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General v. specific vulnerabilities: polygenic risk scores and higher-order psychopathology dimensions in the Adolescent Brain Cognitive Development (ABCD) Study

Published online by Cambridge University Press:  14 September 2021

Monika A. Waszczuk Open the ORCID record for Monika A. Waszczuk [Opens in a new window] ,
Jiaju Miao ,
Anna R. Docherty ,
Andrey A. Shabalin ,
Katherine G. Jonas ,
Giorgia Michelini  and
Roman Kotov
Monika A. Waszczuk*
Affiliation:
Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
Jiaju Miao
Affiliation:
Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
Anna R. Docherty
Affiliation:
Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
Andrey A. Shabalin
Affiliation:
Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
Katherine G. Jonas
Affiliation:
Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
Giorgia Michelini
Affiliation:
Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
Roman Kotov
Affiliation:
Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
*
Author for correspondence: Monika A. Waszczuk, E-mail: [email protected]
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Abstract

Background

Polygenic risk scores (PRSs) capture genetic vulnerability to psychiatric conditions. However, PRSs are often associated with multiple mental health problems in children, complicating their use in research and clinical practice. The current study is the first to systematically test which PRSs associate broadly with all forms of childhood psychopathology, and which PRSs are more specific to one or a handful of forms of psychopathology.

Methods

The sample consisted of 4717 unrelated children (mean age = 9.92, s.d. = 0.62; 47.1% female; all European ancestry). Psychopathology was conceptualized hierarchically as empirically derived general factor (p-factor) and five specific factors: externalizing, internalizing, neurodevelopmental, somatoform, and detachment. Partial correlations explored associations between psychopathology factors and 22 psychopathology-related PRSs. Regressions tested which level of the psychopathology hierarchy was most strongly associated with each PRS.

Results

Thirteen PRSs were significantly associated with the general factor, most prominently Chronic Multisite Pain-PRS (r = 0.098), ADHD-PRS (r = 0.079), and Depression-PRS (r = 0.078). After adjusting for the general factor, Depression-PRS, Neuroticism-PRS, PTSD-PRS, Insomnia-PRS, Chronic Back Pain-PRS, and Autism-PRS were not associated with lower order factors. Conversely, several externalizing PRSs, including Adventurousness-PRS and Disinhibition-PRS, remained associated with the externalizing factor (|r| = 0.040–0.058). The ADHD-PRS remained uniquely associated with the neurodevelopmental factor (r = 062).

Conclusions

PRSs developed to predict vulnerability to emotional difficulties and chronic pain generally captured genetic risk for all forms of childhood psychopathology. PRSs developed to predict vulnerability to externalizing difficulties, e.g. disinhibition, tended to be more specific in predicting behavioral problems. The results may inform translation of existing PRSs to pediatric research and future clinical practice.

