COMBINING NEUROIMAGING AND GENETICS FOR HERITABILITY MEASUREMENTS

描述（由申请人提供）： 神经影像学和基因组学是现代神经科学的两个主要发展领域。然而，尽管将这两种方法结合起来很有吸引力，但它们很少被合并以充分利用其力量。这些领域虽然不同，但具有共同的特征，例如使用复杂的计算建模获得非常精确的非侵入性定量信息的能力。基于定量神经影像的表型分析和统计基因组学的结合创造了一个新的领域-基因组成像。这个申请的目的是指导职业发展奖，是过渡彼得Kochunov从一个神经影像学专家在基因组成像的独立调查员。这将结合联合收割机神经成像和分析基因组学方法来执行研究，这将有助于更好地了解大脑结构的遗传基础。这可能为发现新的分子靶点和其他表征、治疗和潜在预防大脑疾病的方法提供基础，并将构成基础神经科学的重大进步。基因复杂的脑部疾病每年花费美国高达1.2万亿美元。深入了解使个人易患这些类型疾病的生物学基础，有望产生新的治疗策略，并大大减轻这一巨大的健康和经济负担。这种训练的结果将导致基因组图像处理方法，通过该方法可以对描述谱系类型场景中受试者间大脑变异性的多维表型进行统计基因组学测量。该项目将使用从狒狒和人类谱系获得的最先进的3D MR脑图像和遗传图谱来计算脑回灰质厚度、皮质形状和区域白质各向异性的遗传控制。我们将进行物种间比较，并研究人类年龄相关萎缩趋势的基因组学。数据样本由200只纯种狒狒（Papio hamadryas）和来自先前研究的墨西哥-美国血统的约1000名人类受试者组成，这些受试者针对10 cM遗传连锁图谱进行基因分型，并使用最先进的MR结构成像方案成像。将开发新的方差分量方法，以允许对MR衍生的多维表型进行遗传分析。除了生成有价值的新数据外，该项目还将使Peter Kochunov能够接受分析基因组学方面的严格培训，并积累执行基因组研究的第一手经验。

DESCRIPTION (provided by applicant): Neuroimaging and genomics are two primary evolving arenas of modern neuroscience. However, despite the attractiveness of combining these two approaches they have rarely been merged to fully capitalize on their power. These fields, while disparate, have common features such as an ability to obtain very precise, non-invasive quantitative information using complex computational modeling. Bridging quantitative neuroimaging based phenotyping and statistical genomics creates a new field - genomic imaging. The purpose of this application for a mentored career development award, is to transition Peter Kochunov from a neuroimaging specialist to an independent investigator in genomic imaging. This will combine neuroimaging and analytical genomics methodologies to execute studies that will contribute to a better understating of the genetic underpinnings of brain structures. This could provide the basis for discovering new molecular targets and other approaches for the characterization, treatment and potential prevention of brain disorders and will constitute a significant advance for basic neuroscience. Genetically complex brain diseases cost the United States as much as $1.2 trillion annually. Insight into the biological underpinnings that predispose individuals to these types of illnesses hold the promise of yielding new therapeutic strategies and a significant reduction of this considerable health and financial burden. The outcome of this training will result in genomic image processing methods by which one can perform statistical genomics measurements on multi-dimensional phenotypes describing inter-subject cerebral variability in a pedigree-type scenario. This project will use state-of-the-art 3D MR brain images obtained from baboon and human pedigrees and genetic maps to calculate genetic control of gyral gray matter thickness, cortical shapes and regional white-matter anisotropy. We will perform interspecies comparisons and study the genomics of age-related atrophy trends in humans. The data sample consists of 200 pedigreed baboons (Papio hamadryas) and ~1000 human subjects from a previously studied Mexican- American pedigree that are genotyped for a 10 cM genetic linkage map and imaged with a state-of-the-art MR structural imaging protocol. Novel variance components methods will be developed to allow genetic analysis of the MR-derived multi-dimensional phenotypes. In addition to generating valuable, new data, this project will allow Peter Kochunov to undertake rigorous training in analytical genomics and accumulate first hand experience in executing genomic studies.