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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳為堅 | zh_TW |
| dc.contributor.advisor | Wei J. Chen | en |
| dc.contributor.author | 任雅文 | zh_TW |
| dc.contributor.author | Ya-Wen Jen | en |
| dc.date.accessioned | 2024-08-29T16:11:41Z | - |
| dc.date.available | 2024-08-30 | - |
| dc.date.copyright | 2024-08-29 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-09 | - |
| dc.identifier.citation | 1. Sullivan, P.F., Kendler, K.S. & Neale, M.C. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry 60, 1187-92 (2003).
2. Hilker, R. et al. Heritability of Schizophrenia and Schizophrenia Spectrum Based on the Nationwide Danish Twin Register. Biol Psychiatry 83, 492-498 (2018). 3. Schizophrenia Working Group of the Psychiatric Genomics, C. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421-7 (2014). 4. Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat Genet 51, 1670-1678 (2019). 5. Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502-508 (2022). 6. Perkins, D.O. et al. Polygenic Risk Score Contribution to Psychosis Prediction in a Target Population of Persons at Clinical High Risk. Am J Psychiatry 177, 155-163 (2020). 7. Ahangari, M. et al. Relationship between polygenic risk scores and symptom dimensions of schizophrenia and schizotypy in multiplex families with schizophrenia. Br J Psychiatry 223, 301-308 (2023). 8. Ferraro, L. et al. Premorbid Adjustment and IQ in Patients With First-Episode Psychosis: A Multisite Case-Control Study of Their Relationship With Cannabis Use. Schizophr Bull 46, 517-529 (2020). 9. Mallet, J., Le Strat, Y., Dubertret, C. & Gorwood, P. Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis. J Clin Med 9(2020). 10. Jaffe, A.E. Postmortem human brain genomics in neuropsychiatric disorders--how far can we go? Curr Opin Neurobiol 36, 107-11 (2016). 11. Zhu, Y., Wang, L., Yin, Y. & Yang, E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci Rep 7, 5435 (2017). 12. Gamazon, E.R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet 47, 1091-8 (2015). 13. Liang, S.G. & Greenwood, T.A. The impact of clinical heterogeneity in schizophrenia on genomic analyses. Schizophr Res 161, 490-5 (2015). 14. Lien, Y.-J. et al. A Genome-Wide Linkage Scan for Distinct Subsets of Schizophrenia Characterized by Age at Onset and Neurocognitive Deficits. Plos One 6, e24103 (2011). 15. Chen, W.J. Taiwan Schizophrenia Linkage Study: lessons learned from endophenotype-based genome-wide linkage scans and perspective. Am J Med Genet B Neuropsychiatr Genet 162b, 636-47 (2013). 16. Ruderfer, D.M. et al. Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol Psychiatry 19, 1017-1024 (2014). 17. Smigielski, L. et al. Polygenic risk scores across the extended psychosis spectrum. Transl Psychiatry 11, 600 (2021). 18. Kety, S.S., Rosenthal, D., Wender, P.H., Schulsinger, F. & Jacobsen, B. Mental illness in the biological and adoptive families of adopted individuals who have become schizophrenic: a preliminary report based on psychiatric interviews. Proc Annu Meet Am Psychopathol Assoc, 147-65 (1975). 19. Kendler, K.S., McGuire, M., Gruenberg, A.M. & Walsh, D. Schizotypal symptoms and signs in the Roscommon Family Study. Their factor structure and familial relationship with psychotic and affective disorders. Arch Gen Psychiatry 52, 296-303 (1995). 20. Fanous, A., Gardner, C., Walsh, D. & Kendler, K.S. Relationship between positive and negative symptoms of schizophrenia and schizotypal symptoms in nonpsychotic relatives. Arch Gen Psychiatry 58, 669-73 (2001). 21. Nenadic, I. et al. Polygenic risk for schizophrenia and schizotypal traits in non-clinical subjects. Psychol Med 52, 1069-1079 (2022). 22. Owen, M.J., Sawa, A. & Mortensen, P.B. Schizophrenia. Lancet 388, 86-97 (2016). 23. Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet 50, 538-548 (2018). 24. Huckins, L.M. et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat Genet 51, 659-674 (2019). 25. Zhang, C. et al. Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene. BMC Med 20, 169 (2022). 26. Zhang, C. et al. Brain transcriptome-wide association study implicates novel risk genes underlying schizophrenia risk. Psychol Med, 1-11 (2023). 27. Pain, O. et al. Imputed gene expression risk scores: a functionally informed component of polygenic risk. Hum Mol Genet 30, 727-738 (2021). 