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32 results on '"Taylor, Niall"'

1. Developing Healthcare Language Model Embedding Spaces

Author

Taylor, Niall, Schofield, Dan, Kormilitzin, Andrey, Joyce, Dan W, and Nevado-Holgado, Alejo

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text. We explore specialized pre-training to adapt smaller LLMs to different healthcare datasets. Three methods are assessed: traditional masked language modeling, Deep Contrastive Learning for Unsupervised Textual Representations (DeCLUTR), and a novel pre-training objective utilizing metadata categories from the healthcare settings. These schemes are evaluated on downstream document classification tasks for each dataset, with additional analysis of the resultant embedding spaces. Contrastively trained models outperform other approaches on the classification tasks, delivering strong performance from limited labeled data and with fewer model parameter updates required. While metadata-based pre-training does not further improve classifications across the datasets, it yields interesting embedding cluster separability. All domain adapted LLMs outperform their publicly available general base LLM, validating the importance of domain-specialization. This research illustrates efficient approaches to instill healthcare competency in compact LLMs even under tight computational budgets, an essential capability for responsible and sustainable deployment in local healthcare settings. We provide pre-training guidelines for specialized healthcare LLMs, motivate continued inquiry into contrastive objectives, and demonstrates adaptation techniques to align small LLMs with privacy-sensitive medical tasks.

Published
2024

2. Bespoke Large Language Models for Digital Triage Assistance in Mental Health Care

Author

Taylor, Niall, Kormilitzin, Andrey, Lorge, Isabelle, Nevado-Holgado, Alejo, and Joyce, Dan W

Subjects
Computer Science - Artificial Intelligence
Abstract

Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) -- a particularly important use-case for mental health where a majority of routinely-collected patient data lacks structured, machine-readable content. A significant problem for the the United Kingdom's National Health Service (NHS) are the long waiting lists for specialist mental healthcare. According to NHS data, in each month of 2023, there were between 370,000 and 470,000 individual new referrals into secondary mental healthcare services. Referrals must be triaged by clinicians, using clinical information contained in the patient's EHR to arrive at a decision about the most appropriate mental healthcare team to assess and potentially treat these patients. The ability to efficiently recommend a relevant team by ingesting potentially voluminous clinical notes could help services both reduce referral waiting times and with the right technology, improve the evidence available to justify triage decisions. We present and evaluate three different approaches for LLM-based, end-to-end ingestion of variable-length clinical EHR data to assist clinicians when triaging referrals. Our model is able to deliver triage recommendations consistent with existing clinical practices and it's architecture was implemented on a single GPU, making it practical for implementation in resource-limited NHS environments where private implementations of LLM technology will be necessary to ensure confidential clinical data is appropriately controlled and governed.

Published
2024

3. Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks

Author

Taylor, Niall, Ghose, Upamanyu, Rohanian, Omid, Nouriborji, Mohammadmahdi, Kormilitzin, Andrey, Clifton, David, and Nevado-Holgado, Alejo

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods. We present an investigation into the suitability of different PEFT methods to clinical decision-making tasks, across a range of model sizes, including extremely small models with as few as $25$ million parameters. Our analysis shows that the performance of most PEFT approaches varies significantly from one task to another, with the exception of LoRA, which maintains relatively high performance across all model sizes and tasks, typically approaching or matching full fine-tuned performance. The effectiveness of PEFT methods in the clinical domain is evident, particularly for specialised models which can operate on low-cost, in-house computing infrastructure. The advantages of these models, in terms of speed and reduced training costs, dramatically outweighs any performance gain from large foundation LLMs. Furthermore, we highlight how domain-specific pre-training interacts with PEFT methods and model size, and discuss how these factors interplay to provide the best efficiency-performance trade-off. Full code available at: tbd.

Published
2024

4. Detecting the Clinical Features of Difficult-to-Treat Depression using Synthetic Data from Large Language Models

Author

Lorge, Isabelle, Joyce, Dan W., Taylor, Niall, Nevado-Holgado, Alejo, Cipriani, Andrea, and Kormilitzin, Andrey

Subjects
Computer Science - Computation and Language
Abstract

Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where despite treatment, they continue to experience significant burden. We sought to develop a Large Language Model (LLM)-based tool capable of interrogating routinely-collected, narrative (free-text) electronic health record (EHR) data to locate published prognostic factors that capture the clinical syndrome of DTD. In this work, we use LLM-generated synthetic data (GPT3.5) and a Non-Maximum Suppression (NMS) algorithm to train a BERT-based span extraction model. The resulting model is then able to extract and label spans related to a variety of relevant positive and negative factors in real clinical data (i.e. spans of text that increase or decrease the likelihood of a patient matching the DTD syndrome). We show it is possible to obtain good overall performance (0.70 F1 across polarity) on real clinical data on a set of as many as 20 different factors, and high performance (0.85 F1 with 0.95 precision) on a subset of important DTD factors such as history of abuse, family history of affective disorder, illness severity and suicidality by training the model exclusively on synthetic data. Our results show promise for future healthcare applications especially in applications where traditionally, highly confidential medical data and human-expert annotation would normally be required.

Published
2024

6. Building common understanding: seeking consensus and defining social prescribing across contexts – a collective commentary on a Delphi study

Author

Muhl, Caitlin, Mulligan, Kate, Giurca, Bogdan Chiva, Polley, Marie J., Bloch, Gary, Nowak, Dominik Alex, Osborn-Forde, Charlotte, Hsiung, Sonia, Lee, Kheng Hock, Herrmann, Wolfram J., Baker, James Robert, Stokes-Lampard, Dame Helen Jayne, Everington, Sir Sam, Dixon, Michael, Wachsmuth, Isabelle, Figueiredo, Cristiano, Skjerning, Halfdan Thorsø, Rojatz, Daniela, Chen, Yu-Da, Heijnders, Miriam L., Wallace, Carolyn, Howarth, Michelle, Watanabe, Daisuke, Bertotti, Marcello, Jansson, Anu Helena, Althini, Susanna, Holzinger, Felix, Dooler, Darren Glyn, Brand, Siân, Anfilogoff, Tim James, Fancourt, Daisy, Nelson, Michelle L. A., Tierney, Stephanie, Leitch, Alison, Nam, Hae-Kweun, Card, Kiffer G., Hayes, Daniel, Slade, Siân, Essam, Marie Anne, Palmer, Gay Anthia, Welch, Vivian Andrea, Robinson, David, Hilsgen, Laurie, Taylor, Niall, Nielsen, Rasmus Østergaard, Vidovic, Dragana, McDaid, Emer Maeve, Hoffmeister, Louíse Viecili, Bonehill, Jill, Siegel, Alan, Bártová, Alžběta, Acurio-Páez, David, Mendive, Juan Manuel, and Husk, Kerryn

Published
2024
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7. Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio

Author

Nevado-Holgado, Alejo, Espie, Colin, Liakata, Maria, Henry, Alasdair, Gu, Jenny, Taylor, Niall, Saunders, Kate, Walker, Tom, and Miller, Chris

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

SleepioTM is a digital mobile phone and web platform that uses techniques from cognitive behavioural therapy (CBT) to improve sleep in people with sleep difficulty. As part of this process, Sleepio captures data about the sleep behaviour of the users that have consented to such data being processed. For neural networks, the scale of the data is an opportunity to train meaningful models translatable to actual clinical practice. In collaboration with Big Health, the therapeutics company that created and utilizes Sleepio, we have analysed data from a random sample of 401,174 sleep diaries and built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner. We demonstrate that this neural network is more accurate than standard statistical methods in predicting the sleep quality of an individual based on his/her behaviour from the last 10 days. We compare model performance in a wide range of hyperparameter settings representing various scenarios. We further show that the neural network can be used to produce personalised recommendations of what sleep habits users should follow to maximise sleep quality, and show that these recommendations are substantially better than the ones generated by standard methods. We finally show that the neural network can explain the recommendation given to each participant and calculate confidence intervals for each prediction, all of which are essential for clinicians to be able to adopt such a tool in clinical practice.

