Journal article
Redefining Phenotypes to Advance Psychiatric Genetics: Implications From Hierarchical Taxonomy of Psychopathology
Journal of abnormal psychology (1965), Vol.129(2), pp.143-161
02/01/2020
DOI: 10.1037/abn0000486
PMCID: PMC6980897
PMID: 31804095
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.
Details
- Title: Subtitle
- Redefining Phenotypes to Advance Psychiatric Genetics: Implications From Hierarchical Taxonomy of Psychopathology
- Creators
- Monika A. Waszczuk - Stony Brook UniversityNicholas R. Eaton - Stony Brook UniversityRobert F. Krueger - University of MinnesotaAlexander J. Shackman - University of Maryland, College ParkIrwin D. Waldman - Emory UniversityDavid H. Zald - Vanderbilt UniversityBenjamin B. Lahey - University of ChicagoChristopher J. Patrick - Florida State UniversityChristopher C. Conway - Fordham UniversityJohan Ormel - University Medical Center GroningenSteven E. Hyman - Broad InstituteEiko Fried - Leiden UniversityMiriam K. Forbes - Macquarie UniversityAnna R. Docherty - University of UtahRobert R. Althoff - University of VermontBo Bach - Slagelse HospitalMichael Chmielewski - Southern Methodist UniversityColin G. DeYoung - University of MinnesotaKelsie T. Forbush - University of KansasMichael Hallquist - Pennsylvania State UniversityChristopher J. Hopwood - University of California, DavisMasha Y. Ivanova - University of UtahKatherine G. Jonas - Stony Brook UniversityRobert D. Latzman - Georgia State UniversityKristian E. Markon - University of IowaStephanie N. Mullins-Sweatt - Oklahoma State UniversityAaron L. Pincus - Pennsylvania State UniversityUlrich Reininghaus - Maastricht UniversitySusan C. South - Purdue University West LafayetteJennifer L. Tackett - Northwestern UniversityDavid Watson - University of Notre DameAidan G. C. Wright - University of PittsburghRoman Kotov - Stony Brook University
- Resource Type
- Journal article
- Publication Details
- Journal of abnormal psychology (1965), Vol.129(2), pp.143-161
- Publisher
- Amer Psychological Assoc
- DOI
- 10.1037/abn0000486
- PMID
- 31804095
- PMCID
- PMC6980897
- ISSN
- 0021-843X
- eISSN
- 1939-1846
- Number of pages
- 19
- Grant note
- 647209 / European Research Council Consolidator Grant; European Research Council (ERC) DA040717; MH107444 / National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA University of Maryland
- Language
- English
- Date published
- 02/01/2020
- Academic Unit
- Psychological and Brain Sciences
- Record Identifier
- 9984627326802771
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