28. Rodriguez-Lopez, J., Arrojo, M., Paz, E., Paramo, M. & Costas, J. Identification of relevant hub genes for early intervention at gene coexpression modules with altered predicted expression in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 98, 109815 (2020). 29. Prohens, L. et al. Gene expression imputation provides clinical and biological insights into treatment-resistant schizophrenia polygenic risk. Psychiatry Res 332, 115722 (2024). 30. Birnbaum, R. & Weinberger, D.R. Genetic insights into the neurodevelopmental origins of schizophrenia. Nat Rev Neurosci 18, 727-740 (2017). 31. Iasevoli, F. et al. Relationships between early age at onset of psychotic symptoms and treatment resistant schizophrenia. Early Interv Psychiatry 16, 352-362 (2022). 32. Wang, K.S., Liu, X., Zhang, Q., Aragam, N. & Pan, Y. Genome-wide association analysis of age at onset in schizophrenia in a European-American sample. Am J Med Genet B Neuropsychiatr Genet 156B, 671-80 (2011). 33. Bergen, S.E. et al. Genetic modifiers and subtypes in schizophrenia: investigations of age at onset, severity, sex and family history. Schizophr Res 154, 48-53 (2014). 34. Woolston, A.L. et al. Genetic loci associated with an earlier age at onset in multiplex schizophrenia. Sci Rep 7, 6486 (2017). 35. Zhan, N., Sham, P.C., So, H.C. & Lui, S.S.Y. The genetic basis of onset age in schizophrenia: evidence and models. Front Genet 14, 1163361 (2023). 36. Hwu, H.G. et al. Taiwan schizophrenia linkage study: the field study. Am J Med Genet B Neuropsychiatr Genet 134b, 30-6 (2005). 37. Faraone, S.V. et al. Genome scan of Han Chinese schizophrenia families from Taiwan: confirmation of linkage to 10q22.3. Am J Psychiatry 163, 1760-6 (2006). 38. Rees, E. et al. Analysis of exome sequence in 604 trios for recessive genotypes in schizophrenia. Transl Psychiatry 5, e607 (2015). 39. Wang, S.H. et al. Advanced Paternal Age and Early Onset of Schizophrenia in Sporadic Cases: Not Confounded by Parental Polygenic Risk for Schizophrenia. Biol Psychiatry 86, 56-64 (2019). 40. Barbeira, A.N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun 9, 1825 (2018). 41. Gandal, M.J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693-697 (2018). 42. Nurnberger, J.I., Jr. et al. Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics Initiative. Arch Gen Psychiatry 51, 849-59 (1994). 43. Beck, L.H., Bransome, E.D., Jr., Mirsky, A.F., Rosvold, H.E. & Sarason, I. A continuous performance test of brain damage. J Consult Psychol 20, 343-50 (1956). 44. Chen, W.J. et al. Sustained attention deficit and schizotypal personality features in nonpsychotic relatives of schizophrenic patients. Am J Psychiatry 155, 1214-20 (1998). 45. Lin, S.H. et al. Performance on the Wisconsin Card Sorting Test in families of schizophrenia patients with different familial loadings. Schizophr Bull 39, 537-46 (2013). 46. Peterson, R.E. et al. Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations. Cell 179, 589-603 (2019). 47. Gandal, M.J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362(2018). 48. Nagelkerke, N.J.D. A Note on a General Definition of the Coefficient of Determination. Biometrika 78, 691-692 (1991). 49. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559-75 (2007). 50. Bortolato, B., Miskowiak, K.W., Kohler, C.A., Vieta, E. & Carvalho, A.F. Cognitive dysfunction in bipolar disorder and schizophrenia: a systematic review of meta-analyses. Neuropsychiatr Dis Treat 11, 3111-25 (2015). 51. Deng, M. et al. Associations between polygenic risk, negative symptoms, and functional connectome topology during a working memory task in early-onset schizophrenia. Schizophrenia (Heidelb) 8, 54 (2022). 52. Sada-Fuente, E. et al. Common genetic variants contribute to heritability of age at onset of schizophrenia. Transl Psychiatry 13, 201 (2023). 53. Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet 51, 592-599 (2019). 54. Fiorica, P.N. & Wheeler, H.E. Transcriptome association studies of neuropsychiatric traits in African Americans implicate PRMT7 in schizophrenia. PeerJ 7, e7778 (2019). 55. Cabana-Dominguez, J. et al. Transcriptomic risk scores for attention deficit/hyperactivity disorder. Mol Psychiatry 28, 3493-3502 (2023). 