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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 馮嬿臻 | zh_TW |
| dc.contributor.advisor | Yen-Chen Anne Femg | en |
| dc.contributor.author | 賴誼謙 | zh_TW |
| dc.contributor.author | YI-CHIEN LAI | en |
| dc.date.accessioned | 2025-09-19T16:17:55Z | - |
| dc.date.available | 2025-09-20 | - |
| dc.date.copyright | 2025-09-19 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-01 | - |
| dc.identifier.citation | 1. https://vizhub.healthdata.org/gbd-results/.
2. Bobo, W.V., The Diagnosis and Management of Bipolar I and II Disorders: Clinical Practice Update. Mayo Clin Proc, 2017. 92(10): p. 1532-1551. 3. Merikangas, K.R., et al., Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication. Arch Gen Psychiatry, 2007. 64(5): p. 543-52. 4. Spencer, H. and H. Spencer, The principles of psychology. Vol. 1. 1870: Williams and Norgate London. 5. Cohen, R., et al., Impairments of Attention and Effort Among Patients With Major Affective Disorders. The Journal of Neuropsychiatry and Clinical Neurosciences, 2001. 13(3): p. 385-395. 6. Maalouf, F.T., et al., Impaired sustained attention and executive dysfunction: bipolar disorder versus depression-specific markers of affective disorders. Neuropsychologia, 2010. 48(6): p. 1862-1868. 7. Rock, P.L., et al., Cognitive impairment in depression: a systematic review and meta-analysis. Psychological medicine, 2014. 44(10): p. 2029-2040. 8. Martínez‐Arán, A., et al., Cognitive impairment in euthymic bipolar patients: implications for clinical and functional outcome. Bipolar disorders, 2004. 6(3): p. 224-232. 9. Thalamuthu, A., et al., Genome-wide interaction study with major depression identifies novel variants associated with cognitive function. Molecular psychiatry, 2022. 27(2): p. 1111-1119. 10. Bryzgalov, L.O., et al., Novel functional variants at the GWAS-implicated loci might confer risk to major depressive disorder, bipolar affective disorder and schizophrenia. BMC neuroscience, 2018. 19: p. 41-56. 11. Alemany, S., et al., A genome-wide association study of attention function in a population-based sample of children. PloS one, 2016. 11(9): p. e0163048. 12. Ferreira, M.A., et al., Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet, 2008. 40(9): p. 1056-8. 13. Dhingra, N.K., M.A. Freed, and R.G. Smith, Voltage-gated sodium channels improve contrast sensitivity of a retinal ganglion cell. Journal of Neuroscience, 2005. 25(35): p. 8097-8103. 14. Hatzimanolis, A., et al., Bipolar disorder ANK3 risk variant effect on sustained attention is replicated in a large healthy population. Psychiatric genetics, 2012. 22(4): p. 210-213. 15. Clark, L., S.D. Iversen, and G.M. Goodwin, Sustained attention deficit in bipolar disorder. The British Journal of Psychiatry, 2002. 180(4): p. 313-319. 16. Van Der Meere, J., N. Börger, and T. Van Os, Sustained attention in major unipolar depression. Perceptual and motor skills, 2007. 104(3_suppl): p. 1350-1354. 17. Endicott, J. and R.L. Spitzer, A diagnostic interview: the schedule for affective disorders and schizophrenia. Arch Gen Psychiatry, 1978. 35(7): p. 837-44. 18. Hall, R.C., Global assessment of functioning: a modified scale. Psychosomatics, 1995. 36(3): p. 267-275. 19. Beck, A.T., R.A. Steer, and G. Brown, Beck depression inventory–II. Psychological assessment, 1996. 20. Young, R.C., et al., A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry, 1978. 133: p. 429-35. 21. Cornblatt, B.A., et al., The Continuous Performance Test, identical pairs version (CPT-IP): I. New findings about sustained attention in normal families. Psychiatry research, 1988. 