Cloud Computing for AD

AD 云计算

基本信息

批准号：
10827623
负责人：
OLIVIER LICHTARGE
金额：
$ 17.62万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10827623
关键词：
Acceleration Affect Aging Algorithms Alleles Alzheimer associated neurodegeneration Alzheimer&apos s Disease Alzheimer&apos s disease brain Alzheimer&apos s disease model Alzheimer&apos s disease patient Alzheimer&apos s disease related dementia Alzheimer&apos s disease risk Alzheimer’s disease biomarker Amyloid beta-Protein Apolipoprotein E Attention Automobile Driving Autopsy Back Benign Biological Assay Biological Markers Biological Models Blinded Brain Calculi Candidate Disease Gene Cell Culture Techniques Cells Classification Clinical Clinical assessments Cloud Computing Code Communities Data Dementia Development Disease Disease stratification Drosophila genus Elderly Evaluation Evolution Face Fogs Functional disorder Future Gender Gene Expression Profile Gene Modified Gene Mutation Genes Genetic Genetic Markers Genome Genomics Goals Heritability Human Human Genome Individual Intervention Link Machine Learning Medicine Missense Mutation Modeling Molecular Morbidity - disease rate Mus Mutation Mutation Analysis Neuronal Dysfunction Neurons Noise Onset of illness Outcome Pathogenesis Pathogenicity Pathway interactions Patients Performance Pharmaceutical Preparations Phenotype Population Population Attributable Risks Preventive Proteins Recording of previous events Regression Analysis Research Resolution Rest Risk Risk Assessment Running Signal Transduction Social Impacts Sorting Stratification Symptoms System Testing Therapeutic Therapeutic Trials Thinness Time Translating Untranslated RNA Validation Variant Western Blotting Woman Work causal variant clinical risk cognitive computing cohort design drug development economic impact experimental study fitness gene discovery gene network genetic architecture genetic variant genome sequencing genome wide association study genomic variation human data in vivo evaluation innovation insight machine learning framework mathematical analysis mathematical learning mathematical model men nerve stem cell neuropathology neurotoxicity novel novel strategies prevent programs risk stratification robot assistance screening social success tau Proteins text searching theories tool

项目摘要

Cognitive Computing of Alzheimer’s Disease Genes and Risk The molecular basis and genetic architecture of dementia remain a puzzle. As no drug yet prevents, delays, or reverses it, aging populations potentially face a tidal threat of incipient and socially disruptive Alzheimer’s Disease (AD) cases. Genome-wide association studies (GWAS) have linked over 100 loci with AD and explain much of population attributable risk, but only a fraction of heritability. This heritability gap means it remains difficult to design and assess which surveillance, screening, preventive, and stratification programs are effective. In turn, this hinders therapeutic trials. The challenge in translating genetic variants into patient classifications is twofold. First, AD is polygenic, so relevant disease driving mutations are spread thin across a multitude of different genes and patients. Second, current interpretations of the deleterious effects of mutations lack accuracy, so the impactful few cannot be distinguished from the benign multitude in any given subject. These problems compound and fog the statistical genetics of AD risk and morbidity with poor signal to noise ratio. The crux of our solution is to add a massive amount of new information, exploit it efficiently through computation, then perform rigorous multi-pronged experimental validation. We start from the hypothesis that AD arises through mutational perturbations that affect functional pathways beyond the built-in evolutionary tolerances. New algorithms compute these excessive mutational forces and place them in integrative machine learning frameworks to sort between AD patients and controls, and which can also reflect functional interactions among proteins or genes. Innovations include a mathematical model of evolution based on calculus; ensemble machine learning over human genome variations; and harmonic analysis of mutational perturbations in functional networks. The outcome will, for the first time, integrate genomic variations relevant to AD in the context of all relevant evolutionary history and all known functional interactions. In practice, this will increase power and resolution, enable gender-specific analysis and AD stratification of men and women, and identify new and experimentally validated AD genes. To carry out this program, AIM 1 will fuse a novel mathematical analysis of evolution with machine learning and network wavelet theory. This will yield complementary integrative approaches to identify genes and mutations that sort AD vs healthy subjects based on the abnormal mutational burden of rare gene variants in sequenced cohorts. AIM 2 will focus similar tools on patients and controls with known paradoxical phenotypes that run counter to their APOEɛ2/4 status. The results will identify modifier genes that drive AD in APOEɛ2 carriers or that protect APOEɛ4 carriers from AD. AIM 3 will provide direct experimental validation, leveraging high-throughput, robot-assisted genetic modifier screening in Drosophila models of Tau or amyloid-beta peptide neurotoxicity. Promising targets will be further confirmed in mammalian neuronal cell culture. The work will validate a new approach to enlarge our understanding of genetic complexity in Alzheimer’s Disease for the identification of gene drivers and modifiers to guide clinical assessment of AD risk stratification.

阿尔茨海默病基因和风险的认知计算痴呆症的分子基础和遗传结构仍然是一个谜，因为目前还没有药物可以预防、延迟或治疗。相反，老龄化人口可能面临早期和社会破坏性阿尔茨海默氏症的威胁疾病 (AD) 病例。全基因组关联研究 (GWAS) 已将 100 多个位点与 AD 联系起来并进行了解释。大部分人口归因于风险，但只有一小部分是遗传性，这种遗传性差距意味着它仍然存在。很难设计和评估哪些监测、筛查、预防和分层计划是有效的。反过来，这阻碍了治疗试验将遗传变异转化为患者分类的挑战。首先，AD 是多基因的，因此相关的疾病驱动突变分散在多个群体中。其次，目前缺乏对突变有害影响的解释。准确性，因此在任何特定主题中，无法将有影响力的少数人与良性的多数人区分开来。由于信噪比较差，这些问题使 AD 风险和发病率的统计遗传学变得更加复杂和模糊。我们解决方案的关键是添加大量新信息，通过计算有效地利用它，然后我们从 AD 产生的假设开始进行严格的多管齐下的实验验证。影响功能途径超出内在进化耐受性的突变扰动。算法计算这些过度的突变力并将它们放入综合机器学习中 AD 患者和对照组之间的分类框架，也可以反映之间的功能相互作用创新包括基于微积分的进化数学模型；了解人类基因组变异；以及功能突变扰动的调和分析其结果将首次将与 AD 相关的基因组变异整合到所有的背景中。相关的进化历史和所有已知的功能相互作用在实践中，这将增加力量和能力。决议，实现性别特定分析和男性和女性的 AD 分层，并确定新的和为了执行该计划，AIM 1 将融合一种新颖的数学分析。机器学习和网络小波理论的进化这将产生互补的综合。识别基因和突变的方法，根据异常突变对 AD 与健康受试者进行分类 AIM 2 测序队列中罕见基因变异的负担将针对患有此病的患者和对照者使用类似的工具。与 APOEɛ2/4 状态相反的已知矛盾表型结果将识别修饰基因。驱动 APOEɛ2 载体中的 AD 或保护 APOEɛ4 载体免受 AIM 3 的影响将提供直接实验。验证，利用 Tau 或 Tau 果蝇模型中的高通量、机器人辅助遗传修饰筛选淀粉样蛋白-β肽的神经毒性将在哺乳动物神经细胞中得到进一步证实。这项工作将验证一种新方法，以扩大我们对阿尔茨海默病遗传复杂性的理解。用于识别疾病基因驱动因素和修饰因素，以指导 AD 风险分层的临床评估。