Blar i forfatter Det helsevitenskapelige fakultet "Ambatipudi, Srikant"

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    • DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility 

      Ambatipudi, Srikant; Horvath, Steve; Perrier, Flavie; Cuenin, Cyrille; Hernandez-Vargas, Hector; Le Calvez-Kelm, Florence; Durand, Geoffroy; Byrnes, Graham; Ferrari, Pietro; Bouaoun, Liacine; Sklias, Athena; Chajès, Véronique; Overvad, Kim; Severi, Gianluca; Baglietto, Laura; Clavel-Chapelon, Françoise; Kaaks, Rudolf; Barrdahl, Myrto; Boeing, Heiner; Trichopoulou, Antonia; Lagiou, Pagona; Naska, Androniki; Masala, Giovanna; Agnoli, Claudia; Polidoro, Silvia; Tumino, Rosario; Panico, Salvatore; Dollé, Martijn; Peeters, Petra H.M.; Onland-Moret, N. Charlotte; Sandanger, Torkjel M; Nøst, Therese Haugdahl; Weiderpass, Elisabete; Quirós, José Ramón; Agudo, Antonio; Rodriguez-Barranco, Miguel; Huerta Castaño, José María; Barricarte, Aurelio; Fernández, Ander Matheu; Travis, Ruth C.; Vineis, Paolo; Muller, David C.; Riboli, Elio; Gunter, Marc; Romieu, Isabelle; Herceg, Zdenko (Journal article; Tidsskriftartikkel; Peer reviewed, 2017-02-28)
      Aim of the study: A vast majority of human malignancies are associated with ageing, and age is a strong predictor of cancer risk. Recently, DNA methylation-based marker of ageing, known as ‘epigenetic clock’, has been linked with cancer risk factors. This study aimed to evaluate whether the epigenetic clock is associated with breast cancer risk susceptibility and to identify potential epigenetics-based ...

    • Identifying and correcting epigenetics measurements for systematic sources of variation 

      Perrier, Flavie; Novoloaca, Alexei; Ambatipudi, Srikant; Baglietto, Laura; Ghantous, Akram; Perduca, Vittorio; Barrdahl, Myrto; Harlid, Sophia; Ong, Ken K; Cardona, Alexia; Polidoro, Silvia; Nøst, Therese Haugdahl; Overvad, Kim; Omichessan, Hanane; Dollé, Martijn; Bamia, Christina; Huerta, José María; Vineis, Paolo; Herceg, Zdenko; Romieu, Isabelle; Ferrari, Pietro (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-03-21)
      <i>Background</i>: Methylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets ...