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Polygenic association between attention-deficit/hyperactivity disorder liability and cognitive impairments
- Isabella Vainieri, Joanna Martin, Anna-Sophie Rommel, Philip Asherson, Tobias Banaschewski, Jan Buitelaar, Bru Cormand, Jennifer Crosbie, Stephen V. Faraone, Barbara Franke, Sandra K. Loo, Ana Miranda, Iris Manor, Robert D. Oades, Kirstin L. Purves, J. Antoni Ramos-Quiroga, Marta Ribasés, Herbert Roeyers, Aribert Rothenberger, Russell Schachar, Joseph Sergeant, Hans-Christoph Steinhausen, Pieter J. Vuijk, Alysa E. Doyle, Jonna Kuntsi
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- Journal:
- Psychological Medicine / Volume 52 / Issue 14 / October 2022
- Published online by Cambridge University Press:
- 03 February 2021, pp. 3150-3158
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- Article
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Background
A recent genome-wide association study (GWAS) identified 12 independent loci significantly associated with attention-deficit/hyperactivity disorder (ADHD). Polygenic risk scores (PRS), derived from the GWAS, can be used to assess genetic overlap between ADHD and other traits. Using ADHD samples from several international sites, we derived PRS for ADHD from the recent GWAS to test whether genetic variants that contribute to ADHD also influence two cognitive functions that show strong association with ADHD: attention regulation and response inhibition, captured by reaction time variability (RTV) and commission errors (CE).
MethodsThe discovery GWAS included 19 099 ADHD cases and 34 194 control participants. The combined target sample included 845 people with ADHD (age: 8–40 years). RTV and CE were available from reaction time and response inhibition tasks. ADHD PRS were calculated from the GWAS using a leave-one-study-out approach. Regression analyses were run to investigate whether ADHD PRS were associated with CE and RTV. Results across sites were combined via random effect meta-analyses.
ResultsWhen combining the studies in meta-analyses, results were significant for RTV (R2 = 0.011, β = 0.088, p = 0.02) but not for CE (R2 = 0.011, β = 0.013, p = 0.732). No significant association was found between ADHD PRS and RTV or CE in any sample individually (p > 0.10).
ConclusionsWe detected a significant association between PRS for ADHD and RTV (but not CE) in individuals with ADHD, suggesting that common genetic risk variants for ADHD influence attention regulation.
Contributors
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- By Aakash Agarwala, Linda S. Aglio, Rae M. Allain, Paul D. Allen, Houman Amirfarzan, Yasodananda Kumar Areti, Amit Asopa, Edwin G. Avery, Patricia R. Bachiller, Angela M. Bader, Rana Badr, Sibinka Bajic, David J. Baker, Sheila R. Barnett, Rena Beckerly, Lorenzo Berra, Walter Bethune, Sascha S. Beutler, Tarun Bhalla, Edward A. Bittner, Jonathan D. Bloom, Alina V. Bodas, Lina M. Bolanos-Diaz, Ruma R. Bose, Jan Boublik, John P. Broadnax, Jason C. Brookman, Meredith R. Brooks, Roland Brusseau, Ethan O. Bryson, Linda A. Bulich, Kenji Butterfield, William R. Camann, Denise M. Chan, Theresa S. Chang, Jonathan E. Charnin, Mark Chrostowski, Fred Cobey, Adam B. Collins, Mercedes A. Concepcion, Christopher W. Connor, Bronwyn Cooper, Jeffrey B. Cooper, Martha Cordoba-Amorocho, Stephen B. Corn, Darin J. Correll, Gregory J. Crosby, Lisa J. Crossley, Deborah J. Culley, Tomas Cvrk, Michael N. D'Ambra, Michael Decker, Daniel F. Dedrick, Mark Dershwitz, Francis X. Dillon, Pradeep Dinakar, Alimorad G. Djalali, D. John Doyle, Lambertus Drop, Ian F. Dunn, Theodore E. Dushane, Sunil Eappen, Thomas Edrich, Jesse M. Ehrenfeld, Jason M. Erlich, Lucinda L. Everett, Elliott S. Farber, Khaldoun Faris, Eddy M. Feliz, Massimo Ferrigno, Richard S. Field, Michael G. Fitzsimons, Hugh L. Flanagan Jr., Vladimir Formanek, Amanda A. Fox, John A. Fox, Gyorgy