Results 41 to 50 of about 1,101,785 (286)

Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci

Breast Cancer Research, 2022
Background Mammographic density (MD) phenotypes, including percent density (PMD), area of dense tissue (DA), and area of non-dense tissue (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes are highly heritable. However,
Hongjie Chen, Shaoqi Fan, Jennifer Stone, Deborah J. Thompson, Julie Douglas, Shuai Li, Christopher Scott, Manjeet K. Bolla, Qin Wang, Joe Dennis, Kyriaki Michailidou, Christopher Li, Ulrike Peters, John L. Hopper, Melissa C. Southey, Tu Nguyen-Dumont, Tuong L. Nguyen, Peter A. Fasching, Annika Behrens, Gemma Cadby, Rachel A. Murphy, Kristan Aronson, Anthony Howell, Susan Astley, Fergus Couch, Janet Olson, Roger L. Milne, Graham G. Giles, Christopher A. Haiman, Gertraud Maskarinec, Stacey Winham, Esther M. John, Allison Kurian, Heather Eliassen, Irene Andrulis, D. Gareth Evans, William G. Newman, Per Hall, Kamila Czene, Anthony Swerdlow, Michael Jones, Marina Pollan, Pablo Fernandez-Navarro, Daniel S. McConnell, Vessela N. Kristensen, NBCS Investigators, Joseph H. Rothstein, Pei Wang, Laurel A. Habel, Weiva Sieh, Alison M. Dunning, Paul D. P. Pharoah, Douglas F. Easton, Gretchen L. Gierach, Rulla M. Tamimi, Celine M. Vachon, Sara Lindström   +56 more
doaj   +1 more source

A large multi-ethnic genome-wide association study identifies novel genetic loci for intraocular pressure. [PDF]

, 2017
Elevated intraocular pressure (IOP) is a major risk factor for glaucoma, a leading cause of blindness. IOP heritability has been estimated to up to 67%, and to date only 11 IOP loci have been reported, accounting for 1.5% of IOP variability.
Banda, Yambazi, Choquet, Hélène, Hoffmann, Thomas J, Jorgenson, Eric, Kvale, Mark N, Melles, Ronald, Nair, K Saidas, Risch, Neil, Schaefer, Catherine, Thai, Khanh K, Yin, Jie   +10 more
core   +3 more sources

Genome-wide association studies [PDF]

Nature Reviews Methods Primers, 2021
Genome- wide association studies (GWAS) test hundreds of thousands of genetic variants across many genomes to find those statistically associated with a specific trait or disease. This methodology has generated a myriad of robust associations for a range of traits and diseases, and the number of associated variants is expected to grow steadily as GWAS ...
Emil Uffelmann, Qin Qin Huang, Nchangwi Syntia Munung, Jantina de Vries, Yukinori Okada, Alicia R. Martin, Hilary C. Martin, Tuuli Lappalainen, Danielle Posthuma   +8 more
openaire   +2 more sources

Guidelines for Genome-Wide Association Studies

PLoS Genetics, 2012
Genome-wide association studies (GWAS) have revolutionized human genetics. They have led to the identification of thousands of loci that affect both normal variation and susceptibility to disease, and have clarified our understanding of the genetic architecture of complex traits.
Gregory S Barsh, Gregory P Copenhaver, Greg Gibson, Scott M Williams   +3 more
openaire   +4 more sources

Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20

BMC Genetics, 2018
Background Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine Learning group at the GAW20 to ...
Burcu Darst, Corinne D. Engelman, Ye Tian, Justo Lorenzo Bermejo   +3 more
doaj   +1 more source

Genome- and Transcriptome-wide Association Studies to Discover Candidate Genes for Diverse Root Phenotypes in Cultivated Rice

Rice, 2023
Root system architecture plays a crucial role in nutrient and water absorption during rice production. Genetic improvement of the rice root system requires elucidating its genetic control.
Shujun Wei, Ryokei Tanaka, Taiji Kawakatsu, Shota Teramoto, Nobuhiro Tanaka, Matthew Shenton, Yusaku Uga, Shiori Yabe   +7 more
doaj   +1 more source

Replication in Genome-Wide Association Studies

Statistical Science, 2009
Replication helps ensure that a genotype-phenotype association observed in a genome-wide association (GWA) study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. We discuss prerequisites for exact replication, issues of heterogeneity, advantages and disadvantages of different methods of data ...
Kraft, Peter, Zeggini, Eleftheria, Ioannidis, John P. A.   +2 more
openaire   +6 more sources

Genome-Wide Association Studies in Glioma [PDF]

Cancer Epidemiology, Biomarkers & Prevention, 2018
Abstract Since the first reports in 2009, genome-wide association studies (GWAS) have been successful in identifying germline variants associated with glioma susceptibility. In this review, we describe a chronological history of glioma GWAS, culminating in the most recent study comprising 12,496 cases and 18,190 controls. We additionally
Kinnersley, B, Houlston, RS, Bondy, ML
openaire   +3 more sources

Transcriptome-wide association study of multiple myeloma identifies candidate susceptibility genes

Human Genomics, 2019
Background While genome-wide association studies (GWAS) of multiple myeloma (MM) have identified variants at 23 regions influencing risk, the genes underlying these associations are largely unknown. To identify candidate causal genes at these regions and
Molly Went, Ben Kinnersley, Amit Sud, David C. Johnson, Niels Weinhold, Asta Försti, Mark van Duin, Giulia Orlando, Jonathan S. Mitchell, Rowan Kuiper, Brian A. Walker, Walter M. Gregory, Per Hoffmann, Graham H. Jackson, Markus M. Nöthen, Miguel Inacio da Silva Filho, Hauke Thomsen, Annemiek Broyl, Faith E. Davies, Unnur Thorsteinsdottir, Markus Hansson, Martin Kaiser, Pieter Sonneveld, Hartmut Goldschmidt, Kari Stefansson, Kari Hemminki, Björn Nilsson, Gareth J. Morgan, Richard S. Houlston   +28 more
doaj   +1 more source