Published
2022

8. Clinical Prompt Learning with Frozen Language Models

Author

Taylor, Niall, Zhang, Yi, Joyce, Dan, Nevado-Holgado, Alejo, and Kormilitzin, Andrey

Subjects
Computer Science - Computation and Language, ACM-class: J.2
Abstract

Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown impressive performance on a number of natural language tasks with common benchmarking text datasets in full, few-shot, and zero-shot train-evaluation setups. Recently, it has even been observed that large but frozen pre-trained language models (PLMs) with prompt learning outperform smaller but fine-tuned models. However, as with many recent NLP trends, the performance of even the largest PLMs such as GPT-3 do not perform well on specialized domains (e.g. medical text), and the common practice to achieve State of the Art (SoTA) results still consists of pre-training and fine-tuning the PLMs on downstream tasks. The reliance on fine-tuning large PLMs is problematic in clinical settings where data is often held in non-GPU environments, and more resource efficient methods of training specialized domain models is crucial. We investigated the viability of prompt learning on clinically meaningful decision tasks and directly compared with more traditional fine-tuning methods. Results are partially in line with the prompt learning literature, with prompt learning able to match or improve on traditional fine-tuning with substantially fewer trainable parameters and requiring less training data. We argue that prompt learning therefore provides lower computational resource costs applicable to clinical settings, that can serve as an alternative to fine-tuning ever increasing in size PLMs. Complementary code to reproduce experiments presented in this work can be found at: https://github.com/NtaylorOX/Public_Clinical_Prompt., Comment: 18 pages, 6 figures, 6 tables

Published
2022

10. Rationale production to support clinical decision-making

Author

Taylor, Niall, Sha, Lei, Joyce, Dan W, Lukasiewicz, Thomas, Nevado-Holgado, Alejo, and Kormilitzin, Andrey

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

The development of neural networks for clinical artificial intelligence (AI) is reliant on interpretability, transparency, and performance. The need to delve into the black-box neural network and derive interpretable explanations of model output is paramount. A task of high clinical importance is predicting the likelihood of a patient being readmitted to hospital in the near future to enable efficient triage. With the increasing adoption of electronic health records (EHRs), there is great interest in applications of natural language processing (NLP) to clinical free-text contained within EHRs. In this work, we apply InfoCal, the current state-of-the-art model that produces extractive rationales for its predictions, to the task of predicting hospital readmission using hospital discharge notes. We compare extractive rationales produced by InfoCal to competitive transformer-based models pretrained on clinical text data and for which the attention mechanism can be used for interpretation. We find each presented model with selected interpretability or feature importance methods yield varying results, with clinical language domain expertise and pretraining critical to performance and subsequent interpretability., Comment: Machine Learning for Health (ML4H) - Extended Abstract

Published
2021

11. Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews

Author

Wang, Bo, Wu, Yue, Taylor, Niall, Lyons, Terry, Liakata, Maria, Nevado-Holgado, Alejo J, and Saunders, Kate E A

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning, 60L10
Abstract

Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders. However, their overlapping symptoms and common comorbidity make it challenging for the clinicians to distinguish the two conditions on the basis of a clinical interview. In this work, we first present a new multi-modal dataset containing interviews involving individuals with BD or BPD being interviewed about a non-clinical topic . We investigate the automatic detection of the two conditions, and demonstrate a good linear classifier that can be learnt using a down-selected set of features from the different aspects of the interviews and a novel approach of summarising these features. Finally, we find that different sets of features characterise BD and BPD, thus providing insights into the difference between the automatic screening of the two conditions.

Published
2020

12. Clinical Prompt Learning With Frozen Language Models

Author

Taylor, Niall, Zhang, Yi, Joyce, Dan W., Gao, Ziming, Kormilitzin, Andrey, and Nevado-Holgado, Alejo

Abstract

When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work suggests that for some tasks, directly prompting the pretrained model matches or surpasses fine-tuning in performance with few or no model parameter updates required. The use of prompts with language models for natural language processing (NLP) tasks is known as prompt learning. We investigated the viability of prompt learning on clinically meaningful decision tasks and directly compared this with more traditional fine-tuning methods. Results show that prompt learning methods were able to match or surpass the performance of traditional fine-tuning with up to 1000 times fewer trainable parameters, less training time, less training data, and lower computation resource requirements. We argue that these characteristics make prompt learning a very desirable alternative to traditional fine-tuning for clinical tasks, where the computational resources of public health providers are limited, and where data can often not be made available or not be used for fine-tuning due to patient privacy concerns. The complementary code to reproduce the experiments presented in this work can be found at https://github.com/NtaylorOX/Public_Clinical_Prompt.

Published
2024
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15. A HaemAtlas: characterizing gene expression in differentiated human blood cells

Author

Watkins, Nicholas A., Gusnanto, Arief, de Bono, Bernard, De, Subhajyoti, Miranda-Saavedra, Diego, Hardie, Debbie L., Angenent, Will G.J., Attwood, Antony P., Ellis, Peter D., Erber, Wendy, Foad, Nicola S., Garner, Stephen F., Isacke, Clare M., Jolley, Jennifer, Koch, Kerstin, Macaulay, Iain C., Morley, Sarah L., Rendon, Augusto, Rice, Kate M., Taylor, Niall, Thijssen-Timmer, Daphne C., Tijssen, Marloes R., van der Schoot, C. Ellen, Wernisch, Lorenz, Winzer, Thilo, Dudbridge, Frank, Buckley, Christopher D., Langford, Cordelia F., Teichmann, Sarah, Göttgens, Berthold, Ouwehand, Willem H., and on behalf of the Bloodomics Consortium

Published
2009
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16. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Author

Burton, Paul R., Clayton, David G., Cardon, Lon R., Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P., McCarthy, Mark I., Ouwehand, Willem H., Samani, Nilesh J., Todd, John A., Donnelly, Peter, Barrett, Jeffrey C., Davison, Dan, Easton, Doug, Evans, David, Leung, Hin-Tak, Marchini, Jonathan L., Morris, Andrew P., Spencer, Chris C. A., Tobin, Martin D., Attwood, Antony P., Boorman, James P., Cant, Barbara, Everson, Ursula, Hussey, Judith M., Jolley, Jennifer D., Knight, Alexandra S., Koch, Kerstin, Meech, Elizabeth, Nutland, Sarah, Prowse, Christopher V., Stevens, Helen E., Taylor, Niall C., Walters, Graham R., Walker, Neil M., Watkins, Nicholas A., Winzer, Thilo, Jones, Richard W., McArdle, Wendy L., Ring, Susan M., Strachan, David P., Pembrey, Marcus, Breen, Gerome, St Clair, David, Caesar, Sian, Gordon-Smith, Katherine, Jones, Lisa, Fraser, Christine, Green, Elaine K., Grozeva, Detelina, Hamshere, Marian L., Holmans, Peter A., Jones, Ian R., Kirov, George, Moskvina, Valentina, Nikolov, Ivan, O'Donovan, Michael C., Owen, Michael J., Collier, David A., Elkin, Amanda, Farmer, Anne, Williamson, Richard, McGuffin, Peter, Young, Allan H., Ferrier, I. Nicol, Ball, Stephen G., Balmforth, Anthony J., Barrett, Jennifer H., Bishop, D. Timothy, Iles, Mark M., Maqbool, Azhar, Yuldasheva, Nadira, Hall, Alistair S., Braund, Peter S., Dixon, Richard J., Mangino, Massimo, Stevens, Suzanne, Thompson, John R., Bredin, Francesca, Tremelling, Mark, Parkes, Miles, Drummond, Hazel, Lees, Charles W., Nimmo, Elaine R., Satsangi, Jack, Fisher, Sheila A., Forbes, Alastair, Lewis, Cathryn M., Onnie, Clive M., Prescott, Natalie J., Sanderson, Jeremy, Mathew, Christopher G., Barbour, Jamie, Mohiuddin, M. Khalid, Todhunter, Catherine E., Mansfield, John C., Ahmad, Tariq, Cummings, Fraser R., Jewell, Derek P., Webster, John, Brown, Morris J., Lathrop, G. Mark, Connell, John, Dominiczak, Anna, Marcano, Carolina A. Braga, Burke, Beverley, Dobson, Richard, Gungadoo, Johannie, Lee, Kate L., Munroe, Patricia B., Newhouse, Stephen J., Onipinla, Abiodun, Wallace, Chris, Xue, Mingzhan, Caulfield, Mark, Farrall, Martin, Barton, Anne, Bruce, Ian N., Donovan, Hannah, Eyre, Steve, Gilbert, Paul D., Hider, Samantha L., Hinks, Anne M., John, Sally L., Potter, Catherine, Silman, Alan J., Symmons, Deborah P. M., Thomson, Wendy, Worthington, Jane, Dunger, David B., Widmer, Barry, Frayling, Timothy M., Freathy, Rachel M., Lango, Hana, Perry, John R. B., Shields, Beverley M., Weedon, Michael N., Hattersley, Andrew T., Hitman, Graham A., Walker, Mark, Elliott, Kate S., Groves, Christopher J., Lindgren, Cecilia M., Rayner, Nigel W., Timpson, Nicholas J., Zeggini, Eleftheria, Newport, Melanie, Sirugo, Giorgio, Lyons, Emily, Vannberg, Fredrik, Hill, Adrian V. S., Bradbury, Linda A., Farrar, Claire, Pointon, Jennifer J., Wordsworth, Paul, Brown, Matthew A., Franklyn, Jayne A., Heward, Joanne M., Simmonds, Matthew J., Gough, Stephen C. L., Seal, Sheila, Stratton, Michael R., Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J., Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A., Bumpstead, Suzannah J., Chaney, Amy, Downes, Kate, Ghori, Mohammed J. R., Gwilliam, Rhian, Hunt, Sarah E., Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Widden, Claire, Withers, David, Cardin, Niall J., Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdottir, Ingileif B., Howie, Bryan N., Su, Zhan, Teo, Yik Ying, Vukcevic, Damjan, Bentley, David, and Compston, Alistair

