Talbot, Susan, Padhji, Desmond, Sleeman, Matthew, Finch, Donna, Herath, Athula, Lindholm, Catharina, Jenkins, Martin, Ho, Meilien, Hollis, Sally, Marshall, Chris, Parker, Gerry, Page, Matt, Edwards, Hannah, Cuza, Alexandru, Gozzard, Neil, Pandis, Ioannis, Rowe, Anthony, Capdevila, Francisco Bonachela, Loza, Matthew J., Curran, Mark, Verbeeck, Denny, Dan Baker, Mela, Christopher M., Vranic, Ivana, Mela, Catherine T., Wright, Stephen, Rowell, Lucy, Vernon, Emma, Joseph, Nina, Payne, Neil, Rao, Ravi, Binks, Michael, Belson, Alexandra, Ludbrook, Valerie, Hicks, Kirsty, Tipney, Hannah, Ellis, Joanne, Hasan, Samiul, Didierlaurent, Arnaud, Burny, Wivine, Haynes, Andrea, Larminie, Chris, Harris, Ray, Dastros-Pitei, Daniela, Carini, Claudio, Kola, Blerina, Jelinsky, Scott, Hodge, Martin, Maciejewski, Mateusz, Ziemek, Daniel, Schulz-Knappe, Peter, Zucht, Hans-Dieter, Budde, Petra, Coles, Mark, Butler, James A., Read, Simon, and Consortium, The RA-MAP

Subjects
Arthritis, Rheumatoid, Proteomics, Statistics and Probability, Vaccines, Humans, Library and Information Sciences, Statistics, Probability and Uncertainty, Monocytes, Autoantibodies, Biological Specimen Banks, Computer Science Applications, Education, Information Systems
Abstract

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.

Published
2022
Full Text
View/download PDF

37. Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial.

Author

UCL - SSS/IREC/RUMA - Pôle de Pathologies rhumatismales, UCL - (SLuc) Service de rhumatologie, Rivellese, Felice, Surace, Anna E A, Goldmann, Katriona, Sciacca, Elisabetta, Çubuk, Cankut, Giorli, Giovanni, John, Christopher R, Nerviani, Alessandra, Fossati-Jimack, Liliane, Thorborn, Georgina, Ahmed, Manzoor, Prediletto, Edoardo, Church, Sarah E, Hudson, Briana M, Warren, Sarah E, McKeigue, Paul M, Humby, Frances, Bombardieri, Michele, Barnes, Michael R, Lewis, Myles J, Pitzalis, Costantino, R4RA collaborative group, UCL - SSS/IREC/RUMA - Pôle de Pathologies rhumatismales, UCL - (SLuc) Service de rhumatologie, Rivellese, Felice, Surace, Anna E A, Goldmann, Katriona, Sciacca, Elisabetta, Çubuk, Cankut, Giorli, Giovanni, John, Christopher R, Nerviani, Alessandra, Fossati-Jimack, Liliane, Thorborn, Georgina, Ahmed, Manzoor, Prediletto, Edoardo, Church, Sarah E, Hudson, Briana M, Warren, Sarah E, McKeigue, Paul M, Humby, Frances, Bombardieri, Michele, Barnes, Michael R, Lewis, Myles J, Pitzalis, Costantino, and R4RA collaborative group

Abstract

Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5-20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment-response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients.

Published
2022

39. Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney

Author

Wain, Louise V., Vaez, Ahmad, Jansen, Rick, Joehanes, Roby, van der Most, Peter J., Erzurumluoglu, A. Mesut, O’Reilly, Paul F., Cabrera, Claudia P., Warren, Helen R., Rose, Lynda M., Verwoert, Germaine C., Hottenga, Jouke-Jan, Strawbridge, Rona J., Esko, Tonu, Arking, Dan E., Hwang, Shih-Jen, Guo, Xiuqing, Kutalik, Zoltan, Trompet, Stella, Shrine, Nick, Teumer, Alexander, Ried, Janina S., Bis, Joshua C., Smith, Albert V., Amin, Najaf, Nolte, Ilja M., Lyytikäinen, Leo-Pekka, Mahajan, Anubha, Wareham, Nicholas J., Hofer, Edith, Joshi, Peter K., Kristiansson, Kati, Traglia, Michela, Havulinna, Aki S., Goel, Anuj, Nalls, Mike A., Sõber, Siim, Vuckovic, Dragana, Luan, Jian’an, Del Greco M., Fabiola, Ayers, Kristin L., Marrugat, Jaume, Ruggiero, Daniela, Lopez, Lorna M., Niiranen, Teemu, Enroth, Stefan, Jackson, Anne U., Nelson, Christopher P., Huffman, Jennifer E., Zhang, Weihua, Marten, Jonathan, Gandin, Ilaria, Harris, Sarah E., Zemunik, Tatijana, Lu, Yingchang, Evangelou, Evangelos, Shah, Nabi, de Borst, Martin H., Mangino, Massimo, Prins, Bram P., Campbell, Archie, Li-Gao, Ruifang, Chauhan, Ganesh, Oldmeadow, Christopher, Abecasis, Gonçalo, Abedi, Maryam, Barbieri, Caterina M., Barnes, Michael R., Batini, Chiara, Beilby, John, Blake, Tineka, Boehnke, Michael, Bottinger, Erwin P., Braund, Peter S., Brown, Morris, Brumat, Marco, Campbell, Harry, Chambers, John C., Cocca, Massimiliano, Collins, Francis, Connell, John, Cordell, Heather J., Damman, Jeffrey J., Davies, Gail, de Geus, Eco J., de Mutsert, Renée, Deelen, Joris, Demirkale, Yusuf, Doney, Alex S.F., Dörr, Marcus, Farrall, Martin, Ferreira, Teresa, Frånberg, Mattias, Gao, He, Giedraitis, Vilmantas, Gieger, Christian, Giulianini, Franco, Gow, Alan J., Hamsten, Anders, Harris, Tamara B., Hofman, Albert, Holliday, Elizabeth G., Hui, Jennie, Jarvelin, Marjo-Riitta, Johansson, Åsa, Johnson, Andrew D., Jousilahti, Pekka, Jula, Antti, Kähönen, Mika, Kathiresan, Sekar, Khaw, Kay-Tee, Kolcic, Ivana, Koskinen, Seppo, Langenberg, Claudia, Larson, Marty, Launer, Lenore J., Lehne, Benjamin, Liewald, David C.M., Lin, Li, Lind, Lars, Mach, François, Mamasoula, Chrysovalanto, Menni, Cristina, Mifsud, Borbala, Milaneschi, Yuri, Morgan, Anna, Morris, Andrew D., Morrison, Alanna C., Munson, Peter J., Nandakumar, Priyanka, Nguyen, Quang Tri, Nutile, Teresa, Oldehinkel, Albertine J., Oostra, Ben A., Org, Elin, Padmanabhan, Sandosh, Palotie, Aarno, Paré, Guillaume, Pattie, Alison, Penninx, Brenda W.J.H., Poulter, Neil, Pramstaller, Peter P., Raitakari, Olli T., Ren, Meixia, Rice, Kenneth, Ridker, Paul M., Riese, Harriëtte, Ripatti, Samuli, Robino, Antonietta, Rotter, Jerome I., Rudan, Igor, Saba, Yasaman, Saint Pierre, Aude, Sala, Cinzia F., Sarin, Antti-Pekka, Schmidt, Reinhold, Scott, Rodney, Seelen, Marc A., Shields, Denis C., Siscovick, David, Sorice, Rossella, Stanton, Alice, Stott, David J., Sundström, Johan, Swertz, Morris, Taylor, Kent D., Thom, Simon, Tzoulaki, Ioanna, Tzourio, Christophe, Uitterlinden, André G., Völker, Uwe, Vollenweider, Peter, Wild, Sarah, Willemsen, Gonneke, Wright, Alan F., Yao, Jie, Thériault, Sébastien, Conen, David, Attia, John, Sever, Peter, Debette, Stéphanie, Mook-Kanamori, Dennis O., Zeggini, Eleftheria, Spector, Tim D., van der Harst, Pim, Palmer, Colin N.A., Vergnaud, Anne-Claire, Loos, Ruth J.F., Polasek, Ozren, Starr, John M., Girotto, Giorgia, Hayward, Caroline, Kooner, Jaspal S., Lindgren, Cecila M., Vitart, Veronique, Samani, Nilesh J., Tuomilehto, Jaakko, Gyllensten, Ulf, Knekt, Paul, Deary, Ian J., Ciullo, Marina, Elosua, Roberto, Keavney, Bernard D., Hicks, Andrew A., Scott, Robert A., Gasparini, Paolo, Laan, Maris, Liu, YongMei, Watkins, Hugh, Hartman, Catharina A., Salomaa, Veikko, Toniolo, Daniela, Perola, Markus, Wilson, James F., Schmidt, Helena, Zhao, Jing Hua, Lehtimäki, Terho, van Duijn, Cornelia M., Gudnason, Vilmundur, Psaty, Bruce M., Peters, Annette, Rettig, Rainer, James, Alan, Jukema, J. Wouter, Strachan, David P., Palmas, Walter, Metspalu, Andres, Ingelsson, Erik, Boomsma, Dorret I., Franco, Oscar H., Bochud, Murielle, Newton-Cheh, Christopher, Munroe, Patricia B., Elliott, Paul, Chasman, Daniel I., Chakravarti, Aravinda, Knight, Joanne, Morris, Andrew P., Levy, Daniel, Tobin, Martin D., Snieder, Harold, Caulfield, Mark J., and Ehret, Georg B.

Published
2017
Full Text
View/download PDF

40. GEOexplorer: a webserver for gene expression analysis and visualisation

Author

Hunt, Guy P, primary, Grassi, Luigi, additional, Henkin, Rafael, additional, Smeraldi, Fabrizio, additional, Spargo, Thomas P, additional, Kabiljo, Renata, additional, Koks, Sulev, additional, Ibrahim, Zina, additional, Dobson, Richard J B, additional, Al-Chalabi, Ammar, additional, Barnes, Michael R, additional, and Iacoangeli, Alfredo, additional

Published
2022
Full Text
View/download PDF

41. Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial

Author

Rivellese, Felice, Surace, Anna E A, Goldmann, Katriona, Sciacca, Elisabetta, Çubuk, Cankut, Giorli, Giovanni, John, Christopher R, Nerviani, Alessandra, Fossati-Jimack, Liliane, Thorborn, Georgina, Ahmed, Manzoor, Prediletto, Edoardo, Church, Sarah E, Hudson, Briana M, Warren, Sarah E, McKeigue, Paul M, Humby, Frances, Bombardieri, Michele, Barnes, Michael R, Lewis, Myles J, Pitzalis, Costantino, R4RA collaborative group, Taylor, PC, group, R4RA collaborative, UCL - SSS/IREC/RUMA - Pôle de Pathologies rhumatismales, and UCL - (SLuc) Service de rhumatologie

Subjects
Arthritis, Rheumatoid, Antirheumatic Agents, Biopsy, Antibodies, Monoclonal, Humans, General Medicine, Antibodies, Monoclonal, Humanized, Rituximab, General Biochemistry, Genetics and Molecular Biology, Biomarkers
Abstract

Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5–20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment–response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients.

