Integrative analysis of genomic and epigenomic architecture in cognitive decline

描述（由申请人提供）：老年人的认知能力下降和阿尔茨海默病（AD）是该年龄组中最常见的导致严重下降的特定疾病，是一个巨大且日益严重的人文和公共卫生问题。这种轨迹是一种具有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能力下降表型的结构，以了解已知和新的风险因素的功能特征。由于认知能力的下降可能不会遵循线性趋势，我们将比较多个统计模型的表现，以检查这一特征，从线性混合效应模型开始，然后转向复杂性不断增加的模型，包括惩罚和二次样条模型。使用这些努力中确定的最佳统计模型，我们将探索这种增强的认知轨迹估计与现有数据集之间的关系，这些数据集包括（1）全基因组基因型数据和（2）来自同一个体的背外侧前额叶皮层的全基因组DNA甲基化谱。此外，还将检查这些认知下降的估计值与来自相同受试者大脑相同区域的microRNA（miRNA）谱的新数据集的关联。此外，将我们的分析限制在与认知能力下降相关的DNA甲基化和miRNA特征上，我们将评估这两种测量方法相互作用的证据，这两种方法捕获了大脑转录状态的不同方面。虽然分别检查遗传，甲基化和转录因子很重要，但评估它们如何共同影响认知也会产生信息性结果。因此，我们建议使用模型简化技术，如LASSO，来创建一个综合的认知衰退模型，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，这些特征是认知衰退的风险因素。我们将从已知的临床和遗传成分开始，然后考虑在本提案的早期分析中发现的基因组，DNA甲基化和miRNA特征。在整个提案中，我们将产生与认知能力下降的遗传结构相关的已知和新的风险因素的综合模型，其次是AD。最终，这些模型将为开发新的研究和分析计划提供理想的基础，这些研究和分析计划将推动我作为独立研究者的职业生涯。此外，这些研究肯定会产生许多线索，对这些线索的追求将通过创造额外的资助机会来促进我的职业生涯。 公共卫生相关性：该提案的目标是了解认知衰退的基因组，表观基因组和转录组结构。了解这种变化如何影响衰老和人类认知功能将使研究人员更好地了解认知衰退和认知疾病的发病，如AD。这可能有助于开发新的治疗方法，以延迟并有望预防阿尔茨海默病和认知能力下降的发作，并开发临床测试，可以预测谁有患阿尔茨海默病和认知能力下降的风险。

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. PUBLIC HEALTH RELEVANCE: The goal of this proposal is to understand the genomic, epigenomic and transcriptomic architecture of cognitive decline. In understanding how this variation affects aging and human cognitive function will allow investigators to better understand the onset of cognitive decline and diseases of cognition, such as AD. This may help to develop new treatments to delay and hopefully prevent onset of Alzheimer's disease and cognitive decline, and to develop clinical tests that may be able to predict who is at risk of developing Alzheimer's disease and cognitive decline.