Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations
Liu J, Borsari B, Li Y, Liu S, Gao Y, Xin X, Lou S, Jensen M, Garrido-Martín D, Verplaetse T, Ash G, Zhang J, Girgenti M, Roberts W, Gerstein M. Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations. Cell 2024 PMID: 39706190, DOI: 10.1016/j.cell.2024.11.012.Peer-Reviewed Original ResearchGenome-wide association studiesCase-control genome-wide association studyMultivariate genome-wide association studyGenetic lociAssociation studiesGenetic dataGenetic associationPhenotypeGeneticsEnvironmental factorsDetection powerElfn1Adolescent Brain Cognitive DevelopmentLociGenesPsychiatric disordersADORA3Digital phenotypingUsing a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements
Pratt H, Andrews G, Shedd N, Phalke N, Li T, Pampari A, Jensen M, Wen C, Consortium P, Gandal M, Geschwind D, Gerstein M, Moore J, Kundaje A, Colubri A, Weng Z. Using a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements. Science Advances 2024, 10: eadj4452. PMID: 38781344, PMCID: PMC11114231, DOI: 10.1126/sciadv.adj4452.Peer-Reviewed Original ResearchConceptsEpigenetic dataCell-type-specific gene regulationCis-regulatory elementsComprehensive atlasGenetic variants associated with psychiatric disordersLineage-specific transcription factorsBrain cell typesMammalian elementsPsychENCODE ConsortiumNoncoding regionsEvolutionary historyGene regulationRegulatory elementsSequence mutationsTranscription factorsSequence syntaxRegulatory informationPrimate-specific sequencesBinding sitesHuman traitsCell typesFunctional implicationsPsychiatric disordersSequenceFetal brain development