56. Fabbri, C., Leggio, G.M., Drago, F. & Serretti, A. Imputed expression of schizophrenia-associated genes and cognitive measures in patients with schizophrenia. Mol Genet Genomic Med 10, e1942 (2022). 57. Winden, K.D. et al. The organization of the transcriptional network in specific neuronal classes. Mol Syst Biol 5, 291 (2009). 58. Gordon, A. et al. Transcriptomic networks implicate neuronal energetic abnormalities in three mouse models harboring autism and schizophrenia-associated mutations. Mol Psychiatry 26, 1520-1534 (2021). 59. He, T. & Chan, K.C.C. Measuring Boundedness for Protein Complex Identification in PPI Networks. IEEE/ACM Trans Comput Biol Bioinform (2018). 60. Ebstein, F., Kloetzel, P.M., Kruger, E. & Seifert, U. Emerging roles of immunoproteasomes beyond MHC class I antigen processing. Cell Mol Life Sci 69, 2543-58 (2012). 61. Lam, Y.A. et al. Inhibition of the ubiquitin-proteasome system in Alzheimer's disease. Proc Natl Acad Sci U S A 97, 9902-6 (2000). 62. Bousman, C.A. et al. Preliminary evidence of ubiquitin proteasome system dysregulation in schizophrenia and bipolar disorder: convergent pathway analysis findings from two independent samples. Am J Med Genet B Neuropsychiatr Genet 153B, 494-502 (2010). 63. Lee, Y.H., Kim, J.H. & Song, G.G. Pathway analysis of a genome-wide association study in schizophrenia. Gene 525, 107-15 (2013). 64. Minelli, A. et al. Proteasome system dysregulation and treatment resistance mechanisms in major depressive disorder. Transl Psychiatry 5, e687 (2015). 65. Ansar, M. et al. Biallelic variants in PSMB1 encoding the proteasome subunit beta6 cause impairment of proteasome function, microcephaly, intellectual disability, developmental delay and short stature. Hum Mol Genet 29, 1132-1143 (2020). 66. Ying, K. et al. Diverse Ras-related GTPase DIRAS2, downregulated by PSMD2 in a proteasome-mediated way, inhibits colorectal cancer proliferation by blocking NF-kappaB signaling. Int J Biol Sci 18, 1039-1050 (2022). 67. He, H.Y. et al. Neuronal membrane proteasomes regulate neuronal circuit activity in vivo and are required for learning-induced behavioral plasticity. Proc Natl Acad Sci U S A 120, e2216537120 (2023). 68. Breuss, M.W., Leca, I., Gstrein, T., Hansen, A.H. & Keays, D.A. Tubulins and brain development - The origins of functional specification. Mol Cell Neurosci 84, 58-67 (2017). 69. Rustici, G. et al. ArrayExpress update--trends in database growth and links to data analysis tools. Nucleic Acids Res 41, D987-90 (2013). 70. Van Schoor, E. et al. Frontotemporal Lobar Degeneration Case with an N-Terminal TUBA4A Mutation Exhibits Reduced TUBA4A Levels in the Brain and TDP-43 Pathology. Biomolecules 12(2022). 71. Mourier, A., Ruzzenente, B., Brandt, T., Kuhlbrandt, W. & Larsson, N.G. Loss of LRPPRC causes ATP synthase deficiency. Hum Mol Genet 23, 2580-92 (2014). 72. Cuillerier, A. et al. Loss of hepatic LRPPRC alters mitochondrial bioenergetics, regulation of permeability transition and trans-membrane ROS diffusion. Hum Mol Genet 26, 3186-3201 (2017). 73. Zhang, G., Li, S., Cheng, K.W. & Chou, T.F. AAA ATPases as therapeutic targets: Structure, functions, and small-molecule inhibitors. Eur J Med Chem 219, 113446 (2021). 74. Melliou, S. et al. Regionally defined proteomic profiles of human cerebral tissue and organoids reveal conserved molecular modules of neurodevelopment. Cell Rep 39, 110846 (2022). 75. Hou, J. & Cui, H. CSN6: a promising target for cancer prevention and therapy. Histol Histopathol 35, 645-652 (2020). 76. Wei, N. & Deng, X.W. The COP9 signalosome. Annu Rev Cell Dev Biol 19, 261-86 (2003). 77. Lee, M.H., Zhao, R., Phan, L. & Yeung, S.C. Roles of COP9 signalosome in cancer. Cell Cycle 10, 3057-66 (2011). 78. Zheng, Q. et al. Dysregulation of Ubiquitin-Proteasome System in Neurodegenerative Diseases. Front Aging Neurosci 8, 303 (2016). 79. Tse, W.K., You, M.S., Ho, S.H. & Jiang, Y.J. The deubiquitylating enzyme Cops6 regulates different developmental processes during early zebrafish embryogenesis. Int J Dev Biol 55, 19-24 (2011). 80. Park, Y. et al. A Bayesian approach to mediation analysis predicts 206 causal target genes in Alzheimer’s disease. bioRxiv, 219428 (2017). 81. Liang, Y. et al. Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries. Genome Biol 23, 23 (2022). 82. Hu, X. et al. Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program. Am J Hum Genet 109, 857-870 (2022). 