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): The Wellcome Trust Case Control Consortium; Management Committee; Paul R. Burton [1]; David G. Clayton [2]; Lon R. Cardon [3]; Nick Craddock [4]; Panos Deloukas [5]; Audrey Duncanson [6]; [...]

Published
2007
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18. The anti-vax phenomenon

Author

Whitehead, Martin, primary, Taylor, Niall, additional, Gough, Alex, additional, Chambers, Danny, additional, Jessop, Mike, additional, and Hyde, Phil, additional

Published
2019
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19. No scientific basis to homeopathy

Author

Whitehead, Martin, primary, Jessop, Mike, additional, Gough, Alex, additional, Taylor, Niall, additional, Hyde, Phil, additional, Atkinson, Martin, additional, and Guthrie, Arlo, additional

Published
2018
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20. Support for Defra's position on homeopathy

Author

Whitehead, Martin, primary, Jessop, Mike, additional, Gough, Alex, additional, Taylor, Niall, additional, Atkinson, Martin, additional, Hyde, Phil, additional, Price, Alison, additional, and Guthrie, Arlo, additional

Published
2018
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21. Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

Author

Nejentsev, Sergey, Howson, Joanna M. M., Walker, Neil M., Szeszko, Jeffrey, Field, Sarah F., Stevens, Helen E., Reynolds, Pamela, Hardy, Matthew, King, Erna, Masters, Jennifer, Hulme, John, Maier, Lisa M., Smyth, Deborah, Bailey, Rebecca, Cooper, Jason D., Ribas, Gloria, Campbell, R. Duncan, Clayton, David G., Todd, John A., Burton, Paul R., Cardon, Lon R., Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P., McCarthy, Mark I., Ouwehand, Willem H., Samani, Nilesh J., Donnelly, Peter, Barrett, Jeffrey C., Davison, Dan, Easton, Doug, Evans, David, Leung, Hin-Tak, Marchini, Jonathan L., Morris, Andrew P., Spencer, Chris C. A., Tobin, Martin D., Attwood, Antony P., Boorman, James P., Cant, Barbara, Everson, Ursula, Hussey, Judith M., Jolley, Jennifer D., Knight, Alexandra S., Koch, Kerstin, Meech, Elizabeth, Nutland, Sarah, Prowse, Christopher V., Taylor, Niall C., Walters, Graham R., Watkins, Nicholas A., Winzer, Thilo, Jones, Richard W., McArdle, Wendy L., Ring, Susan M., Strachan, David P., Pembrey, Marcus, Breen, Gerome, St Clair, David, Caesar, Sian, Gordon-Smith, Katherine, Jones, Lisa, Fraser, Christine, Green, Elaine K., Grozeva, Detelina, Hamshere, Marian L., Holmans, Peter A., Jones, Ian R., Kirov, George, Moskvina, Valentina, Nikolov, Ivan, O'Donovan, Michael C., Owen, Michael J., Collier, David A., Elkin, Amanda, Farmer, Anne, Williamson, Richard, McGuffin, Peter, Young, Allan H., Nicol Ferrier, I., Ball, Stephen G., Balmforth, Anthony J., Barrett, Jennifer H., Bishop, D. Timothy, Iles, Mark M., Maqbool, Azhar, Yuldasheva, Nadira, Hall, Alistair S., Braund, Peter S., Dixon, Richard J., Mangino, Massimo, Stevens, Suzanne, Thompson, John R., Bredin, Francesca, Tremelling, Mark, Parkes, Miles, Drummond, Hazel, Lees, Charles W., Nimmo, Elaine R., Satsangi, Jack, Fisher, Sheila A., Forbes, Alastair, Lewis, Cathryn M., Onnie, Clive M., Prescott, Natalie J., Sanderson, Jeremy, Mathew, Christopher G., Barbour, Jamie, Khalid Mohiuddin, M., Todhunter, Catherine E., Mansfield, John C., Ahmad, Tariq, Cummings, Fraser R., Jewell, Derek P., Webster, John, Brown, Morris J., Lathrop, G. Mark, Connell, John, Dominiczak, Anna, Braga, Carolina A., Burke, Beverley, Dobson, Richard, Gungadoo, Johannie, Lee, Kate L., Munroe, Patricia B., Newhouse, Stephen J., Onipinla, Abiodun, Wallace, Chris, Xue, Mingzhan, Caulfield, Mark, Farrall, Martin, Barton, Anne, Bruce, Ian N., Donovan, Hannah, Eyre, Steve, Gilbert, Paul D., Hider, Samantha L., Hinks, Anne M., John, Sally L., Potter, Catherine, Silman, Alan J., Symmons, Deborah P. M., Thomson, Wendy, Worthington, Jane, Dunger, David B., Widmer, Barry, Frayling, Timothy M., Freathy, Rachel M., Lango, Hana, Perry, John R. B., Shields, Beverley M., Weedon, Michael N., Hattersley, Andrew T., Hitman, Graham A., Walker, Mark, Elliott, Kate S., Groves, Christopher J., Lindgren, Cecilia M., Rayner, Nigel W., Timpson, Nicholas J., Zeggini, Eleftheria, Newport, Melanie, Sirugo, Giorgio, Lyons, Emily, Vannberg, Fredrik, Hill, Adrian V. S., Bradbury, Linda A., Farrar, Claire, Pointon, Jennifer J., Wordsworth, Paul, Brown, Matthew A., Franklyn, Jayne A., Heward, Joanne M., Simmonds, Matthew J., Gough, Stephen C. L., Seal, Sheila, Stratton, Michael R., Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J., Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A., Bryan, Claire, Bumpstead, Suzannah J., Chaney, Amy, Downes, Kate, Ghori, Jilur, Gwilliam, Rhian, Hunt, Sarah E., Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Withers, David, Cardin, Niall J., Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdottir, Ingeleif B., Howie, Bryan N., Su, Zhan, Ying Teo, Yik, Vukcevic, Damjan, Bentley, David, and Compston, Alistair

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Sergey Nejentsev [1, 57]; Joanna M. M. Howson (corresponding author) [1, 57]; Neil M. Walker [1]; Jeffrey Szeszko [1]; Sarah F. Field [1]; Helen E. Stevens [1]; Pamela Reynolds [...]

Published
2007
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22. Homeopathy and cancer

Author

Whitehead, Martin, primary, Chambers, Danny, additional, Taylor, Niall, additional, Jessop, Mike, additional, Gough, Alex, additional, Atkinson, Martin, additional, Hyde, Phil, additional, McKenzie, Brennen, additional, and Guthrie, Arlo, additional

Published
2016
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25. Animals can't choose.