26(2): p. 223-238. 22. Taliun, D., et al., Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature, 2021. 590(7845): p. 290-299. 23. Yang, J., et al., GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics, 2011. 88(1): p. 76-82. 24. Ge, T., et al., Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nature communications, 2019. 10(1): p. 1776. 25. Howard, D.M., et al., Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature neuroscience, 2019. 22(3): p. 343-352. 26. Giannakopoulou, O., et al., The genetic architecture of depression in individuals of East Asian ancestry: a genome-wide association study. JAMA psychiatry, 2021. 78(11): p. 1258-1269. 27. Lane, J.M., et al., Biological and clinical insights from genetics of insomnia symptoms. Nature genetics, 2019. 51(3): p. 387-393. 28. Mullins, N., et al., Dissecting the shared genetic architecture of suicide attempt, psychiatric disorders, and known risk factors. Biological psychiatry, 2022. 91(3): p. 313-327. 29. Coleman, J.R., et al., The genetics of the mood disorder spectrum: genome-wide association analyses of more than 185,000 cases and 439,000 controls. Biological psychiatry, 2020. 88(2): p. 169-184. 30. Liu, H., et al., TTLL11 gene is associated with sustained attention performance and brain networks: A genome‐wide association study of a healthy Chinese sample. Genes, Brain and Behavior, 2023. 22(1): p. e12835. 31. Alemany, S., et al., New suggestive genetic loci and biological pathways for attention function in adult attention‐deficit/hyperactivity disorder. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2015. 168(6): p. 459-470. 32. Baum, A., et al., A genome-wide association study implicates diacylglycerol kinase eta (DGKH) and several other genes in the etiology of bipolar disorder. Molecular psychiatry, 2008. 13(2): p. 197-207. 33. Grove, W.M., et al., Familial prevalence and coaggregation of schizotypy indicators: a multitrait family study. Journal of abnormal psychology, 1991. 100(2): p. 115. 34. Antila, M., et al., Heritability of cognitive functions in families with bipolar disorder. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2007. 144(6): p. 802-808. 35. Chen, X., et al., Decreased sustained attention, processing speed and verbal learning memory in patients with insomnia in Chinese young and middle-aged adults: a cross-sectional study. Sleep and Biological Rhythms, 2020. 18(3): p. 225-232. 36. Brownlow, J.A., K.E. Miller, and P.R. Gehrman, Insomnia and cognitive performance. Sleep medicine clinics, 2019. 15(1): p. 71. 37. Chesin, M.S., et al., Improvements in executive attention, rumination, cognitive reactivity, and mindfulness among high–suicide risk patients participating in adjunct mindfulness-based cognitive therapy: preliminary findings. The journal of alternative and complementary medicine, 2016. 22(8): p. 642-649. 38. Piani, M.C., et al., Sustained attention alterations in major depressive disorder: a review of fMRI studies employing Go/No-Go and CPT tasks. Journal of affective disorders, 2022. 303: p. 98-113. 39. Sax, K.W., et al., Frontosubcortical neuroanatomy and the continuous performance test in mania. American Journal of Psychiatry, 1999. 156(1): p. 139-141. 40. Najt, P., et al., Attention deficits in bipolar disorder: a comparison based on the Continuous Performance Test. Neuroscience letters, 2005. 379(2): p. 122-126. 41. Stahl, E.A., et al., Genome-wide association study identifies 30 loci associated with bipolar disorder. Nature genetics, 2019. 