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Redefining phenotypes to advance psychiatric genetics: Implications from hierarchical taxonomy of psychopathology.

  • Author(s): Waszczuk, Monika A
  • Eaton, Nicholas R
  • Krueger, Robert F
  • Shackman, Alexander J
  • Waldman, Irwin D
  • Zald, David H
  • Lahey, Benjamin B
  • Patrick, Christopher J
  • Conway, Christopher C
  • Ormel, Johan
  • Hyman, Steven E
  • Fried, Eiko I
  • Forbes, Miriam K
  • Docherty, Anna R
  • Althoff, Robert R
  • Bach, Bo
  • Chmielewski, Michael
  • DeYoung, Colin G
  • Forbush, Kelsie T
  • Hallquist, Michael
  • Hopwood, Christopher J
  • Ivanova, Masha Y
  • Jonas, Katherine G
  • Latzman, Robert D
  • Markon, Kristian E
  • Mullins-Sweatt, Stephanie N
  • Pincus, Aaron L
  • Reininghaus, Ulrich
  • South, Susan C
  • Tackett, Jennifer L
  • Watson, David
  • Wright, Aidan GC
  • Kotov, Roman
  • et al.
Abstract

Genetic discovery in psychiatry and clinical psychology is hindered by suboptimal phenotypic definitions. We argue that the hierarchical, dimensional, and data-driven classification system proposed by the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium provides a more effective approach to identifying genes that underlie mental disorders, and to studying psychiatric etiology, than current diagnostic categories. Specifically, genes are expected to operate at different levels of the HiTOP hierarchy, with some highly pleiotropic genes influencing higher order psychopathology (e.g., the general factor), whereas other genes conferring more specific risk for individual spectra (e.g., internalizing), subfactors (e.g., fear disorders), or narrow symptoms (e.g., mood instability). We propose that the HiTOP model aligns well with the current understanding of the higher order genetic structure of psychopathology that has emerged from a large body of family and twin studies. We also discuss the convergence between the HiTOP model and findings from recent molecular studies of psychopathology indicating broad genetic pleiotropy, such as cross-disorder SNP-based shared genetic covariance and polygenic risk scores, and we highlight molecular genetic studies that have successfully redefined phenotypes to enhance precision and statistical power. Finally, we suggest how to integrate a HiTOP approach into future molecular genetic research, including quantitative and hierarchical assessment tools for future data-collection and recommendations concerning phenotypic analyses. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

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