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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

基本信息

批准号：
10436879
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
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/10436879
关键词：
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-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 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 Western Blotting Woman Work base case control causal variant clinical risk cognitive computing cohort design disorder control drug development economic impact experimental study fitness gene discovery gene network genetic architecture genetic variant genome sequencing genome wide association study genomic biomarker genomic variation human data in vivo evaluation innovation insight learning network 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 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。由于没有药物可以预防、延迟或逆转它，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潮汐威胁疾病（AD）病例。全基因组关联研究（GWAS）已经将100多个位点与AD联系起来，并解释了大部分的人口归因风险，但只有一小部分的遗传性。这种遗传性差距意味着它仍然存在难以设计和评估哪些监测、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。将遗传变异转化为患者分类的挑战是双重的首先，AD是多基因的，因此相关的疾病驱动突变在众多的基因组中分布很薄。不同的基因和患者。其次，目前对突变有害影响的解释缺乏准确性，所以在任何给定的主题中，有影响力的少数人无法与良性的多数人区分开来。这些这些问题使AD风险和发病率的统计遗传学变得复杂和模糊，信噪比很差。的我们解决方案的关键是增加大量的新信息，通过计算有效地利用它，然后进行严格的多方面实验验证。我们从假设开始，AD是通过影响功能途径的突变扰动超出了固有的进化容忍度。新算法计算这些过度的突变力并将其置于综合机器学习中在AD患者和对照组之间进行分类的框架，也可以反映AD患者和对照组之间的功能性相互作用。蛋白质或基因。创新包括基于微积分的进化数学模型;集成机器人类基因组变异的研究;以及功能基因组中突变扰动的谐波分析。网络.结果将首次整合与AD相关的基因组变异，相关的进化历史和所有已知的功能相互作用。实际上，这将增加功率，解决，使性别特异性分析和男性和女性的AD分层，并确定新的和实验验证的AD基因。为了执行这一计划，AIM 1将融合一种新的数学分析，进化与机器学习和网络小波理论。这将产生互补的一体化方法来识别基因和突变，分类AD与健康受试者的基础上，异常突变，测序队列中罕见基因变异的负担。AIM 2将把类似的工具集中在患者和对照组上，与APOE β 2/4状态相反的已知矛盾表型。结果将确定修饰基因在APOE β 2携带者中驱动AD或保护APOE β 4携带者免受AD。AIM 3将提供直接的实验验证，利用高通量，机器人辅助的遗传修饰剂筛选果蝇模型的Tau或淀粉样β肽神经毒性有希望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基因驱动因子和修饰因子的疾病，以指导AD风险分层的临床评估。