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Integration of evidence across human and model organism studies: A meeting report.

H C Palmer RohanEmma C JohnsonHyejung WonRenato PolimantiManav KapoorApurva S ChitreMolly A BogueChelsie E Benca-BachmanClarissa C ParkerAnurag VermaTimothy J ReynoldsJason ErnstMichael BraySoo Bin KwonDongbing LaiBryan C QuachNathan C GaddisLaura M SabaHao ChenMichael HawrylyczShan ZhangYuan ZhouSpencer MahaffeyChristian FischerSandra Sanchez-RoigeAnita BandrowskiQing LuLi ShenPhilipp P HenrichJoshua C GrayLaura J BierutDana B HancockHoward J EdenbergEric O JohnsonEric J NestlerPeter B BarrPjotr PrinsDesmond J SmithSchahram AkbarianThorgeir E ThorgeirssonDave WaltonErich J BakerDaniel A JacobsonAbraham A PalmerMichael F MilesElissa J CheslerJake EmersonArpana AgrawalMaryann E MartoneRobert W Williams
Published in: Genes, brain, and behavior (2021)
The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.
Keyphrases
  • electronic health record
  • endothelial cells
  • big data
  • gene expression
  • dna methylation
  • induced pluripotent stem cells
  • healthcare
  • social media
  • genome wide
  • machine learning
  • deep learning