Author

Gough, Alex, Jaggar, David, Ramey, David, Imrie, Robert, Devaney, Valeri, Taylor, Niall, and Kerr, Morag

Subjects
LETTERS to the editor, VETERINARY medicine
Abstract

A letter to the editor is presented in response to an article related to the application of veterinary medicine in the January 21, 2006 issue.

Published
2006

26. Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

Author

Nejentsev, Sergey, Howson, Joanna M. M., Walker, Neil M., Szeszko, Jeffrey, Field, Sarah F., Stevens, Helen E., Reynolds, Pamela, Hardy, Matthew, King, Erna, Masters, Jennifer, Hulme, John, Maier, Lisa M., Smyth, Deborah, Bailey, Rebecca, Cooper, Jason D., Ribas, Gloria, Campbell, R. Duncan, Clayton, David G., Todd, John A., Burton, Paul R., Cardon, Lon R., Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P., McCarthy, Mark I., Ouwehand, Willem H., Samani, Nilesh J., Donnelly, Peter, Barrett, Jeffrey C., Davison, Dan, Easton, Doug, Evans, David, Leung, Hin-Tak, Marchini, Jonathan L., Morris, Andrew P., Spencer, Chris C. A., Tobin, Martin D., Attwood, Antony P., Boorman, James P., Cant, Barbara, Everson, Ursula, Hussey, Judith M., Jolley, Jennifer D., Knight, Alexandra S., Koch, Kerstin, Meech, Elizabeth, Nutland, Sarah, Prowse, Christopher V., Taylor, Niall C., Walters, Graham R., Watkins, Nicholas A., Winzer, Thilo, Jones, Richard W., McArdle, Wendy L., Ring, Susan M., Strachan, David P., Pembrey, Marcus, Breen, Gerome, St Clair, David, Caesar, Sian, Gordon-Smith, Katherine, Jones, Lisa, Fraser, Christine, Green, Elaine K., Grozeva, Detelina, Hamshere, Marian L., Holmans, Peter A., Jones, Ian R., Kirov, George, Moskvina, Valentina, Nikolov, Ivan, O’Donovan, Michael C., Owen, Michael J., Collier, David A., Elkin, Amanda, Farmer, Anne, Williamson, Richard, McGuffin, Peter, Young, Allan H., Nicol Ferrier, I., Ball, Stephen G., Balmforth, Anthony J., Barrett, Jennifer H., Bishop, D. Timothy, Iles, Mark M., Maqbool, Azhar, Yuldasheva, Nadira, Hall, Alistair S., Braund, Peter S., Dixon, Richard J., Mangino, Massimo, Stevens, Suzanne, Thompson, John R., Bredin, Francesca, Tremelling, Mark, Parkes, Miles, Drummond, Hazel, Lees, Charles W., Nimmo, Elaine R., Satsangi, Jack, Fisher, Sheila A., Forbes, Alastair, Lewis, Cathryn M., Onnie, Clive M., Prescott, Natalie J., Sanderson, Jeremy, Mathew, Christopher G., Barbour, Jamie, Khalid Mohiuddin, M., Todhunter, Catherine E., Mansfield, John C., Ahmad, Tariq, Cummings, Fraser R., Jewell, Derek P., Webster, John, Brown, Morris J., Lathrop, G. Mark, Connell, John, Dominiczak, Anna, Braga, Carolina A., Burke, Beverley, Dobson, Richard, Gungadoo, Johannie, Lee, Kate L., Munroe, Patricia B., Newhouse, Stephen J., Onipinla, Abiodun, Wallace, Chris, Xue, Mingzhan, Caulfield, Mark, Farrall, Martin, Barton, Anne, Bruce, Ian N., Donovan, Hannah, Eyre, Steve, Gilbert, Paul D., Hider, Samantha L., Hinks, Anne M., John, Sally L., Potter, Catherine, Silman, Alan J., Symmons, Deborah P. M., Thomson, Wendy, Worthington, Jane, Dunger, David B., Widmer, Barry, Frayling, Timothy M., Freathy, Rachel M., Lango, Hana, Perry, John R. B., Shields, Beverley M., Weedon, Michael N., Hattersley, Andrew T., Hitman, Graham A., Walker, Mark, Elliott, Kate S., Groves, Christopher J., Lindgren, Cecilia M., Rayner, Nigel W., Timpson, Nicholas J., Zeggini, Eleftheria, Newport, Melanie, Sirugo, Giorgio, Lyons, Emily, Vannberg, Fredrik, Hill, Adrian V. S., Bradbury, Linda A., Farrar, Claire, Pointon, Jennifer J., Wordsworth, Paul, Brown, Matthew A., Franklyn, Jayne A., Heward, Joanne M., Simmonds, Matthew J., Gough, Stephen C. L., Seal, Sheila, Stratton, Michael R., Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J., Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A., Bryan, Claire, Bumpstead, Suzannah J., Chaney, Amy, Downes, Kate, Ghori, Jilur, Gwilliam, Rhian, Hunt, Sarah E., Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Withers, David, Cardin, Niall J., Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdóttir, Ingeleif B., Howie, Bryan N., Su, Zhan, Ying Teo, Yik, Vukcevic, Damjan, Bentley, David, Compston, Alistair, Nejentsev, Sergey, Howson, Joanna M. M., Walker, Neil M., Szeszko, Jeffrey, Field, Sarah F., Stevens, Helen E., Reynolds, Pamela, Hardy, Matthew, King, Erna, Masters, Jennifer, Hulme, John, Maier, Lisa M., Smyth, Deborah, Bailey, Rebecca, Cooper, Jason D., Ribas, Gloria, Campbell, R. Duncan, Clayton, David G., Todd, John A., Burton, Paul R., Cardon, Lon R., Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P., McCarthy, Mark I., Ouwehand, Willem H., Samani, Nilesh J., Donnelly, Peter, Barrett, Jeffrey C., Davison, Dan, Easton, Doug, Evans, David, Leung, Hin-Tak, Marchini, Jonathan L., Morris, Andrew P., Spencer, Chris C. A., Tobin, Martin D., Attwood, Antony P., Boorman, James P., Cant, Barbara, Everson, Ursula, Hussey, Judith M., Jolley, Jennifer D., Knight, Alexandra S., Koch, Kerstin, Meech, Elizabeth, Nutland, Sarah, Prowse, Christopher V., Taylor, Niall C., Walters, Graham R., Watkins, Nicholas A., Winzer, Thilo, Jones, Richard W., McArdle, Wendy L., Ring, Susan M., Strachan, David P., Pembrey, Marcus, Breen, Gerome, St Clair, David, Caesar, Sian, Gordon-Smith, Katherine, Jones, Lisa, Fraser, Christine, Green, Elaine K., Grozeva, Detelina, Hamshere, Marian L., Holmans, Peter A., Jones, Ian R., Kirov, George, Moskvina, Valentina, Nikolov, Ivan, O’Donovan, Michael C., Owen, Michael J., Collier, David A., Elkin, Amanda, Farmer, Anne, Williamson, Richard, McGuffin, Peter, Young, Allan H., Nicol Ferrier, I., Ball, Stephen G., Balmforth, Anthony J., Barrett, Jennifer H., Bishop, D. Timothy, Iles, Mark M., Maqbool, Azhar, Yuldasheva, Nadira, Hall, Alistair S., Braund, Peter S., Dixon, Richard J., Mangino, Massimo, Stevens, Suzanne, Thompson, John R., Bredin, Francesca, Tremelling, Mark, Parkes, Miles, Drummond, Hazel, Lees, Charles W., Nimmo, Elaine R., Satsangi, Jack, Fisher, Sheila A., Forbes, Alastair, Lewis, Cathryn M., Onnie, Clive M., Prescott, Natalie J., Sanderson, Jeremy, Mathew, Christopher G., Barbour, Jamie, Khalid Mohiuddin, M., Todhunter, Catherine E., Mansfield, John C., Ahmad, Tariq, Cummings, Fraser R., Jewell, Derek P., Webster, John, Brown, Morris J., Lathrop, G. Mark, Connell, John, Dominiczak, Anna, Braga, Carolina A., Burke, Beverley, Dobson, Richard, Gungadoo, Johannie, Lee, Kate L., Munroe, Patricia B., Newhouse, Stephen J., Onipinla, Abiodun, Wallace, Chris, Xue, Mingzhan, Caulfield, Mark, Farrall, Martin, Barton, Anne, Bruce, Ian N., Donovan, Hannah, Eyre, Steve, Gilbert, Paul D., Hider, Samantha L., Hinks, Anne M., John, Sally L., Potter, Catherine, Silman, Alan J., Symmons, Deborah P. M., Thomson, Wendy, Worthington, Jane, Dunger, David B., Widmer, Barry, Frayling, Timothy M., Freathy, Rachel M., Lango, Hana, Perry, John R. B., Shields, Beverley M., Weedon, Michael N., Hattersley, Andrew T., Hitman, Graham A., Walker, Mark, Elliott, Kate S., Groves, Christopher J., Lindgren, Cecilia M., Rayner, Nigel W., Timpson, Nicholas J., Zeggini, Eleftheria, Newport, Melanie, Sirugo, Giorgio, Lyons, Emily, Vannberg, Fredrik, Hill, Adrian V. S., Bradbury, Linda A., Farrar, Claire, Pointon, Jennifer J., Wordsworth, Paul, Brown, Matthew A., Franklyn, Jayne A., Heward, Joanne M., Simmonds, Matthew J., Gough, Stephen C. L., Seal, Sheila, Stratton, Michael R., Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J., Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A., Bryan, Claire, Bumpstead, Suzannah J., Chaney, Amy, Downes, Kate, Ghori, Jilur, Gwilliam, Rhian, Hunt, Sarah E., Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Withers, David, Cardin, Niall J., Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdóttir, Ingeleif B., Howie, Bryan N., Su, Zhan, Ying Teo, Yik, Vukcevic, Damjan, Bentley, David, and Compston, Alistair

