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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Domenico Paolo Alessandro Bria Carlo Greco Marco Russano Sara Ramella Paolo Soda Rosa Sicilia Department of Engineering Unit of Computer Systems & Bioinformatics Università Campus Bio-Medico di Roma Italy Department of Electrical and Information Engineering University of Cassino and Southern Latium Cassino Italy Department of Medicine and Surgery Research Unit of Radiation Oncology Università Campus Bio-Medico di Roma Italy Operative Research Unit of Radiation Oncology Fondazione Policlinico Universitario Campus Bio-Medico Italy Operative Research Unit of Medical Oncology Fondazione Policlinico Universitario Campus Bio-Medico Italy Department of Radiation Sciences Radiation Physics Biomedical Engineering Umeå University Sweden

The widespread adoption of electronic health records (EHRs) offers a valuable opportunity to support clinical research by containing crucial patient information, including diagnoses, symptoms, medications, lab tests, and more. Despite the success of deep learning for biomedical Named Entity Recognition (NER), the literature in this field still presents a gap regarding applications focused on lung cancer for the Italian language. Hence, this paper presents a transformer-based approach to extract named entities from Italian clinical notes related to Non-Small Cell Lung Cancer (NSCLC). We introduce a novel set of 25 clinical entities related to NSCLC building a corpus annotated for NER. We apply a state-of the-art model pre-trained on Italian biomedical texts to the manually annotated clinical reports of a cohort of 257 patients suffering from NSCLC, successfully dealing with class-imbalance problems and obtaining promising performance (average F1-score of 84.3%). We also compared our method with two other pre-trained state-of-the-art models showing that the domain specific knowledge offered by the proposed approach is necessary to achieve higher performance. These findings also showcase the feasibility of using transformers to extract biomedical information in the Italian language.

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Optimal Brain Dissection in Dense Autoencoders: Towards Determining Feature Importance in -Omics Data

Optimal Brain Dissection in Dense Autoencoders: Towards Dete...

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BioInspired Processing (BIP), IEEE International Conference on

作者： Fin Amin Lisa Van den Broeck Ive De Smet Anna M. Locke Rossangela Sozzani Department of Electrical and Computer Engineering North Carolina State University Raleigh USA Protealis NV Ghent Belgium Department of Plant Biotechnology and Bioinformatics VIB Center for Plant Systems Biology Ghent University Ghent Belgium Soybean & Nitrogen Fixation Research Unit USDA-ARS Raleigh USA NC Plant Sciences Initiative North Carolina State University Raleigh USA

Recently, there has been increased interest in ma-chine learning explainability. Understanding the complex relationship between input features of a model and their respective outputs is of increased relevance, especially in biological science. In this paper, we introduce Optimal Brain Dissection (OBD), an innovative methodology designed to examine the importance of first-layer connections in a biology-inspired autoencoder. We incorporated regulator-target interactions within the first autoencoder layer, representing biological regulatory networks, and identified their importance to the reconstruction error, a critical aspect in navigating the complexity of high-dimensional omics data. Through a combination of pruning techniques and counterfactual reasoning, OBD offers a method to quantify feature importance, factoring in both weight magnitude and time-to-laziness. To implement this method, we propose a Dense Autoencoder (DAE) architecture, aiming for increased efficiency and reduced computation. Tailored for omics data, the DAE employs skip concatenations and circumvents non-existent target-target interactions. Our approach aims to understand the relative importance of connections for autoencoder performance, a critical step towards better counter-factual reasoning for neural networks.

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Fully automated evaluation of paraspinal muscle morphology and composition in patients with low back pain

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Intelligence-Based Medicine 2024年 9卷 100130-100130页

作者： Giaccone, Paolo Russo, Fabrizio Volpecina, Manuel Mallio, Carlo Augusto Papalia, Giuseppe Francesco Vadalà, Gianluca Denaro, Vincenzo Vollero, Luca Merone, Mario Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome 00128 Italy Research Unit of Orthopaedic and Trauma Surgery Fondazione Policlinico Universitario Campus Bio-Medico Via Alvaro del Portillo 200 Rome 00128 Italy Research Unit of Orthopaedic and Trauma Surgery Department of Medicine and Surgery Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome 00128 Italy Research Unit of Diagnostic Imaging and Interventional Radiology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome 00128 Italy

