Cognitive Computing of Alzheimer's Disease Genes and Risk

阿尔茨海默病基因和风险的认知计算

• 批准号: 10219658
• 负责人: OLIVIER LICHTARGE
• 金额: $ 80万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 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/10219658
• 关键词: Affect; Aging; Algorithms; Alleles; Alzheimer associated neurodegeneration; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease model; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Amyloid beta-42; Amyloid beta-Protein; Apolipoprotein E; Attention; Automobile Driving; Autopsy; Back; Benign; Biological Assay; Biological Models; Blinded; Brain; Calculi; Candidate Disease Gene; Cell Culture Techniques; Cells; Classification; Clinical; Clinical assessments; Code; Communities; Data; Dementia; Development; Disease; Disease stratification; Drosophila genus; Elderly; Evaluation; Evolution; Face; Fogs; Functional disorder; Future; Gender; Gene Expression Profile; Gene Mutation; Gene-Modified; Genes; Genetic; Genetic Markers; Genome; Goals; Heritability; Human; Human Genome; Immunoblotting; 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; Stratification; Symptoms; System; Testing; Text; Therapeutic; Therapeutic Trials; Thinness; Time; Translating; Untranslated RNA; Validation; Variant; Woman; Work; base; case control; causal variant; clinical risk; cognitive computing; cohort; design; disorder control; drug development; economic impact; experimental study; fitness; gene discovery; genetic architecture; genetic variant; genome sequencing; genome wide association study; genomic biomarker; genomic variation; human data; in vivo evaluation; innovation; insight; learning network; 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; theories; tool

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 相反，老龄化人口可能面临初期和社会颠覆性阿尔茨海默氏症的潮汐威胁 疾病(AD)病例。全基因组关联研究已经将100多个基因座与阿尔茨海默病联系起来，并解释了 大部分人群可归因于风险，但只有一小部分可遗传性。这种遗传性差距意味着它仍然存在 很难设计和评估哪种监测、筛查、预防和分层方案是有效的。 反过来，这又阻碍了治疗试验。将基因变异转化为患者分类的挑战是 两重。首先，阿尔茨海默病是多基因的，因此相关的疾病驱动突变分布在许多 不同的基因和病人。其次，目前对突变有害影响的解释还不充分。 精确度，所以在任何给定的主题中，都不能区分有影响力的少数人和善良的大多数人。这些 问题使AD风险和发病率的统计遗传学变得复杂而模糊，而且信噪比很低。这个 我们的解决方案的关键是添加大量的新信息，通过计算高效地利用它， 然后进行严格的多管齐下的实验验证。我们从这样一个假设开始：AD是通过 超出内在进化耐受性的影响功能通路的突变扰动。新的 算法计算这些过多的变异力，并将它们放入综合机器学习中 对AD患者和对照组进行分类的框架，还可以反映AD患者和对照组之间的功能交互 蛋白质或基因。创新包括基于微积分的进化数学模型；系列机 对人类基因组变异的学习；以及泛函中突变扰动的调和分析 网络。结果将第一次将与阿尔茨海默病相关的基因组变异整合到所有 相关的进化历史和所有已知的功能相互作用。在实践中，这将增加功率和 解决方案，使男女能够进行针对性别的分析和AD分层，并确定新的和 经过实验验证的AD基因。为了执行这个计划，AIM 1将融合一种新的数学分析 利用机器学习和网络小波理论进行进化。这将产生互补的一体化 基于异常突变识别AD与健康受试者的基因和突变的方法 测序队列中稀有基因变异的负担。Aim 2将把类似的工具集中在患者和对照组身上 已知的与其载脂蛋白ɛ2/4状态相反的矛盾表型。结果将确定修饰基因 在APOEɛ2携带者中驱动AD或保护APOEɛ4携带者免受AD。目标3将提供直接的实验 利用高通量、机器人辅助的遗传修饰物筛选在Tau或Tau的果蝇模型中的验证 淀粉样β蛋白多肽的神经毒性。前景看好的靶点将在哺乳动物神经细胞中得到进一步证实 文化。这项工作将验证一种新的方法，以扩大我们对阿尔茨海默氏症遗传复杂性的理解 用于识别疾病的基因驱动因素和修饰因素，以指导AD风险分层的临床评估。