Kessler, R. C., Chiu, W. T., Demler, O. & Walters, E. E. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 62, 617–627 (2005).
Google Scholar
Klein Hofmeijer-Sevink, M. et al. Clinical relevance of comorbidity in anxiety disorders: a report from the Netherlands Study of Depression and Anxiety (NESDA). J. Affect. Disord. 137, 106–112 (2012).
Google Scholar
Investing in Mental Health (WHO, 2003); https://apps.who.int/iris/bitstream/handle/10665/42823/9241562579.pdf
Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).
Google Scholar
Kotov, R. et al. The Hierarchical Taxonomy of Psychopathology (HiTOP): a dimensional alternative to traditional nosologies. J. Abnorm. Psychol. 126, 454–477 (2017).
Google Scholar
Beam, E., Potts, C., Poldrack, R. A. & Etkin, A. A data-driven framework for mapping domains of human neurobiology. Nat. Neurosci. 24, 1733–1744 (2021).
Google Scholar
Dalgleish, T., Black, M., Johnston, D. & Bevan, A. Transdiagnostic approaches to mental health problems: current status and future directions. J. Consult. Clin. Psychol. 88, 179–195 (2020).
Google Scholar
Lee, P. H. et al. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482.e11 (2019).
Google Scholar
Grotzinger, A. D. et al. Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat. Genet. 54, 548–559 (2022).
Google Scholar
Mallard, T. T., Grotzinger, A. D. & Smoller, J. W. Examining the shared etiology of psychopathology with genome-wide association studies. Physiol. Rev. 103, 1645–1665 (2023).
Google Scholar
Smoller, J. W. et al. Psychiatric genetics and the structure of psychopathology. Mol. Psychiatry 24, 409–420 (2019).
Google Scholar
Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).
Google Scholar
Frei, O. et al. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nat. Commun. 10, 2417 (2019).
Google Scholar
Jones, H. J. et al. Investigating the genetic architecture of general and specific psychopathology in adolescence. Transl. Psychiatry 8, 145 (2018).
Google Scholar
The Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
Caspi, A. et al. The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clin. Psychol. Sci. 2, 119–137 (2014).
Google Scholar
Lahey, B. B. et al. Measuring the hierarchical general factor model of psychopathology in young adults. Int. J. Methods Psychiatr. Res. 27, e1593 (2018).
Google Scholar
Markon, K. E. Modeling psychopathology structure: a symptom-level analysis of Axis I and II disorders. Psychol. Med. 40, 273–288 (2010).
Google Scholar
Markon, K. E. Bifactor and hierarchical models: specification, inference, and interpretation. Annu. Rev. Clin. Psychol. 15, 51–69 (2019).
Google Scholar
Sprooten, E., Franke, B. & Greven, C. U. The P-factor and its genomic and neural equivalents: an integrated perspective. Mol. Psychiatry 27, 38–48 (2021).
Google Scholar
Carey, C. E. et al. Principled distillation of multidimensional UK Biobank data reveals insights into the correlated human phenome. Preprint at medRxiv https://doi.org/10.1101/2022.09.02.22279546 (2022)
Caspi, A. et al. Longitudinal assessment of mental health disorders and comorbidities across 4 decades among participants in the Dunedin Birth Cohort Study. JAMA Netw. Open 3, e203221 (2020).
Google Scholar
Caspi, A. & Moffitt, T. E. All for one and one for all: mental disorders in one dimension. Am. J. Psychiatry 175, 831–844 (2018).
Google Scholar
Lahey, B. B., Krueger, R. F., Rathouz, P. J., Waldman, I. D. & Zald, D. H. Validity and utility of the general factor of psychopathology. World Psychiatry 16, 142–144 (2017).
Google Scholar
Lahey, B. B., Moore, T. M., Kaczkurkin, A. N. & Zald, D. H. Hierarchical models of psychopathology: empirical support, implications, and remaining issues. World Psychiatry 20, 57–63 (2021).
Google Scholar
Watts, A. L., Greene, A. L., Bonifay, W. & Fried, E. I. A critical evaluation of the p-factor literature. Nat. Rev. Psychol. 3, 108–122 (2024).
Google Scholar
van Bork, R. et al. Latent variable models and networks: statistical equivalence and testability. Multivar. Behav. Res. 56, 175–198 (2019).