83. Fanous, A.H. & Kendler, K.S. Genetic heterogeneity, modifier genes, and quantitative phenotypes in psychiatric illness: searching for a framework. Mol Psychiatry 10, 6-13 (2005). 84. Bruinooge, A. et al. Genetic predictors of gene expression associated with psychiatric comorbidity in patients with inflammatory bowel disease - A pilot study. Genomics 113, 919-932 (2021). 85. Chen, H.H. et al. Genetically regulated expression in late-onset Alzheimer's disease implicates risk genes within known and novel loci. Transl Psychiatry 11, 618 (2021). 86. Jasper, E.A. et al. Genetically-predicted placental gene expression is associated with birthweight and adult body mass index. Sci Rep 13, 322 (2023). 87. Chen, W.J., Hsiao, C.K., Hsiao, L.L. & Hwu, H.G. Performance of the Continuous Performance Test among community samples. Schizophr Bull 24, 163-74 (1998). 88. van Os, J., Linscott, R.J., Myin-Germeys, I., Delespaul, P. & Krabbendam, L. A systematic review and meta-analysis of the psychosis continuum: evidence for a psychosis proneness-persistence-impairment model of psychotic disorder. Psychol Med 39, 179-95 (2009). 89. Barrantes-Vidal, N., Grant, P. & Kwapil, T.R. The role of schizotypy in the study of the etiology of schizophrenia spectrum disorders. Schizophr Bull 41(Suppl 2), S408-16 (2015). 90. Liddle, P.F. The symptoms of chronic schizophrenia. A re-examination of the positive-negative dichotomy. Br J Psychiatry 151, 145-51 (1987). 91. Liddle, P.F. & Barnes, T.R. Syndromes of chronic schizophrenia. Br J Psychiatry 157, 558-61 (1990). 92. Lien, Y.J. et al. The multidimensionality of schizotypy in nonpsychotic relatives of patients with schizophrenia and its applications in ordered subsets linkage analysis of schizophrenia. Am J Med Genet B Neuropsychiatr Genet 153b, 1-9 (2010). 93. Stefanis, N.C. et al. Factorial composition of self-rated schizotypal traits among young males undergoing military training. Schizophr Bull 30, 335-50 (2004). 94. Compton, M.T., Goulding, S.M., Bakeman, R. & McClure-Tone, E.B. Confirmation of a four-factor structure of the Schizotypal Personality Questionnaire among undergraduate students. Schizophr Res 111, 46-52 (2009). 95. Tsaousis, I., Zouraraki, C., Karamaouna, P., Karagiannopoulou, L. & Giakoumaki, S.G. The validity of the Schizotypal Personality Questionnaire in a Greek sample: Tests of measurement invariance and latent mean differences. Compr Psychiatry 62, 51-62 (2015). 96. Linney, Y.M. et al. A quantitative genetic analysis of schizotypal personality traits. Psychol Med 33, 803-16 (2003). 97. Lin, C.C. et al. Genetic and environmental influences on schizotypy among adolescents in Taiwan: a multivariate twin/sibling analysis. Behavior genetics 37, 334-44 (2007). 98. Ericson, M., Tuvblad, C., Raine, A., Young-Wolff, K. & Baker, L.A. Heritability and longitudinal stability of schizotypal traits during adolescence. Behavior genetics 41, 499-511 (2011). 99. Vollema, M.G., Sitskoorn, M.M., Appels, M.C. & Kahn, R.S. Does the Schizotypal Personality Questionnaire reflect the biological-genetic vulnerability to schizophrenia? Schizophr Res 54, 39-45 (2002). 100. Moreno-Izco, L. et al. Ten-year stability of self-reported schizotypal personality features in patients with psychosis and their healthy siblings. Psychiatry Res 227, 283-9 (2015). 101. Teraishi, T. et al. Relationship between lifetime suicide attempts and schizotypal traits in patients with schizophrenia. Plos One 9, e107739 (2014). 102. Brosey, E. & Woodward, N.D. Schizotypy and clinical symptoms, cognitive function, and quality of life in individuals with a psychotic disorder. Schizophr Res 166, 92-7 (2015). 103. Tarbox, S.I. & Pogue-Geile, M.F. A multivariate perspective on schizotypy and familial association with schizophrenia: a review. Clinical psychology review 31, 1169-82 (2011). 104. Linscott, R.J., Morton, S.E. & Investigators, G. The Latent Taxonicity of Schizotypy in Biological Siblings of Probands With Schizophrenia. Schizophr Bull 44, 922-932 (2018). 105. Stefanis, N.C. et al. Impact of schizophrenia candidate genes on schizotypy and cognitive endophenotypes at the population level. Biol Psychiatry 62, 784-92 (2007). 106. Kircher, T. et al. Association of the DTNBP1 genotype with cognition and personality traits in healthy subjects. Psychol Med 39, 1657-65 (2009). 