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研究生:趙經軒
研究生(外文):CHAO, CHING-HSUAN
論文名稱:探討臺灣族群中脂質相關性狀之基因型綜觀並利用孟德爾隨機化進行老藥新用之預測
論文名稱(外文):Investigation of Genotypic Landscape and Drug Repurposing Using Mendelian Randomization among Lipid-related Traits in the Taiwanese Population
指導教授:張偉嶠
指導教授(外文):CHANG, WEI-CHIAO
口試委員:張偉嶠楊欣洲卓爾婕
口試委員(外文):CHANG, WEI-CHIAOYANG, HSIN-CHOUCHO, ER-CHIEH
口試日期:2022-06-13
學位類別:碩士
校院名稱:臺北醫學大學
系所名稱:藥學系碩士班
學門:醫藥衛生學門
學類:藥學學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:英文
論文頁數:163
中文關鍵詞:脂質血漿濃度全基因組關聯性研究孟德爾隨機化老藥新用高血脂症類血管生成素三臺灣族群
外文關鍵詞:lipid plasma concentrationgenome-wide association studyMendelian randomizationdrug repurposinghyperlipidemiaANGPTL3Taiwanese population
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脂質相關性狀包含低密度脂蛋白(LDL-C)、高密度脂蛋白(HDL-C)、總膽固醇(TC)以及三酸甘油脂(TG)先前研究指出與心血管疾病風險相關並且可用於評估疾病的進程。脂質相關性狀之全基因體關聯性研究(genome-wide association study, GWAS)大部分探討於西方族群,尤其對於臺灣族群而言,資料尚未充足,且於心血管或代謝疾病而言,目前仍然需要有更多藥物選擇。因此本研究之目的有二,一為利用全基因體關聯性研究探討臺灣族群中脂質相關性狀與基因型之相關性;二為利用孟德爾隨機化研究(Mendelian randomization, MR)應用於老藥新用探討臺灣族群潛在心血管代謝相關疾病藥物靶點。
我們先利用臺灣人體生物資料庫中59448位受試者之基因型與脂質相關性狀對全基因體中常見遺傳變異位點進行關聯性分析,並透過遺傳力分析、遺傳相關性分析、細胞種類富集度分析、路徑分析提供脂質相關性狀於臺灣族群中基因型之綜覽。接著利用孟德爾隨機化研究進行老藥新用探討。老藥新用為一種找尋已核准藥物或已進入臨床試驗藥物新適應症之策略,此策略的優勢為降低在早期臨床試驗中失敗率。孟德爾隨機化利用遺傳變異位點作為工具變量探討暴露與結果之間之因果推論,並提供一個類似虛擬隨機分派試驗概念可應用於藥物靶點預測。於是我們使用臺灣人體資料庫中脂質相關性狀與心血管或代謝疾病之資料並整合藥物靶點之基因資料庫資訊探討潛在藥物靶點透過改變脂質相關性狀進而影響心血管或代謝疾病之風險。
我們發現當性狀為LDL-C,共發現30個顯著基因座; 當性狀為HDL-C,共發現42個顯著基因座; 當性狀為TC,共發現39個顯著基因座; 當性狀為TG,共發現27個顯著基因座。並且在這些性狀當中,8個顯著基因座可能新發現於臺灣族群。我們進一步聚焦於rs10922108 (CFH)、rs79039367 (RABGAP1L)
此兩顯著位點與其基因座,透過搜尋其等位基因在不同族群間之頻率以及表達/剪接數量性狀基因座,我們認為此二位點分別對LDL-C與HDL-C性狀在臺灣族群中具有特異之關聯性。經過孟德爾隨機化分析與共定位分析將基因做等級排序,我們共發現13個顯著基因潛在透過LDL-C影響高血脂症之風險,亦發現17個顯著基因潛在透過TG影響高血脂症之風險。其中包含已知血脂相關基因(如HMGCR, PCSK9, APOC3),代表我們建立之分析流程具有一定程度的可信度。最後我們提供ANGPTL3在臺灣族群中透過TG影響高血脂症風險之孟德爾隨機化研究結果。
綜合以上,我們利用了全基因組關聯性研究提供脂質相關性狀之基因型綜覽,並且建立之孟德爾隨機化分析流程未來亦可應用於其他疾病與其對應之生物標記進行老藥新用之預測。

Lipid-related traits such as low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C), total cholesterol (TC) and triglyceride (TG) are considered as be the risk factors of cardiovascular diseases. These traits have been used to evaluate disease progression. Most of the lipid-related Genome-wide association studies (GWASs) were conducted in the Western populations, so the data in Asian population is limited especially for Taiwanese population. In addition, for the cardiovascular or metabolic related diseases, it is still needed to identify new potential drug targets for choice. Therefore, there are two study aims. The first is to investigate the relationship between the genotype data and lipid-related traits using GWAS in the Taiwanese population, and the second is to investigate the potential drug targets of cardiovascular or metabolic related diseases by conducting drug repurposing using Mendelian randomization (MR) method.
