Publications

Les publications qui suivent sont des exemples de recherches rendues possibles grâce aux données de CanPath et de ses cohortes régionales.

2024

Genomic Analysis Identifies Risk Factors in Restless Legs Syndrome

Auteurs : Fulya Akçimen, Ruth Chia, Sara Saez-Atienzar, Paola Ruffo, Memoona Rasheed, Jay P. Ross, Calwing Liao, Anindita Ray, Patrick A. Dion, Sonja W. Scholz , Guy A. Rouleau, and Bryan J. Traynor

The researchers studied 9,851 Restless Legs Syndrome (RLS) cases and 38,957 controls of European ancestry from CARTaGENE, Canadian Longitudinal Study on Aging, and All of Us biobanks in Canada and the U.S. They found nine genetic locations linked to RLS, including one new location (LMX1B), and identified two related genes (GLO1 and ELFN1). The study also revealed genetic overlaps between RLS and traits like neuroticism, depression, and intelligence. This research enhances our understanding of RLS’s genetic factors.

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2022

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Auteurs : Wei Zhou, Masahiro Kanai, Kuan-Han H Wu, Humaira Rasheed, Kristin Tsuo, Jibril B Hirbo, Ying Wang, Arjun Bhattacharya, Huiling Zhao, Shinichi Namba, Ida Surakka, Brooke N Wolford, Valeria Lo Faro, Esteban A Lopera-Maya, Kristi Läll, Marie-Julie Favé, Juulia J Partanen, Sinéad B Chapman, Juha Karjalainen, Mitja Kurki, Mutaamba Maasha, Ben M Brumpton, Sameer Chavan, Tzu-Ting Chen, Michelle Daya, Yi Ding, Yen-Chen A Feng, Lindsay A Guare, Christopher R Gignoux, Sarah E Graham, Whitney E Hornsby, Nathan Ingold, Said I Ismail, Ruth Johnson, Triin Laisk, Kuang Lin, Jun Lv, Iona Y Millwood, Sonia Moreno-Grau, Kisung Nam, Priit Palta, Anita Pandit, Michael H Preuss, Chadi Saad, Shefali Setia-Verma, Unnur Thorsteinsdottir, Jasmina Uzunovic, Anurag Verma, Matthew Zawistowski, Xue Zhong, Nahla Afifi, Kawthar M Al-Dabhani, Asma Al Thani, Yuki Bradford, Archie Campbell, Kristy Crooks, Geertruida H de Bock, Scott M Damrauer, Nicholas J Douville, Sarah Finer, Lars G Fritsche, Eleni Fthenou, Gilberto Gonzalez-Arroyo, Christopher J Griffiths, Yu Guo, Karen A Hunt, Alexander Ioannidis, Nomdo M Jansonius, Takahiro Konuma, Ming Ta Michael Lee, Arturo Lopez-Pineda, Yuta Matsuda, Riccardo E Marioni, Babak Moatamed, Marco A Nava-Aguilar, Kensuke Numakura, Snehal Patil, Nicholas Rafaels, Anne Richmond, Agustin Rojas-Muñoz, Jonathan A Shortt, Peter Straub, Ran Tao, Brett Vanderwerff, Manvi Vernekar, Yogasudha Veturi, Kathleen C Barnes, Marike Boezen, Zhengming Chen, Chia-Yen Chen, Judy Cho, George Davey Smith, Hilary K Finucane, Lude Franke, Eric R Gamazon, Andrea Ganna, Tom R Gaunt, Tian Ge, Hailiang Huang, Jennifer Huffman, Nicholas Katsanis, Jukka T Koskela, Clara Lajonchere, Matthew H Law, Liming Li, Cecilia M Lindgren, Ruth J F Loos, Stuart MacGregor, Koichi Matsuda, Catherine M Olsen, David J Porteous, Jordan A Shavit, Harold Snieder, Tomohiro Takano, Richard C Trembath, Judith M Vonk, David C Whiteman, Stephen J Wicks, Cisca Wijmenga, John Wright, Jie Zheng, Xiang Zhou, Philip Awadalla, Michael Boehnke, Carlos D Bustamante, Nancy J Cox, Segun Fatumo, Daniel H Geschwind, Caroline Hayward, Kristian Hveem, Eimear E Kenny, Seunggeun Lee, Yen-Feng Lin, Hamdi Mbarek, Reedik Mägi, Hilary C Martin, Sarah E Medland, Yukinori Okada, Aarno V Palotie, Bogdan Pasaniuc, Daniel J Rader, Marylyn D Ritchie, Serena Sanna, Jordan W Smoller, Kari Stefansson, David A van Heel, Robin G Walters, Sebastian Zöllner; Biobank of the Americas; Biobank Japan Project; BioMe; BioVU; CanPath - Ontario Health Study; China Kadoorie Biobank Collaborative Group; Colorado Center for Personalized Medicine; deCODE Genetics; Estonian Biobank; FinnGen; Generation Scotland; Genes & Health Research Team; LifeLines; Mass General Brigham Biobank; Michigan Genomics Initiative; National Biobank of Korea; Penn Medicine BioBank; Qatar Biobank; QSkin Sun and Health Study; Taiwan Biobank; HUNT Study; UCLA ATLAS Community Health Initiative; Uganda Genome Resource; UK Biobank; Alicia R Martin, Cristen J Willer, Mark J Daly, Benjamin M Neale

L’Initiative mondiale de méta-analyse des biobanques est un réseau collaboratif de 23 biobanques, représentant plus de 2,2 millions de participants consentants dont les données génétiques sont liées aux dossiers de santé électroniques. Cet effort de collaboration améliorera le pouvoir des études d’association pangénomiques pour les maladies, profitera aux maladies peu étudiées et améliorera la prédiction des risques.

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