107. Yasuda, Y. et al. Impact on schizotypal personality trait of a genome-wide supported psychosis variant of the ZNF804A gene. Neuroscience letters 495, 216-20 (2011). 108. Stefanis, N.C. et al. Variation in psychosis gene ZNF804A is associated with a refined schizotypy phenotype but not neurocognitive performance in a large young male population. Schizophr Bull 39, 1252-60 (2013). 109. Fanous, A.H. et al. Significant correlation in linkage signals from genome-wide scans of schizophrenia and schizotypy. Mol Psychiatry 12, 958-65 (2007). 110. Ortega-Alonso, A. et al. Genome-Wide Association Study of Psychosis Proneness in the Finnish Population. Schizophr Bull 43, 1304-1314 (2017). 111. Torkamani, A., Wineinger, N.E. & Topol, E.J. The personal and clinical utility of polygenic risk scores. Nat Rev Genet 19, 581-590 (2018). 112. Sieradzka, D. et al. Are genetic risk factors for psychosis also associated with dimension-specific psychotic experiences in adolescence? Plos One 9, e94398 (2014). 113. Zammit, S. et al. A population-based study of genetic variation and psychotic experiences in adolescents. Schizophr Bull 40, 1254-62 (2014). 114. Jones, H.J. et al. Phenotypic Manifestation of Genetic Risk for Schizophrenia During Adolescence in the General Population. JAMA Psychiatry 73, 221-8 (2016). 115. Hatzimanolis, A. et al. Stress-Dependent Association Between Polygenic Risk for Schizophrenia and Schizotypal Traits in Young Army Recruits. Schizophr Bull 44, 338-347 (2018). 116. Legge, S.E. et al. Association of Genetic Liability to Psychotic Experiences With Neuropsychotic Disorders and Traits. JAMA Psychiatry 76, 1256-1265 (2019). 117. Nenadic, I. et al. Polygenic risk for schizophrenia and schizotypal traits in non-clinical subjects. Psychol Med, 1-11 (2020). 118. van Os, J. et al. Evidence that polygenic risk for psychotic disorder is expressed in the domain of neurodevelopment, emotion regulation and attribution of salience. Psychol Med 47, 2421-2437 (2017). 119. van Os, J. et al. Replicated evidence that endophenotypic expression of schizophrenia polygenic risk is greater in healthy siblings of patients compared to controls, suggesting gene-environment interaction. The EUGEI study. Psychol Med, 1-14 (2019). 120. Derks, E.M. et al. Investigation of the genetic association between quantitative measures of psychosis and schizophrenia: a polygenic risk score analysis. Plos One 7, e37852 (2012). 121. Kendler, K.S., Lieberman, J.A. & Walsh, D. The Structured Interview for Schizotypy (SIS): a preliminary report. Schizophr Bull 15, 559-71 (1989). 122. Andreasen, N.C. The Scale for the Assessment of Negative Symptoms (SANS): conceptual and theoretical foundations. Br J Psychiatry Suppl, 49-58 (1989). 123. de Moor, M.H. et al. Meta-analysis of genome-wide association studies for personality. Mol Psychiatry 17, 337-49 (2012). 124. Stahl, E.A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet 51, 793-803 (2019). 125. 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). 126. Hu, L.t. & Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6, 1-55 (1999). 127. Rosseel, Y. lavaan: An R Package for Structural Equation Modeling. 2012 48, 36 (2012). 128. Lee, S.H., Goddard, M.E., Wray, N.R. & Visscher, P.M. A better coefficient of determination for genetic profile analysis. Genet Epidemiol 36, 214-24 (2012). 129. Christensen, A.P., Gross, G.M., Golino, H.F., Silvia, P.J. & Kwapil, T.R. Exploratory Graph Analysis of the Multidimensional Schizotypy Scale. Schizophr Res 206, 43-51 (2019). 130. Bedwell, J.S. et al. Latent factor modeling of four schizotypy dimensions with theory of mind and empathy. Plos One 9, e113853 (2014). 131. Siever, L.J. & Gunderson, J.G. The search for a schizotypal personality: historical origins and current status. Compr Psychiatry 24, 199-212 (1983). 132. Fonseca-Pedrero, E. et al. The structure of schizotypal personality traits: a cross-national study. Psychol Med 48, 451-462 (2018). 133. Tiego, J. et al. Dissecting Schizotypy and Its Association With Cognition and Polygenic Risk for Schizophrenia in a Nonclinical Sample. Schizophr Bull 49, 1217-1228 (2023). 134. Kotov, R. et al. Validating dimensions of psychosis symptomatology: Neural correlates and 20-year outcomes. J Abnorm Psychol 125, 1103-1119 (2016). 