Google Scholar
Waldman, I. D. et al. Recommendations for adjudicating among alternative structural models of psychopathology. Clin. Psychol. Sci. 11, 616–640 (2023).
Google Scholar
Watts, A. L., Lane, S. P., Bonifay, W., Steinley, D. & Meyer, F. A. C. Building theories on top of, and not independent of, statistical models: the case of the p-factor. Psychol. Inq. 31, 310–320 (2020).
Google Scholar
Clapp Sullivan, M. L. et al. Beyond the factor indeterminacy problem using genome-wide association data. Nat. Hum. Behav. 8, 205–218 (2024).
Google Scholar
Vandekerckhove, J., Matzke, D. & Wagenmakers, E.-J. Model comparison and the principle of parsimony. In The Oxford Handbook of Computational and Mathematical Psychology (eds. Busemeyer, J. R., Wang, Z., Townsend, J. T. & Eidels, A.) 300–319 (Oxford Univ. Press, 2015).
Sodini, S. M., Kemper, K. E., Wray, N. R. & Trzaskowski, M. Comparison of genotypic and phenotypic correlations: Cheverud’s conjecture in humans. Genetics 209, 941–948 (2018).
Google Scholar
Willis, J. H., Coyne, J. A. & Kirkpatrick, M. Can one predict the evolution of quantitative characters without genetics? Evolution 45, 441–444 (1991).
Google Scholar
Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).
Google Scholar
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
Google Scholar
Karlsson Linnér, R. et al. Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction. Nat. Neurosci. 24, 1367–1376 (2021).
Google Scholar
Williams, C. M. et al. Guidelines for evaluating the comparability of down-sampled GWAS summary statistics. Behav. Genet. 53, 404–415 (2023).
Google Scholar
Mallard, T. T. et al. Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities. Cell Genomics 2, 100140 (2022).
Google Scholar
Sydnor, V. J. et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron 109, 2820–2846 (2021).
Google Scholar
Markello, R. D. et al. neuromaps: structural and functional interpretation of brain maps. Nat. Methods 19, 1472–1479 (2022).
Google Scholar
Karlson, E. W., Boutin, N. T., Hoffnagle, A. G. & Allen, N. L. Building the Partners HealthCare Biobank at Partners Personalized Medicine: informed consent, return of research results, recruitment lessons and operational considerations. J. Pers. Med. 6, 2 (2016).
Google Scholar
Cheverud, J. M. A comparison of genetic and phenotypic correlations. Evolution 42, 958–968 (1988).
Google Scholar
Turkheimer, E., Pettersson, E. & Horn, E. E. A phenotypic null hypothesis for the genetics of personality. Annu. Rev. Psychol. 65, 515–540 (2014).
Google Scholar
Weiner, D. J. et al. Polygenic architecture of rare coding variation across 394,783 exomes. Nature 614, 492–499 (2023).
Google Scholar
Xie, C. et al. A shared neural basis underlying psychiatric comorbidity. Nat. Med. 29, 1232–1242 (2023).
Google Scholar
Goodkind, M. et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry 72, 305–315 (2015).
Google Scholar
Taylor, J. J. et al. A transdiagnostic network for psychiatric illness derived from atrophy and lesions. Nat. Hum. Behav. 7, 420–429 (2023).
Google Scholar
Grotzinger, A. D. et al. Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis. Nat. Commun. 14, 946 (2023).
Google Scholar
Cai, N. et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat. Genet. 52, 437–447 (2020).
Google Scholar
Bulik-Sullivan, B. K. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Google Scholar
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Google Scholar
Ruan, Y. et al. Improving polygenic prediction in ancestrally diverse populations. Nat. Genet. 54, 573–580 (2022).
Google Scholar
Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).
Google Scholar
Abdellaoui, A., Smit, D. J. A., Brink, W., van den, Denys, D. & Verweij, K. J. H. Genomic relationships across psychiatric disorders including substance use disorders. Drug Alcohol Depend. 220, 108535 (2021).
Google Scholar
Thorp, J. G. et al. Symptom-level modelling unravels the shared genetic architecture of anxiety and depression. Nat. Hum. Behav. 5, 1432–1442 (2021).
Google Scholar
Abdellaoui, A. The evolutionary dance between culture, genes, and everything in between. Behav. Brain Sci. 45, e153 (2022).
Google Scholar
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).
Google Scholar
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Google Scholar
Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage 166, 400–424 (2018).
Google Scholar
Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).