Abstract

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group.

27. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Author

Burton, Paul R., Clayton, David G., Cardon, Lon R., Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P., McCarthy, Mark I., Ouwehand, Willem H., Samani, Nilesh J., Todd, John A., Donnelly, Peter, Barrett, Jeffrey C., Davison, Dan, Easton, Doug, Evans, David, Leung, Hin-Tak, Marchini, Jonathan L., Morris, Andrew P., Spencer, Chris C. A., Tobin, Martin D., Attwood, Antony P., Boorman, James P., Cant, Barbara, Everson, Ursula, Hussey, Judith M., Jolley, Jennifer D., Knight, Alexandra S., Koch, Kerstin, Meech, Elizabeth, Nutland, Sarah, Prowse, Christopher V., Stevens, Helen E., Taylor, Niall C., Walters, Graham R., Walker, Neil M., Watkins, Nicholas A., Winzer, Thilo, Jones, Richard W., McArdle, Wendy L., Ring, Susan M., Strachan, David P., Pembrey, Marcus, Breen, Gerome, St Clair, David, Caesar, Sian, Gordon-Smith, Katherine, Jones, Lisa, Fraser, Christine, Green, Elaine K., Grozeva, Detelina, Hamshere, Marian L., Holmans, Peter A., Jones, Ian R., Kirov, George, Moskvina, Valentina, Nikolov, Ivan, O'Donovan, Michael C., Owen, Michael J., Collier, David A., Elkin, Amanda, Farmer, Anne, Williamson, Richard, McGuffin, Peter, Young, Allan H., Ferrier, I. Nicol, Ball, Stephen G., Balmforth, Anthony J., Barrett, Jennifer H., Bishop, D. Timothy, Iles, Mark M., Maqbool, Azhar, Yuldasheva, Nadira, Hall, Alistair S., Braund, Peter S., Dixon, Richard J., Mangino, Massimo, Stevens, Suzanne, Thompson, John R., Bredin, Francesca, Tremelling, Mark, Parkes, Miles, Drummond, Hazel, Lees, Charles W., Nimmo, Elaine R., Satsangi, Jack, Fisher, Sheila A., Forbes, Alastair, Lewis, Cathryn M., Onnie, Clive M., Prescott, Natalie J., Sanderson, Jeremy, Mathew, Christopher G., Barbour, Jamie, Mohiuddin, M. Khalid, Todhunter, Catherine E., Mansfield, John C., Ahmad, Tariq, Cummings, Fraser R., Jewell, Derek P., Webster, John, Brown, Morris J., Lathrop, G. Mark, Connell, John, Dominiczak, Anna, Marcano, Carolina A. Braga, Burke, Beverley, Dobson, Richard, Gungadoo, Johannie, Lee, Kate L., Munroe, Patricia B., Newhouse, Stephen J., Onipinla, Abiodun, Wallace, Chris, Xue, Mingzhan, Caulfield, Mark, Farrall, Martin, Barton, Anne, and Genomics (BRAGGS), The Biologics in RA Genetics, Bruce, Ian N., Donovan, Hannah, Eyre, Steve, Gilbert, Paul D., Hider, Samantha L., Hinks, Anne M., John, Sally L., Potter, Catherine, Silman, Alan J., Symmons, Deborah P. M., Thomson, Wendy, Worthington, Jane, Dunger, David B., Widmer, Barry, Frayling, Timothy M., Freathy, Rachel M., Lango, Hana, Perry, John R. B., Shields, Beverley M., Weedon, Michael N., Hattersley, Andrew T., Hitman, Graham A., Walker, Mark, Elliott, Kate S., Groves, Christopher J., Lindgren, Cecilia M., Rayner, Nigel W., Timpson, Nicholas J., Zeggini, Eleftheria, Newport, Melanie, Sirugo, Giorgio, Lyons, Emily, Vannberg, Fredrik, Hill, Adrian V. S., Bradbury, Linda A., Farrar, Claire, Pointon, Jennifer J., Wordsworth, Paul, Brown, Matthew A., Franklyn, Jayne A., Heward, Joanne M., Simmonds, Matthew J., Gough, Stephen C. L., Seal, Sheila, Susceptibility Collaboration (UK), Breast Cancer, Stratton, Michael R., Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J., Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A., Bumpstead, Suzannah J., Chaney, Amy, Downes, Kate, Ghori, Mohammed J. R., Gwilliam, Rhian, Hunt, Sarah E., Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Widden, Claire, Withers, David, Cardin, Niall J., Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdóttir, Ingileif B., Howie, Bryan N., Su, Zhan, Teo, Yik Ying, Vukcevic, Damjan, Bentley, David, Compston, Alistair, Burton, Paul R., Clayton, David G., Cardon, Lon R., Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P., McCarthy, Mark I., Ouwehand, Willem H., Samani, Nilesh J., Todd, John A., Donnelly, Peter, Barrett, Jeffrey C., Davison, Dan, Easton, Doug, Evans, David, Leung, Hin-Tak, Marchini, Jonathan L., Morris, Andrew P., Spencer, Chris C. A., Tobin, Martin D., Attwood, Antony P., Boorman, James P., Cant, Barbara, Everson, Ursula, Hussey, Judith M., Jolley, Jennifer D., Knight, Alexandra S., Koch, Kerstin, Meech, Elizabeth, Nutland, Sarah, Prowse, Christopher V., Stevens, Helen E., Taylor, Niall C., Walters, Graham R., Walker, Neil M., Watkins, Nicholas A., Winzer, Thilo, Jones, Richard W., McArdle, Wendy L., Ring, Susan M., Strachan, David P., Pembrey, Marcus, Breen, Gerome, St Clair, David, Caesar, Sian, Gordon-Smith, Katherine, Jones, Lisa, Fraser, Christine, Green, Elaine K., Grozeva, Detelina, Hamshere, Marian L., Holmans, Peter A., Jones, Ian R., Kirov, George, Moskvina, Valentina, Nikolov, Ivan, O'Donovan, Michael C., Owen, Michael J., Collier, David A., Elkin, Amanda, Farmer, Anne, Williamson, Richard, McGuffin, Peter, Young, Allan H., Ferrier, I. Nicol, Ball, Stephen G., Balmforth, Anthony J., Barrett, Jennifer H., Bishop, D. Timothy, Iles, Mark M., Maqbool, Azhar, Yuldasheva, Nadira, Hall, Alistair S., Braund, Peter S., Dixon, Richard J., Mangino, Massimo, Stevens, Suzanne, Thompson, John R., Bredin, Francesca, Tremelling, Mark, Parkes, Miles, Drummond, Hazel, Lees, Charles W., Nimmo, Elaine R., Satsangi, Jack, Fisher, Sheila A., Forbes, Alastair, Lewis, Cathryn M., Onnie, Clive M., Prescott, Natalie J., Sanderson, Jeremy, Mathew, Christopher G., Barbour, Jamie, Mohiuddin, M. Khalid, Todhunter, Catherine E., Mansfield, John C., Ahmad, Tariq, Cummings, Fraser R., Jewell, Derek P., Webster, John, Brown, Morris J., Lathrop, G. Mark, Connell, John, Dominiczak, Anna, Marcano, Carolina A. Braga, Burke, Beverley, Dobson, Richard, Gungadoo, Johannie, Lee, Kate L., Munroe, Patricia B., Newhouse, Stephen J., Onipinla, Abiodun, Wallace, Chris, Xue, Mingzhan, Caulfield, Mark, Farrall, Martin, Barton, Anne, and Genomics (BRAGGS), The Biologics in RA Genetics, Bruce, Ian N., Donovan, Hannah, Eyre, Steve, Gilbert, Paul D., Hider, Samantha L., Hinks, Anne M., John, Sally L., Potter, Catherine, Silman, Alan J., Symmons, Deborah P. M., Thomson, Wendy, Worthington, Jane, Dunger, David B., Widmer, Barry, Frayling, Timothy M., Freathy, Rachel M., Lango, Hana, Perry, John R. B., Shields, Beverley M., Weedon, Michael N., Hattersley, Andrew T., Hitman, Graham A., Walker, Mark, Elliott, Kate S., Groves, Christopher J., Lindgren, Cecilia M., Rayner, Nigel W., Timpson, Nicholas J., Zeggini, Eleftheria, Newport, Melanie, Sirugo, Giorgio, Lyons, Emily, Vannberg, Fredrik, Hill, Adrian V. S., Bradbury, Linda A., Farrar, Claire, Pointon, Jennifer J., Wordsworth, Paul, Brown, Matthew A., Franklyn, Jayne A., Heward, Joanne M., Simmonds, Matthew J., Gough, Stephen C. L., Seal, Sheila, Susceptibility Collaboration (UK), Breast Cancer, Stratton, Michael R., Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J., Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A., Bumpstead, Suzannah J., Chaney, Amy, Downes, Kate, Ghori, Mohammed J. R., Gwilliam, Rhian, Hunt, Sarah E., Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Widden, Claire, Withers, David, Cardin, Niall J., Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdóttir, Ingileif B., Howie, Bryan N., Su, Zhan, Teo, Yik Ying, Vukcevic, Damjan, Bentley, David, and Compston, Alistair

