【佳學(xué)基因檢測】是如何怎加老年癡呆癥基因檢測位點(diǎn)并提高風(fēng)險(xiǎn)評估正確性的？
2017年，估計(jì)有550萬美國人患有老年癡呆癥，65歲以上人群的患病率為10% 年。在沒有重大醫(yī)學(xué)突破的情況下，估計(jì)到2050年，僅在美國就有1380萬人患有阿爾茨海默病。阿爾茨海默病是美國第六大死亡原因，但這可能被低估，因?yàn)樵摬〉牟l(fā)癥，如肺炎，通常被記錄為主要死亡原因。阿爾茨海默病的特征是神經(jīng)元死亡和關(guān)鍵的神經(jīng)病理學(xué)變化，包括β-淀粉樣蛋白和過度磷酸化的tau纏結(jié)的沉積。全基因組關(guān)聯(lián)研究（GWAS）已經(jīng)確定了阿爾茨海默病的遺傳風(fēng)險(xiǎn)因素，并為疾病病因提供了新的見解。GWAS對74046個(gè)個(gè)體（25580個(gè)病例和48466個(gè)對照）進(jìn)行薈萃分析，確定了19個(gè)遺傳風(fēng)險(xiǎn)位點(diǎn)[，此后隨著樣本量的增加，該位點(diǎn)增加到約24個(gè)位點(diǎn)。這些數(shù)據(jù)的生物途徑分析涉及免疫系統(tǒng)和脂質(zhì)代謝以及tau結(jié)合和淀粉樣前體蛋白代謝，盡管疾病作用機(jī)制尚未確定。An estimated 5.5 million Americans were living with Alzheimer’s disease in 2017, with a prevalence of 10% for people over the age of 65 years [1]. In the absence of a significant medical breakthrough, the number of people living with Alzheimer’s disease is estimated to reach 13.8 million in the USA alone by 2050 [1]. Alzheimer’s disease is the sixth leading cause of death in the USA, but this is likely to be an underestimation as complications of the disease, such as pneumonia, are often recorded as the primary cause of death. Alzheimer’s disease is characterised by neuronal death and key neuropathological changes, including the deposition of β-amyloid and hyperphosphorylated tau tangles. Genome-wide association studies (GWAS) have identified genetic risk factors for Alzheimer’s disease and provided novel insights into disease aetiology. A GWAS meta-analysis of 74,046 individuals (25,580 cases and 48,466 controls) identified 19 genetic risk loci [2], which has since increased to some 24 loci with larger sample sizes [3]. Biological pathway analyses of these data implicate the immune system and lipid metabolism as well as tau binding and amyloid precursor protein metabolism [2], although a disease mechanism of action has yet to be established.
在GWAS中，報(bào)道了具有賊低P值的單核苷酸多態(tài)性（SNP）的顯著關(guān)聯(lián)，但該信號可以由SNP所在的連鎖不平衡區(qū)內(nèi)的一個(gè)（或多個(gè)）變體來解釋。此外，GWAS基因座可能包含影響其他基因表達(dá)的多個(gè)基因或區(qū)域。需要更多的分析來闡明遺傳變異和疾病風(fēng)險(xiǎn)之間統(tǒng)計(jì)關(guān)聯(lián)的生物學(xué)機(jī)制。一種方法是識別SNP變異與基因表達(dá)差異相關(guān)的位點(diǎn)，稱為表達(dá)數(shù)量性狀位點(diǎn)（eQTL）。全基因組基因表達(dá)數(shù)據(jù)已成功地與SNP基因型數(shù)據(jù)相結(jié)合，以確定風(fēng)險(xiǎn)基因的優(yōu)先級，并揭示對一系列精神疾病易感性的潛在機(jī)制[4-7]。這種方法可以在基因表達(dá)和SNP基因型數(shù)據(jù)都可用的病例和對照組中進(jìn)行。然而，這些數(shù)據(jù)集可能具有有限的樣本量，并且由于反向因果關(guān)系而受到混淆，因?yàn)榛虮磉_(dá)的變化可能受到疾病狀態(tài)或藥物治療的影響。
In GWAS, significant associations are reported for a single nucleotide polymorphism (SNP) with the lowest P value, but the signal could be explained by one (or more) variant within the linkage disequilibrium block where that SNP resides. Furthermore, GWAS loci may contain multiple genes or regions that affect the expression of other genes. Additional analyses are required to elucidate the biological mechanisms that underlie statistical associations between genetic variants and disease risk. One method is to identify loci where SNP variation is associated with differences in gene expression, called expression quantitative trait loci (eQTL). Genome-wide gene expression data has been successfully integrated with SNP genotype data to prioritise risk genes and reveal possible mechanisms underlying susceptibility to a range of psychiatric disorders [4–7]. This approach may be performed in cases and controls for whom both gene expression and SNP genotype data are available. However, these data sets are likely to have limited sample size and suffer from confounding from reverse causality as variation in gene expression may be influenced by disease status or drug treatment.
