G
Guy A. Rouleau
Researcher at Montreal Neurological Institute and Hospital
Publications - 935
Citations - 75050
Guy A. Rouleau is an academic researcher from Montreal Neurological Institute and Hospital. The author has contributed to research in topics: Gene & Genome-wide association study. The author has an hindex of 129, co-authored 884 publications receiving 65892 citations. Previous affiliations of Guy A. Rouleau include Utrecht University & University of Helsinki.
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Journal ArticleDOI
Expanded CAG Repeats in ATXN1, ATXN2, ATXN3, and HTT in the 1000 Genomes Project
Fulya Akçimen,Fulya Akçimen,Jay P. Ross,Jay P. Ross,Calwing Liao,Calwing Liao,Dan Spiegelman,Patrick A. Dion,Patrick A. Dion,Guy A. Rouleau,Guy A. Rouleau +10 more
TL;DR: The results suggest that there may be asymptomatic small expanded repeats in almost 0.5% of populations affected by spinocerebellar ataxia and Huntington disease.
Journal ArticleDOI
Restriction Map of a YAC and Cosmid Contig Encompassing the Oculopharyngeal Muscular Dystrophy Candidate Region on Chromosome 14q11.2–q13
Ya-Gang Xie,Daniel Rochefort,Bernard Brais,Heidi Carmen Howard,Fei-Yu Han,Lu-Ping Gou,Patrícia Maciel,Catherina Larsson,Guy A. Rouleau +8 more
TL;DR: The YAC and cosmid contigs will facilitate the identification of genes lying within the OPMD candidate interval and five putative CpG islands were identified.
Journal ArticleDOI
Association study of essential tremor genetic loci in Parkinson's disease.
Jay P. Ross,Jay P. Ross,Sadaf Mohtashami,Sadaf Mohtashami,Etienne Leveille,Amelie Johnson,Lan Xiong,Patrick A. Dion,Patrick A. Dion,Edward A. Fon,Edward A. Fon,Yves Dauvilliers,Nicolas Dupré,Guy A. Rouleau,Guy A. Rouleau,Ziv Gan-Or,Ziv Gan-Or +16 more
TL;DR: None of the variants tested in the present study was significantly associated with PD, and results do not support a role of ET-associated genetic variants in PD.
Journal ArticleDOI
Erratum: Common variants in P2RY11 are associated with narcolepsy (Nature Genetics (2011) 43 (66-71))
Birgitte Rahbek Kornum,Minae Kawashima,Juliette Faraco,Ling Lin,Thomas J Rico,Stephanie Hesselson,Robert C. Axtell,Hedwich F. Kuipers,Karin Weiner,Alexandra Hamacher,Matthias U. Kassack,Fang Han,Stine Knudsen,Jing Li,Xiaosong Dong,Juliane Winkelmann,Giuseppe Plazzi,Sona Nevsimalova,Seung-Chul Hong,Yutaka Honda,Makoto Honda,Birgit Högl,Thanh G.N. Ton,Jacques Montplaisir,Patrice Bourgin,David Kemlink,Yu-Shu Huang,Simon C. Warby,Mali Einen,Jasmin L Eshragh,Taku Miyagawa,Alex Desautels,Elisabeth Ruppert,Per Egil Hesla,Francesca Poli,Fabio Pizza,Birgit Frauscher,Jong-Hyun Jeong,Sung-Pil Lee,Kingman P Strohl,William T. Longstreth,Mark N. Kvale,Marie Dobrovolna,Maurice M. Ohayon,Gerald T. Nepom,H-Erich Wichmann,Guy A. Rouleau,Christian Gieger,Douglas F. Levinson,Pablo V. Gejman,Thomas Meitinger,Paul E. Peppard,Terry Young,Poul Jennum,Lawrence Steinman,Katsushi Tokunaga,Pui-Yan Kwok,Neil Risch,Joachim Hallmayer,Emmanuel Mignot +59 more
TL;DR: In the version of this article initially published, the percentage reduction of P2RY11 expression in CD8+ T lymphocytes was incorrectly given as 339%, when it should have been 72%.
Journal ArticleDOI
Prediction of lithium response using genomic data.
Will Stone,Abraham Nunes,Kazufumi Akiyama,Nirmala Akula,Raffaella Ardau,Jean-Michel Aubry,Lena Backlund,Lena Backlund,Michael Bauer,Frank Bellivier,Frank Bellivier,Pablo Cervantes,Hsi-Chung Chen,Caterina Chillotti,Cristiana Cruceanu,Alexandre Dayer,Franziska Degenhardt,Maria Del Zompo,Andreas J. Forstner,Andreas J. Forstner,Mark A. Frye,Janice M. Fullerton,Maria Grigoroiu-Serbanescu,Paul Grof,Ryota Hashimoto,Liping Hou,Esther Jiménez,Tadafumi Kato,John R. Kelsoe,Sarah Kittel-Schneider,Po-Hsiu Kuo,Ichiro Kusumi,Catharina Lavebratt,Catharina Lavebratt,Mirko Manchia,Mirko Manchia,Lina Martinsson,Manuel Mattheisen,Francis J. McMahon,Vincent Millischer,Vincent Millischer,Philip B. Mitchell,Markus M. Nöthen,Claire O'Donovan,Norio Ozaki,Claudia Pisanu,Andreas Reif,Marcella Rietschel,Guy A. Rouleau,Janusz K. Rybakowski,Martin Schalling,Martin Schalling,Peter R. Schofield,Thomas G. Schulze,Giovanni Severino,Alessio Squassina,Alessio Squassina,Julia Veeh,Eduard Vieta,Thomas Trappenberg,Martin Alda +60 more
TL;DR: In this paper, the authors evaluated the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data using the largest existing genomic dataset (n = 2210 across 14 international sites; 29% responders).