Abstract

There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined approx2,000 individuals for each of 7 major diseases and a shared set of approx3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 times 10-7: 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10-5 and 5 times 10-7) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource

28. Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants

Author

Burton, Paul R, Clayton, David G, Cardon, Lon R, Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P, McCarthy, Mark I, Ouwehand, Willem H, Samani, Nilesh J, Todd, John A, Donnelly (Chair), Peter, Barrett, Jeffrey C, Davison, Dan, Donnelly, Peter, Easton, Doug, Evans, David M, Leung, Hin-Tak, Marchini, Jonathan L, Morris, Andrew P, Spencer, Chris CA, Tobin, Martin D, Attwood, Antony P, Boorman, James P, Cant, Barbara, Everson, Ursula, Hussey, Judith M, Jolley, Jennifer D, Knight, Alexandra S, Koch, Kerstin, Meech, Elizabeth, Nutland, Sarah, Prowse, Christopher V, Stevens, Helen E, Taylor, Niall C, Walters, Graham R, Walker, Neil M, Watkins, Nicholas A, Winzer, Thilo, Jones, Richard W, McArdle, Wendy L, Ring, Susan M, Strachan, David P, Pembrey, Marcus, Breen, Gerome, Clair, David St, Caesar, Sian, Gordon-Smith, Katharine, Jones, Lisa, Fraser, Christine, Green, Elaine K, Grozeva, Detelina, Hamshere, Marian L, Holmans, Peter A, Jones, Ian R, Kirov, George, Moskivina, Valentina, Nikolov, Ivan, O'Donovan, Michael C, Owen, Michael J, Collier, David A, Elkin, Amanda, Farmer, Anne, Williamson, Richard, McGuffin, Peter, Young, Allan H, Ferrier, I Nicol, Ball, Stephen G, Balmforth, Anthony J, Barrett, Jennifer H, Bishop, Timothy D, Iles, Mark M, Maqbool, Azhar, Yuldasheva, Nadira, Hall, Alistair S, Braund, Peter S, Dixon, Richard J, Mangino, Massimo, Stevens, Suzanne, Thompson, John R, Bredin, Francesca, Tremelling, Mark, Parkes, Miles, Drummond, Hazel, Lees, Charles W, Nimmo, Elaine R, Satsangi, Jack, Fisher, Sheila A, Forbes, Alastair, Lewis, Cathryn M, Onnie, Clive M, Prescott, Natalie J, Sanderson, Jeremy, Matthew, Christopher G, Barbour, Jamie, Mohiuddin, M Khalid, Todhunter, Catherine E, Mansfield, John C, Ahmad, Tariq, Cummings, Fraser R, Jewell, Derek P, Webster, John, Brown, Morris J, Lathrop, Mark G, Connell, John, Dominiczak, Anna, Marcano, Carolina A Braga, Burke, Beverley, Dobson, Richard, Gungadoo, Johannie, Lee, Kate L, Munroe, Patricia B, Newhouse, Stephen J, Onipinla, Abiodun, Wallace, Chris, Xue, Mingzhan, Caulfield, Mark, Farrall, Martin, Barton, Anne, Bruce, Ian N, Donovan, Hannah, Eyre, Steve, Gilbert, Paul D, Hilder, Samantha L, Hinks, Anne M, John, Sally L, Potter, Catherine, Silman, Alan J, Symmons, Deborah PM, Thomson, Wendy, Worthington, Jane, Dunger, David B, Widmer, Barry, Frayling, Timothy M, Freathy, Rachel M, Lango, Hana, Perry, John R B, Shields, Beverley M, Weedon, Michael N, Hattersley, Andrew T, Hitman, Graham A, Walker, Mark, Elliott, Kate S, Groves, Christopher J, Lindgren, Cecilia M, Rayner, Nigel W, Timpson, Nicolas J, Zeggini, Eleftheria, Newport, Melanie, Sirugo, Giorgio, Lyons, Emily, Vannberg, Fredrik, Hill, Adrian V S, Bradbury, Linda A, Farrar, Claire, Pointon, Jennifer J, Wordsworth, Paul, Brown, Matthew A, Franklyn, Jayne A, Heward, Joanne M, Simmonds, Matthew J, Gough, Stephen CL, Seal, Sheila, Stratton, Michael R, Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J, Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A, Bumpstead, Suzannah J, Chaney, Amy, Downes, Kate, Ghori, Mohammed JR, Gwilliam, Rhian, Hunt, Sarah E, Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Widden, Claire, Withers, David, Cardin, Niall J, Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdóttir, Ingeleif B, Howie, Bryan N, Su, Zhan, Teo, Yik Ying, Vukcevic, Damjan, Bentley, David, Mitchell, Sarah L, Newby, Paul R, Brand, Oliver J, Carr-Smith, Jackie, Pearce, Simon H S, Gough, Stephen C L, McGinnis, R, Keniry, A, Deloukas, P, Reveille, John D, Zhou, Xiaodong, Sims, Anne-Marie, Dowling, Alison, Taylor, Jacqueline, Doan, Tracy, Davis, John C, Savage, Laurie, Ward, Michael M, Learch, Thomas L, Weisman, Michael H, Burton, Paul R, Clayton, David G, Cardon, Lon R, Craddock, Nick, Deloukas, Panos, Duncanson, Audrey, Kwiatkowski, Dominic P, McCarthy, Mark I, Ouwehand, Willem H, Samani, Nilesh J, Todd, John A, Donnelly (Chair), Peter, Barrett, Jeffrey C, Davison, Dan, Donnelly, Peter, Easton, Doug, Evans, David M, Leung, Hin-Tak, Marchini, Jonathan L, Morris, Andrew P, Spencer, Chris CA, Tobin, Martin D, Attwood, Antony P, Boorman, James P, Cant, Barbara, Everson, Ursula, Hussey, Judith M, Jolley, Jennifer D, Knight, Alexandra S, Koch, Kerstin, Meech, Elizabeth, Nutland, Sarah, Prowse, Christopher V, Stevens, Helen E, Taylor, Niall C, Walters, Graham