51(5): p. 793-803. 42. Li, H.-J., et al., Novel risk loci associated with genetic risk for bipolar disorder among Han Chinese individuals: a genome-wide association study and meta-analysis. JAMA psychiatry, 2021. 78(3): p. 320-330. 43. Cardno, A.G., et al., Heritability estimates for psychotic disorders: the Maudsley twin psychosis series. Archives of general psychiatry, 1999. 56(2): p. 162-168. 44. Kendler, K.S., et al., A pilot Swedish twin study of affective illness, including hospital-and population-ascertained subsamples. Archives of General Psychiatry, 1993. 50(9): p. 699-706. 45. Palmer, D.S., et al., Exome sequencing in bipolar disorder identifies AKAP11 as a risk gene shared with schizophrenia. Nature Genetics, 2022. 54(5): p. 541-547. 46. Organization, W.H., Suicide worldwide in 2021: global health estimates. 2025: World Health Organization. 47. Ministry of Health and Welfare, T.S.o.c.o.d., 2024. Retrieved from https://www.mohw.gov.tw. 48. Statistics, N.C.f.H., WISQARS fatal injuries: Mortality reports. Hyattsville, MD: Author, 2019. 49. Control, C.f.D. and Prevention, Fatal injury reports. Atlanta: CDC. URL (accessed 5 March 2018): https://www. cdc. gov/injury/wisqars/fatal_injury_reports. html, 2016. 50. Harris, E.C. and B. Barraclough, Suicide as an outcome for mental disorders. A meta-analysis. British journal of psychiatry, 1997. 170(3): p. 205-228. 51. Christiansen, E. and B. Frank Jensen, Risk of repetition of suicide attempt, suicide or all deaths after an episode of attempted suicide: a register-based survival analysis. Australian & New Zealand Journal of Psychiatry, 2007. 41(3): p. 257-265. 52. Cullberg, J., D. Wasserman, and C.G. Stefansson, Who commits suicide after a suicide attempt? An 8 to 10 year follow up in a suburban catchment area. Acta Psychiatrica Scandinavica, 1988. 77(5): p. 598-603. 53. Mullins, N., et al., GWAS of suicide attempt in psychiatric disorders and association with major depression polygenic risk scores. American journal of psychiatry, 2019. 176(8): p. 651-660. 54. Perlis, R.H., et al., Genome-wide association study of suicide attempts in mood disorder patients. American Journal of Psychiatry, 2010. 167(12): p. 1499-1507. 55. DiBlasi, E., et al., Rare protein‐coding variants implicate genes involved in risk of suicide death. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2021. 186(8): p. 508-520. 56. Wilkerson, M.D., et al., Uncommon protein-coding variants associated with suicide attempt in a diverse sample of US Army soldiers. Biological psychiatry, 2024. 96(1): p. 15-25. 57. Endicott, J. and R.L. Spitzer, A Diagnostic Interview: The Schedule for Affective Disorders and Schizophrenia. Archives of General Psychiatry, 1978. 35(7): p. 837-844. 58. American Psychiatric Association, A. and A.P. Association, Diagnostic and statistical manual of mental disorders: DSM-IV. Vol. 4. 1994: American psychiatric association Washington, DC. 59. DeFelice, M., et al., Blended Genome Exome (BGE) as a Cost Efficient Alternative to Deep Whole Genomes or Arrays. bioRxiv, 2024: p. 2024.04. 03.587209. 60. Mahajan, A. and N. Robertson, Rare Variant Quality Control, in Assessing Rare Variation in Complex Traits: Design and Analysis of Genetic Studies, E. Zeggini and A. Morris, Editors. 2015, Springer New York: New York, NY. p. 33-43. 61. Wang, J., et al., Genome measures used for quality control are dependent on gene function and ancestry. Bioinformatics, 2015. 31(3): p. 318-323. 62. Panoutsopoulou, K. and K. Walter, Quality control of common and rare variants. Genetic Epidemiology: Methods and Protocols, 2018: p. 25-36. 