135. Jauhar, S., Johnstone, M. & McKenna, P.J. Schizophrenia. Lancet 399, 473-486 (2022). 136. Cardno, A.G., Sham, P.C., Murray, R.M. & McGuffin, P. Twin study of symptom dimensions in psychoses. Br J Psychiatry 179, 39-45 (2001). 137. Fanous, A.H. et al. Genome-wide association study of clinical dimensions of schizophrenia: polygenic effect on disorganized symptoms. Am J Psychiatry 169, 1309-17 (2012). 138. Legge, S.E. et al. Associations Between Schizophrenia Polygenic Liability, Symptom Dimensions, and Cognitive Ability in Schizophrenia. JAMA Psychiatry 78, 1143-1151 (2021). 139. Kendler, K.S. et al. The Roscommon Family Study. III. Schizophrenia-related personality disorders in relatives. Arch Gen Psychiatry 50, 781-8 (1993). 140. Vassos, E. et al. Correlation and familial aggregation of dimensions of psychosis in affected sibling pairs from China. Br J Psychiatry 193, 305-10 (2008). 141. Abe, C. et al. Genetic risk for bipolar disorder and schizophrenia predicts structure and function of the ventromedial prefrontal cortex. J Psychiatry Neurosci 46, E441-E450 (2021). 142. Tronchin, G. et al. Cognitive and Clinical Predictors of Prefrontal Cortical Thickness Change Following First-Episode of Psychosis. Psychiatry Res Neuroimaging 302, 111100 (2020). 143. Power, R.A. et al. Polygenic risk scores for schizophrenia and bipolar disorder predict creativity. Nat Neurosci 18, 953-5 (2015). 144. Lo, M.T. et al. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat Genet 49, 152-156 (2017). 145. Smeland, O.B. et al. Identification of genetic loci shared between schizophrenia and the Big Five personality traits. Sci Rep 7, 2222 (2017) | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95125 | - |
| dc.description.abstract | 本論文共包括兩部分,欲藉由思覺失調症家庭樣本,透過多基因風險分數(Polygenic risk scores, PRS) 量化思覺失調症的多基因遺傳風險,並探討其與均質性狀,包括:早發性思覺失調症與類思覺失調病質性狀之間的關聯性。此外,本研究亦通過量化基因調控表現來揭示基因變異對基因表達的功能性影響,以理解思覺失調症的生物機轉。
第一部分,利用將過去全基因組關聯分析 (Genome-wide association studies, GWAS) 的結果轉化為基因調控機制並建構出思覺失調症的基因調控表達風險分數 (Predicted genetically regulated expression risk scores for schizophrenia, SZ-GeRS),藉以探討早發性思覺失調症的遺傳架構並尋找其相關的核心基因。在此研究中,我們使用595位來自314個多發性思覺失調症家庭具有發病資料紀錄的患病手足進行分析,而這些患者中有223位被定義為早發性思覺失調症 (Early-onset Schizophrenia),372位則為晚發性 (Late-onset Schizophrenia)。 首先,我們針對這些患者進行 SZ-GeRS的計算,SZ-GeRS 是基於思覺失調症轉錄組關聯研究 (Transcriptome-wide association study) 中對應基因的效應大小 (effect size),加權預測基因調控表達量 (Predicted genetically regulated expression, GeRS) 後計算而得。在本研究中,我們除了評估了每個基因的 SZ-GeRS 與早發性思覺失調症之間的關聯性,還進一步考慮了多基因遺傳風險,評估了幾種基於基因效應累積所產生的基因調控表達風險分數與早發性思覺失調症的關聯性,其中包括:p值閾值化基因調控表達風險分數 (p-value thresholding GeRS)、共表達基因模組之基因調控表達風險分數 (module-based GeRS)、以及核心基因之基因調控表達風險分數 (hub-genes based GeRS)。我們發現,由精神疾病相關的基因共表達模組 (gene co-expression modules) 所建構的 module-based GeRS 當中,有一個模組與早發性思覺失調症顯著相關且能解釋2.3% 的變異。接著,我們在另一個獨立的驗證樣本中驗證這一個 module-based GeRS 區辨早發與晚發的能力。在此模組得到驗證後,我們針對落在這個模組中用於計算 module-based GeRS 的基因進行生物路徑分析 (Ingenuity Pathway Analysis),進而辨識出六個樞紐基因,包括 RUVBL2、COPS6、TUBA4A、PSMB5、PSMB2 和LRPPRC。而基於這些樞紐基因計算的 hub-genes based GeRS,解釋了0.5%的早發性思覺失調症變異。此外,我們還發現,在控制思覺失調症的 PRS (SZ-PRS) 和潛在共變數後,由精神疾病相關的基因共表達模組所建構的 module-based GeRS 可以在此研究樣本及驗證樣本中各別額外解釋早發性思覺失調症中2.5%和0.7%的變異。 本研究顯示,基因型預測之基因調控表達風險分數提供獨立於多基因風險分數以外的獨立訊息,且具有潛力可辨識出與早發性思覺失調症相關的潛在風險基因,並為進一步理解早發性思覺失調症的生物學機制提供了新的視角。而釐清這些潛在樞紐基因的生物功能性仍然有許多挑戰,值得未來更近一步的探討與研究。 本論文的第二部分,欲透過多基因風險分數探討思覺失調症和類思覺失調病質性狀 (Schizotypal traits) 之間重疊遺傳決定因素的潛在關聯。 本研究樣本為來自315個多發性思覺失調症家庭中具有Schizotypal traits測量資料的538位患者及其543位未罹病的家屬。患者的思覺失調症症狀 (Schizophrenia Symptoms) 使用「活性症狀量表(Scales for the Assessment of Positive Symptoms, SAPS)」以及 「負性症狀量表(Scales for the Assessment of Negative Symptoms, SANS)」評估,而未罹病家屬的類思覺失調病質 (Schizotypy) 則利用「修正版準精神分裂性人格違常結構化問卷 (Modified Structured Interview for Schizotypy, SIS) 」來評估。我們參照過去的因素分析結果,對本研究樣本SAPS、SANS及SIS量表進行驗證性因素分析 ,並確認患者的三因子思覺失調症症狀 (包括:正向症狀、負向症狀、思維紊亂因子) 及未罹病家屬的四因子類思覺失調病質特徵 (包括:正向因子、負向因子、人際敏感因子、及社會退縮/內向性因子)。 首先,我們使用精神病基因組聯盟(Psychiatric Genomics Consortium)提供的針對思覺失調症的大型病例對照研究的統合分析結果作為發現樣本,計算本研究中每個樣本的SZ-PRS。接著,我們分別在患者及未罹病的家屬中比較SZ-PRS與不同因子的 Schizotypal traits 之間的關係。結果顯示,SZ-PRS僅與患者的三個思覺失調症症狀因子中的思維紊亂因子 (Disorganization factor) 相關,並且與未受影響親屬的四個類精神分裂症因素中的社會退縮/內向性因子 (Social isolation/Introversion factor) 相關。 此外,本研究進一步合併患者和其未罹病的親屬樣本,並計算出「對齊性正向思覺失調症狀-類思覺失調病質」因子 (Aligned Positive Schizophrenic-Schizotypy factor) 以及「對齊性負向思覺失調症狀-類思覺失調病質」因子 (Aligned Negative Schizophrenic-Schizotypy factor)。我們發現在合併樣本後,比起 Aligned Positive Schizophrenic-Schizotypy factor,SZ-PRS 可以解釋更多Aligned Negative Schizophrenic-Schizotypy factor 的變異。且與單獨分析患者和未罹病的親屬樣本相比,合併樣本中SZ-PRS與各因子的關聯效應略有增加。 最後,由於思覺失調症與其他精神疾病的基因遺傳風險具有高度重疊性,因此我們額外利用合併後樣本評估雙性情緒障礙症 (Bipolar Disorder, BIP)、重鬱症 (Major Depression Disorder, MDD)、及五個一般人格特質 (General Personality Traits) 的多基因風險分數與兩個「對齊性思覺失調症狀-類思覺失調病質」因子的相關性。結果顯示,「對齊性思覺失調症狀-類思覺失調病質」因子僅與SZ-PRS相關,而與BIP、MDD或一般人格特徵的PRS皆無關。我們的研究結果支持類思覺失調症的負向因子比正向因子更能反應思覺失調症的遺傳易感性的假設。 總結來說,本論文展示SZ-PRS及SZ-GeRS在探索思覺失調症均質亞型性狀的遺傳架構和生物機轉上之價值。 | zh_TW |