Google Scholar
Williams, C. M. et al. High intelligence is not associated with a greater propensity for mental health disorders. Eur. Psychiatry 66, e3 (2023).
Google Scholar
R. Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).
Revelle, W. Psych: Procedures for Psychological, Psychometric, and Personality Research (Northwestern Univ., 2021).
Finch, W. H. Using fit statistic differences to determine the optimal number of factors to retain in an exploratory factor analysis. Educ. Psychol. Meas. 80, 217–241 (2020).
Google Scholar
Fabrigar, L. R., Wegener, D., MacCallum, R. C. & Strahan, E. J. Evaluating the use of exploratory factor analysis in psychological research. Psychol. Methods 4, 272–299 (1999).
Google Scholar
Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2012).
Google Scholar
Kolenikov, S. & Bollen, K. A. Testing negative error variances: is a Heywood case a symptom of misspecification? Sociol. Methods Res. 41, 124–167 (2012).
Google Scholar
Kline, R. B. Principles and Practice of Structural Equation Modeling 4th edn (Guilford Publications, 2015).
Marsh, H. W., Hau, K.-T. & Wen, Z. In search of golden rules: comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Struct. Equ. Model. 11, 320–341 (2004).
Google Scholar
Jiang, L., Zheng, Z., Fang, H. & Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat. Genet. 53, 1616–1621 (2021).
Google Scholar
Yang, J., Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs. Proc. Natl Acad. Sci. USA 113, E4579–E4580 (2016).
Google Scholar
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
Google Scholar
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Google Scholar
de la Fuente, J., Davies, G., Grotzinger, A. D., Tucker-Drob, E. M. & Deary, I. J. A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data. Nat. Hum. Behav. 5, 49–58 (2021).
Google Scholar
McArdle, J. J. & Goldsmith, H. H. Alternative common factor models for multivariate biometric analyses. Behav. Genet. 20, 569–608 (1990).
Google Scholar
Lloyd-Jones, L. R. et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat. Commun. 10, 5086 (2019).
Google Scholar
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Google Scholar
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Google Scholar
Hu, L. & Bentler, P. M. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6, 1–55 (1999).
Google Scholar
Boutin, N. T. et al. The evolution of a large biobank at Mass General Brigham. J. Pers. Med. 12, 1323 (2022).
Google Scholar
Wei, W.-Q. et al. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS ONE 12, e0175508 (2017).
Google Scholar
Zheutlin, A. B. et al. Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems. Am. J. Psychiatry 176, 846–855 (2019).
Google Scholar
Carroll, R. J., Bastarache, L. & Denny, J. C. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics 30, 2375–2376 (2014).
Google Scholar
Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. NeuroImage 178, 540–551 (2018).
Google Scholar
Seidlitz, J. et al. Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. Neuron 97, 231–247.e7 (2018).
Google Scholar
Váša, F. et al. Adolescent tuning of association cortex in human structural brain networks. Cereb. Cortex 28, 281–294 (2018).
Google Scholar
Walters, R. K. et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat. Neurosci. 21, 1656–1669 (2018).
Google Scholar
Meier, S. M. et al. Genetic variants associated with anxiety and stress-related disorders: a genome-wide association study and mouse-model study. JAMA Psychiatry 76, 924–932 (2019).
Google Scholar
Forstner, A. J. et al. Genome-wide association study of panic disorder reveals genetic overlap with neuroticism and depression. Mol. Psychiatry 26, 4179–4190 (2021).
Google Scholar
Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
Google Scholar
Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).
Google Scholar
Duncan, L. et al. Significant locus and metabolic genetic correlations revealed in genome-wide association study of anorexia nervosa. Am. J. Psychiatry 174, 850–858 (2017).
Google Scholar
Arnold, P. D. et al. Revealing the complex genetic architecture of obsessive–compulsive disorder using meta-analysis. Mol. Psychiatry 23, 1181–1188 (2018).
Google Scholar
Duncan, L. E. et al. Largest GWAS of PTSD (N = 20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol. Psychiatry 23, 666–673 (2018).
Google Scholar
Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
Google Scholar
Erlangsen, A. et al. Genetics of suicide attempts in individuals with and without mental disorders: a population-based genome-wide association study. Mol. Psychiatry 25, 2410–2421 (2020).
Google Scholar
#Characterizing #phenotypic #genetic #structure #psychopathology #Biobank #Nature #Mental #Health
Image Source : www.nature.com