Frendl, Tanja S. Frey, Samuel M. Galvagno Jr., Edward R. Garcia, Jonathan D. Gates, Cosmin Gauran, Brian J. Gelfand, Simon Gelman, Alexander C. Gerhart, Peter Gerner, Omid Ghalambor, Christopher J. Gilligan, Christian D. Gonzalez, Noah E. Gordon, William B. Gormley, Thomas J. Graetz, Wendy L. Gross, Amit Gupta, James P. Hardy, Seetharaman Hariharan, Miriam Harnett, Philip M. Hartigan, Joaquim M. Havens, Bishr Haydar, Stephen O. Heard, James L. Helstrom, David L. Hepner, McCallum R. Hoyt, Robert N. Jamison, Karinne Jervis, Stephanie B. Jones, Swaminathan Karthik, Richard M. Kaufman, Shubjeet Kaur, Lee A. Kearse Jr., John C. Keel, Scott D. Kelley, Albert H. Kim, Amy L. Kim, Grace Y. Kim, Robert J. Klickovich, Robert M. Knapp, Bhavani S. Kodali, Rahul Koka, Alina Lazar, Laura H. Leduc, Stanley Leeson, Lisa R. Leffert, Scott A. LeGrand, Patricio Leyton, J. Lance Lichtor, John Lin, Alvaro A. Macias, Karan Madan, Sohail K. Mahboobi, Devi Mahendran, Christine Mai, Sayeed Malek, S. Rao Mallampati, Thomas J. Mancuso, Ramon Martin, Matthew C. Martinez, J. A. Jeevendra Martyn, Kai Matthes, Tommaso Mauri, Mary Ellen McCann, Shannon S. McKenna, Dennis J. McNicholl, Abdel-Kader Mehio, Thor C. Milland, Tonya L. K. Miller, John D. Mitchell, K. Annette Mizuguchi, Naila Moghul, David R. Moss, Ross J. Musumeci, Naveen Nathan, Ju-Mei Ng, Liem C. Nguyen, Ervant Nishanian, Martina Nowak, Ala Nozari, Michael Nurok, Arti Ori, Rafael A. Ortega, Amy J. Ortman, David Oxman, Arvind Palanisamy, Carlo Pancaro, Lisbeth Lopez Pappas, Benjamin Parish, Samuel Park, Deborah S. Pederson, Beverly K. Philip, James H. Philip, Silvia Pivi, Stephen D. Pratt, Douglas E. Raines, Stephen L. Ratcliff, James P. Rathmell, J. Taylor Reed, Elizabeth M. Rickerson, Selwyn O. Rogers Jr., Thomas M. Romanelli, William H. Rosenblatt, Carl E. Rosow, Edgar L. Ross, J. Victor Ryckman, Mônica M. Sá Rêgo, Nicholas Sadovnikoff, Warren S. Sandberg, Annette Y. Schure, B. Scott Segal, Navil F. Sethna, Swapneel K. Shah, Shaheen F. Shaikh, Fred E. Shapiro, Torin D. Shear, Prem S. Shekar, Stanton K. Shernan, Naomi Shimizu, Douglas C. Shook, Kamal K. Sikka, Pankaj K. Sikka, David A. Silver, Jeffrey H. Silverstein, Emily A. Singer, Ken Solt, Spiro G. Spanakis, Wolfgang Steudel, Matthias Stopfkuchen-Evans, Michael P. Storey, Gary R. Strichartz, Balachundhar Subramaniam, Wariya Sukhupragarn, John Summers, Shine Sun, Eswar Sundar, Sugantha Sundar, Neelakantan Sunder, Faraz Syed, Usha B. Tedrow, Nelson L. Thaemert, George P. Topulos, Lawrence C. Tsen, Richard D. Urman, Charles A. Vacanti, Francis X. Vacanti, Joshua C. Vacanti, Assia Valovska, Ivan T. Valovski, Mary Ann Vann, Susan Vassallo, Anasuya Vasudevan, Kamen V. Vlassakov, Gian Paolo Volpato, Essi M. Vulli, J. Matthias Walz, Jingping Wang, James F. Watkins, Maxwell Weinmann, Sharon L. Wetherall, Mallory Williams, Sarah H. Wiser, Zhiling Xiong, Warren M. Zapol, Jie Zhou
- Edited by Charles Vacanti, Scott Segal, Pankaj Sikka, Richard Urman
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- Book:
- Essential Clinical Anesthesia
- Published online:
- 05 January 2012
- Print publication:
- 11 July 2011, pp xv-xxviii
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16 - Online access and processing of LiDAR topography data
- from Part V - Web services and scientific workflows
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- By Christopher J. Crosby, University of California-San Diego, J Ramón Arrowsmith, Arizona State University, Viswanath Nandigam, University of California-San Diego, Chaitanya Baru, University of California-San Diego
- Edited by G. Randy Keller, University of Oklahoma, Chaitanya Baru, University of California, San Diego
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- Book:
- Geoinformatics
- Published online:
- 25 October 2011
- Print publication:
- 19 May 2011, pp 251-265
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Summary
Introduction
