Integrative analysis of genomic and epigenomic architecture in cognitive decline

认知衰退中基因组和表观基因组结构的综合分析

• 批准号: 8897221
• 负责人: Lori Beth Chibnik
• 金额: $ 15.42万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8897221
• 关键词: Accounting; Affect; Age; Aging; Alzheimer' s Disease; Architecture; Biology of Aging; Brain; Brain region; Case-Control Studies; Cataloging; Catalogs; Categories; Cells; Clinical; Cognition; Cognition Disorders; Cognitive; Computational Biology; DNA Methylation; Data; Data Set; Diagnostic; Disease; Disease susceptibility; Education; Funding; Funding Opportunities; Gene Expression Profile; Genes; Genetic; Genetic Transcription; Genome; Genomic DNA; Genomics; Genotype; Goals; Grant; Human; Hypermethylation; Impaired cognition; Individual; Knowledge acquisition; Linear Models; Measurement; Measures; Mediating; Medical Genetics; Memory; Mentors; Methods; MicroRNAs; Modeling; Molecular Conformation; Neurology; Neuropsychological Tests; Non-linear Models; Performance; Persons; Phenotype; Prefrontal Cortex; Process; Public Health; RNA; Religion and Spirituality; Research; Research Design; Research Personnel; Risk; Risk Factors; Role; Single Nucleotide Polymorphism; Site; Statistical Methods; Statistical Models; Study Subject; Susceptibility Gene; Techniques; Testing; Tissues; Training; United States National Institutes of Health; Untranslated RNA; Variant; Work; age group; aging brain; base; career; cognitive function; cognitive performance; cohort; epigenome; epigenomics; gene discovery; genetic variant; genome-wide; genome-wide analysis; high throughput analysis; interest; methylome; non-genetic; novel; performance tests; pre-clinical; prevent; skills; statistics; time use; tool; trait; transcriptomics; trend

DESCRIPTION (provided by applicant): Cognitive decline among older people and Alzheimer's disease (AD), the specific condition most frequently responsible for severe decline in this age group, are large and growing humanistic and public health problems. This trajectory is a human trait with a heritable component, but is also associated with non-genetic factors. We propose to explore the architecture of the phenotype of decline in global cognition to inform the functional characterization of both known and novel risk factors. Since the decline in cognition may not follow a linear trend, we will compare the performance of multiple statistical models with which to examine this trait, starting with a linear mixed effects model and moving to models of increasing complexity, including penalized and quadratic spline models. Using the optimal statistical model determined in these efforts, we will explore the relationship between this enhanced estimate of cognitive trajectories with existing sets of data cataloguing (1) genome-wide genotype data and (2) genome-wide DNA methylation profiles of the dorsolateral prefrontal cortex from the same individuals. In addition, these estimates of cognitive decline will also be examined for associations with a novel dataset of microRNA (miRNA) profiles from the same region of the brain in the same subjects. Further, limiting our analyses to DNA methylation and miRNA features associated with cognitive decline, we will assess for evidence of interaction in these two types of measures, which capture different aspects of the transcriptional state of the brain. While examining genetic, methylomic and transcriptomic factors separately is important, informative results will also come from assessing how they all work together to affect cognition. Therefore, we propose to use model reduction techniques, such as LASSO, to create a comprehensive model of cognitive decline that incorporates non-redundant clinical, neuropathologic, genetic, DNA methylation and miRNA features that are risk factors for cognitive decline. We will start with the known clinical and genetic components and then consider genomic, DNA methylation and miRNA traits discovered in earlier analyses in this proposal. Throughout this proposal, we will produce integrated models of known and new risk factors that relate to the genetic architecture of cognitive decline, and secondarily to AD. Ultimately, these models will provide an ideal basis for developing new studies and analytic plans that will drive my own career as an independent investigator. Moreover, many leads will certainly emerge from these studies, the pursuit of which will serve to further my career by creating additional funding opportunities.

描述（由申请人提供）：老年人的认知能力下降和阿尔茨海默氏病（AD）是该年龄段最常导致严重下降的特定疾病，是人文主义和公共卫生的越来越大。这种轨迹是具有可遗传成分的人类特征，但也与非遗传因素有关。我们建议探索全球认知衰落表型的结构，以告知已知风险因素和新型风险因素的功能表征。由于认知的下降可能不会遵循线性趋势，因此我们将比较多个统计模型的性能，以检查该性状，从线性混合效应模型开始，并转变为增加复杂性的模型，包括受惩罚和二次样本模型。使用在这些工作中确定的最佳统计模型，我们将探讨这种增强的认知轨迹估计值与现有数据分解集（1）全基因组基因型数据以及（2）（2）全基因组DNA甲基化谱的背侧前额叶甲基甲基化谱。此外，还将检查这些对与来自同一受试者同一大脑同一区域的MicroRNA（miRNA）谱的新数据集的关联的认知下降估计值。此外，将我们的分析限制为DNA甲基化和与认知下降相关的miRNA特征，我们将评估这两种类型的措施中相互作用的证据，这些措施捕获了大脑转录状态的不同方面。在分别检查遗传学，甲基甲基和转录组因子方面很重要，但内容丰富的结果也将来自评估它们如何共同工作以影响认知。因此，我们建议使用模型还原技术（例如Lasso）来创建一个全面的认知下降模型，该模型结合了非冗余的临床，神经病理学，遗传学，DNA甲基化和miRNA特征，这是认知能力下降的风险因素。我们将从已知的临床和遗传成分开始，然后考虑在此提案中早期分析中发现的基因组，DNA甲基化和miRNA特征。在整个建议中，我们将生成与认知下降的遗传结构以及与AD相关的综合模型和新的风险因素。最终，这些模型将为制定新的研究和分析计划提供理想的基础，从而推动我作为独立研究者的职业。此外，这些研究肯定会出现许多潜在客户，这些研究的追求将通过创造额外的资金机会来促进我的职业发展。