Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-26T09:17:56.918Z Has data issue: false hasContentIssue false

Using major depression polygenic risk scores to explore the depressive symptom continuum

Published online by Cambridge University Press:  10 June 2020

Bradley S. Jermy
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
Saskia P. Hagenaars
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
Kylie P. Glanville
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
Jonathan R. I. Coleman
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
David M. Howard
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
Gerome Breen
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
Evangelos Vassos
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
Cathryn M. Lewis*
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK Department of Medical & Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
*
Author for correspondence: Cathryn Lewis, E-mail: [email protected]

Abstract

Background

Major depression (MD) is often characterised as a categorical disorder; however, observational studies comparing sub-threshold and clinical depression suggest MD is continuous. Many of these studies do not explore the full continuum and are yet to consider genetics as a risk factor. This study sought to understand if polygenic risk for MD could provide insight into the continuous nature of depression.

Methods

Factor analysis on symptom-level data from the UK Biobank (N = 148 957) was used to derive continuous depression phenotypes which were tested for association with polygenic risk scores (PRS) for a categorical definition of MD (N = 119 692).

Results

Confirmatory factor analysis showed a five-factor hierarchical model, incorporating 15 of the original 18 items taken from the PHQ-9, GAD-7 and subjective well-being questionnaires, produced good fit to the observed covariance matrix (CFI = 0.992, TLI = 0.99, RMSEA = 0.038, SRMR = 0.031). MD PRS associated with each factor score (standardised β range: 0.057–0.064) and the association remained when the sample was stratified into case- and control-only subsets. The case-only subset had an increased association compared to controls for all factors, shown via a significant interaction between lifetime MD diagnosis and MD PRS (p value range: 2.23 × 10−3–3.94 × 10−7).

Conclusions

An association between MD PRS and a continuous phenotype of depressive symptoms in case- and control-only subsets provides support against a purely categorical phenotype; indicating further insights into MD can be obtained when this within-group variation is considered. The stronger association within cases suggests this variation may be of particular importance.

Type
Original Article
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

These authors share senior authorship.