Real-time sensor networks, space and airborne-based remote sensing, real-time geodesy and seismology, massive geospatial databases, and large computational models are all enabling new and exciting research on the forefront of the earth sciences. However, with these technologies comes a prodigious increase in the volume and complexity of scientific data that must be efficiently managed, archived, distributed, processed, and integrated in order for it to be of use to the scientific community. Data volume, processing expertise, or computing resource requirements may be a barrier to the scientific community's access to and effective use of these datasets. An emerging solution is a shared cyberinfrastructure that provides access to data, tools, and computing resources. A key objective of geoinformatics initiatives (e.g., Sinha, 2000) is to build such cyberinfrastructure for the geosciences through collaboration between earth scientists and computer scientists.
Airborne LiDAR (Light Distance And Ranging) data have emerged as one of the most powerful tools available for documenting the Earth's topography and its masking vegetation at high resolution (defined here as pixel dimensions less than 2 meters). LiDAR-derived digital elevation models (DEMs) are typically of a resolution more than an order of magnitude better than the best-available 10-meter DEMs. The ability to use these data to construct 2.5-D and 3-D models of the Earth's topography and vegetation is rapidly making them an indispensable tool for earth science research (e.g., Carter et al., 2001).
15 - Scientific workflows for the geosciences: An emerging approach to building integrated data analysis systems
- from Part V - Web services and scientific workflows
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- By Ilkay Altintas, University of California-San Diego, Daniel Crawl, University of California-San Diego, Christopher J Crosby, University of California-San Diego, Peter Cornillon, University of Rhode Island
- Edited by G. Randy Keller, University of Oklahoma, Chaitanya Baru, University of California, San Diego
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- Book:
- Geoinformatics
- Published online:
- 25 October 2011
- Print publication:
- 19 May 2011, pp 237-250
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Summary
Scientific method and the influence of technology
Due to the increasing number and sophistication of data acquisition technologies, the amount of raw data acquired has vastly increased over the last couple of decades (Berman, 2008). This explosion of scientific data, growth in scientific knowledge, and the increase in the number of studies that require access to knowledge from multiple scientific disciplines amplify the complexity of scientific problems. In order to answer these “grand challenge” scientific questions, scientists use computational methods that are evolving almost daily. The basic scientific method, however, remains the same for the individual scientist. Scientists still start with a set of questions, then observe phenomena, gather data, develop hypotheses, perform tests, negate or modify hypotheses, reiterate the process with various data, and finally come up with a new set of questions, theories, or laws (http://en.wikipedia.org/wiki/Scientific_method). A recent change in this scientific method is that it is continuously being transformed with the advances in computer science and technology. The simplest examples of this transformation are use of personal computers to record scientific activity and the way scientists publish and search for publications online. More advanced technologies within the scientific process include sensor-based observatories to collect data in real time, supercomputers to run simulations, domain-specific data archives that give access to heterogeneous data, and online interfaces to distribute computational experiments and monitor resources.