Chronic Low Back Pain (LBP) is one of the most prevalent musculoskeletal conditions and is the leading cause of disability worldwide. The morphology and composition of lumbar paraspinal muscles, in terms of infiltrated adipose tissue, constitute important guidelines for diagnosis and treatment choice but still require manual procedures to be assessed. We developed a fully automated artificial intelligence based algorithm both to segment paraspinal muscles from MRI scans through a U-Net architecture and to estimate the amount of fatty infiltrations by a home-made intensity- and region-based processing;we further validated our results by statistical assessment of the accuracy and agreement between our automated measures and the clinically reported values, achieving dice scores greater than 95 % on the preliminary segmentation task, as well as an excellent degree of agreement on the following area estimates (ICC_2,1 = 0.89). Furthermore, we employed an external public dataset to validate our model generalization abilities, reaching dice scores greater than 94 % with an average processing time of 21.92s(±3.38s) per subject. Hence, a deterministic and reliable measuring tool is proposed, without any manual confounding effect, to efficiently support daily clinical practice in LBP management. © 2023 The Authors

关键词： Artificial intelligence computer vision Deep learning Fat infiltrations Low back pain Neural networks

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Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition

arXiv

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arXiv 2023年

作者： Zhang, Xiaoqing Zhao, Jilu Li, Yan Wu, Hao Zhou, Xiangtian Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China National Clinical Research Center for Ocular Diseases Eye Hospital Wenzhou Medical University Wenzhou325027 China State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China Research Unit of Myopia Basic Research and Clinical Prevention and Control Chinese Academy of Medical Sciences Wenzhou325027 China Singapore Eye Research Institute 169856 Singapore

Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on designing complex architectures but rarely explored the pathology distribution prior of PM. To tackle this issue, we propose an efficient pyramid channel attention (EPCA) module, which fully leverages the potential of the clinical pathology prior of PM with pyramid pooling and multi-scale context fusion. Then, we construct EPCA-Net for automatic PM recognition based on fundus images by stacking a sequence of EPCA modules. Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters. In addition, we construct a PM recognition benchmark termed PM-fundus by collecting fundus images of PM from publicly available datasets. The comprehensive experiments demonstrate the superiority of EPCA-Net over state-of-the-art methods in the PM recognition task. For example, EPCA-Net achieves 97.56% accuracy and outperforms ViT by 2.85% accuracy on the PM-fundus dataset. The results also show that our method based on the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of previous methods based on traditional fine-tuning paradigm with fewer tunable parameters, which has the potential to leverage more natural image foundation models to address the PM recognition task in limited medical data regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Pathology

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A Graph Neural Network-based Model with Out-of-Distribution Robustness for Enhancing Antiretroviral Therapy Outcome Prediction for HIV-1

arXiv

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arXiv 2023年

作者： Teodoro, Giulia Di Siciliano, Federico Guarrasi, Valerio Vandamme, Anne-Mieke Ghisetti, Valeria Sönnerborg, Anders Zazzi, Maurizio Silvestri, Fabrizio Palagi, Laura Sapienza University of Rome Department of Computer Control and Management Engineering Antonio Ruberti Rome00185 Italy EuResist Network Rome00152 Italy Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Rome00128 Italy Department of Medical Biotechnologies University of Siena Siena53100 Italy KU Leuven Department of Microbiology Immunology and Transplantation Rega Institute for Medical Research Clinical and Epidemiological Virology Leuven Belgium Center for Global Health and Tropical Medicine Instituto de Higiene e Medicina Tropical Universidade Nova de Lisboa Lisbon1349-008 Portugal Molecular Biology and Microbiology Unit Amedeo di Savoia Hospital ASL Città di Torino Turin10128 Italy Karolinska Institutet Division of Infectious Diseases Department of Medicine Huddinge Stockholm14152 Sweden Karolinska University Hospital Department of Infectious Diseases Stockholm14186 Sweden

Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced therapy representation. To tackle this issue, we introduce a novel joint fusion model, which combines features from a Fully Connected (FC) Neural Network and a Graph Neural Network (GNN) in a multi-modality fashion. Our model uses both tabular data about genetic sequences and a knowledge base derived from Stanford drug-resistance mutation tables, which serve as benchmark references for deducing in-vivo treatment efficacy based on the viral genetic sequence. By leveraging this knowledge base structured as a graph, the GNN component enables our model to adapt to imbalanced data distributions and account for Out-of-Distribution (OoD) drugs. We evaluated these models’ robustness against OoD drugs in the test set. Our comprehensive analysis demonstrates that the proposed model consistently outperforms the FC model. These results underscore the advantage of integrating Stanford scores in the model, thereby enhancing its generalizability and robustness, but also extending its utility in contributing in more informed clinical decisions with limited data availability. The source code is available at https://***/federicosiciliano/*** Codes 68 © 2023, CC BY-NC-ND.