Published
2021
Full Text
View/download PDF

42. Gene-centric meta-analyses for central adiposity traits in up to 57 412 individuals of European descent confirm known loci and reveal several novel associations

Author

Yoneyama, Sachiko, Guo, Yiran, Lanktree, Matthew B., Barnes, Michael R., Elbers, Clara C., Karczewski, Konrad J, Padmanabhan, Sandosh, Bauer, Florianne, Baumert, Jens, Beitelshees, Amber, Berenson, Gerald S., Boer, Jolanda M.A., Burke, Gregory, Cade, Brian, Chen, Wei, Cooper-Dehoff, Rhonda M., Gaunt, Tom R., Gieger, Christian, Gong, Yan, Gorski, Mathias, Heard-Costa, Nancy, Johnson, Toby, Lamonte, Michael J., Mcdonough, Caitrin, Monda, Keri L., Onland-Moret, Charlotte N., Nelson, Christopher P., OʼConnell, Jeffrey R., Ordovas, Jose, Peter, Inga, Peters, Annette, Shaffer, Jonathan, Shen, Haiqinq, Smith, Erin, Speilotes, Liz, Thomas, Fridtjof, Thorand, Barbara, Verschuren, Monique W. M., Anand, Sonia S., Dominiczak, Anna, Davidson, Karina W., Hegele, Robert A., Heid, Iris, Hofker, Marten H., Huggins, Gordon S., Illig, Thomas, Johnson, Julie A., Kirkland, Susan, König, Wolfgang, Langaee, Taimour Y., Mccaffery, Jeanne, Melander, Olle, Mitchell, Braxton D., Munroe, Patricia, Murray, Sarah S., Papanicolaou, George, Redline, Susan, Reilly, Muredach, Samani, Nilesh J., Schork, Nicholas J., Van Der Schouw, Yvonne T., Shimbo, Daichi, Shuldiner, Alan R., Tobin, Martin D., Wijmenga, Cisca, Yusuf, Salim, Hakonarson, Hakon, Lange, Leslie A., Demerath, Ellen W, Fox, Caroline S., North, Kari E, Reiner, Alex P., Keating, Brendan, and Taylor, Kira C.

Published
2014
Full Text
View/download PDF

44. Using real‐world data to guide ustekinumab dosing strategies for psoriasis: a prospective pharmacokinetic‐pharmacodynamic study

Author

Pan, Shan, Tsakok, Teresa, Dand, Nick, Lonsdale, Dagan O., Loeff, Floris C., Bloem, Karien, de Vries, Annick, Baudry, David, Duckworth, Michael, Mahil, Satveer, Pushpa‐Rajah, Angela, Russell, Alice, Alsharqi, Ali, Becher, Gabrielle, Murphy, Ruth, Wahie, Shyamal, Wright, Andrew, Griffiths, Christopher E.M., Reynolds, Nick J., Barker, Jonathan, Warren, Richard B., Burden, A. David, Rispens, Theo, Standing, Joseph F., Smith, Catherine H., Benham, Marilyn, Evans, Ian, Hussain, Sagair, Kirby, Brian, Lawson, Linda, Mason, Kayleigh, McElhone, Kathleen, Ormerod, Anthony, Owen, Caroline, Barnes, Michael R., Di Meglio, Paola, Emsley, Richard, Evans, Andrea, and Payne, Katherine

Abstract

Variation in response to biologic therapy for inflammatory diseases, such as psoriasis, is partly driven by variation in drug exposure. Real‐world psoriasis data were used to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for the first‐line therapeutic antibody ustekinumab. The impact of differing dosing strategies on response was explored. Data were collected from a UK prospective multicenter observational cohort (491 patients on ustekinumab monotherapy, drug levels, and anti‐drug antibody measurements on 797 serum samples, 1,590 measurements of Psoriasis Area Severity Index (PASI)). Ustekinumab PKs were described with a linear one‐compartment model. A maximum effect (Emax) model inhibited progression of psoriatic skin lesions in the turnover PD mechanism describing PASI evolution while on treatment. A mixture model on half‐maximal effective concentration identified a potential nonresponder group, with simulations suggesting that, in future, the model could be incorporated into a Bayesian therapeutic drug monitoring “dashboard” to individualize dosing and improve treatment outcomes.

Published
2020

45. Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study

Author

Cabrera, Claudia P., Manson, Joanna, Shepherd, Joanna M., Torrance, Hew D., Watson, David, Longhi, M. Paula, Hoti, Mimoza, Patel, Minal B., O'Dwyer, Michael, Nourshargh, Sussan, Pennington, Daniel J., Barnes, Michael R., and Brohi, Karim

Subjects
Multiple organ failure -- Risk factors, Inflammation -- Risk factors, Biological sciences
Abstract

Background Severe trauma induces a widespread response of the immune system. This 'genomic storm' can lead to poor outcomes, including Multiple Organ Dysfunction Syndrome (MODS). MODS carries a high mortality and morbidity rate and adversely affects long-term health outcomes. Contemporary management of MODS is entirely supportive, and no specific therapeutics have been shown to be effective in reducing incidence or severity. The pathogenesis of MODS remains unclear, and several models are proposed, such as excessive inflammation, a second-hit insult, or an imbalance between pro- and anti-inflammatory pathways. We postulated that the hyperacute window after trauma may hold the key to understanding how the genomic storm is initiated and may lead to a new understanding of the pathogenesis of MODS. Methods and findings We performed whole blood transcriptome and flow cytometry analyses on a total of 70 critically injured patients (Injury Severity Score [ISS] [greater than or equal to] 25) at The Royal London Hospital in the hyperacute time period within 2 hours of injury. We compared transcriptome findings in 36 critically injured patients with those of 6 patients with minor injuries (ISS [less than or equal to] 4). We then performed flow cytometry analyses in 34 critically injured patients and compared findings with those of 9 healthy volunteers. Immediately after injury, only 1,239 gene transcripts (4%) were differentially expressed in critically injured patients. By 24 hours after injury, 6,294 transcripts (21%) were differentially expressed compared to the hyperacute window. Only 202 (16%) genes differentially expressed in the hyperacute window were still expressed in the same direction at 24 hours postinjury. Pathway analysis showed principally up-regulation of pattern recognition and innate inflammatory pathways, with down-regulation of adaptive responses. Immune deconvolution, flow cytometry, and modular analysis suggested a central role for neutrophils and Natural Killer (NK) cells, with underexpression of T- and B cell responses. In the transcriptome cohort, 20 critically injured patients later developed MODS. Compared with the 16 patients who did not develop MODS (NoMODS), maximal differential expression was seen within the hyperacute window. In MODS versus NoMODS, 363 genes were differentially expressed on admission, compared to only 33 at 24 hours postinjury. MODS transcripts differentially expressed in the hyperacute window showed enrichment among diseases and biological functions associated with cell survival and organismal death rather than inflammatory pathways. There was differential up-regulation of NK cell signalling pathways and markers in patients who would later develop MODS, with down-regulation of neutrophil deconvolution markers. This study is limited by its sample size, precluding more detailed analyses of drivers of the hyperacute response and different MODS phenotypes, and requires validation in other critically injured cohorts. Conclusions In this study, we showed how the hyperacute postinjury time window contained a focused, specific signature of the response to critical injury that led to widespread genomic activation. A transcriptomic signature for later development of MODS was present in this hyperacute window; it showed a strong signal for cell death and survival pathways and implicated NK cells and neutrophil populations in this differential response., Author(s): Claudia P. Cabrera 1, Joanna Manson 2, Joanna M. Shepherd 2,*, Hew D. Torrance 3, David Watson 1, M. Paula Longhi 4, Mimoza Hoti 5, Minal B. Patel 1, [...]

Published
2017
Full Text
View/download PDF

46. LGR4 deficiency results in delayed puberty through impaired Wnt/β-catenin signaling

Author

Mancini, Alessandra, Howard, Sasha R., Marelli, Federica, Cabrera, Claudia P., Barnes, Michael R., Sternberg, Michael J.E., Leprovost, Morgane, Hadjidemetriou, Irene, Monti, Elena, David, Alessia, Wehkalampi, Karoliina, Oleari, Roberto, Lettieri, Antonella, Vezzoli, Valeria, Vassart, Gilbert, Cariboni, Anna, Bonomi, Marco, Garcia, Marie-Isabelle, Guasti, Leonardo, Dunkel, Leo, Mancini, Alessandra, Howard, Sasha R., Marelli, Federica, Cabrera, Claudia P., Barnes, Michael R., Sternberg, Michael J.E., Leprovost, Morgane, Hadjidemetriou, Irene, Monti, Elena, David, Alessia, Wehkalampi, Karoliina, Oleari, Roberto, Lettieri, Antonella, Vezzoli, Valeria, Vassart, Gilbert, Cariboni, Anna, Bonomi, Marco, Garcia, Marie-Isabelle, Guasti, Leonardo, and Dunkel, Leo

Abstract

The initiation of puberty is driven by an upsurge in hypothalamic gonadotropin-releasing hormone (GnRH) secretion. In turn, GnRH secretion upsurge depends on the development of a complex GnRH neuroendocrine network during embryonic life. Although delayed puberty (DP) affects up to 2% of the population, is highly heritable, and is associated with adverse health outcomes, the genes underlying DP remain largely unknown. We aimed to discover regulators by whole-exome sequencing of 160 individuals of 67 multigenerational families in our large, accurately phenotyped DP cohort. LGR4 was the only gene remaining after analysis that was significantly enriched for potentially pathogenic, rare variants in 6 probands. Expression analysis identified specific Lgr4 expression at the site of GnRH neuron development. LGR4 mutant proteins showed impaired Wnt/β-catenin signaling, owing to defective protein expression, trafficking, and degradation. Mice deficient in Lgr4 had significantly delayed onset of puberty and fewer GnRH neurons compared with WT, whereas lgr4 knockdown in zebrafish embryos prevented formation and migration of GnRH neurons. Further, genetic lineage tracing showed strong Lgr4-mediated Wnt/β-catenin signaling pathway activation during GnRH neuron development. In conclusion, our results show that LGR4 deficiency impairs Wnt/β-catenin signaling with observed defects in GnRH neuron development, resulting in a DP phenotype., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2020

47. Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals.

