Results 11 to 20 of about 41,997,054 (284)

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies [PDF]

Human Genetics and Genomics Advances, 2023
Abstract Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological
Daiwei Zhang, Boran Gao, Qidi Feng, Ani Manichaikul, Gina M. Peloso, Russell P. Tracy, Peter Durda, Kent D. Taylor, Yongmei Liu, W. Craig Johnson, Stacey Gabriel, Namrata Gupta, Joshua D. Smith, Francois Aguet, Kristin G. Ardlie, Thomas W. Blackwell, Robert E. Gerszten, Stephen S. Rich, Jerome I. Rotter, Laura J. Scott, Xiang Zhou, Seunggeun Lee   +21 more
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Probabilistic fine-mapping of transcriptome-wide association studies [PDF]

Nature Genetics, 2019
Transcriptome-wide association studies using predicted expression have identified thousands of genes whose locally regulated expression is associated with complex traits and diseases. In this work, we show that linkage disequilibrium induces significant gene-trait associations at non-causal genes as a function of the expression quantitative trait loci ...
Nicholas Mancuso, Malika K. Freund, Ruth Johnson, Huwenbo Shi, Gleb Kichaev, Alexander Gusev, Bogdan Pasaniuc   +6 more
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Transcriptome‐wide association study of breast cancer risk by estrogen‐receptor status [PDF]

Genetic Epidemiology, 2020
AbstractPrevious transcriptome‐wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome‐wide association studies (GWAS), but analyses of breast cancer subtype‐specific associations have been limited.
Feng, Helian, Gusev, Alexander, Pasaniuc, Bogdan, Wu, Lang, Long, Jirong, Abu-Full, Zomoroda, Aittomäki, Kristiina, Andrulis, Irene L, Anton-Culver, Hoda, Antoniou, Antonis C, Arason, Adalgeir, Arndt, Volker, Aronson, Kristan J, Arun, Banu K, Asseryanis, Ella, Auer, Paul L, Azzollini, Jacopo, Balmaña, Judith, Barkardottir, Rosa B, Barnes, Daniel R, Barrowdale, Daniel, Beckmann, Matthias W, Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Białkowska, Katarzyna, Blanco, Ana, Blomqvist, Carl, Boeckx, Bram, Bogdanova, Natalia V, Bojesen, Stig E, Bolla, Manjeet K, Bonanni, Bernardo, Borg, Ake, Brauch, Hiltrud, Brenner, Hermann, Briceno, Ignacio, Broeks, Annegien, Brüning, Thomas, Burwinkel, Barbara, Cai, Qiuyin, Caldés, Trinidad, Caligo, Maria A, Campbell, Ian, Canisius, Sander, Campa, Daniele, Carter, Brian D, Carter, Jonathan, Castelao, Jose E, Chang-Claude, Jenny, Chanock, Stephen J, Christiansen, Hans, Chung, Wendy K, Claes, Kathleen BM, Clarke, Christine L, GEMO Study Collaborators, EMBRACE Collaborators, GC-HBOC study Collaborators, Couch, Fergus J, Cox, Angela, Cross, Simon S, Cybulski, Cezary, Czene, Kamila, Daly, Mary B, de la Hoya, Miguel, De Leeneer, Kim, Dennis, Joe, Devilee, Peter, Diez, Orland, Domchek, Susan M, Dörk, Thilo, Dos-Santos-Silva, Isabel, Dunning, Alison M, Dwek, Miriam, Eccles, Diana M, Ejlertsen, Bent, Ellberg, Carolina, Engel, Christoph, Eriksson, Mikael, Fasching, Peter A, Fletcher, Olivia, Flyger, Henrik, Fostira, Florentia, Friedman, Eitan, Fritschi, Lin, Frost, Debra, Gabrielson, Marike, Ganz, Patricia A, Gapstur, Susan M, Garber, Judy, García-Closas, Montserrat, García-Sáenz, José A, Gaudet, Mia M, Giles, Graham G, Glendon, Gord, Godwin, Andrew K, Goldberg, Mark S, Goldgar, David E, González-Neira, Anna, Greene, Mark H, Gronwald, Jacek, Guénel, Pascal, Haiman, Christopher A, Hall, Per, Hamann, Ute, Hake, Christopher, He, Wei, Heyworth, Jane, Hogervorst, Frans BL, Hollestelle, Antoinette, Hooning, Maartje J, Hoover, Robert N, Hopper, John L, Huang, Guanmengqian, Hulick, Peter J, Humphreys, Keith, Imyanitov, Evgeny N, ABCTB Investigators, HEBON Investigators, BCFR Investigators, OCGN Investigators, Isaacs, Claudine, Jakimovska, Milena, Jakubowska, Anna, James, Paul, Janavicius, Ramunas, Jankowitz, Rachel C, John, Esther M, Johnson, Nichola, Joseph, Vijai, Jung, Audrey, Karlan, Beth Y, Khusnutdinova, Elza, Kiiski, Johanna I, Konstantopoulou, Irene, Kristensen, Vessela N, Laitman, Yael, Lambrechts, Diether, Lazaro, Conxi, Leroux, Dominique, Leslie, Goska, Lester, Jenny, Lesueur, Fabienne, Lindor, Noralane, Lindström, Sara, Lo, Wing-Yee, Loud, Jennifer T, Lubiński, Jan, Makalic, Enes, Mannermaa, Arto, Manoochehri, Mehdi, Manoukian, Siranoush, Margolin, Sara, Martens, John WM, Martinez, Maria E, Matricardi, Laura, Maurer, Tabea, Mavroudis, Dimitrios, McGuffog, Lesley, Meindl, Alfons, Menon, Usha, Michailidou, Kyriaki, Kapoor, Pooja M, Miller, Austin, Montagna, Marco, Moreno, Fernando, Moserle, Lidia, Mulligan, Anna M, Muranen, Taru A, Nathanson, Katherine L, Neuhausen, Susan L, Nevanlinna, Heli, Nevelsteen, Ines, Nielsen, Finn C, Nikitina-Zake, Liene, Offit, Kenneth, Olah, Edith, Olopade, Olufunmilayo I, Olsson, Håkan, Osorio, Ana, Papp, Janos, Park-Simon, Tjoung-Won, Parsons, Michael T, Pedersen, Inge S, Peixoto, Ana, Peterlongo, Paolo, Peto, Julian, Pharoah, Paul DP, Phillips, Kelly-Anne, Plaseska-Karanfilska, Dijana, Poppe, Bruce, Pradhan, Nisha, Prajzendanc, Karolina, Presneau, Nadege, Punie, Kevin, Pylkäs, Katri, Radice, Paolo, Rantala, Johanna, Rashid, Muhammad Usman, Rennert, Gad, Romero, Atocha, Saloustros, Emmanouil, Sandler, Dale P, Santos, Catarina, Sawyer, Elinor J, Schoemaker, Minouk J, Scott, Rodney J, Simard, Jacques, Skytte, Anne-Bine, Stone, Jennifer, Tapper, William J, Taylor, Jack A, Teixeira, Manuel R, Terry, Mary Beth, Teulé, Alex, Tischkowitz, Marc, Torres, Diana, Vega, Ana, Viel, Alessandra, Weinberg, Clarice R, Weitzel, Jeffrey N, Winqvist, Robert, Chenevix-Trench, Georgia, Zheng, Wei   +223 more
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MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies [PDF]

