Article
Prediction of lithium response using genomic data.
Scientific reports - 13 Jan 2021
Stone William, Nunes Abraham, Akiyama Kazufumi, Akula Nirmala, Ardau Raffaella, Aubry Jean-Michel, Backlund Lena, Bauer Michael, Bellivier Frank, Cervantes Pablo, Chen Hsi-Chung, Chillotti Caterina, Cruceanu Cristiana, Dayer Alexandre, Degenhardt Franziska, Del Zompo Maria, Forstner Andreas J, Frye Mark, Fullerton Janice M, Grigoroiu-Serbanescu Maria, Grof Paul, Hashimoto Ryota, Hou Liping, Jiménez Esther, Kato Tadafumi, Kelsoe John, Kittel-Schneider Sarah, Kuo Po-Hsiu, Kusumi Ichiro, Lavebratt Catharina, Manchia Mirko, Martinsson Lina, Mattheisen Manuel, McMahon Francis J, Millischer Vincent, Mitchell Philip B, Nöthen Markus M, O'Donovan Claire, Ozaki Norio, Pisanu Claudia, Reif Andreas, Rietschel Marcella, Rouleau Guy, Rybakowski Janusz, Schalling Martin, Schofield Peter R, Schulze Thomas G, Severino Giovanni, Squassina Alessio, Veeh Julia, Vieta Eduard, Trappenberg Thomas, Alda Martin
Abstract excerpt
Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response...
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