Author

Surendran, Praveen, Feofanova, Elena V, Lahrouchi, Najim, Ntalla, Ioanna, Karthikeyan, Savita, Cook, James, Chen, Lingyan, Mifsud, Borbala, Yao, Chen, Kraja, Aldi T, Cartwright, James H, Hellwege, Jacklyn N, Giri, Ayush, Tragante, Vinicius, Thorleifsson, Gudmar, Liu, Dajiang J, Prins, Bram P, Stewart, Isobel D, Cabrera, Claudia P, Eales, James M, Akbarov, Artur, Auer, Paul L, Bielak, Lawrence F, Bis, Joshua C, Braithwaite, Vickie S, Brody, Jennifer A, Daw, E Warwick, Warren, Helen R, Drenos, Fotios, Nielsen, Sune Fallgaard, Faul, Jessica D, Fauman, Eric B, Fava, Cristiano, Ferreira, Teresa, Foley, Christopher N, Franceschini, Nora, Gao, He, Giannakopoulou, Olga, Giulianini, Franco, Gudbjartsson, Daniel F, Guo, Xiuqing, Harris, Sarah E, Havulinna, Aki S, Helgadottir, Anna, Huffman, Jennifer E, Hwang, Shih-Jen, Kanoni, Stavroula, Kontto, Jukka, Larson, Martin G, Li-Gao, Ruifang, Lindström, Jaana, Lotta, Luca A, Lu, Yingchang, Luan, Jian'an, Mahajan, Anubha, Malerba, Giovanni, Masca, Nicholas G D, Mei, Hao, Menni, Cristina, Mook-Kanamori, Dennis O, Mosen-Ansorena, David, Müller-Nurasyid, Martina, Paré, Guillaume, Paul, Dirk S, Perola, Markus, Poveda, Alaitz, Rauramaa, Rainer, Richard, Melissa, Richardson, Tom G, Sepúlveda, Nuno, Sim, Xueling, Smith, Albert V, Smith, Jennifer A, Staley, James R, Stanáková, Alena, Sulem, Patrick, Thériault, Sébastien, Thorsteinsdottir, Unnur, Trompet, Stella, Varga, Tibor V, Velez Edwards, Digna R, Veronesi, Giovanni, Weiss, Stefan, Willems, Sara M, Yao, Jie, Young, Robin, Yu, Bing, Zhang, Weihua, Zhao, Jing-Hua, Zhao, Wei, Evangelou, Evangelos, Aeschbacher, Stefanie, Asllanaj, Eralda, Blankenberg, Stefan, Bonnycastle, Lori L, Bork-Jensen, Jette, Brandslund, Ivan, Braund, Peter S, Burgess, Stephen, Cho, Kelly, Christensen, Cramer, Connell, John, Mutsert, Renée de, Dominiczak, Anna F, Dörr, Marcus, Eiriksdottir, Gudny, Farmaki, Aliki-Eleni, Gaziano, J Michael, Grarup, Niels, Grove, Megan L, Hallmans, Göran, Hansen, Torben, Have, Christian T, Heiss, Gerardo, Jørgensen, Marit E, Jousilahti, Pekka, Kajantie, Eero, Kamat, Mihir, Käräjämäki, AnneMari, Karpe, Fredrik, Koistinen, Heikki A, Kovesdy, Csaba P, Kuulasmaa, Kari, Laatikainen, Tiina, Lannfelt, Lars, Lee, I-Te, Lee, Wen-Jane, Linneberg, Allan, Martin, Lisa W, Moitry, Marie, Nadkarni, Girish, Neville, Matt J, Palmer, Colin N A, Papanicolaou, George J, Pedersen, Oluf, Peters, James, Poulter, Neil, Rasheed, Asif, Rasmussen, Katrine L, Rayner, N William, Mägi, Reedik, Renström, Frida, Rettig, Rainer, Rossouw, Jacques, Schreiner, Pamela J, Sever, Peter S, Sigurdsson, Emil L, Skaaby, Tea, Sun, Yan V, Sundström, Johan, Thorgeirsson, Gudmundur, Esko, Tõnu, Trabetti, Elisabetta, Tsao, Philip S, Tuomi, Tiinamaija, Turner, Stephen T, Tzoulaki, Ioanna, Vaartjes, Ilonca, Vergnaud, Anne-Claire, Willer, Cristen J, Wilson, Peter W F, Witte, Daniel R, Yonova-Doing, Ekaterina, Zhang, He, Aliya, Naheed, Almgren, Peter, Amouyel, Philippe, Asselbergs, Folkert W, Barnes, Michael R, Blakemore, Alexandra I, Boehnke, Michael, Bots, Michiel L, Bottinger, Erwin P, Buring, Julie E, Chambers, John C, Chen, Yii-Der Ida, Chowdhury, Rajiv, Conen, David, Correa, Adolfo, Davey Smith, George, Boer, Rudolf A de, Deary, Ian J, Dedoussis, George, Deloukas, Panos, Di Angelantonio, Emanuele, Elliott, Paul, Felix, Stephan B, Ferrières, Jean, Ford, Ian, Fornage, Myriam, Franks, Paul W, Franks, Stephen, Frossard, Philippe, Gambaro, Giovanni, Gaunt, Tom R, Groop, Leif, Gudnason, Vilmundur, Harris, Tamara B, Hayward, Caroline, Hennig, Branwen J, Herzig, Karl-Heinz, Ingelsson, Erik, Tuomilehto, Jaakko, Järvelin, Marjo-Riitta, Jukema, J Wouter, Kardia, Sharon L R, Kee, Frank, Kooner, Jaspal S, Kooperberg, Charles, Launer, Lenore J, Lind, Lars, Loos, Ruth J F, Majumder, Abdulla Al Shafi, Laakso, Markku, McCarthy, Mark I, Melander, Olle, Mohlke, Karen L, Murray, Alison D, Nordestgaard, Børge Grønne, Orho-Melander, Marju, Packard, Chris J, Padmanabhan, Sandosh, Palmas, Walter, Polasek, Ozren, Porteous, David J, Prentice, Andrew M, Province, Michael A, Relton, Caroline L, Rice, Kenneth, Ridker, Paul M, Rolandsson, Olov, Rosendaal, Frits R, Rotter, Jerome I, Rudan, Igor, Salomaa, Veikko, Samani, Nilesh J, Sattar, Naveed, Sheu, Wayne H-H, Smith, Blair H, Soranzo, Nicole, Spector, Timothy D, Starr, John M, Sebert, Sylvain, Taylor, Kent D, Lakka, Timo A, Timpson, Nicholas J, Tobin, Martin D, van der Harst, Pim, van der Meer, Peter, Ramachandran, Vasan S, Verweij, Niek, Virtamo, Jarmo, Völker, Uwe, Weir, David R, Zeggini, Eleftheria, Charchar, Fadi J, Wareham, Nicholas J, Langenberg, Claudia, Tomaszewski, Maciej, Butterworth, Adam S, Caulfield, Mark J, Danesh, John, Edwards, Todd L, Holm, Hilma, Hung, Adriana M, Lindgren, Cecilia M, Liu, Chunyu, Manning, Alisa K, Morris, Andrew P, Morrison, Alanna C, O'Donnell, Christopher J, Psaty, Bruce M, Saleheen, Danish, Stefansson, Kari, Boerwinkle, Eric, Chasman, Daniel I, Levy, Daniel, Newton-Cheh, Christopher, Munroe, Patricia B, Howson, Joanna M