PLOS Genetics, 2020
Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association.
Arjun Bhattacharya, Yun Li, Michael I. Love   +2 more
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Integrative approaches for large-scale transcriptome-wide association studies [PDF]

Nature Genetics, 2015
Abstract Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. In this work we introduce a powerful strategy that integrates gene expression measurements with large-scale genome-wide association data to identify genes whose cis ...
Alexander Gusev, Arthur Ko, Huwenbo Shi, Gaurav Bhatia, Wonil Chung, Brenda W J H Penninx, Rick Jansen, Eco J C de Geus, Dorret I Boomsma, Fred A Wright, Patrick F Sullivan, Elina Nikkola, Marcus Alvarez, Mete Civelek, Aldons J Lusis, Terho Lehtimäki, Emma Raitoharju, Mika Kähönen, Ilkka Seppälä, Olli T Raitakari, Johanna Kuusisto, Markku Laakso, Alkes L Price, Päivi Pajukanta, Bogdan Pasaniuc   +24 more
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Transcriptome-wide association study: Opportunity and challenges for cancer studies

Insights in Biology and Medicine, 2022
Genome-wide association studies (GWAS) have uncovered thousands of single nucleotide polymorphism (SNP) loci that are associated with complex traits. However, the majority of GWAS discoveries are located in non-coding regions and the biological mechanisms behind these associations are not well understood.
Kendy Shang, Jingni He
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Transcriptome-wide association study identifies susceptibility genes for rheumatoid arthritis [PDF]

Arthritis Research & Therapy, 2021
Objective To identify rheumatoid arthritis (RA)-associated susceptibility genes and pathways through integrating genome-wide association study (GWAS) and gene expression profile data. Methods A transcriptome-wide association study (TWAS) was conducted by
Cuiyan Wu, Sijian Tan, Li Liu, Shiqiang Cheng, Peilin Li, Wenyu Li, Huan Liu, Feng’e Zhang, Sen Wang, Yujie Ning, Yan Wen, Feng Zhang   +11 more
doaj   +3 more sources

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

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
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On the interpretation of transcriptome-wide association studies [PDF]

PLOS Genetics, 2021
Abstract Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a geneticrelationship between gene expression and a ...
de Leeuw, Christiaan, Werme, Josefin, Savage, Jeanne E., Peyrot, Wouter J., Posthuma, Danielle   +4 more
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Multi-tissue transcriptome-wide association studies [PDF]

Genetic Epidemiology, 2020
Abstract Many genetic mutations affecting phenotypes are presumed to do so via altering gene expression in particular cells or tissues, but identifying the specific genes involved has been challenging. A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by first learning a predictive model on an ...
Grinberg, Nastasiya F, Wallace, Chris
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