R, Walker, Neil M, Watkins, Nicholas A, Winzer, Thilo, Jones, Richard W, McArdle, Wendy L, Ring, Susan M, Strachan, David P, Pembrey, Marcus, Breen, Gerome, Clair, David St, Caesar, Sian, Gordon-Smith, Katharine, Jones, Lisa, Fraser, Christine, Green, Elaine K, Grozeva, Detelina, Hamshere, Marian L, Holmans, Peter A, Jones, Ian R, Kirov, George, Moskivina, Valentina, Nikolov, Ivan, O'Donovan, Michael C, Owen, Michael J, Collier, David A, Elkin, Amanda, Farmer, Anne, Williamson, Richard, McGuffin, Peter, Young, Allan H, Ferrier, I Nicol, Ball, Stephen G, Balmforth, Anthony J, Barrett, Jennifer H, Bishop, Timothy D, Iles, Mark M, Maqbool, Azhar, Yuldasheva, Nadira, Hall, Alistair S, Braund, Peter S, Dixon, Richard J, Mangino, Massimo, Stevens, Suzanne, Thompson, John R, Bredin, Francesca, Tremelling, Mark, Parkes, Miles, Drummond, Hazel, Lees, Charles W, Nimmo, Elaine R, Satsangi, Jack, Fisher, Sheila A, Forbes, Alastair, Lewis, Cathryn M, Onnie, Clive M, Prescott, Natalie J, Sanderson, Jeremy, Matthew, Christopher G, Barbour, Jamie, Mohiuddin, M Khalid, Todhunter, Catherine E, Mansfield, John C, Ahmad, Tariq, Cummings, Fraser R, Jewell, Derek P, Webster, John, Brown, Morris J, Lathrop, Mark G, Connell, John, Dominiczak, Anna, Marcano, Carolina A Braga, Burke, Beverley, Dobson, Richard, Gungadoo, Johannie, Lee, Kate L, Munroe, Patricia B, Newhouse, Stephen J, Onipinla, Abiodun, Wallace, Chris, Xue, Mingzhan, Caulfield, Mark, Farrall, Martin, Barton, Anne, Bruce, Ian N, Donovan, Hannah, Eyre, Steve, Gilbert, Paul D, Hilder, Samantha L, Hinks, Anne M, John, Sally L, Potter, Catherine, Silman, Alan J, Symmons, Deborah PM, Thomson, Wendy, Worthington, Jane, Dunger, David B, Widmer, Barry, Frayling, Timothy M, Freathy, Rachel M, Lango, Hana, Perry, John R B, Shields, Beverley M, Weedon, Michael N, Hattersley, Andrew T, Hitman, Graham A, Walker, Mark, Elliott, Kate S, Groves, Christopher J, Lindgren, Cecilia M, Rayner, Nigel W, Timpson, Nicolas J, Zeggini, Eleftheria, Newport, Melanie, Sirugo, Giorgio, Lyons, Emily, Vannberg, Fredrik, Hill, Adrian V S, Bradbury, Linda A, Farrar, Claire, Pointon, Jennifer J, Wordsworth, Paul, Brown, Matthew A, Franklyn, Jayne A, Heward, Joanne M, Simmonds, Matthew J, Gough, Stephen CL, Seal, Sheila, Stratton, Michael R, Rahman, Nazneen, Ban, Maria, Goris, An, Sawcer, Stephen J, Compston, Alastair, Conway, David, Jallow, Muminatou, Rockett, Kirk A, Bumpstead, Suzannah J, Chaney, Amy, Downes, Kate, Ghori, Mohammed JR, Gwilliam, Rhian, Hunt, Sarah E, Inouye, Michael, Keniry, Andrew, King, Emma, McGinnis, Ralph, Potter, Simon, Ravindrarajah, Rathi, Whittaker, Pamela, Widden, Claire, Withers, David, Cardin, Niall J, Ferreira, Teresa, Pereira-Gale, Joanne, Hallgrimsdóttir, Ingeleif B, Howie, Bryan N, Su, Zhan, Teo, Yik Ying, Vukcevic, Damjan, Bentley, David, Mitchell, Sarah L, Newby, Paul R, Brand, Oliver J, Carr-Smith, Jackie, Pearce, Simon H S, Gough, Stephen C L, McGinnis, R, Keniry, A, Deloukas, P, Reveille, John D, Zhou, Xiaodong, Sims, Anne-Marie, Dowling, Alison, Taylor, Jacqueline, Doan, Tracy, Davis, John C, Savage, Laurie, Ward, Michael M, Learch, Thomas L, and Weisman, Michael H

Abstract

We have genotyped 14,436 nonsynonymous SNPs (nsSNPs) and 897 major histocompatibility complex (MHC) tag SNPs from 1,000 independent cases of ankylosing spondylitis (AS), autoimmune thyroid disease (AITD), multiple sclerosis (MS) and breast cancer (BC). Comparing these data against a common control dataset derived from 1,500 randomly selected healthy British individuals, we report initial association and independent replication in a North American sample of two new loci related to ankylosing spondylitis, ARTS1 and IL23R, and confirmation of the previously reported association of AITD with TSHR and FCRL3. These findings, enabled in part by increased statistical power resulting from the expansion of the control reference group to include individuals from the other disease groups, highlight notable new possibilities for autoimmune regulation and suggest that IL23R may be a common susceptibility factor for the major 'seronegative' diseases.

29. Complementary and alternative medicines.

Author

Whitehead, Martin, Price, Alison, Jessop, Mike, Gough, Alex, Taylor, Niall, Atkinson, Martin, Hyde, Phil, Chambers, Danny, McKenzie, Brennen, and Guthrie, Arlo

Subjects
ALTERNATIVE medicine, VETERINARY medicine, HOMEOPATHIC veterinary medicine, MEDICINAL plant use in animals, VETERINARIANS
Published
2017
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30. Developing healthcare language model embedding spaces.

Author

Taylor N, Schofield D, Kormilitzin A, Joyce DW, and Nevado-Holgado A

Subjects
Humans, Delivery of Health Care, Deep Learning, Natural Language Processing
Abstract