关键词： Graph neural networks

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Building an international consortium for tracking coronavirus health status (vol 78, pg 931, 2020)

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NATURE MEDICINE 2020年第8期26卷 1309-1309页

作者： Segal, Eran Zhang, Feng Lin, Xihong King, Gary Shalem, Ophir Shilo, Smadar Allen, William E. Alquaddoomi, Faisal Altae-Tran, Han Anders, Simon Balicer, Ran Bauman, Tal Bonilla, Ximena Booman, Gisel Chan, Andrew T. Cohen, Ori Coletti, Silvano Davidson, Natalie Dor, Yuval Drew, David A. Elemento, Olivier Evans, Georgina Ewels, Phil Gale, Joshua Gavrieli, Amir Geiger, Benjamin Grad, Yonatan H. Greene, Casey S. Hajirasouliha, Iman Jerala, Roman Kahles, Andre Kallioniemi, Olli Keshet, Ayya Kocarev, Ljupco Landua, Gregory Meir, Tomer Muller, Aline Nguyen, Long H. Oresic, Matej Ovchinnikova, Svetlana Peterson, Hedi Prodanova, Jana Rajagopal, Jay Raetsch, Gunnar Rossman, Hagai Rung, Johan Sboner, Andrea Sigaras, Alexandros Spector, Tim Steinherz, Ron Stevens, Irene Vilo, Jaak Wilmes, Paul Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Howard Hughes Medical Institute Chevy Chase Maryland USA Broad Institute of MIT and Harvard Cambridge MA USA Departments of Biostatistics and Statistics Harvard T.H. Chan School of Public Health Harvard University Cambridge MA USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Pediatric Diabetes Unit Ruth Rappaport Children’s Hospital Rambam Healthcare Campus Haifa Israel Harvard Society of Fellows Harvard University Cambridge MA USA ETH Zurich NEXUS Personalized Health Technologies Zurich Switzerland Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Center for Molecular Biology (ZMBH) University of Heidelberg Heidelberg Germany Clalit Research Institute Clalit Health Services Ramat Gan Israel Mapping and Geo-Information Engineering Civil and Environmental Engineering Faculty The Technion Haifa Israel ETH Zurich Department for Computer Science Zurich University Hospital Zurich Medical Informatics Zurich and SIB Swiss Institute of Bioinformatics Zurich Switzerland Regen Network Mar del Plata Argentina Massachusetts General Hospital (MGH) Boston MA USA Chelonia Applied Science Allschwil Switzerland IMRIC Developmental Biology and Cancer Research School of Medicine The Hebrew University Jerusalem Israel Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Science for Life Laboratory (SciLifeLab) Department of Biochemistry and Biophysics Stockholm University Stockholm Sweden http://*** Canada Department of Immunology Weizmann Institute of Scienc

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Computational platforms and environments Databases Infectious diseases

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On the instability of further pre-training: Does a single sentence matter to BERT?

Natural Language Processing Journal

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Natural Language Processing Journal 2023年 5卷

作者： Luca Bacco Gosse Minnema Tommaso Caselli Felice Dell’Orletta Mario Merone Malvina Nissim Unit of Computer Systems and Bioinformatics Department of Engineering University “Campus Bio-Medico di Roma” Via Alvaro del Portillo 21 Rome 00128 Italy ItaliaNLP Lab Institute of Computational Linguistics “Antonio Zampolli” National Research Council Via Giuseppe Moruzzi 1 Pisa 56124 Italy Center for Language and Cognition Faculty of Arts University of Groningen Oude Kijk in’t Jatstraat 26 Groningen 9712 EK The Netherlands

We observe a remarkable instability in BERT-like models: minimal changes in the internal representations of BERT, as induced by one-step further pre-training with even a single sentence, can noticeably change the behaviour of subsequently fine-tuned models. While the pre-trained models seem to be essentially the same, also by means of established similarity assessment techniques, the measurable tiny changes appear to substantially impact the models’ tuning path, leading to significantly different fine-tuned systems and affecting downstream performance. After testing a very large number of combinations, which we briefly summarize, the experiments reported in this short paper focus on an intermediate phase consisting of a single-step and single-sentence masked language modeling stage and its impact on a sentiment analysis task. We discuss a series of unexpected findings which leave some open questions over the nature and stability of further pre-training.

关键词： Natural language processing Large language models Further pre-training Transformers Instability BERT

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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