另一種方法是將GWAS發(fā)現(xiàn)與大型國際財(cái)團(tuán)（如多組織基因型組織表達(dá)（GTEx）項(xiàng)目[8]和CommonMind財(cái)團(tuán)（CMC））提供的獨(dú)立基因表達(dá)數(shù)據(jù)相結(jié)合。GTEx（版本7）包含與714名供體53個(gè)組織（包括13個(gè)大腦區(qū)域）的基因表達(dá)相關(guān)的SNP基因型數(shù)據(jù)，CMC包含646名供體背外側(cè)前額葉皮質(zhì)的基因表達(dá)數(shù)據(jù)。這些數(shù)據(jù)代表了一個(gè)寶貴的資源，可以用來量化多個(gè)組織中基因調(diào)控表達(dá)與感興趣表型之間的關(guān)聯(lián)。關(guān)聯(lián)測試可以使用轉(zhuǎn)錄組插補(bǔ)方法實(shí)現(xiàn)的基于基因的方法進(jìn)行[5,9,10]，該方法減少了單變量測試的高水平多重測試，并提高了從強(qiáng)功能SNP信號和適度信號組合中識別性狀相關(guān)位點(diǎn)的能力。使用GWAS匯總統(tǒng)計(jì)數(shù)據(jù)進(jìn)行轉(zhuǎn)錄組學(xué)插補(bǔ)，而不需要個(gè)人層面的數(shù)據(jù)，這使得這些方法可以應(yīng)用于大規(guī)模GWAS薈萃分析結(jié)果。在此，我們將一種稱為S-PrediXcan的轉(zhuǎn)錄組學(xué)插補(bǔ)方法應(yīng)用于阿爾茨海默病GWAS匯總統(tǒng)計(jì)，以探索與該疾病相關(guān)的基因表達(dá)的遺傳成分。然后，我們利用這些數(shù)據(jù)進(jìn)行精細(xì)定位，以確定具有疾病相關(guān)位點(diǎn)的候選致病基因的優(yōu)先級，并確定可能提供有關(guān)阿爾茨海默病路徑和過程的生物學(xué)意義信息的外周組織。
An alternative method is to integrate GWAS findings with independent gene expression data provided by large international consortia, such as the multi-tissue Genotype-Tissue Expression (GTEx) project [8] and the CommonMind Consortium (CMC). GTEx (version 7) contains SNP genotype data linked to gene expression across 53 tissues from 714 donors, including 13 brain regions, and the CMC contains gene expression data from the dorsolateral prefrontal cortex of 646 donors. These data represent a valuable resource with which to quantify the association between genetically regulated expression in multiple tissues and the phenotype of interest. Association testing can be carried out using a gene-based approach implemented by transcriptomic imputation approaches [5, 9, 10] which reduce the high level of multiple testing from single-variant tests and increase power to identify trait-associated loci both from a strong functional SNP signal and from a combination of modest signals. The application of transcriptomic imputation using GWAS summary statistics without the need for individual-level data allows these methods to be applied to large-scale GWAS meta-analysis results. Here, we apply a transcriptomic imputation approach called S-PrediXcan to Alzheimer’s disease GWAS summary statistics in order to explore the genetic component of gene expression associated with the disorder. We then use these data in a fine-mapping approach to prioritise candidate causal genes with disease-implicated loci, and identify peripheral tissues that might provide biologically meaningful information on Alzheimer’s disease pathways and processes.(責(zé)任編輯：佳學(xué)基因)
