APOEomic: Searching for APOE interacting risk factors using omics data

描述(由申请人提供)：最近，针对晚发性阿尔茨海默病(LOAD)的几项全基因组关联研究(GWAS)已经发表。到目前为止，已发表的每一种GWAs都将载脂蛋白E(APOE)基因定位为人类基因组中最强的负荷风险信号。这导致我们和其他人假设，这个大的信号可以1.压倒其他较小的效应，这些效应是在APOE E4基因座上发生的；2.被分解成映射到基因组同一区域的多个风险基因座。第一种假设是合理的，考虑到在我们之前的GWA中，我们绘制了一种只在APOE E4阳性个体中存在的效应。第二个假设也是有效的，因为这种类型的影响(即多个风险位点映射到基因组的同一区域)已经在其他神经疾病中看到。例如，对于17号染色体连锁的额颞性痴呆(FTDP-17)，已经发现微管相关蛋白Tau基因(MAPT)和原颗粒蛋白(PRGN)的突变。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来定位与APOE E4等位基因或独立于APOE E4等位基因一起作用的新的负荷风险基因座。我们提出以下建议：为了测试我们的APOE E4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(目标1a)。我们还将在更多的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(目标1b)。为了遵循我们的APOE E4上位性效应，我们发现与APOE E4一起作用的SNPs之后将检查另一个队列(目标2a)，并在该区域内进行测序，以寻找更多的变异(目标2b)。最后，我们将通过检查转录表达和蛋白质谱(目标3)来映射我们在AIMS 1或2中映射的任何变体的下游影响，我们的合作拥有执行这项工作的独特技能和数据集。Huentelman博士和Myers博士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，使用的技术与本申请中建议的类似。他们可以接触到一个独特的队列，该队列由大约1600名经过神经病理学验证的个体组成，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另外一组约18,000名具有临床特征的样本，以复制任何效果。他们也 拥有计算能力(48核/576 GB内存计算机和通过TGen独立的2700核集群，以及迈阿密大学5000个CPU中的一个)以及执行所有AIMS涉及的生物信息学分析的专业知识。

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.