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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