References

Adams, M. J., Hill, W. D., Howard, D. M., Dashti, H. S., Davis, K. A. S., Campbell, A., … McIntosh, A. M. (2019). Factors associated with sharing e-mail information and mental health survey participation in large population cohorts. International Journal of Epidemiology. [published online ahead of print Jul 1]. doi:10.1093/ije/dyz134.Google Scholar
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). Arlington, VA: American Psychiatric Publishing.Google Scholar
Bartlett, M. S. (1950). Tests of significance in factor analysis. British Journal of Statistical Psychology, 3(2), 7785. doi:10.1111/j.2044-8317.1950.tb00285.x.CrossRefGoogle Scholar
Beavers, A. S., Lounsbury, J. W., Richards, J. K., Huck, S. W., Skolits, G. J., & Esquivel, S. L. (2013). Practical considerations for using exploratory factor analysis in educational research. Practical Assessment, Research & Evaluation, 18(1), 6. Retrieved from https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1303&context=pare.Google Scholar
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., … Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203. doi:10.1038/s41586-018-0579-z.CrossRefGoogle ScholarPubMed
Byers, A. L., Vittinghoff, E., Lui, L-Y., Hoang, T., Blazer, D. G., Covinsky, K. E., … Yaffe, K. (2012). Twenty-year depressive trajectories among older women. Archives of General Psychiatry, 69(10), 10731079. doi:10.1001/archgenpsychiatry.2012.43.CrossRefGoogle ScholarPubMed
Carroll, J. B. (1961). The nature of the data, or how to choose a correlation coefficient. Psychometrika, 26(4), 347372. doi:10.1007/BF02289768.CrossRefGoogle Scholar
Choi, S. W., & O'Reilly, P. F. (2019). PRSice-2: Polygenic risk score software for biobank-scale data. Gigascience, 8(7), doi:10.1093/gigascience/giz082.CrossRefGoogle ScholarPubMed
Cinar, O., & Viechtbauer, W. (2016). PoolR: Package for pooling the results from (dependent) tests. Retrieved from https://rdrr.io/github/ozancinar/poolR/man/poolr-package.html.Google Scholar
Coleman, J. R., Peyrot, W. J., Purves, K. L., Davis, K. A., Rayner, C., Choi, S. W., … Breen, G. (2020). Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank. Molecular Psychiatry, [published online ahead of print Jan 23], 117. doi:10.1038/s41380-019-0546-6.Google ScholarPubMed
Cuijpers, P, de Graaf, R., & van Dorsselaer, S. (2004). Minor depression: Risk profiles, functional disability, health care use and risk of developing major depression. Journal of Affective Disorders, 79(1), 7179. doi:10.1016/S0165-0327(02)00348-8.CrossRefGoogle ScholarPubMed
Davis, K. A., Coleman, J. R., Adams, M., Allen, N., Breen, G., Cullen, B., … Hotopf, M. (2020). Mental health in UK Biobank – development, implementation and results from an online questionnaire completed by 157 366 participants: A reanalysis. BJPsych Open, 6(2), e18. doi: 10.1192/bjo.2019.100.CrossRefGoogle ScholarPubMed
Elhai, J. D., Contractor, A. A., Tamburrino, M., Fine, T. H., Prescott, M. R., Shirley, E., … Calabrese, J. R. (2012). The factor structure of major depression symptoms: A test of four competing models using the Patient Health Questionnaire-9. Psychiatry Research, 199(3), 169173. doi:10.1016/j.psychres.2012.05.018.CrossRefGoogle ScholarPubMed
Euesden, J., Lewis, C. M., & O'Reilly, P. F. (2014). PRSice: Polygenic risk score software. Bioinformatics (Oxford, England), 31(9), 14661468. doi:10.1093/bioinformatics/btu848.CrossRefGoogle ScholarPubMed
Gadermann, A. M., Guhn, M., & Zumbo, B. D. (2012). Estimating ordinal reliability for Likert type and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research, and Evaluation, 17(1), 3. Retrieved from https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1247&context=pare.Google Scholar
Hankin, B. L., Fraley, R. C., Lahey, B. B., & Waldman, I. D. (2005). Is depression best viewed as a continuum or discrete category? A taxometric analysis of childhood and adolescent depression in a population-based sample. Journal of Abnormal Psychology, 114(1), 96110. doi:10.1037/0021-843X.114.1.96.CrossRefGoogle ScholarPubMed
Helzer, J. E., Kraemer, H. C., & Krueger, R. F. (2006). The feasibility and need for dimensional psychiatric diagnoses. Psychological Medicine, 36(12), 16711680. doi:10.1017/S003329170600821X.CrossRefGoogle ScholarPubMed
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179185. doi:10.1007/bf02289447.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. doi:10.1038/s41593-018-0326-7.CrossRefGoogle ScholarPubMed