| dc.description.abstract | This dissertation comprises two main parts. It aims to quantify the polygenic risk of schizophrenia (SZ) using polygenic risk scores (PRS) in family samples with SZ and to investigate its association with homogeneous traits, including early-onset SZ and schizotypal traits. Additionally, this dissertation seeks to uncover the functional impact of genetic variation on gene expression by quantifying gene regulatory expression, thereby advancing our understanding of the biological mechanisms underlying SZ.
Study I aimed to investigate the underlying biological mechanisms of early-onset SZ and identify associated hub genes. This was achieved by translating Genome-wide Association Studies (GWAS) results into regulatory mechanisms and constructing genetically regulated gene-expression risk scores for SZ (SZ-GeRS). In this study, we analyzed 595 individuals from 314 multiplex schizophrenia families, with 223 classified as early-onset SZ and 372 as late-onset SZ. First, we calculated the SZ-GeRS for these patients. The SZ-GeRS is derived by weighting the predicted genetically regulated expression based on the effect sizes of corresponding genes from transcriptome-wide association studies (TWAS) on SZ. Then, we evaluated the association between individual genes' SZ-GeRS and early-onset SZ. Given the polygenic architecture of SZ, we also assessed several types of gene-summing GeRS, including p-value thresholding GeRS, module-based GeRS, and hub-genes-based GeRS. We found that a module-based GeRS from psychiatric disorder-related co-expression modules is significantly associated with early-onset SZ, explaining 2.3% of the variance. Also, we validated the ability of this module-based GeRS to distinguish between early-onset and late-onset SZ in an independent validation sample. Upon validation of this module, we conducted an Ingenuity Pathway Analysis on the genes within this module used to calculate the module-based GeRS, identifying six hub genes: RUVBL2, COPS6, TUBA4A, PSMB5, PSMB2, and LRPPRC. The hub-genes-based GeRS, calculated from these hub genes, accounted for 0.5% of the variance in early-onset SZ. Additionally, we found that, after controlling for PRS for SZ and potential covariates, this module-based GeRS independently explained an additional 2.5% and 0.7% of the variance in early-onset SZ in the study sample and validation sample, respectively. This study demonstrates that SZ-GeRS provide an independent effect over SZ-PRS on early-onset SZ and have the potential to identify putative risk genes associated with early-onset SZ. However, elucidating the biological functions of these potential hub genes remains a significant challenge, warranting further investigation and research in the future. Study II aimed to investigate the associations of overlapping genetic determinants between schizophrenia and schizotypal traits through PRS. This study included 538 patients with SZ and 543 unaffected relatives from 315 multiplex schizophrenia families, all of whom have data on schizotypal traits. We assessed schizotypy in unaffected relatives using a modified Structured Interview for Schizotypy (SIS) and schizophrenic symptoms in patients using the SAPS/SANS scales. Confirmatory factor analysis confirmed three symptom factors in patients (Psychotic, Negative, Disorganized) and four schizotypal traits in unaffected relatives (Positive Schizotypy, Negative Schizotypy, Social Isolation/Introversion, Interpersonal Sensitivity). First, we calculated the SZ-PRS for each sample, and then separately compared the relationship between SZ-PRS and different factors of schizotypal traits in patients and their unaffected relatives. We found that the SZ-PRS was correlated only with Disorganization factor from three Schizophrenic symptom factors in