M, Surendran, Praveen, Feofanova, Elena V, Lahrouchi, Najim, Ntalla, Ioanna, Karthikeyan, Savita, Cook, James, Chen, Lingyan, Mifsud, Borbala, Yao, Chen, Kraja, Aldi T, Cartwright, James H, Hellwege, Jacklyn N, Giri, Ayush, Tragante, Vinicius, Thorleifsson, Gudmar, Liu, Dajiang J, Prins, Bram P, Stewart, Isobel D, Cabrera, Claudia P, Eales, James M, Akbarov, Artur, Auer, Paul L, Bielak, Lawrence F, Bis, Joshua C, Braithwaite, Vickie S, Brody, Jennifer A, Daw, E Warwick, Warren, Helen R, Drenos, Fotios, Nielsen, Sune Fallgaard, Faul, Jessica D, Fauman, Eric B, Fava, Cristiano, Ferreira, Teresa, Foley, Christopher N, Franceschini, Nora, Gao, He, Giannakopoulou, Olga, Giulianini, Franco, Gudbjartsson, Daniel F, Guo, Xiuqing, Harris, Sarah E, Havulinna, Aki S, Helgadottir, Anna, Huffman, Jennifer E, Hwang, Shih-Jen, Kanoni, Stavroula, Kontto, Jukka, Larson, Martin G, Li-Gao, Ruifang, Lindström, Jaana, Lotta, Luca A, Lu, Yingchang, Luan, Jian'an, Mahajan, Anubha, Malerba, Giovanni, Masca, Nicholas G D, Mei, Hao, Menni, Cristina, Mook-Kanamori, Dennis O, Mosen-Ansorena, David, Müller-Nurasyid, Martina, Paré, Guillaume, Paul, Dirk S, Perola, Markus, Poveda, Alaitz, Rauramaa, Rainer, Richard, Melissa, Richardson, Tom G, Sepúlveda, Nuno, Sim, Xueling, Smith, Albert V, Smith, Jennifer A, Staley, James R, Stanáková, Alena, Sulem, Patrick, Thériault, Sébastien, Thorsteinsdottir, Unnur, Trompet, Stella, Varga, Tibor V, Velez Edwards, Digna R, Veronesi, Giovanni, Weiss, Stefan, Willems, Sara M, Yao, Jie, Young, Robin, Yu, Bing, Zhang, Weihua, Zhao, Jing-Hua, Zhao, Wei, Evangelou, Evangelos, Aeschbacher, Stefanie, Asllanaj, Eralda, Blankenberg, Stefan, Bonnycastle, Lori L, Bork-Jensen, Jette, Brandslund, Ivan, Braund, Peter S, Burgess, Stephen, Cho, Kelly, Christensen, Cramer, Connell, John, Mutsert, Renée de, Dominiczak, Anna F, Dörr, Marcus, Eiriksdottir, Gudny, Farmaki, Aliki-Eleni, Gaziano, J Michael, Grarup, Niels, Grove, Megan L, Hallmans, Göran, Hansen, Torben, Have, Christian T, Heiss, Gerardo, Jørgensen, Marit E, Jousilahti, Pekka, Kajantie, Eero, Kamat, Mihir, Käräjämäki, AnneMari, Karpe, Fredrik, Koistinen, Heikki A, Kovesdy, Csaba P, Kuulasmaa, Kari, Laatikainen, Tiina, Lannfelt, Lars, Lee, I-Te, Lee, Wen-Jane, Linneberg, Allan, Martin, Lisa W, Moitry, Marie, Nadkarni, Girish, Neville, Matt J, Palmer, Colin N A, Papanicolaou, George J, Pedersen, Oluf, Peters, James, Poulter, Neil, Rasheed, Asif, Rasmussen, Katrine L, Rayner, N William, Mägi, Reedik, Renström, Frida, Rettig, Rainer, Rossouw, Jacques, Schreiner, Pamela J, Sever, Peter S, Sigurdsson, Emil L, Skaaby, Tea, Sun, Yan V, Sundström, Johan, Thorgeirsson, Gudmundur, Esko, Tõnu, Trabetti, Elisabetta, Tsao, Philip S, Tuomi, Tiinamaija, Turner, Stephen T, Tzoulaki, Ioanna, Vaartjes, Ilonca, Vergnaud, Anne-Claire, Willer, Cristen J, Wilson, Peter W F, Witte, Daniel R, Yonova-Doing, Ekaterina, Zhang, He, Aliya, Naheed, Almgren, Peter, Amouyel, Philippe, Asselbergs, Folkert W, Barnes, Michael R, Blakemore, Alexandra I, Boehnke, Michael, Bots, Michiel L, Bottinger, Erwin P, Buring, Julie E, Chambers, John C, Chen, Yii-Der Ida, Chowdhury, Rajiv, Conen, David, Correa, Adolfo, Davey Smith, George, Boer, Rudolf A de, Deary, Ian J, Dedoussis, George, Deloukas, Panos, Di Angelantonio, Emanuele, Elliott, Paul, Felix, Stephan B, Ferrières, Jean, Ford, Ian, Fornage, Myriam, Franks, Paul W, Franks, Stephen, Frossard, Philippe, Gambaro, Giovanni, Gaunt, Tom R, Groop, Leif, Gudnason, Vilmundur, Harris, Tamara B, Hayward, Caroline, Hennig, Branwen J, Herzig, Karl-Heinz, Ingelsson, Erik, Tuomilehto, Jaakko, Järvelin, Marjo-Riitta, Jukema, J Wouter, Kardia, Sharon L R, Kee, Frank, Kooner, Jaspal S, Kooperberg, Charles, Launer, Lenore J, Lind, Lars, Loos, Ruth J F, Majumder, Abdulla Al Shafi, Laakso, Markku, McCarthy, Mark I, Melander, Olle, Mohlke, Karen L, Murray, Alison D, Nordestgaard, Børge Grønne, Orho-Melander, Marju, Packard, Chris J, Padmanabhan, Sandosh, Palmas, Walter, Polasek, Ozren, Porteous, David J, Prentice, Andrew M, Province, Michael A, Relton, Caroline L, Rice, Kenneth, Ridker, Paul M, Rolandsson, Olov, Rosendaal, Frits R, Rotter, Jerome I, Rudan, Igor, Salomaa, Veikko, Samani, Nilesh J, Sattar, Naveed, Sheu, Wayne H-H, Smith, Blair H, Soranzo, Nicole, Spector, Timothy D, Starr, John M, Sebert, Sylvain, Taylor, Kent D, Lakka, Timo A, Timpson, Nicholas J, Tobin, Martin D, van der Harst, Pim, van der Meer, Peter, Ramachandran, Vasan S, Verweij, Niek, Virtamo, Jarmo, Völker, Uwe, Weir, David R, Zeggini, Eleftheria, Charchar, Fadi J, Wareham, Nicholas J, Langenberg, Claudia, Tomaszewski, Maciej, Butterworth, Adam S, Caulfield, Mark J, Danesh, John, Edwards, Todd L, Holm, Hilma, Hung, Adriana M, Lindgren, Cecilia M, Liu, Chunyu, Manning, Alisa K, Morris, Andrew P, Morrison, Alanna C, O'Donnell, Christopher J, Psaty, Bruce M, Saleheen, Danish, Stefansson, Kari, Boerwinkle, Eric, Chasman, Daniel I, Levy, Daniel, Newton-Cheh, Christopher, Munroe, Patricia B, and Howson, Joanna M M

