2011-08-012024-05-13https://scholars.lib.ntu.edu.tw/handle/123456789/650427摘要：葛瑞夫茲氏病是世界造成甲狀腺機能亢進以及甲狀腺眼病變的最重要原因，是一個在臨床及基礎研究都很重要的自體免疫疾病。本病因何引起尚未清楚，證據顯示遺傳因素扮演重要角色。為了找出本病的病因，過去十年來已經做了非常多聯鎖分析但成果有限。而無數的關連研究報告中，也僅有 HLA，CTLA4 以及 PTPN22 等較可信。全世界到目前只有一個全基因體規模關連研究，但該研究涵蓋性不足（只做了大約 15,000 個在基因上頭的SNP）。總括來說，本疾病的研究有太多可進步的空間。本團隊過去十年來在葛瑞夫茲氏病的遺傳研究上已建立穩固基礎，簡述如下：首先，我們已收集全球最多檢體（1000個病人，1000個對照組以及807個家族檢體）。第二，我們先前成功利用家族檢體發現一個新的危險 SNP。第三，我們是全球唯一利用連鎖分析證明 HLA 重要性的團隊。第四，我們已就所有六個典型 HLA 位點進行基因定型並發現在我們族群內特有的致病因子。第五，我們已完成先期全基因體規模關連研究（246病人，468對照組），並在 HLA 及另外幾個區域發現信號。第六，我們已在利用基因資訊建立葛瑞夫茲氏病疾病危險性預估模型。新計畫期間，我們將對本病完成全球第一個有完整涵蓋性的全基因體規模關連研究。兩階段性全基因體規模關連研究已被證明可節省大量支出卻保有幾乎所有的檢力，而因為我們已經有了246病人以及1000對照組的基因型，我們可在第一階段再額外省下百分之八十九的支出。我們也將整合寶貴的HLA資訊進入分析，且將測試 HLA 和其他基因的交互作用。之後我們將選取最重要的20個信號來在第二階段做驗證。一旦發現了可能的致病基因，將進行實驗來測試他們對功能的影響，進而找出真正的致病基因。而一旦建立了疾病危險性預估模型則在臨床上更可以馬上應用。發現致病基因將能夠大大幫助科學研究。更進一步來說，發現本族群特有之致病基因更是重要，因為其他族群的研究永遠沒有辦法提供此等資訊。而一個好的疾病危險性預估模型將有助於個人化醫療。最後，發現葛瑞夫茲氏病的致病基因不僅對本疾病的研究與治療有幫助，甚至可能一併嘉惠其他自體免疫性疾病的研究。<br> Abstract: Graves’ disease [GD (MIM 27500)], the worldwide leading cause of hyperthyroidismand thyroid eye disease, is a common organ-specific autoimmune disorder with both clinicaland scientific importance. The etiology of GD is generally accepted to be multifactorial, withseveral lines of evidence showing significant genetic influence.Many linkage analyses were performed in the past 10 years, with unsatisfactory results.Association studies have been conducted on numerous candidate genes/regions. Among theregions with positive results, only the HLA region, CTLA4 and PTPN22 seem to be promising.The only genome-scale association study for GD had inadequate genome coverage (~15,000nonsynonymous SNP). Regarding to current progress of GD genetic study, there is plenty ofroom for improvement.Our group has built a solid foundation on genetic study of GD during the past 10 years.First, we have set up the largest GD collection in the world (1,000 GD cases, 1,000 controlsand 807 family samples). Second, we conducted a family-based association study on CTLA4and identified a novel susceptibility SNP (rs733618). Third, we are the only group worldwideto demonstrate significant linkage signal for GD at the HLA region. Fourth, we have finisheda large-scale high-quality HLA genotyping (4-digit resolution for all 6 classical HLA loci) andidentified risk/protective alleles of GD in our own population. Fifth, we conducted a pilotGWAS (246 GD cases/ 468 controls) which demonstrated reliable quality and positiveassociation signals at HLA and a couple of other regions. Sixth, we are in a good position tobuild a GD risk prediction model based on our population-specific genetic information.In the upcoming new project period, we will finish the first GWAS with unbiasedgenome coverage for GD. A two-stage GWAS substantially saves the cost while maintainingthe power. During the first stage, we will genotype additional 154 individuals withgenome-wide SNP coverage, therefore complete a first-stage GWAS with 400 cases and 1000controls. Because we already have genome-wide SNP genotypes for 246 cases and 1,000controls, we actually save 89% of cost in this stage. Our valuable HLA genotypes will also beincorporated into GWAS analysis, and interaction of HLA with other SNPs will be tested. Inthe second-stage GWAS, we plan to pick top 20 SNPs for replication in 600 GD cases and600 controls. The most promising GD susceptibility/protective signals will further be testedfor replication in our ~800 family samples and other unrelated individuals. We will thenperform functional assays to identify and verify the genuine causative variants. Theimmediate clinically relevant application is to build a GD risk prediction model based ongenetic information of HLA loci and newly identified susceptibility genes. At the same time,we will also keep recruiting new GD patients.Identification of susceptibility genes will pave the road for better scientific research.Discovery of population-specific risk alleles/genes will be invaluable because this kind ofknowledge can never be generated from other populations. A good disease risk predictionmodel has the potential for individualized medicine. Furthermore, breakthrough in GD studymight even shed light on research of other autoimmune diseases.