63. Dyer, S.C., et al., Ensembl 2025. Nucleic Acids Research, 2025. 53(D1): p. D948-D957. 64. Adzhubei, I.A., et al., A method and server for predicting damaging missense mutations. Nature methods, 2010. 7(4): p. 248-249. 65. Ng, P.C. and S. Henikoff, SIFT: Predicting amino acid changes that affect protein function. Nucleic acids research, 2003. 31(13): p. 3812-3814. 66. Feng, Y.-C.A., et al., Ultra-rare genetic variation in the epilepsies: a whole-exome sequencing study of 17,606 individuals. The American Journal of Human Genetics, 2019. 105(2): p. 267-282. 67. Rivas, M.A., et al., Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science, 2015. 348(6235): p. 666-9. 68. Visscher, P.M., et al., Five years of GWAS discovery. The American Journal of Human Genetics, 2012. 90(1): p. 7-24. 69. Wang, X., Firth logistic regression for rare variant association tests. 2014, Frontiers Media SA. p. 187. 70. Aleksander, S.A., et al., The gene ontology knowledgebase in 2023. Genetics, 2023. 224(1): p. iyad031. 71. Ashburner, M., et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000. 25(1): p. 25-9. 72. Palmer, D.S., et al., Exome sequencing in bipolar disorder identifies AKAP11 as a risk gene shared with schizophrenia. Nature genetics, 2022. 54(5): p. 541-547. 73. Satterstrom, F.K., et al., Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell, 2020. 180(3): p. 568-584. e23. 74. Singh, T., B.M. Neale, and M.J. Daly, Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia. MedRxiv, 2020: p. 2020.09. 18.20192815. 75. Carithers, L.J., et al., A novel approach to high-quality postmortem tissue procurement: the GTEx project. Biopreservation and biobanking, 2015. 13(5): p. 311-319. 76. Judy, J.T., et al., Association study of serotonin pathway genes in attempted suicide. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2012. 159(1): p. 112-119. 77. Lee, S., et al., Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. The American Journal of Human Genetics, 2012. 91(2): p. 224-237. 78. Lee, S., M.C. Wu, and X. Lin, Optimal tests for rare variant effects in sequencing association studies. Biostatistics, 2012. 13(4): p. 762-775. 79. Ganna, A., et al., Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nature neuroscience, 2016. 19(12): p. 1563-1565. 80. Erlangsen, A., et al., Genetics of suicide attempts in individuals with and without mental disorders: a population-based genome-wide association study. Molecular Psychiatry, 2020. 25(10): p. 2410-2421. 81. Li, Q.S., et al., Genome-wide association study meta-analysis of suicide death and suicidal behavior. Molecular psychiatry, 2023. 28(2): p. 891-900. 82. Singh, T., et al., Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature, 2022. 604(7906): p. 509-516. 83. Nechifor, M., Changement of cations concentration in unipolar and bipolar disorders. 2006. 84. González‐Castro, T.B., et al., Identification of gene ontology and pathways implicated in suicide behavior: Systematic review and enrichment analysis of GWAS studies. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2019. 180(5): p. 320-329. 85. Lewis, C.M. and E. Vassos, Polygenic risk scores: from research tools to clinical instruments. Genome medicine, 2020. 12(1): p. 44. 86. Murray, G.K., et al., Could polygenic risk scores be useful in psychiatry?: a review. JAMA psychiatry, 2021. 78(2): p. 210-219. 87. Karczewski, K.J., et al., The mutational constraint spectrum quantified from variation in 141,456 humans. Nature, 2020. 581(7809): p. 434-443. 