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Títol: Prediction of lithium response using genomic data
Autor: Jiménez Martínez, Ester
Vieta i Pascual, Eduard, 1963-
Matèria: Liti
Membranes cel·lulars
Lithium
Cell membranes
Data de publicació: 13-gen-2021
Publicat per: Nature Publishing Group
Resum: 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 literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen's kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures.
Nota: Reproducció del document publicat a: https://doi.org/10.1038/s41598-020-80814-z
És part de: Scientific Reports, 2021, vol. 11, p. 1155
URI: https://hdl.handle.net/2445/175324
Recurs relacionat: https://doi.org/10.1038/s41598-020-80814-z
ISSN: 2045-2322
Apareix en les col·leccions:Articles publicats en revistes (Medicina)
Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)

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