請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99366完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳沛隆 | zh_TW |
| dc.contributor.advisor | Pei-Lung Chen | en |
| dc.contributor.author | 段德敏 | zh_TW |
| dc.contributor.author | De-Min Duan | en |
| dc.date.accessioned | 2025-09-09T16:10:21Z | - |
| dc.date.available | 2025-09-10 | - |
| dc.date.copyright | 2025-09-09 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-06-08 | - |
| dc.identifier.citation | References
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99366 | - |
| dc.description.abstract | 胸主動脈瘤與剝離症候群 (TAAD) 是一種隱蔽且潛在致命的疾病。在本研究中，針對來自 NTU TAAD cohort的 125位受試者，使用基於次世代定序技術的 NTU TAAD 基因組 (29 genes) 進行篩檢，得出了 42 個基因診斷結果。此基因組篩檢在證實早期診斷及對已知主動脈擴張患者進行剝離風險分層方面展現出高效性。基因組的特異性為 82.4%，陽性預測值 (PPV) 為 87%。然而，其靈敏度為 54.8%，在此世代研究中的檢測率為 43%。值得注意的是，皮膚擴張紋可能作為初步篩查指標，適合在進行進一步、更全面的檢查之前使用。
此外，在我們對結構變異檢測算法的比較研究中，評估了五個先進的結構變異檢測算法和商業軟體 DRAGEN 的性能，使用來自 GIAB v0.6 Tier 1 基準集以及著名的 HGSVC2 基準集的短讀序全基因組數據。每種算法均獨立測試，並且在多樣協議 (multiple agreement) 和聯合 (union) 策略下進行了多種組合測試。聯合策略達到了更高的召回率 (recall rate)，而多樣協議策略則展現出更優秀的精確度 (precision)。我們的分析顯示，每種算法具有內在的優勢和劣勢，導致所檢測到的結構變異類型和大小有所不同。組合策略有效協調了這些差異，提升了總體性能，並達到與商業軟體 DRAGEN 相當的 F1 分數。 在世代研究中，未找到致病基因的 83 位前驅患者中，34 位臨床特徵為主動脈瘤或剝離篩選為 NTU TAAD WGS cohort的試驗者並接受了全基因組定序和基因變異篩選，過程中使用兩組虛擬基因組篩選變異，分別為專注型 (focused panel) 和廣譜型 (broad spectrum panel)。在這 34 位前驅患者中，經過專注型虛擬基因資組過濾後，在 5 位患者中發現了致病變異點，包括在 FBN1 基因中發現的一個單核苷酸變異點 (SNV)、兩個剪接位點變異 (splice site variant) 和兩個結構變異 (structural variant)。廣譜虛擬基因組篩選後每位患者大約識別出 260 個候選變異。為了偵測全新的致病基因，針對剩餘的 29 位患者的廣譜基因組篩選出來之候選變異進行了基因層級的交集分析，此方法對於發現潛在導致胸主動脈瘤和剝離病理的新基因至關重要。 | zh_TW |
| dc.description.abstract | Thoracic aortic aneurysm and dissection (TAAD) is a silent yet potentially severe condition. In the NTU TAAD cohort study, 125 probands were screened using a next-generation sequencing NTU TAAD panel comprising 29 genes, leading to 42 genetic diagnoses. The panel effectively confirmed early-stage cases and stratified dissection risk, showing a specificity of 82.4% and a positive predictive value (PPV) of 87%. However, sensitivity was 54.8%, with a detection rate of 41%. Notably, skin striae distensae may serve as an early screening indicator before comprehensive examinations.
Our comparative study evaluated five advanced SV detection algorithms alongside the commercial software DRAGEN using short-read whole-genome sequencing data from the GIAB v0.6 Tier 1 and HGSVC2 benchmark sets. The union strategy improved recall, while the multiple agreement method enhanced precision. Each algorithm exhibited strengths and limitations, affecting SV detection variability. Combining approaches optimized performance, yielding F1 scores similar to those of DRAGEN. Among 83 probands previously testing negative for disease-causing genes, 34 underwent whole-genome sequencing and variant prioritization in the NTU TAAD WGS cohort. We applied focused (169 genes) and broad-spectrum (3197 genes) virtual panels, identifying disease-causing variants in 5 probands, including 1 SNV, 2 splice site variants, and 2 structural variants in FBN1. The broad-spectrum panel yielded roughly 260 candidate variants per proband, and gene-level intersection analysis among the remaining 29 probands provided insights into potential novel genetic contributors to TAAD pathology. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-09T16:10:21Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-09T16:10:21Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Table of Contents
口試委員審定書 i Acknowledgement ii 摘要 iii Abstract iv Table of contents v List of figures viii List of tables ix Supplementary x 1. Introduction 1 1.1 Anatomy of aorta 1 1.2 Normal aortic size 3 1.3 Definition of aortic aneurysm and dissection 3 1.4 Revised Ghent criteria for diagnosis of Marfan syndrome 4 1.5 Causes of aortic aneurysm and dissection 6 1.6 Genetic diagnosis for Mendelian disease 9 1.7 Distinct types of genetic variants 9 1.7.1 Single nucleotide variants (SNVs) 9 1.7.2 Small indels 10 1.7.3 Structural variants (SVs) 11 1.7.4 Mobile element insertions (MEIs) 12 1.8 Next generation sequencing for monogenic disease 14 2. Gene panel analysis of NTU TAAD cohort 15 2.1 Background 15 2.2 Aims 15 2.3 Materials and Methods 16 2.3.1 Enrollment of participants 16 2.3.2 Establishment of NTU TAAD panel (29 genes) 16 2.3.3 Library construction and next generation sequencing 17 2.3.4 Computation resources 18 2.3.5 Variant calling and annotation 19 2.3.6 Prioritization and interpretation of variants 20 2.3.7 Confirmation of identified variants 21 2.3.8 Statistics 22 2.4 Results 22 2.4.1 Baseline characteristics of NTU TAAD