88. Rivas, M.A., et al., Effect of predicted protein-truncating genetic variants on the human transcriptome. Science, 2015. 348(6235): p. 666-669. 89. Liao, C., et al., 53. RARE CODING VARIATION ACROSS 149,356 INDIVIDUALS IDENTIFIES OVER 20 NOVEL GENES ASSOCIATED WITH BIPOLAR DISORDER. European Neuropsychopharmacology, 2024. 87: p. 77-78. 90. Sequeira, A., et al., Global brain gene expression analysis links glutamatergic and GABAergic alterations to suicide and major depression. PloS one, 2009. 4(8): p. e6585. 91. Genovese, G., et al., Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nature neuroscience, 2016. 19(11): p. 1433-1441. 92. Karczewski, K.J., et al., Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes. Cell genomics, 2022. 2(9). 93. Rebhan, M., et al., GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics (Oxford, England), 1998. 14(8): p. 656-664. 94. Safran, M., et al., GeneCards Version 3: the human gene integrator. Database, 2010. 2010: p. baq020. 95. Wainschtein, P., et al., Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nature genetics, 2022. 54(3): p. 263-273. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99919 | - |
| dc.description.abstract | 背景:
持續性注意力缺陷與自殺企圖 (Suicide Attempt, SA)是常見於情感性疾病患者(如重度憂鬱症(Major Depressive Disorder, MDD)與雙相情感障礙 (Bipolar Disorder, BP))的嚴重臨床議題。了解這些特徵背後的遺傳因子有助於釐清其生物機制,並提升風險預測能力。本論文分為兩個部分:(1)使用持續性注意力測驗(Continuous Performance Test, CPT)資料進行全基因組關聯分析 (GWAS)及多基因風險分數 (Polygenic Risk Score, PRS)分析,探討其與持續注意力的關聯性;(2)利用全外顯子定序進行針對自殺企圖的罕見變異負荷檢定。 方法: 第一部分,我們分析了804位完成CPT測驗的情感性疾患患者的SNP資料(個案為雙相情感障礙,對照為重度憂鬱症)。針對CPT的各項指標(hit rate, false alarm rate, sensitivity 及 response criterion indices)進行GWAS分析,並利用Genome-wide Complex Trait Analysis(GCTA)方法估算其SNP遺傳力。我們亦依據MDD、失眠與SA等外部GWAS資料計算PRS,以探討其對注意力表現的影響。 第二部分,我們分析了來自台灣646位參與者的全外顯子定序(Whole Exome Sequencing, WES)資料,比較BP病人中自殺企圖者與非企圖者之間的差異。為探討罕見功能性變異對SA風險的貢獻,我們使用Firth邏輯斯迴歸進行WES的負荷分析,並進一步以Firth邏輯斯迴歸與Sequence Kernel Association Test(SKAT)進行基因層級的負荷檢定,評估各基因對SA風險的影響。此外,我們進行基因集富集分析以檢驗功能性相關基因群,並建構結合PRS與罕見變異攜帶狀態的聯合模型,以評估其對SA風險的整體效應。 結果: 在CPT分析中,GWAS結果未發現達到全基因體顯著性的位點,且各指標的SNP遺傳力估算值皆偏低。然而,MDD、失眠與SA的PRS與特定注意力指標(特別是false alarm rate與sensitivity)呈現顯著關聯,顯示注意力缺陷與情感性疾患風險可能具有共同的遺傳架構。 在罕見變異負荷檢定中,雖無單一基因或基因集達顯著性門檻,但蛋白截斷變異(Protein-Truncating Variants, PTVs)表現出較高的效應量,可能對SA風險具有較強影響。ANKRD60、PACS2 與 MYLK 等訊號較強之基因於基因層級分析中被識別出,與電壓閘控離子通道活性與血清素路徑相關的基因集亦呈現中等程度的富集現象。在聯合模型中,PRS與罕見變異攜帶狀態結合後與SA的關聯性更為穩定且顯著,顯示在本研究族群中,常見變異可能較罕見變異在自殺風險中扮演更重要的角色。相對地,罕見變異的效應可能受限於樣本數與統計力不足。 結論: 經本研究分析,發現MDD、失眠與SA的PRS與BP患者的持續性注意力表現有顯著關聯,結果顯示不同情感性疾患在神經認知功能的遺傳架構上可能具備異質性。特別是在BP患者中,遺傳風險對注意力缺陷的影響可能更為直接,進一步突顯未來評估認知功能時應考量疾病類型的遺傳背景。 針對自殺企圖之罕見變異分析,雖整體未達統計顯著,但觀察到PTVs可能具有相對較高的效應值,部分基因與神經發育路徑有潛在連結。此外,PRS與SA之間的穩定關聯,亦提示在現階段族群樣本中,常見變異可能較罕見變異更具預測價值。 總結而言,本研究強調整合常見與罕見變異分析對掌握精神疾病複雜遺傳特徵的重要性。雖然樣本數有限且具特定族群侷限性,本論文為東亞地區罕見的遺傳資料庫提供了寶貴的資料,並初步揭示與持續性注意力與自殺風險相關的生物路徑。未來研究應結合更大規模、更多元族群的資料與功能性後續驗證,以推動情感性疾患的精準醫療發展。 | zh_TW |
| dc.description.abstract | Background
Sustained attentional deficits and Suicide attempt (SA) are serious clinical issues frequently observed in individuals with mood disorders such as major depressive disorder (MDD) and bipolar disorder (BP). Understanding the genetic factors underlying these traits may help clarify their biological mechanisms and support the prediction of risk. This thesis includes two parts: (1) a genome-wide association study (GWAS) and polygenic risk score (PRS) analysis of sustained attention using Continuous Performance Test (CPT) data, and (2) a series of rare variants burden tests of SA using whole-exome sequencing (WES). Materials and Methods For Part 1, we analyzed genome-wide single-nucleotide polymorphism (SNP) data from 