We firstly conducted a GWAS for lipid-related traits using 59,448 subjects genotype data from Taiwan Biobank, and then we conducted the heritability analysis, genetic correlation analysis, cell type enrichment analysis, and pathway analysis to provide the genetic landscape of the Taiwanese population. Second, we conducted the drug repurposing using the MR. Drug repurposing is a strategy to identify new indication for approved or investigated drugs. The advantage of such approaches is to reduce the risk of failure in the early phase of the clinical trial. We utilized genetic variants as instrument variables in MR to infer the causal relationship of exposure to outcome and to provide a concept of the virtual randomized controlled trial opportunities. Therefore, we integrated information of the drug target gene databases and Taiwan Biobank to investigate the potential drug targets affected the risk of cardiovascular or metabolic related diseases mediated by the lipid concentration by MR.
We found 30 GWS loci for LDL-C, 42 GWS loci for HDL-C, 39 GWS loci for TC, and 27 GWS loci for TG, and 8 loci were newly identified loci among the lipid-related traits. We particularly focused on 2 candidate newly identified loci. The first one is the locus that the lead SNP was rs10922108 in CFH gene for LDL-C. The second one is the locus that the lead SNP was rs79039367 in RABGAP1L gene for HDL-C. Furthermore, our results revealed that these two loci were specific associated with the corresponding trait in the Taiwanese population by exploring the allele frequencies in different populations and the expression/splicing quantitative trait locus information in the GTEx database. After conducting the MR analysis and colocalization analysis to classify the genes. 13 MR robust significant genes were identified for hyperlipidemia through LDL-C and 17 MR robust significant genes for hyperlipidemia through TG. Well-known lipid-related genes such as HMGCR, PCSK9 or APOC3 are in the gene list, which indicated our analytical pipeline is robustness and reliable. Finally, we provided the MR evidence in the Taiwanese population for ANGPTL3 of the causal role of TG to hyperlipidemia.
In conclusion, we provided the genotypic landscape for lipid-related traits by GWAS in the Taiwanese population and the MR analytical pipeline can also be applied into other diseases and their corresponding biomarkers for drug repurposing to predict the potential drug targets in the future.