Hybels, C. F., Blazer, D. G., & Pieper, C. F. (2001). Toward a threshold for subthreshold depression: An analysis of correlates of depression by severity of symptoms using data from an elderly community sample. The Gerontologist, 41(3), 357365. doi:10.1093/geront/41.3.357.CrossRefGoogle Scholar
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 3136. doi:10.1007/BF02291575.CrossRefGoogle Scholar
Keller, M. C. (2014). Gene x environment interaction studies have not properly controlled for potential confounders: The problem and the (simple) solution. Biological Psychiatry, 75, 1824. doi:10.1016/j.biopsych.2013.09.006.CrossRefGoogle Scholar
Kendler, K. S., Aggen, S. H., & Neale, M. C. (2013). Evidence for multiple genetic factors underlying DSM-IV criteria for major depression. JAMA Psychiatry. 70(6), 599607. doi:10.1001/jamapsychiatry.2013.751.CrossRefGoogle ScholarPubMed
Kendler, K. S., & Gardner, C. O. Jr. (1998). Boundaries of major depression: An evaluation of DSM-IV criteria. American Journal of Psychiatry, 155(2), 172177. doi:10.1176/ajp.155.2.172.Google ScholarPubMed
Kessler, R. C., Andrews, G., Mroczek, D., Ustun, B., & Wittchen, H. U. (1998). The World Health Organization composite international diagnostic interview short-form (CIDI-SF). International Journal of Methods in Psychiatric Research, 7(4), 171185. doi:10.1002/mpr.47.CrossRefGoogle Scholar
Kessler, R. C., Zhao, S., Blazer, D. G., & Swartz, M. (1997). Prevalence, correlates, and course of minor depression and major depression in the National Comorbidity Survey. Journal of Affective Disorders, 45(1–2), 1930. doi:10.1016/s0165-0327(97)00056-6.CrossRefGoogle ScholarPubMed
Kocalevent, R-D., Hinz, A., & Brähler, E. (2013). Standardization of the depression screener Patient Health Questionnaire (PHQ-9) in the general population. General Hospital Psychiatry, 35(5), 551555. doi:10.1016/j.genhosppsych.2013.04.006.CrossRefGoogle ScholarPubMed
Kraemer, H. C., Noda, A., & O'Hara, R. (2004). Categorical versus dimensional approaches to diagnosis: Methodological challenges. Journal of Psychiatric Research, 38(1), 1725. doi:10.1016/s0022-3956(03)00097-9.CrossRefGoogle ScholarPubMed
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606613. doi:10.1046/j.1525-1497.2001.016009606.x.CrossRefGoogle ScholarPubMed
Kuchibhatla, M. N., Fillenbaum, G. G., Hybels, C. F., & Blazer, D. G. (2012). Trajectory classes of depressive symptoms in a community sample of older adults. Acta Psychiatrica Scandinavica, 125(6), 492501. doi:10.1111/j.1600-0447.2011.01801.x.CrossRefGoogle Scholar
Liu, R. T. (2016). Taxometric evidence of a dimensional latent structure for depression in an epidemiological sample of children and adolescents. Psychological Medicine, 46(6), 12651275. doi:10.1017/S0033291715002792.CrossRefGoogle Scholar
Löwe, B., Decker, O., Müller, S., Brähler, E., Schellberg, D., Herzog, W., & Herzberg, P. Y. (2008). Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Medical Care, 46(3), 266274. doi:10.1097/MLR.0b013e318160d093.CrossRefGoogle ScholarPubMed
Moffitt, T. E., Harrington, H., Caspi, A., Kim-Cohen, J., Goldberg, D., Gregory, A. M., & Poulton, R. (2007). Depression and generalized anxiety disorder: Cumulative and sequential comorbidity in a birth cohort followed prospectively to age 32 years. Archives of General Psychiatry, 64(6), 651660. doi:10.1001/archpsyc.64.6.651.CrossRefGoogle Scholar
Nyholt, D. R. (2004). A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. The American Journal of Human Genetics, 74(4), 765769. doi:10.1086/383251.CrossRefGoogle ScholarPubMed
Olbert, C. M., Gala, G. J., & Tupler, L. A. (2014). Quantifying heterogeneity attributable to polythetic diagnostic criteria: Theoretical framework and empirical application. Journal of Abnormal Psychology, 123(2), 452462. doi:10.1037/a0036068.CrossRefGoogle ScholarPubMed
Purves, K. L., Coleman, J. R., Meier, S. M., Rayner, C., Davis, K. A., Cheesman, R., … Eley, T. C. (2019). A major role for common genetic variation in anxiety disorders. Molecular Psychiatry, [published online ahead of print Nov 20], 112. doi:10.1038/s41380-019-0559-1.Google Scholar
Revelle, W. (2017). Psych: Procedures for psychological, psychometric, and personality research. Retrieved from: https://CRAN.R-project.org/package=psych.Google Scholar
Rodríguez, M. R., Nuevo, R., Chatterji, S., & Ayuso-Mateos, J. L. (2012). Definitions and factors associated with subthreshold depressive conditions: A systematic review. BMC Psychiatry, 12(1), 181. doi:10.1186/1471-244X-12-181.CrossRefGoogle ScholarPubMed
