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【Trust科技基因检测】是如何怎加老年痴呆症基因检测位点并提高风险评估正确性的？

【Trust科技基因】是如何怎加老年痴呆症基因检测位点并提高风险评估正确性的？ 2017年，估计有550万美国人患有老年痴呆症，65岁以上人群的患病率为10%年。在没有重大医学突破的情况下，估计到2050年，仅在美国就有1380万人患有阿尔茨海默病。阿尔茨海默病是美国第六大死亡原因，但这可能被低估，因为该病的并发症，如肺炎，通常被记录为主要死亡原因。阿尔茨海默病的特征是神

【Trust科技基因检测】是如何怎加老年痴呆症基因检测位点并提高风险评估正确性的？

2017年，估计有550万美国人患有老年痴呆症，65岁以上人群的患病率为10% 年。在没有重大医学突破的情况下，估计到2050年，仅在美国就有1380万人患有阿尔茨海默病。阿尔茨海默病是美国第六大死亡原因，但这可能被低估，因为该病的并发症，如肺炎，通常被记录为主要死亡原因。阿尔茨海默病的特征是神经元死亡和关键的神经病理学变化，包括β-淀粉样蛋白和过度磷酸化的tau缠结的沉积。全基因组关联研究（GWAS）已经确定了阿尔茨海默病的遗传风险因素，并为疾病病因给予了新的见解。GWAS对74046个个体（25580个病例和48466个对照）进行荟萃分析，确定了19个遗传风险位点[，此后随着样本量的增加，该位点增加到约24个位点。这些数据的生物途径分析涉及免疫系统和脂质代谢以及tau结合和淀粉样前体蛋白代谢，尽管疾病作用机制尚未确定。

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，报道了具有最低P值的单核苷酸多态性（SNP）的显著关联，但该信号可以由SNP所在的连锁不平衡区内的一个（或多个）变体来解释。此外，GWAS基因座可能包含影响其他基因表达的多个基因或区域。需要更多的分析来阐明遗传变异和疾病风险之间统计关联的生物学机制。一种方法是识别SNP变异与基因表达差异相关的位点，称为表达数量性状位点（eQTL）。全基因组基因表达数据已成功地与SNP基因型数据相结合，以确定风险基因的优先级，并揭示对一系列精神疾病易感性的潜在机制[4-7]。这种方法可以在基因表达和SNP基因型数据都可用的病例和对照组中进行。然而，这些数据集可能具有有限的样本量，并且由于反向因果关系而受到混淆，因为基因表达的变化可能受到疾病状态或药物治疗的影响。
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.

另一种方法是将GWAS发现与大型国际财团（如多组织基因型组织表达（GTEx）项目[8]和CommonMind财团（CMC））给予的独立基因表达数据相结合。GTEx（版本7）包含与714名供体53个组织（包括13个大脑区域）的基因表达相关的SNP基因型数据，CMC包含646名供体背外侧前额叶皮质的基因表达数据。这些数据代表了一个宝贵的资源，可以用来量化多个组织中基因调控表达与感兴趣表型之间的关联。关联测试可以使用转录组插补方法实现的基于基因的方法进行[5,9,10]，该方法减少了单变量测试的高水平多重测试，并提高了从强功能SNP信号和适度信号组合中识别性状相关位点的能力。使用GWAS汇总统计数据进行转录组学插补，而不需要个人层面的数据，这使得这些方法可以应用于大规模GWAS荟萃分析结果。在此，我们将一种称为S-PrediXcan的转录组学插补方法应用于阿尔茨海默病GWAS汇总统计，以探索与该疾病相关的基因表达的遗传成分。然后，我们利用这些数据进行精细定位，以确定具有疾病相关位点的候选致病基因的优先级，并确定可能给予有关阿尔茨海默病路径和过程的生物学意义信息的外周组织。
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.
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