cohort 22 2.4.2 Physical appearance as a potential prediction marker 26 2.4.3 Prognostic utility of the NTU TAAD panel 31 2.4.4 Multi-gene NGS panel outperforms FBN1-only analysis in predictive diagnosis 33 2.4.5 Genetic characteristics of TAAD patients 34 2.4.6 Disease-causing variants associate with early onset and enhanced systemic manifestations in TAAD patients 35 2.5 Discussions 36 2.5.1 Skin striae distensae as a marker for AoAD screening 36 2.5.2 The gene panel can confirm early-stage AoAD 37 2.5.3 The gene panel can help stratify the risk of dissection 38 2.5.4 High detection rate due to strict inclusion criteria 39 2.6 Conclusions 39 2.7 Limitations 40 3. Comparisons of performances of structural variants detection algorithms in solitary or combination strategy 41 3.1 Background 41 3.2 Aims 42 3.3 Study design 42 3.4 Materials and Methods 43 3.4.1 Truth set 43 3.4.2 SV detection algorithms 43 3.4.3 Calling and refinement of structural variations 44 3.4.4 Combination strategies of multiple algorithms 45 3.4.5 Evaluation of performance for single algorithm and combination strategies 46 3.4.6 Computational resources 48 3.5 Results 48 3.5.1 SV detection methods included in this study 48 3.5.2 Evaluation of SV detection methods based on the GIAB benchmark set and well-referenced cell lines 50 3.5.3 The individual performance of each algorithm 53 3.5.4 Systematic identification of the performances of combination strategies 58 3.6 Discussions 67 3.7 Conclusions 71 3.8 Acknowledgments 71 4. NTU TAAD WGS cohort 73 4.1 Background 73 4.2 Aims 74 4.3 Study design 74 4.4 Materials and Methods 75 4.4.1 Enrollment of participants 75 4.4.2 Establishment of virtual panels 75 4.4.3 Library construction and whole genome sequencing 76 4.4.4 Computation resources 77 4.4.5 Calling and annotation of SNVs and small indels 77 4.4.6 Calling and annotation of structural variants 78 4.4.7 Calling and annotation of mobile element insertions 78 4.4.8 Variant prioritization pathway 1: by Clinvar database 79 4.4.9 Variant prioritization pathway 2: by prediction score 80 4.4.10 Variant prioritization pathway 3: by ACMG classification and 4: by AnnotSV ranking score 80 4.4.11 Classification and interpretation of variants 82 4.4.12 Identification of novel disease-causing gene through gene-level intersection analysis 82 4.4.13 Confirmation of identified variants 83 4.5 Results 83 4.5.1 Baseline characteristics 83 4.5.2 Participant selection and genetic variants prioritization 84 4.5.3 Supported variants filtered through focused virtual panel and broad-spectrum virtual panel 86 4.5.4 Recurrent gene identified via gene-level intersection analysis within a broad-spectrum panel 90 4.6 Discussions 93 4.6.1 Maximizing variant detection through whole genome sequencing 93 4.6.2 Different filtration pathways prevent missed detection of variants 94 4.6.3 Balancing sensitivity and specificity in broad-spectrum virtual panel 95 4.6.4 Intersection analysis between probands gives an opportunity for detection of novel gene 96 4.7 Conclusions 98 5. References 100 6. Appendices 108 | - |
| dc.language.iso | en | - |
| dc.subject | 全基因體定序 | zh_TW |
| dc.subject | 基因組篩選 | zh_TW |
| dc.subject | 胸主動脈瘤與剝離徵侯群 | zh_TW |
| dc.subject | 次世代基因定序 | zh_TW |
| dc.subject | 結構變異檢測算法 | zh_TW |
| dc.subject | thoracic aortic aneurysm and dissection | en |
| dc.subject | gene panel analysis | en |
| dc.subject | structural variants detection | en |
| dc.subject | whole genome sequencing | en |
| dc.subject | next generation sequencing | en |
| dc.title | 胸主動脈瘤與剝離症侯群的系統性基因分析及結構變異檢測算法在單一或組合策略下的性能比較 | zh_TW |
| dc.title | Systemic Genetic Analysis for Thoracic Aortic Aneurysm and Dissection (TAAD) and Comparison of Performances of Structural Variants Detection Algorithms in Solitary or Combination Strategy | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 柯毓麟;楊鎧鍵;許書睿;游治節;邱馨慧 | zh_TW |
| dc.contributor.oralexamcommittee | Yu-Lin Ko;Kai-Chien Yang;Jacob Shujui Hsu;Chih-Chieh Yu;Hsin-Hui Chiu | en |
| dc.subject.keyword | 次世代基因定序,胸主動脈瘤與剝離徵侯群,基因組篩選,結構變異檢測算法,全基因體定序, | zh_TW |
| dc.subject.keyword | next generation sequencing,thoracic aortic aneurysm and dissection,gene panel analysis,structural variants detection,whole genome sequencing, | en |
| dc.relation.page | 127 | - |
| dc.identifier.doi | 10.6342/NTU202500995 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-06-09 | - |
| dc.contributor.author-college | 醫學院 | - |
| dc.contributor.author-dept | 基因體暨蛋白體醫學研究所 | - |
| dc.date.embargo-lift | 2025-09-10 | - |
| 顯示於系所單位： | 基因體暨蛋白體醫學研究所 | |
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