APOEomic: Searching for APOE interacting risk factors using omics data

APOEomic：使用组学数据搜索 APOE 相互作用的风险因素

• 批准号: 8439407
• 负责人: Matt Huentelman
• 金额: $ 35.9万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8439407
• 关键词: Address; Alleles; Alzheimer' s Disease; Alzheimer' s disease risk; Apolipoprotein E; Area; Authorship; Bioinformatics; Biological Assay; Chromosomes, Human, Pair 17; Chromosomes, Human, Pair 19; Clinical; Code; Collaborations; Computers; DNA; Data; Data Set; Dideoxy Chain Termination DNA Sequencing; FTD with parkinsonism; Frontotemporal Dementia; Gene Expression; Gene Expression Profile; Gene Targeting; Genes; Genetic; Genetic Epistasis; Genetic Transcription; Genetic Variation; Genome; Genotype; Goals; Human Genome; Individual; Late Onset Alzheimer Disease; Lead; Link; Luciferases; Maps; Memory; Modeling; Molecular Profiling; Mutation; Outcome; PGRN gene; Pathway interactions; Progranulin; Proteins; Proteome; Proteomics; Publications; Publishing; RNA; Recruitment Activity; Research; Risk; Risk Factors; Sampling; Sequence Analysis; Series; Signal Transduction; Targeted Resequencing; Techniques; Testing; Transcript; Universities; Variant; Work; career; case control; cohort; disorder risk; genome wide association study; genome-wide; nervous system disorder; next generation; next generation sequencing; novel; protein expression; protein profiling; public health relevance; skills; tau Proteins; transcriptomics

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.

描述（由申请人提供）：最近，已经发表了几项关于迟发性阿尔茨海默病（LOAD）的全基因组关联研究（GWAS）。迄今为止，已发表的每个GWAS都将载脂蛋白E（APOE）位点定位为人类基因组中最强的LOAD风险信号。这使得我们和其他人假设，这个大信号可以1。压倒其他较小的影响，在表位与载脂蛋白E E4基因座和2。去卷积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，考虑到在我们以前的GWAS中，我们绘制了一个只存在于APOE E4阳性个体中的效应。第二个假设也是有效的，因为这种类型的影响（即多个风险基因座映射到基因组的同一区域）已经在其他神经系统疾病中观察到。例如，对于与17号染色体连锁的额颞叶痴呆（FTDP-17），已经发现微管相关蛋白Tau基因（MAPT）以及颗粒蛋白前体（PRGN）两者的突变JAPT和PRGN两者都映射在17号染色体上的相同连锁峰内。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LOAD的新风险位点，这些位点与APOE E4等位基因发生上位性作用或独立于APOE E4等位基因发挥作用。 我们建议如下：为了测试我们的APOE E4独立效应，我们将在19号染色体的相同区域内进行额外的NGS测序，以捕获额外的效应（目的1a）。我们还将在其他病例对照样本中对我们从NGS中发现的变体进行基因分型，以确定它们是否独立于APOE E4而增加疾病风险（Aim 1b）。为了跟踪我们的APOE E4上位效应，我们发现与APOE E4共同作用的SNP将通过检查额外的队列（Aim 2a）以及该区域内的测序来发现额外的变体（Aim 2b）来跟踪。最后，我们将通过检查转录本表达和蛋白质谱来绘制我们在目标1或2中绘制的任何变体的下游效应（目标3），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博士在其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，其中使用了与本申请中提出的技术相似的技术。他们可以访问一个独特的队列，其中包含约1600名经过神经病理学验证的个体，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个临床特征样本，以复制任何效果。他们还 拥有计算能力（48核/576 GB内存计算机和一个单独的2，700核集群，通过Tgen和迈阿密大学的5000个CPU之一）以及执行所有目标中涉及的生物信息学分析的专业知识。