804 patients with mood disorders (case: BP, control: MDD) who completed the CPT. GWAS was performed on CPT measures (hit rate, false alarm rate, sensitivity, and response criterion indices), followed by SNP-based heritability estimation using Genome-wide Complex Trait Analysis (GCTA). We also computed PRS based on external GWAS of MDD, insomnia, and SA to examine their association with sustained attention performance. For Part 2, we analyzed WES data from 646 Taiwanese individuals, comparing suicide attempters and non-attempters. To investigate the role of rare functional variants in SA risk, we conducted exome-wide burden tests using Firth’s logistic regression. We also performed gene-based burden tests with both Firth’s logistic regression and the Sequence Kernel Association Test (SKAT) to assess each gene's contribution to SA risk. Additionally, gene set enrichment tests were used to examine functionally grouped genes, and a joint model was developed to evaluate the combined effects of rare variant carrier status and PRS based on a large SA GWAS. Results In the CPT study, GWAS results did not reveal genome-wide significant loci. SNP-based heritability estimates were low across CPT traits. However, PRS for MDD, insomnia, and SA showed significant associations with certain attention measures, particularly false alarm rate and sensitivity, indicating shared genetic architecture between sustained attention deficits and mood disorders risk. In the rare variant burden tests, no individual gene or gene set reached statistical significance. However, PTVs showed higher effect sizes than other variant categories, suggesting a potentially more substantial impact on SA risk. Genes such as ANKRD60, PACS2, and MYLK were identified as top associations with SA, and gene sets related to voltage-gated ion channel activity and the serotonin pathway demonstrated modest enrichment. In the joint model, PRS showed stronger and more consistent associations with SA relative to rare variant carrier status. This suggests that common variants may play a more substantial role in suicide risk within this cohort. In contrast, the effect of rare variants may be limited by sample size and statistical power. Conclusion Based on our analysis, PRS for MDD, insomnia, and SA were significantly linked to sustained attention performance in patients with BP compared to those with MDD. These findings suggest potential differences in the genetic markers of neurocognitive function across various mood disorders. Specifically, genetic risk might have a more direct influence on attention deficits in BP patients, emphasizing the need to consider disorder-specific genetic backgrounds when assessing cognitive impairments. In the rare variant analysis of SA, although no findings reached statistical significance, PTVs appeared to exhibit relatively higher effect sizes, and some of the top-ranked genes may be implicated in neurodevelopmental pathways. Additionally, the more consistent association between PRS and SA supports the possibility that, within the current sample, common variants may offer greater predictive value for suicide risk than rare variants. In conclusion, these findings highlight the importance of combining both rare and common variant analyses to capture the full range of genetic influences on complex psychiatric traits. While limited by a modest sample size and population-specific factors, this thesis adds valuable genetic data from an underrepresented East Asian group. It offers initial insights into the biological pathways related to both sustained attention and suicide risk. Future research with larger, more diverse samples and functional follow-up studies will be essential for validating these findings and advancing precision psychiatry in mood disorders. | en |