the patients as well as with the Social isolation/Introversion factor from four Schizotypy factors in their unaffected relatives. Additionally, this study further pooled the patient and unaffected relative samples and derived the 2-factor schizotypal trait in the pooled sample (i.e., the Aligned Positive/Negative Schizophrenic-Schizotypy). After pooling patients and their unaffected relatives, SZ-PRS explained more variance in the Aligned Negative Schizophrenic-Schizotypy factor than in the Aligned Positive Schizophrenic-Schizotypy factor. Also, the association effects were slightly increased in the pooled sample compared to those of analyzing patients and unaffected relatives separately. Finally, due to the high genetic overlap between schizophrenia and other psychiatric disorders, we assessed the polygenic risk scores for bipolar disorder (BIP), major depressive disorder (MDD), and five general personality traits in relation to the 2-factor schizotypal trait in the pooled sample. The results indicated that the Aligned Schizophrenic-Schizotypy factors were only associated with SZ-PRS but not with PRS for BIP, MDD, or general personality traits. Our findings support the hypothesis that the negative factor of schizotypy more accurately reflects the genetic susceptibility to SZ than the positive factor. Taken together, this dissertation demonstrates the utility of SZ-PRS and GeRS in exploring the genetic architecture and elucidating the biological mechanisms underlying SZ in its homogeneous subtypes. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-29T16:11:41Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-29T16:11:41Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌 謝 ii 中 文 摘 要 iii Abstract vi Contents ix List of Figures xi List of Tables xiv Chapter 2 Study I- Identification of hub genes involved in early-onset schizophrenia using genetically predicted regulated gene-expression risk score for schizophrenia 4 2.1 Background 4 2.2 Methods 7 2.3 Results 15 2.4 Discussion 22 2.5 Figures and Tables 31 2.6 Supplementary Materials 42 Chapter 3 Study II- Associations between polygenic risk score for schizophrenia and schizotypal traits in the multiplex families of patients with schizophrenia 64 3.1 Background 64 3.2 Methods 67 3.3 Results 73 3.4 Discussion 76 3.5 Figures and Tables 83 3.6 Supplementary Materials 90 Chapter 4 Conclusions and Implications 94 References 96 Appendix I- 修正版準精神分裂症結構化問卷 (Modified Structured Interview for Schizotypy, SIS) 112 Appendix II- 活性症狀量表(Scales for the Assessment of Positive Symptoms, SAPS) 136 Appendix III-負性症狀量表(Scales for the Assessment of Negative Symptoms, SANS) 139 Appendix IV- The other publication by Ya-Wen Jen 141 | - |
| dc.language.iso | en | - |
| dc.subject | 多基因風險分數 | zh_TW |
| dc.subject | 全基因組關聯分析 | zh_TW |
| dc.subject | 轉錄組差捕 | zh_TW |
| dc.subject | 基因表現量 | zh_TW |
| dc.subject | 類思覺失調病質 | zh_TW |
| dc.subject | 思覺失調症 | zh_TW |
| dc.subject | 發病年齡 | zh_TW |
| dc.subject | age of onset | en |
| dc.subject | schizophrenia | en |
| dc.subject | schizotypy | en |
| dc.subject | genome-wide association studies | en |
| dc.subject | transcriptome-wide association | en |
| dc.subject | gene expression levels | en |
| dc.subject | polygenic risk scores | en |
| dc.title | 思覺失調症的多基因結構及預測基因調控表達:與早發性思覺失調症及類思覺失調病質性狀之關聯 | zh_TW |
| dc.title | Polygenic Architecture and Predicted Genetically Regulated Expression in Schizophrenia: Associations with Early-Onset Schizophrenia and Schizotypal Traits | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 蕭朱杏;郭柏秀;馮嬿臻;林彥鋒;劉智民 | zh_TW |
| dc.contributor.oralexamcommittee | Chuhsing Kate Hsiao;Po-Hsiu Kuo;Yen-Chen Anne Feng;Yen-Feng Lin;Chih-Ming Liu | en |
| dc.subject.keyword | 思覺失調症,類思覺失調病質,發病年齡,全基因組關聯分析,轉錄組差捕,基因表現量,多基因風險分數, | zh_TW |
| dc.subject.keyword | schizophrenia,schizotypy,age of onset,genome-wide association studies,transcriptome-wide association,gene expression levels,polygenic risk scores, | en |
| dc.relation.page | 141 | - |
| dc.identifier.doi | 10.6342/NTU202402855 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-09 | - |
| dc.contributor.author-college | 公共衛生學院 | - |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | - |
| dc.date.embargo-lift | 2029-08-07 | - |
| 顯示於系所單位: | 流行病學與預防醫學研究所 | |
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