Table Contents
中文摘要 1
Abstract 3
Chapter 1. Introduction 5
1.1 Lipid-related traits 5
1.2 Genome-wide association study (GWAS) 5
1.3 Mendelian randomization study (MR) 6
1.3.1 OVERVIEW 6
1.3.2 ASSUMPTIONS 7
1.4 Drug repurposing 8
1.4.1 OVERVIEW 8
1.4.2 GENOMIC-DRIVEN DRUG DISCOVERY 9
1.4.3 DRUG REPURPOSING BY MENDELIAN RANDOMIZATION 9
1.5 Knowledge gap and the aim of the study 10
Chapter 2. Material and Methods 11
2.1 Study population 11
2.2 Quality control of variants and subjects 11
2.3 Quality control of the phenotypes 12
2.4 Association analysis 12
2.5 Discovery of GWS loci 13
2.6 Conditional analysis 13
2.7 Functional annotation of genome-wide significant (GWS) SNPs 13
2.8 Identification of the novel loci 13
2.9 Pleiotropy of the novel loci 14
2.10 Allele frequencies among different populations of lead SNPs of novel loci 14
2.11 Heritability and genetic correlation analyses 14
2.12 Cell type enrichment analysis 14
2.13 Pathway analysis 15
2.14 Overview of the workflow for drug repurposing 15
2.15 Selection of drug target genes 16
2.16 Selection of proposed instruments of drug target genes 16
2.17 Application of the GWAS summary statistics of the candidate outcomes 16
2.18 Selection of the outcomes 17
2.19 Mendelian randomization analysis 17
2.20 Classification of MR robust significant genes 19
2.21 Phenome-wide scan of candidate therapeutic genes 21
Chapter 3 Results 22
3.1 Overview of the GWAS of lipid-related traits 22
3.2 Genome-wide significant loci among lipid-related traits in the Taiwanese population 22
3.3 Independent GWS SNPs for lipid-related traits 23
3.4 Novel loci for lipid-related traits in Taiwanese population 23
3.5 Heritability for lipid-related traits in Taiwanese population 24
3.6 Genetic correlation among lipid-related traits 24
3.7 The cell types specific to lipid-related traits 25
3.8 Pathway analysis 26
3.9 Drug target genes we selected based on the drug target gene databases 26
3.10 Proposed instruments for the drug target genes 27
3.11 The causal relationships with lipid-related traits and candidate outcomes 27
3.12 Robust MR significant genes identified from the drug target biomarker MR analysis 28
3.13 MR robust significant genes in different tiers 28
3.14 Phenome-wide scan of the candidate therapeutic genes 29
Chapter 4 Discussion and conclusion 30
Chapter 5. Reference 37
Chapter 6. Tables 42
Chapter 7. Figures 122

Tables
Table 1 Sample sizes of candidate outcomes 42
Table 2 The 42 binrary traits and 45 quantitative traits in Taiwan Biobank 43
Table 3 Baseline characteristics of the subjects 47
Table 4 Sample sizes for lipid-related traits after removing outliers 48
Table 5 Results of genome-wide significant loci for lipid-related traits 49
Table 6 The result of conditional analysis for lipid-related traits 59
Table 7 Summary of GWAS results among four lipid-related traits 65
Table 8 Previous traits of lead SNPs in newly identified loci and its r2 > 0.8 SNPs
reported by GWAS catalog 66
Table 9 Allele frequencies of the lead SNPs in newly identified loci 69
Table10 Genetic correlations among lipid-related traits 73
Table11 The significant pathways for lipid-related traits 74
Table12 Drug target genes identified from drug target gene databases 103
Table13 Genetic correlation between exposures and candidate outcomes 108
Table14 Results of genome-wide biomarker MR for the significantly correlated pair
of exposure and outcome 109
Table15 Results of main analysis of robust significant genes 110
Table16 Results of sensitivity analysis of robust significant genes 113
Table17 Multivariable MR results for robust significant genes for LDL-C and
triglyceride on hyperlipidemia 115
Table18 MR robust significant genes in different tiers 116
Table19 Existing related indications for candidate therapeutic genes in the tier 1 and
tier 2 119
Table20 Phenome-wide scan for candidate therapeutic genes in tier 1 and tier 2 121

Figures
Figure 1 The quality control workflow of variants and subjects using in the GWAS
122
Figure 2 The workflow of drug repurposing 123
Figure 3 The analytical pipeline of the MR analysis 124
Figure 4 The Manhattan plots of lipid-related traits 126
Figure 5 The quantile-quantile (Q-Q) plots of the GWAS result for lipid-related
traits 130
Figure 6 The functional annotation of GWS SNPs for lipid-related traits 134
Figure 7 The venn plots of the GWAS results among four lipid-related traits 138
Figure 8 The genetic correlation among lipid-related traits in Taiwanese population
139
Figure 9 Enrichment analyses of cell types in active enhancers 140
Figure10 Subgroup cell type enrichment analyses of the significant associated cell
groups 141
Figure11 Pathway analysis 142
Figure12 The overlap of the 1,076 drug target genes identified from three drug
target gene databases 146
Figure13 The overlap of the drug target genes applied into the drug target biomarker
MR analysis 147
Figure14 The genetic correlation between the exposure and 9 cardiovascular or
metabolic related outcomes 148
Figure15 The manhattan plots of the phenome-wide scan for the candidate
therapeutic genes 149


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