Abstract

Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (P < 5 × 10-8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.

Published
2020
Full Text
View/download PDF

48. A national initiative in data science for health: an evaluation of the UK Farr Institute

Author

Hemingway, Harry, Lyons, Ronan, Li, Qianrui, Buchan, Iain, Ainsworth, John, Pell, Jill, Morris, Andrew, Barnes, Michael R, Bedford, Helen, Bennie, Marion, others, 1100, Caminati, Marco, Hemingway, Harry, Lyons, Ronan, Li, Qianrui, Buchan, Iain, Ainsworth, John, Pell, Jill, Morris, Andrew, Barnes, Michael R, Bedford, Helen, Bennie, Marion, others, 1100, and Caminati, Marco

Abstract

Objective: To evaluate the extent to which the inter-institutional, inter-disciplinary mobilisation of data and skills in the Farr Institute contributed to establishing the emerging field of data science for health in the UK. Design and outcome measures: We evaluated evidence of six domains characterising a new field of science:defining central scientific challenges, demonstrating how the central challenges might be solved,creating novel interactions among groups of scientists,training new types of experts,re-organising universities,demonstrating impacts in society.We carried out citation, network and time trend analyses of publications, and a narrative review of infrastructure, methods and tools. Setting: Four UK centres in London, North England, Scotland and Wales (23 university partners), 2013-2018. Results: 1. The Farr Institute helped define a central scientific challenge publishing a research corpus, demonstrating insights from electronic health record (EHR) and administrative data at each stage of the translational cycle in 593 papers with at least one Farr Institute author affiliation on PubMed. 2. The Farr Institute offered some demonstrations of how these scientific challenges might be solved: it established the first four ISO27001 certified trusted research environments in the UK, and approved more than 1000 research users, published on 102 unique EHR and administrative data sources, although there was no clear evidence of an increase in novel, sustained record linkages. The Farr Institute established open platforms for the EHR phenotyping algorithms and validations (>70 diseases, CALIBER). Sample sizes showed some evidence of increase but remained less than 10% of the UK population in primary care-hospital care linked studies. 3.The Farr Institute created novel interactions among researchers: the co-author publication network expanded from 944 unique co-authors (based on 67 publications in the first 30 months) to 3839 unique co-authors (545 papers in the

Published
2020

49. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.