Keywords

Child Behavior Checklistchildhood psychopathologygeneral factorgeneticpolygenic
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 5 , April 2023 , pp. 1937 - 1946
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Achenbach, T., & Rescorla, L. (2001). Manual for the ASEBA school-age forms and profiles. Child behavior checklist for age 6–18, teacher's report from, youth self-report and integrated system of multi-informant assessment. Burlington, VT: University of Vermont.[Links].Google Scholar
Allegrini, A. G., Cheesman, R., Rimfeld, K., Selzam, S., Pingault, J. B., Eley, T. C., & Plomin, R. (2020). The p factor: Genetic analyses support a general dimension of psychopathology in childhood and adolescence. Journal of Child Psychology and Psychiatry, 61(1), 30–39.CrossRefGoogle Scholar
Bartels, M., Boomsma, D. I., Hudziak, J. J., van Beijsterveldt, T. C., & van den Oord, E. J. (2007). Twins and the study of rater (dis) agreement. Psychological Methods, 12(4), 451.CrossRefGoogle Scholar
Baurley, J. W., Edlund, C. K., Pardamean, C. I., Conti, D. V., & Bergen, A. W. (2016). Smokescreen: A targeted genotyping array for addiction research. BMC Genomics, 17(1), 145.CrossRefGoogle ScholarPubMed
Belsky, D. W., & Harden, K. P. (2019). Phenotypic annotation: Using polygenic scores to translate discoveries from genome-wide association studies from the top down. Current Directions in Psychological Science, 28(1), 82–90.CrossRefGoogle Scholar
Bird, H. R., Gould, M. S., & Staghezza, B. (1992). Aggregating data from multiple informants in child psychiatry epidemiological research. Journal of the American Academy of Child & Adolescent Psychiatry, 31(1), 78–85.CrossRefGoogle ScholarPubMed
Bogdan, R., Baranger, D. A., & Agrawal, A. (2018). Polygenic risk scores in clinical psychology: Bridging genomic risk to individual differences. Annual Review of Clinical Psychology, 14, 119–157.CrossRefGoogle ScholarPubMed
Bornovalova, M. A., Choate, A. M., Fatimah, H., Petersen, K. J., & Wiernik, B. M. (2020). Appropriate use of bifactor analysis in psychopathology research: Appreciating benefits and limitations. Biological Psychiatry, 88(1), 18–27.CrossRefGoogle ScholarPubMed
Brikell, I., Larsson, H., Lu, Y., Pettersson, E., Chen, Q., Kuja-Halkola, R., … Martin, J. (2018). The contribution of common genetic risk variants for ADHD to a general factor of childhood psychopathology. Molecular Psychiatry, 25(8), 1809–1821.CrossRefGoogle ScholarPubMed
Cai, N., Revez, J. A., Adams, M. J., Andlauer, T. F., Breen, G., Byrne, E. M., … Hamilton, S. P. (2020). Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nature Genetics, 52(4), 437–447.CrossRefGoogle ScholarPubMed
Caspi, A., Houts, R. M., Belsky, D. W., Goldman-Mellor, S. J., Harrington, H., Israel, S., … Poulton, R. (2014). The p factor one general psychopathology factor in the structure of psychiatric disorders? Clinical Psychological Science, 2(2), 119–137.CrossRefGoogle Scholar
Compton, W. M., Dowling, G. J., & Garavan, H. (2019). Ensuring the best use of data: The adolescent brain cognitive development study. JAMA Pediatrics, 173(9), 809–810.CrossRefGoogle ScholarPubMed
Demontis, D., Walters, R. K., Martin, J., Mattheisen, M., Als, T. D., Agerbo, E., … Bækvad-Hansen, M. (2019). Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nature Genetics, 51(1), 63–75.CrossRefGoogle ScholarPubMed
Dick, D. M., Barr, P. B., Cho, S. B., Cooke, M. E., Kuo, S. I. C., Lewis, T. J., … Su, J. (2018). Post-GWAS in psychiatric genetics: A developmental perspective on the “other” next steps. Genes, Brain and Behavior, 17(3), e12447.CrossRefGoogle ScholarPubMed
Docherty, A. R., Moscati, A., Dick, D., Savage, J. E., Salvatore, J. E., Cooke, M., … Riley, B. P. (2018). Polygenic prediction of the phenome, across ancestry, in emerging adulthood. Psychological Medicine, 48(11), 1814.CrossRefGoogle ScholarPubMed
Dudbridge, F. (2013). Power and predictive accuracy of polygenic risk scores. PLoS Genetics, 9(3), e1003348.CrossRefGoogle ScholarPubMed
Euesden, J., Lewis, C. M., & O'Reilly, P. F. (2015). PRSice: Polygenic risk score software. Bioinformatics (Oxford, England), 31(9), 1466–1468.Google ScholarPubMed
Forbes, M. K., Tackett, J. L., Markon, K. E., & Krueger, R. F. (2016). Beyond comorbidity: Toward a dimensional and hierarchical approach to understanding psychopathology across the life span. Development and Psychopathology, 28(4pt1), 971–986.CrossRefGoogle Scholar
Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R., Heeringa, S., … Zahs, D. (2018). Recruiting the ABCD sample: Design considerations and procedures. Developmental Cognitive Neuroscience, 32, 16–22.CrossRefGoogle ScholarPubMed