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(1), 136. doi:10.18637/jss.v048.i02.CrossRefGoogle Scholar
Rucci, P., Gherardi, S., Tansella, M., Piccinelli, M., Berardi, D., Bisoffi, G, … Pini, S. (2003). Subthreshold psychiatric disorders in primary care: Prevalence and associated characteristics. Journal of Affective Disorders, 76(1), 171181. doi:10.1016/s0165-0327(02)00087-3.CrossRefGoogle ScholarPubMed
Ruscio, J., Brown, T. A., & Ruscio, A. M. (2009). A taxometric investigation of DSM-IV major depression in a large outpatient sample. Assessment, 16(2), 127144. doi:10.1177/1073191108330065.CrossRefGoogle Scholar
Ruscio, J., Zimmerman, M., McGlinchey, J. B., Chelminski, I., & Young, D. (2007). Diagnosing major depressive disorder XI: A taxometric investigation of the structure underlying DSM-IV symptoms. The Journal of Nervous and Mental Disease, 195(1), 1019. doi:10.1097/01.nmd.0000252025.12014.c4.CrossRefGoogle ScholarPubMed
Solomon, A., Haaga, D. A., & Arnow, B. A. (2001). Is clinical depression distinct from subthreshold depressive symptoms? A review of the continuity issue in depression research. The Journal of Nervous and Mental Disease, 189(8), 498506. doi:10.1097/00005053-200108000-00002.CrossRefGoogle ScholarPubMed
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 10921097. doi:10.1001/archinte.166.10.1092.CrossRefGoogle ScholarPubMed
Sullivan, P. F., Daly, M. J., & O'Donovan, M. (2012). Genetic architectures of psychiatric disorders: The emerging picture and its implications. Nature Reviews Genetics, 13(8), 537551. doi:10.1038/nrg3240.CrossRefGoogle ScholarPubMed
Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000). Genetic epidemiology of major depression: Review and meta-analysis. American Journal of Psychiatry, 157(10), 15521562. doi:10.1176/appi.ajp.157.10.1552.CrossRefGoogle ScholarPubMed
Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington, DC: American Psychological Association. 10694.CrossRefGoogle Scholar
Thurstone, L. L. (1947). Multiple factor analysis; a development and expansion of The Vectors of Mind. Chicago: University of Chicago Press.Google Scholar
van der Sluis, S., Posthuma, D., Nivard, M. G., Verhage, M., & Dolan, C. V. (2013) Power in GWAS: Lifting the curse of the clinical cut-off. Molecular Psychiatry, 18(1), 23. doi:10.1038/mp.2012.65.CrossRefGoogle ScholarPubMed
Vares, E. A., Salum, G. A., Spanemberg, L., Caldieraro, M. A., & Fleck, M. P. (2015). Depression dimensions: Integrating clinical signs and symptoms from the perspectives of clinicians and patients. PLoS ONE, 10(8), e0136037. doi: 10.1371/journal.pone.0136037.CrossRefGoogle ScholarPubMed
Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321327. doi:10.1007/BF02293557.CrossRefGoogle Scholar
Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 1018. doi:10.1111/j.1750-8606.2009.00110.x.CrossRefGoogle ScholarPubMed
Wood, A. R., Esko, T., Yang, J., Vedantam, S., Pers, T. H., Gustafsson, S, … Frayling, T. M. (2014). Defining the role of common variation in the genomic and biological architecture of adult human height. Nature Genetics, 46(11), 11731186. doi:10.1038/ng.3097.CrossRefGoogle ScholarPubMed
World Health Organization. (2017). Depression and other common mental disorders: global health estimates. Retrieved from: https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf.Google Scholar
World Health Organization. (2018). International classification of diseases for mortality and morbidity statistics (11th Revision). Retrieved from: https://icd.who.int/browse11/l-m/en.Google Scholar
Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A. E., Dudbridge, F., & Middeldorp, C. M. (2014). Research review: Polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry, 55(10), 10681087. doi:10.1111/jcpp.12295.CrossRefGoogle ScholarPubMed
Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., … the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics, 50(5), 668. doi:10.1038/s41588-018-0090-3.CrossRefGoogle ScholarPubMed
Zimmerman, M., Ellison, W., Young, D., Chelminski, I., & Dalrymple, K. (2015). How many different ways do patients meet the diagnostic criteria for major depressive disorder? Comprehensive Psychiatry, 56, 2934. doi:10.1016/j.comppsych.2014.09.007.CrossRefGoogle ScholarPubMed
Supplementary material: File

Jermy et al. supplementary material

Jermy et al. supplementary material

Download Jermy et al. supplementary material(File)
File 18.6 MB