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| dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 中文摘要 iii ABSTRACT vi Chapter 1. A Genome‐wide Association Study of Attention Deficit among Patients with Mood Disorders 1 Introduction 1 Introduction and epidemiology of mood disorders 1 Attention deficit in patients with mood disorders 2 Genome-wide analysis for sustained attention in patients with mood disorder 3 The aims 4 Methods 5 Study participants 5 Clinical assessments 5 Attention task 6 Statistical analysis 7 Results 10 Demographic and clinical characteristics among patients with mood disorders 10 GWAS results of CPT 10 SNP-Based Heritability Estimates 11 PRS-CS results of CPT 11 Discussion 12 Chapter 2. Rare Variant Burden in Suicide Attempt 17 Introduction 17 Studies for rare variants in BP patients 17 Rare variants studies for suicidal behavior 18 Aims & study flow 19 Methods 21 Study Participants 21 Whole-Exome Sequencing Data 21 Quality Control 22 Variant Annotation 24 Rare-variant Burden Test 25 Join Modeling of Rare Variants and Polygenic Risk Score 28 Results 29 Socio-demographics, WES Overview 29 Quality Control and Annotation 29 Effects of each functional annotation class on SA risk 30 Rare-variant burden in tissue-specific and candidate gene sets 31 Genes enriched in brain tissues 31 Genes previously associated with BP, SCZ, ASD, and SA 32 Gene sets from GO previously associated with psychiatric disorders 33 Gene-based analysis 34 Join modeling of rare and common variants 35 Discussion 37 Main findings 37 The effect of functional rare variants on SA 37 Interpretation of gene-set burden test findings 38 The main finding of the gene-based burden test 40 Strengths & Limitations 42 Conclusion 43 Chapter 3. Conclusion 45 Reference 46 Tables 54 Figures 59 Supplementary material 81 | - |
| dc.language.iso | en | - |
| dc.subject | 持續性注意力缺陷 | zh_TW |
| dc.subject | 自殺企圖 | zh_TW |
| dc.subject | 情感性疾病 | zh_TW |
| dc.subject | 全基因組關聯分析 | zh_TW |
| dc.subject | 多基因風險分數 | zh_TW |
| dc.subject | 全外顯子定序 | zh_TW |
| dc.subject | 負荷分析 | zh_TW |
| dc.subject | GWAS | en |
| dc.subject | suicide attempt | en |
| dc.subject | mood disorder | en |
| dc.subject | burden analysis | en |
| dc.subject | WES | en |
| dc.subject | PRS | en |
| dc.subject | sustained attention deficits | en |
| dc.title | 情感性疾患臨床表型的遺傳學洞察: 探索台灣樣本中的常見與罕見變異負擔 | zh_TW |
| dc.title | Genetic Insights into Clinical Phenotypes of Mood Disorders: Exploring Common and Rare Variant Burden in a Taiwanese Sample | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 郭柏秀 | zh_TW |
| dc.contributor.coadvisor | Po-Hsiu Kuo | en |
| dc.contributor.oralexamcommittee | 蕭朱杏;盧子彬;林彥鋒 | zh_TW |
| dc.contributor.oralexamcommittee | Chuhsing Kate Hsiao;Tzu-Pin Lu;Yen-Feng Lin | en |
| dc.subject.keyword | 持續性注意力缺陷,自殺企圖,情感性疾病,全基因組關聯分析,多基因風險分數,全外顯子定序,負荷分析, | zh_TW |
| dc.subject.keyword | sustained attention deficits,,suicide attempt,mood disorder,GWAS,PRS,WES,burden analysis, | en |
| dc.relation.page | 120 | - |
| dc.identifier.doi | 10.6342/NTU202502829 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-04 | - |
| 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 | 2030-07-31 | - |
| 顯示於系所單位: | 流行病學與預防醫學研究所 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf 未授權公開取用 | 11.08 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