Goodman, R. (2001). Psychometric properties of the strengths and difficulties questionnaire. Journal of American Academic Child and Adolescent Psychiatry, 40(11), 1337–1345.CrossRefGoogle ScholarPubMed
Grotzinger, A. D., Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., … Deary, I. J. (2019). Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behaviour, 3(5), 513–525.CrossRefGoogle ScholarPubMed
Grove, J., Ripke, S., Als, T. D., Mattheisen, M., Walters, R. K., Won, H., … Anney, R. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature Genetics, 51(3), 431–444.CrossRefGoogle ScholarPubMed
Howard, D. M., Adams, M. J., Clarke, T. K., Hafferty, J. D., Gibson, J., Shirali, M., … McIntosh, A. M. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature Neuroscience, 22(3), 343–352. doi:10.1038/s41593–018-0326-7.CrossRefGoogle ScholarPubMed
Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., … Posthuma, D. (2019). Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk. Nature Genetics, 51(3), 404–413. doi:10.1038/s41588-018-0311-9.CrossRefGoogle ScholarPubMed
Jensen, P. S., Rubio-Stipec, M., Canino, G., Bird, H. R., Dulcan, M. K., Schwab-Stone, M. E., & Lahey, B. B. (1999). Parent and child contributions to diagnosis of mental disorder: Are both informants always necessary? Journal of the American Academy of Child & Adolescent Psychiatry, 38(12), 1569–1579.CrossRefGoogle ScholarPubMed
Johnston, K. J., Adams, M. J., Nicholl, B. I., Ward, J., Strawbridge, R. J., Ferguson, A., … Smith, D. J. (2019). Genome-wide association study of multisite chronic pain in UK biobank. PLoS Genetics, 15(6), e1008164.CrossRefGoogle ScholarPubMed
Jones, H. J., Heron, J., Hammerton, G., Stochl, J., Jones, P. B., Cannon, M., … Linden, D. E. (2018). Investigating the genetic architecture of general and specific psychopathology in adolescence. Translational Psychiatry, 8(1), 145.CrossRefGoogle ScholarPubMed
Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, M., … Zimmerman, M. (2017). The hierarchical taxonomy of psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126(4), 454.CrossRefGoogle ScholarPubMed
Krapohl, E., Euesden, J., Zabaneh, D., Pingault, J., Rimfeld, K., Von Stumm, S., … Plomin, R. (2016). Phenome-wide analysis of genome-wide polygenic scores. Molecular Psychiatry, 21(9), 1188–1193.CrossRefGoogle ScholarPubMed
Lahey, B. B., Applegate, B., Hakes, J. K., Zald, D. H., Hariri, A. R., & Rathouz, P. J. (2012). Is there a general factor of prevalent psychopathology during adulthood? Journal of Abnormal Psychology, 121(4), 971.CrossRefGoogle Scholar
Lahey, B. B., Krueger, R. F., Rathouz, P. J., Waldman, I. D., & Zald, D. H. (2016). A hierarchical causal taxonomy of psychopathology across the life span. Psychological Bulletin, 143(2), 142–186.CrossRefGoogle ScholarPubMed
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121. doi:10.1038/s41588-018-0147-3.CrossRefGoogle ScholarPubMed
Linner, K. R., Biroli, P., Kong, E., Meddens, S. F. W., Wedow, R., Fontana, M. A., … Beauchamp, J. P. (2019). Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nature Genetics, 51(2), 245–257. doi:10.1038/s41588-018-0309-3.CrossRefGoogle Scholar
Liu, M., Jiang, Y., Wedow, R., Li, Y., Brazel, D. M., Chen, F., … Vrieze, S. (2019). Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nature Genetics, 51(2), 237–244. doi:10.1038/s41588-018-0307-5.CrossRefGoogle ScholarPubMed
Locke, A. E., Kahali, B., Berndt, S. I., Justice, A. E., Pers, T. H., Day, F. R., … Speliotes, E. K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197–206. doi:10.1038/nature14177.CrossRefGoogle ScholarPubMed
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584.CrossRefGoogle ScholarPubMed
Martin, J., Taylor, M. J., & Lichtenstein, P. (2017). Assessing the evidence for shared genetic risks across psychiatric disorders and traits. Psychological Medicine, 48(11), 1759–1774.CrossRefGoogle ScholarPubMed
McCarthy, S., Das, S., Kretzschmar, W., Delaneau, O., Wood, A. R., Teumer, A., … Sharp, K. (2016). A reference panel of 64976 haplotypes for genotype imputation. Nature Genetics, 48(10), 1279.Google Scholar
Meng, W., Adams, M. J., Palmer, C. N., Shi, J., Auton, A., Ryan, K. A., … Yau, M. S. (2019). Genome-wide association study of knee pain identifies associations with GDF5 and COL27A1 in UK biobank. Communications Biology, 2(1), 1–8.CrossRefGoogle ScholarPubMed
Merwood, A., Greven, C., Price, T., Rijsdijk, F., Kuntsi, J., McLoughlin, G., … Asherson, P. (2013). Different heritabilities but shared etiological influences for parent, teacher and self-ratings of ADHD symptoms: An adolescent twin study. Psychological Medicine, 43(09), 1973–1984.CrossRefGoogle ScholarPubMed
Michelini, G., Barch, D. M., Tian, Y., Watson, D., Klein, D. N., & Kotov, R. (2019). Delineating and validating higher-order dimensions of psychopathology in the adolescent brain cognitive development (ABCD) study. Translational Psychiatry, 9(1), 1–15.CrossRefGoogle ScholarPubMed