Author

Amgad, Mohamed, Stovgaard, Elisabeth Ida Specht, Balslev, Eva, Thagaard, Jeppe, Chen, Weijie, Dudgeon, Sarah, Sharma, Ashish, Kerner, Jennifer K, Denkert, Carsten, Yuan, Yinyin, AbdulJabbar, Khalid, Wienert, Stephan, Savas, Peter, Voorwerk, Leonie, Beck, Andrew H, Madabhushi, Anant, Hartman, Johan, Sebastian, Manu M, Horlings, Hugo Mark, Hudeček, Jan, Ciompi, Francesco, Moore, David A, Singh, Rajendra, Roblin, Elvire, Balancin, Marcelo Luiz, Mathieu, Marie-Christine, Lennerz, Jochen JK, Kirtani, Pawan, Chen, I-Chun, Braybrooke, Jeremy P, Pruneri, Giancarlo, Demaria, Sandra, Adams, Sylvia, Schnitt, Stuart J, Lakhani, Sunil R, Rojo, Federico, Comerma, Laura, Badve, Sunil, Khojasteh, Mehrnoush, Symmans, W Fraser, Sotiriou, Christos, Gonzalez-Ericsson, Paula, Pogue-Geile, Katherine L, Kim, Seong-Rim, Rimm, David L, Viale, Giuseppe, Hewitt, Stephen M, Bartlett, John M S, Penault-Llorca, Frédérique, Goel, Shom, Lien, Huang-Chun, Loibl, Sibylle, Kos, Zuzana, Loi, Sherene, Hanna, Matthew G, Michiels, Stefan, Kok, Marleen, Nielsen, Torsten O, Lazar, Alexander J, Bago-Horvath, Zsuzsanna, Kooreman, Loes LFS, van der Laak, Jeroen A W M JAWM, Saltz, Joel, Gallas, Brandon D, Kurkure, Uday, Barnes, Michael R., Salgado, Roberto, Cooper, Lee A D, International Immuno-Oncology Biomarker Working Group, Amgad, Mohamed, Stovgaard, Elisabeth Ida Specht, Balslev, Eva, Thagaard, Jeppe, Chen, Weijie, Dudgeon, Sarah, Sharma, Ashish, Kerner, Jennifer K, Denkert, Carsten, Yuan, Yinyin, AbdulJabbar, Khalid, Wienert, Stephan, Savas, Peter, Voorwerk, Leonie, Beck, Andrew H, Madabhushi, Anant, Hartman, Johan, Sebastian, Manu M, Horlings, Hugo Mark, Hudeček, Jan, Ciompi, Francesco, Moore, David A, Singh, Rajendra, Roblin, Elvire, Balancin, Marcelo Luiz, Mathieu, Marie-Christine, Lennerz, Jochen JK, Kirtani, Pawan, Chen, I-Chun, Braybrooke, Jeremy P, Pruneri, Giancarlo, Demaria, Sandra, Adams, Sylvia, Schnitt, Stuart J, Lakhani, Sunil R, Rojo, Federico, Comerma, Laura, Badve, Sunil, Khojasteh, Mehrnoush, Symmans, W Fraser, Sotiriou, Christos, Gonzalez-Ericsson, Paula, Pogue-Geile, Katherine L, Kim, Seong-Rim, Rimm, David L, Viale, Giuseppe, Hewitt, Stephen M, Bartlett, John M S, Penault-Llorca, Frédérique, Goel, Shom, Lien, Huang-Chun, Loibl, Sibylle, Kos, Zuzana, Loi, Sherene, Hanna, Matthew G, Michiels, Stefan, Kok, Marleen, Nielsen, Torsten O, Lazar, Alexander J, Bago-Horvath, Zsuzsanna, Kooreman, Loes LFS, van der Laak, Jeroen A W M JAWM, Saltz, Joel, Gallas, Brandon D, Kurkure, Uday, Barnes, Michael R., Salgado, Roberto, Cooper, Lee A D, and International Immuno-Oncology Biomarker Working Group

Abstract

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring., SCOPUS: re.j, info:eu-repo/semantics/published

Published
2020

50. Clinical Impact of Antibodies against Ustekinumab in Psoriasis: An Observational, Cross-Sectional, Multicenter Study

Author

Loeff, Floris C., primary, Tsakok, Teresa, additional, Dijk, Lisanne, additional, Hart, Margreet H., additional, Duckworth, Michael, additional, Baudry, David, additional, Russell, Alice, additional, Dand, Nick, additional, van Leeuwen, Astrid, additional, Griffiths, Christopher E.M., additional, Reynolds, Nick J., additional, Barker, Jonathan, additional, Burden, A. David, additional, Warren, Richard B., additional, de Vries, Annick, additional, Bloem, Karien, additional, Wolbink, Gerrit Jan, additional, Smith, Catherine H., additional, Rispens, Theo, additional, Benham, Marilyn, additional, Burden, David, additional, Evans, Ian, additional, Griffiths, Christopher, additional, Hussain, Sagair, additional, Kirby, Brian, additional, Lawson, Linda, additional, Mason, Kayleigh, additional, McElhone, Kathleen, additional, Murphy, Ruth, additional, Ormerod, Anthony, additional, Owen, Caroline, additional, Reynolds, Nick, additional, Smith, Catherine, additional, Warren, Richard, additional, Barker, Jonathan N.W.N., additional, Barnes, Michael R., additional, DiMeglio, Paola, additional, Emsley, Richard, additional, Evans, Andrea, additional, Payne, Katherine, additional, and Stocken, Deborah, additional

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results