Pre-trained Large Language Models (LLMs) have revolutionised Natural Language Processing (NLP) tasks, but often struggle when applied to specialised domains such as healthcare. The traditional approach of pre-training on large datasets followed by task-specific fine-tuning is resource-intensive and poorly aligned with the constraints of many healthcare settings. This presents a significant challenge for deploying LLM-based NLP solutions in medical contexts, where data privacy, computational resources, and domain-specific language pose unique obstacles. This study aims to develop and evaluate efficient methods for adapting smaller LLMs to healthcare-specific datasets and tasks. We seek to identify pre-training approaches that can effectively instil healthcare competency in compact LLMs under tight computational budgets, a crucial capability for responsible and sustainable deployment in local healthcare settings. We explore three specialised pre-training methods to adapt smaller LLMs to different healthcare datasets: traditional Masked Language modelling (MLM), Deep Contrastive Learning for Unsupervised Textual Representations (DeCLUTR), and a novel approach utilising metadata categories from healthcare settings. These methods are assessed across multiple healthcare datasets, with a focus on downstream document classification tasks. We evaluate the performance of the resulting LLMs through classification accuracy and analysis of the derived embedding spaces. Contrastively trained models consistently outperform other approaches on classification tasks, delivering strong performance with limited labelled data and fewer model parameter updates. While our novel metadata-based pre-training does not further improve classifications across datasets, it yields interesting embedding cluster separability. Importantly, all domain-adapted LLMs outperform their publicly available, general-purpose base models, validating the importance of domain specialisation. This research demonstrates the efficacy of specialised pre-training methods in adapting compact LLMs to healthcare tasks, even under resource constraints. We provide guidelines for pre-training specialised healthcare LLMs and motivate continued inquiry into contrastive objectives. Our findings underscore the potential of these approaches for aligning small LLMs with privacy-sensitive medical tasks, offering a path toward more efficient and responsible NLP deployment in healthcare settings. This work contributes to the broader goal of making advanced NLP techniques accessible and effective in specialised domains, particularly where resource limitations and data sensitivity are significant concerns., Competing Interests: Declaration of competing interest A.K. A.N.H. and D.W.J. were supported in part by the National Institute of Health Research (NIHR) AI Award for Health and Social Care (AI-AWARD02183). A.K. and A.N.H. declare a research grant from GlaxoSmithKline (unrelated to this work). A.N.H. also received grant funding from Novo Nordisk Pharmaceuticals unrelated to this work. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR or the UK Department of Health and Social Care. This study was supported by CRIS Powered by Akrivia Health, using data, systems and support from the NIHR Oxford Health Biomedical Research Centre (BRC-1215-20005) Research Informatics Team. The views expressed in this publication are those of the authors and not necessarily those of NHS England. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Published
2024
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31. Efficiency at scale: Investigating the performance of diminutive language models in clinical tasks.

Author

Taylor N, Ghose U, Rohanian O, Nouriborji M, Kormilitzin A, Clifton DA, and Nevado-Holgado A

Subjects
Humans, Algorithms, Clinical Decision-Making
Abstract

The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability. This was followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods. We present an investigation into the suitability of different PEFT methods to clinical decision-making tasks, across a range of model sizes, including extremely small models with as few as 25 million parameters. Our analysis shows that the performance of most PEFT approaches varies significantly from one task to another, with the exception of LoRA, which maintains relatively high performance across all model sizes and tasks, typically approaching or matching full fine-tuned performance. The effectiveness of PEFT methods in the clinical domain is evident, particularly for specialised models which can operate on low-cost, in-house computing infrastructure. The advantages of these models, in terms of speed and reduced training costs, dramatically outweighs any performance gain from large foundation LLMs. Furthermore, we highlight how domain-specific pre-training interacts with PEFT methods and model size, finding the domain pre-training to be particularly important in smaller models and discuss how these factors interplay to provide the best efficiency-performance trade-off. Full code available at: https://github.com/nlpie-research/efficient-ml., Competing Interests: Declaration of competing interest AK and ANH and by a research grant from GlaxoSmithKline unrelated to this work. ANH also receives grant funding from Novo Nordisk Pharmaceuticals unrelated to this work. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Published
2024
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32. Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants.

Author

Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A, Kwiatkowski DP, McCarthy MI, Ouwehand WH, Samani NJ, Todd JA, Donnelly P, Barrett JC, Davison D, Easton D, Evans DM, Leung HT, Marchini JL, Morris AP, Spencer CC, Tobin MD, Attwood AP, Boorman JP, Cant B, Everson U, Hussey JM, Jolley JD, Knight AS, Koch K, Meech E, Nutland S, Prowse CV, Stevens HE, Taylor NC, Walters GR, Walker NM, Watkins NA, Winzer T, Jones RW, McArdle WL, Ring SM, Strachan DP, Pembrey M, Breen G, St Clair D, Caesar S, Gordon-Smith K, Jones L, Fraser C, Green EK, Grozeva D, Hamshere ML, Holmans PA, Jones IR, Kirov G, Moskivina V, Nikolov I, O'Donovan MC, Owen MJ, Collier DA, Elkin A, Farmer A, Williamson R, McGuffin P, Young AH, Ferrier IN, Ball SG, Balmforth AJ, Barrett JH, Bishop TD, Iles MM, Maqbool A, Yuldasheva N, Hall AS, Braund PS, Dixon RJ, Mangino M, Stevens S, Thompson JR, Bredin F, Tremelling M, Parkes M, Drummond H, Lees CW, Nimmo ER, Satsangi J, Fisher SA, Forbes A, Lewis CM, Onnie CM, Prescott NJ, Sanderson J, Matthew CG, Barbour J, Mohiuddin MK, Todhunter CE, Mansfield JC, Ahmad T, Cummings FR, Jewell DP, Webster J, Brown MJ, Lathrop MG, Connell J, Dominiczak A, Marcano CA, Burke B, Dobson R, Gungadoo J, Lee KL, Munroe PB, Newhouse SJ, Onipinla A, Wallace C, Xue M, Caulfield M, Farrall M, Barton A, Bruce IN, Donovan H, Eyre S, Gilbert PD, Hilder SL, Hinks AM, John SL, Potter C, Silman AJ, Symmons DP, Thomson W, Worthington J, Dunger DB, Widmer B, Frayling TM, Freathy RM, Lango H, Perry JR, Shields BM, Weedon MN, Hattersley AT, Hitman GA, Walker M, Elliott KS, Groves CJ, Lindgren CM, Rayner NW, Timpson NJ, Zeggini E, Newport M, Sirugo G, Lyons E, Vannberg F, Hill AV, Bradbury LA, Farrar C, Pointon JJ, Wordsworth P, Brown MA, Franklyn JA, Heward JM, Simmonds MJ, Gough SC, Seal S, Stratton MR, Rahman N, Ban M, Goris A, Sawcer SJ, Compston A, Conway D, Jallow M, Newport M, Sirugo G, Rockett KA, Bumpstead SJ, Chaney A, Downes K, Ghori MJ, Gwilliam R, Hunt SE, Inouye M, Keniry A, King E, McGinnis R, Potter S, Ravindrarajah R, Whittaker P, Widden C, Withers D, Cardin NJ, Davison D, Ferreira T, Pereira-Gale J, Hallgrimsdo'ttir IB, Howie BN, Su Z, Teo YY, Vukcevic D, Bentley D, Brown MA, Compston A, Farrall M, Hall AS, Hattersley AT, Hill AV, Parkes M, Pembrey M, Stratton MR, Mitchell SL, Newby PR, Brand OJ, Carr-Smith J, Pearce SH, McGinnis R, Keniry A, Deloukas P, Reveille JD, Zhou X, Sims AM, Dowling A, Taylor J, Doan T, Davis JC, Savage L, Ward MM, Learch TL, Weisman MH, and Brown M

Subjects
Aminopeptidases genetics, Breast Neoplasms epidemiology, Case-Control Studies, Chromosome Mapping, Genetics, Population, Genotype, Haplotypes genetics, Humans, Linkage Disequilibrium, Minor Histocompatibility Antigens, Multiple Sclerosis epidemiology, North America epidemiology, Polymerase Chain Reaction, Receptors, Immunologic genetics, Receptors, Interleukin genetics, Spondylitis, Ankylosing epidemiology, Thyroiditis, Autoimmune epidemiology, Autoimmunity genetics, Breast Neoplasms genetics, Multiple Sclerosis genetics, Polymorphism, Single Nucleotide genetics, Spondylitis, Ankylosing genetics, Thyroiditis, Autoimmune genetics
Abstract

We have genotyped 14,436 nonsynonymous SNPs (nsSNPs) and 897 major histocompatibility complex (MHC) tag SNPs from 1,000 independent cases of ankylosing spondylitis (AS), autoimmune thyroid disease (AITD), multiple sclerosis (MS) and breast cancer (BC). Comparing these data against a common control dataset derived from 1,500 randomly selected healthy British individuals, we report initial association and independent replication in a North American sample of two new loci related to ankylosing spondylitis, ARTS1 and IL23R, and confirmation of the previously reported association of AITD with TSHR and FCRL3. These findings, enabled in part by increased statistical power resulting from the expansion of the control reference group to include individuals from the other disease groups, highlight notable new possibilities for autoimmune regulation and suggest that IL23R may be a common susceptibility factor for the major 'seronegative' diseases.

Published
2007
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