Genome-wide association study identifies 74 loci associated with educational attainment

Nature, 2016
Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals.
A. Okbay, J. Beauchamp, M. Fontana, James J. Lee, T. Pers, C. A. Rietveld, P. Turley, Guo-Bo Chen, V. Emilsson, S. Meddens, Sven Oskarsson, Joseph K. Pickrell, Kevin Thom, P. Timshel, Ronald de Vlaming, A. Abdellaoui, T. Ahluwalia, J. Bacelis, C. Baumbach, G. Bjornsdottir, J. Brandsma, Maria Pina Concas, J. Derringer, N. Furlotte, T. Galesloot, G. Girotto, Richa Gupta, Leanne M. Hall, S. Harris, E. Hofer, M. Horikoshi, J. Huffman, Kadri Kaasik, I. Kalafati, R. Karlsson, A. Kong, J. Lahti, S. J. Lee, C. deLeeuw, P. Lind, K. Lindgren, Tian Liu, M. Mangino, J. Marten, E. Mihailov, Michael B. Miller, P. J. van der Most, C. Oldmeadow, A. Payton, N. Pervjakova, W. Peyrot, Yong Qian, O. Raitakari, R. Rueedi, E. Salvi, B. Schmidt, Katharina E. Schraut, Jianxin Shi, A. Smith, R. Poot, Beate St Pourcain, A. Teumer, G. Thorleifsson, N. Verweij, D. Vuckovic, J. Wellmann, H. Westra, Jingyun Yang, Wei Zhao, Zhihong Zhu, B. Alizadeh, N. Amin, A. Bakshi, S. Baumeister, G. Biino, K. Bønnelykke, P. Boyle, H. Campbell, F. Cappuccio, G. Davies, J. De Neve, P. Deloukas, I. Demuth, Jun Ding, P. Eibich, L. Eisele, N. Eklund, David M. Evans, J. Faul, M. Feitosa, A. Forstner, I. Gandin, Bjarni Gunnarsson, B. Halldórsson, T. Harris, Andrew C. Heath, L. Hocking, Elizabeth G. Holliday, G. Homuth, M. Horan, J. Hottenga, P. D. De Jager, Peter K. Joshi, A. Jugessur, M. Kaakinen, M. Kähönen, S. Kanoni, Liisa Keltigangas-Järvinen, L. Kiemeney, I. Kolčić, S. Koskinen, A. Kraja, M. Kroh, Z. Kutalik, A. Latvala, L. Launer, Maël P. Lebreton, D. Levinson, P. Lichtenstein, P. Lichtner, D. Liewald, LifeLines Cohort Study, A. Loukola, P. Madden, R. Mägi, T. Mäki-Opas, R. Marioni, P. Marques-Vidal, Gerardus A. Meddens, George Mcmahon, C. Meisinger, T. Meitinger, Y. Milaneschi, L. Milani, G. Montgomery, Ronny Myhre, C. Nelson, D. Nyholt, William E. R. Ollier, A. Palotie, L. Paternoster, N. Pedersen, K. Petrovic, D. Porteous, K. Räikkönen, Susan M. Ring, A. Robino, Olga Rostapshova, I. Rudan, A. Rustichini, V. Salomaa, A. Sanders, Antti-Pekka Sarin, H. Schmidt, Rodney J Scott, Blair H. Smith, Jennifer A Smith, J. Staessen, E. Steinhagen-Thiessen, K. Strauch, A. Terracciano, M. Tobin, Sheila Ulivi, S. Vaccargiu, L. Quaye, F. V. van Rooij, C. Venturini, Anna A. E. Vinkhuyzen, U. Völker, H. Völzke, J. Vonk, D. Vozzi, Johannes Waage, E. Ware, G. Willemsen, J. Attia, David A. Bennett, K. Berger, L. Bertram, H. Bisgaard, D. Boomsma, I. Borecki, Ute Bültmann, C. Chabris, F. Cucca, D. Cusi, I. Deary, G. Dedoussis, C. V. van Duijn, J. Eriksson, B. Franke, L. Franke, P. Gasparini, P. Gejman, C. Gieger, H. Grabe, J. Gratten, Patrick J. F. Groenen, V. Gudnason, P. van der Harst, C. Hayward, D. Hinds, W. Hoffmann, E. Hyppönen, W. Iacono, B. Jacobsson, M. Järvelin, K. Jöckel, J. Kaprio, S. Kardia, T. Lehtimäki, Steven F. Lehrer, P. Magnusson, Nicholas G. Martin, M. McGue, A. Metspalu, N. Pendleton, B. Penninx, M. Perola, N. Pirastu, M. Pirastu, O. Polašek, D. Posthuma, C. Power, M. Province, Nilesh J. Samani, D. Schlessinger, R. Schmidt, T. I. Sørensen, T. Spector, K. Stefánsson, U. Thorsteinsdóttir, A. Thurik, N. Timpson, H. Tiemeier, J. Tung, A. Uitterlinden, V. Vitart, P. Vollenweider, David R. Weir, James F. Wilson, Alan F. Wright, Dalton C. Conley, Robert F. Krueger, G. Davey Smith, A. Hofman, D. Laibson, S. Medland, M. Meyer, Jian Yang, M. Johannesson, P. Visscher, T. Esko, P. Koellinger, D. Cesarini, D. Benjamin   +255 more
semanticscholar   +1 more source