Nagel, M., Jansen, P. R., Stringer, S., Watanabe, K., de Leeuw, C. A., Bryois, J., … Posthuma, D. (2018). Meta-analysis of genome-wide association studies for neuroticism in 449484 individuals identifies novel genetic loci and pathways. Nature Genetics, 50(7), 920–927. doi:10.1038/s41588-018-0151-7.CrossRefGoogle Scholar
Newson, J. J., Hunter, D., & Thiagarajan, T. C. (2020). The heterogeneity of mental health assessment. Frontiers in Psychiatry, 11, 76.CrossRefGoogle ScholarPubMed
Nievergelt, C. M., Maihofer, A. X., Klengel, T., Atkinson, E. G., Chen, C. Y., Choi, K. W., … Koenen, K. C. (2019). International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nature Communications, 10(1), 4558. doi:10.1038/s41467-019-12576-w.CrossRefGoogle ScholarPubMed
Pettersson, E., Larsson, H., & Lichtenstein, P. (2016). Common psychiatric disorders share the same genetic origin: A multivariate sibling study of the Swedish population. Molecular Psychiatry, 21(5), 717–721.CrossRefGoogle ScholarPubMed
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., … Daly, M. J. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559–575.CrossRefGoogle ScholarPubMed
Rice, F., Riglin, L., Thapar, A. K., Heron, J., Anney, R., O'donovan, M. C., … Thapar, A. (2018). Characterizing developmental trajectories and the role of neuropsychiatric genetic risk variants in early-onset depression. JAMA Psychiatry, 76(3), 306–313.CrossRefGoogle Scholar
Riglin, L., Thapar, A. K., Leppert, B., Martin, J., Richards, A., Anney, R., … Lahey, B. B. (2019). Using genetics to examine a general liability to childhood psychopathology. Behavior Genetics, 50(4), 213–220.CrossRefGoogle ScholarPubMed
Ripke, S., Walters, J. T., O'Donovan, M. C., & The Schizophrenia Working Group of the Psychiatric Genomics Consortium (2020). Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. MedRxiv.Google Scholar
Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., … Posthuma, D. (2018). Genome-wide association meta-analysis in 269867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 50(7), 912–919. doi:10.1038/s41588-018-0152-6.CrossRefGoogle Scholar
Selzam, S., Coleman, J. R., Caspi, A., Moffitt, T. E., & Plomin, R. (2018). A polygenic p factor for major psychiatric disorders. Translational Psychiatry, 8(1), 1–9.CrossRefGoogle ScholarPubMed
Stahl, E. A., Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., Trubetskoy, V., … Sklar, P. (2019). Genome-wide association study identifies 30 loci associated with bipolar disorder. Nature Genetics, 51(5), 793–803. doi:10.1038/s41588-019-0397-8.CrossRefGoogle ScholarPubMed
Stanger, C., & Lewis, M. (1993). Agreement among parents, teachers, and children on internalizing and externalizing behavior problems. Journal of Clinical Child Psychology, 22(1), 107–116.CrossRefGoogle Scholar
Suri, P., Palmer, M. R., Tsepilov, Y. A., Freidin, M. B., Boer, C. G., Yau, M. S., … Nethander, M. (2018). Genome-wide meta-analysis of 158000 individuals of European ancestry identifies three loci associated with chronic back pain. PLoS Genetics, 14(9), e1007601.CrossRefGoogle Scholar
Thapar, A., & Riglin, L. (2020). The importance of a developmental perspective in psychiatry: What do recent genetic-epidemiological findings show? Molecular Psychiatry, 25(8), 1631–1639.CrossRefGoogle ScholarPubMed
Waldman, I. D., Poore, H. E., Luningham, J. M., & Yang, J. (2020). Testing structural models of psychopathology at the genomic level. World Psychiatry, 19(3), 350–359.CrossRefGoogle ScholarPubMed
Waldman, I. D., Poore, H. E., van Hulle, C., Rathouz, P. J., & Lahey, B. B. (2016). External validity of a hierarchical dimensional model of child and adolescent psychopathology: Tests using confirmatory factor analyses and multivariate behavior genetic analyses. Journal of Abnormal Psychology, 125(8), 1053.CrossRefGoogle ScholarPubMed
Waszczuk, M. A., Eaton, N. R., Krueger, R. F., Shackman, A. J., Waldman, I. D., Zald, D. H., … Kotov, R. (2020). Redefining phenotypes to advance psychiatric genetics: Implications from hierarchical taxonomy of psychopathology. Journal of Abnormal Psychology, 129(2), 143–161. doi: 10.31234/osf.io/sf46g.CrossRefGoogle ScholarPubMed
Watts, A. L., Poore, H. E., & Waldman, I. D. (2019). Riskier tests of the validity of the bifactor model of psychopathology. Clinical Psychological Science, 7(6), 1285–1303.CrossRefGoogle Scholar
Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A., Dudbridge, F., & Middeldorp, C. M. (2014). Research review: Polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry, 55(10), 1068–1087.CrossRefGoogle ScholarPubMed
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