APOEomic: Searching for APOE interacting risk factors using omics data

描述（由申请人提供）：最近，一些晚发性阿尔茨海默病（LOAD）的全基因组关联研究（GWAS）已经发表。迄今为止发表的每个GWAS都将载脂蛋白E （APOE）位点定位为人类基因组中最强的负载风险信号。这导致我们和其他人假设这个大信号可以。压倒其他较小的影响与APOE E4位点和2。它被分解成多个风险位点，映射到基因组的同一区域。第一个假设是合理的，考虑到在我们之前的GWAS中，我们绘制了只存在于APOE E4阳性个体的效应。第二个假设也是有效的，因为这种类型的效应（即多个风险位点映射到基因组的同一区域）已经在其他神经系统疾病中看到。例如，在与17号染色体相关的额颞叶痴呆（FTDP-17）中，发现了微管相关蛋白Tau基因（MAPT）和前颗粒蛋白（PRGN）的突变。MAPT和PRGN都位于17号染色体上的同一连锁峰内。因此，我们建议利用我们的全基因组和下一代测序（NGS）遗传数据，以及转录组和蛋白质组数据集来绘制新的LOAD风险位点，这些位点要么与APOE E4等位基因相互作用，要么独立于APOE E4等位基因。为了测试APOE E4的独立效应，我们将在19号染色体的同一区域进行额外的NGS测序以捕获额外的效应（Aim 1a）。我们还将在其他病例对照样本中对我们从NGS中发现的变异进行基因分型，以确定它们是否独立于APOE E4而增加疾病风险（Aim 1b）。为了研究APOE E4的上位性效应，我们发现与APOE E4一起作用的snp将通过检查另一个队列（Aim 2a）以及在该区域内测序来寻找其他变体（Aim 2b）。最后，我们将通过检查转录物表达和蛋白质谱（目标3）来绘制目标1或2中绘制的任何变异的下游效应。我们的合作拥有独特的技能和数据集来执行这项工作。Drs。Huentelman和Myers在他们职业生涯的大部分时间里都在一起工作，并共同撰写了许多出版物，使用了与本应用程序中提出的类似的技术。他们有机会获得约1600名神经病理学验证个体的独特队列，这将允许分析风险变异以及这些变异的下游变化。他们从卡迪夫大学招募了大约18000个临床特征样本的额外队列来复制任何效果。他们还

DESCRIPTION (provided by applicant): Recently, several genome-wide association studies (GWAS) have been published for late onset Alzheimer's disease (LOAD). Each GWAS that has been published to date has mapped the Apolipoprotein E (APOE) locus as the strongest LOAD risk signal within the human genome. This has led us and others to hypothesize that this large signal can 1. Overwhelm other smaller effects that are in epitasis with the APOE E4 locus and 2. Be de-convoluted into multiple risk loci mapping to the same area of the genome. The first hypothesis is plausible, considering that in our previous GWAS we mapped an effect that was only present in APOE E4 positive individuals. The second hypothesis is also valid in that this type of effect (i.e. multiple risk loci mapping to the same region of the genome) has been seen in other neurological diseases. For example, for Fronto-temporal Dementia Linked to Chromosome 17 (FTDP-17), mutations in both the microtubule associated protein Tau gene (MAPT) as well as Progranulin (PRGN) have been found. Both MAPT and PRGN map within the same linkage peak on chromosome 17. Thus, we propose to leverage our genome-wide and Next Generation Sequencing (NGS) genetic data, as well as transcriptome and proteome datasets to map novel risk loci for LOAD that are acting either in epistasis with or independently of the APOE E4 allele. We propose the following: To test our APOE E4 independent effects, we will perform additional NGS sequencing within the same region of chromosome 19 to capture additional effects (Aim 1a). We will also genotype the variants we found from our NGS in additional case control samples to determine whether they act independently of APOE E4 to increase risk for disease (Aim 1b). To follow our APOE E4 epistatic effects, SNPs which we found to act in conjunction with APOE E4 will be followed by examining an additional cohort (Aim 2a) as well as sequencing within the region to find additional variants (Aim 2b). Finally, we will map the downstream effects of any variants we map in Aims 1 or 2 by examining transcript expression and protein profiles (Aim 3), Our collaboration possesses the unique skills and datasets to perform this work. Drs. Huentelman and Myers have worked together for the greater part of their careers and have co-authored many publications using similar techniques as those proposed in this application. They have access to a unique cohort of ~ 1600 neuropathologically verified individuals, which will allow for both the analysis of risk variation as well as the downstream changes of those variants. They have recruited an additional cohort of ~ 18,000 clinically characterized samples from the University of Cardiff to replicate any effects. They also have access to both the computational power (48-core / 576GB memory computer and a separate 2,700-core cluster through Tgen and one of 5000 CPU at the University of Miami) as well as the expertise to execute the bioinformatics analysis involved in all Aims.