Integrating high-throughput histology with systems genetics through causal graphical models

通过因果图模型将高通量组织学与系统遗传学相结合

• 批准号: 10549831
• 负责人: John Matthew Mahoney
• 金额: $ 36.35万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10549831
• 关键词: Adopted; Affect; Age; Alleles; Automobile Driving; Biological Models; Biomedical Research; Categories; Cell Death; Chromosome Mapping; Clinical; Communities; Complex; Computer Vision Systems; Data; Data Set; Development; Disease; Dissection; Etiology; Evaluation; Feedback; Fibrosis; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Diseases; Genetic Models; Genetic Risk; Genetic Variation; Genetic study; Genome; Genotype; Heritability; Histologic; Histology; Histopathology; Human; Image; Inflammation; Kidney; Kidney Diseases; Maps; Measures; Mediating; Mediation; Mediator; Methodology; Methods; Modeling; Molecular; Molecular Abnormality; Molecular Disease; Molecular Profiling; Mus; Network-based; Oranges; Organ; Organism; Outcome; Outcome Study; Pathologist; Pathway Analysis; Pathway interactions; Patients; Pattern; Phenotype; Physiological; Physiology; Population Heterogeneity; Pre-Clinical Model; Proteome; Proteomics; Public Health; Quantitative Genetics; Quantitative Trait Loci; Renal function; Statistical Models; Structure; System; Testing; Therapeutic Intervention; Time; Tissue-Specific Gene Expression; Tissues; Training; Variant; age related; aged; cohort; deep learning; deep neural network; design; disorder risk; fitness; genetic analysis; genetic approach; genetic risk factor; histological image; human model; image processing; improved; multi-scale modeling; novel; novel strategies; poor health outcome; risk variant; therapeutic development; tool; trait; transcriptome; transcriptomics

PROJECT SUMMARY The overall objective of this proposal is to develop and validate a deep learning analytical framework to integrate histological traits into systems genetics analysis of complex diseases. Mapping the risk genes for poor health outcomes is a major focus of biomedical research, and new approaches to improve genetic mapping power can have a transformative impact on public health. Genetic disease risk manifests through complex interactions between gene regulation and tissue structure, ultimately influencing organ function. However, quantifying tissue structure for quantitative genetic mapping has not been widely adopted. This is partly because histological scoring has traditionally been labor intensive and error prone, and limited to coarse measures (e.g., discrete categories) that are suboptimal for association testing. In contrast, deep neural networks (DNNs) now routinely automate laborious image quantification tasks for histopathology, making them an ideal platform for integrating histology into genetic analysis. Furthermore, unlike human-defined histological scores, DNN readouts enable objective histological trait discovery as a function of genetic, molecular, and physiological variation. In this project, histological features will be rigorously and robustly quantified using DNNs and these data will be integrated into novel multiscale statistical models that will connect genetic, molecular, and histological variation to physiological outcomes. In particular, novel methods will be developed to integrate histology into three major classes of systems genetic analysis, i.e., heritable trait inference, causal mediation analysis, and molecular quantitative trait locus (mQTL) mapping. These methods will be developed and validated using a data set of genetic, histological, transcriptomic, proteomic, and physiological data from a cohort of Diversity Outbred mice used for the study of age-related kidney disease. By using a model system, complex genetic effects and causal mediation hypotheses can be directly tested to validate and refine the analytical framework. The specific aims of this proposal include: Aim 1: Identify maximally heritable histological traits through deep learning on paired genotypes and histological images. Aim 2: Genetically map histological mediators of complex physiological traits using deep learning on histological images. Aim 3: Identify causal paths connecting molecular QTLs (mQTLs) to outcomes through histological mediators. The outcome of this study will be a validated methodological framework for histological systems genetics that is modular, enabling a wide range of users to incorporate appropriate computer vision tools into state-of-the-art systems genetics workflows for any complex disease.

项目摘要 本提案的总体目标是开发和验证深度学习分析框架， 组织学特征转化为复杂疾病的系统遗传学分析。绘制健康不良的风险基因 结果是生物医学研究的一个主要焦点，提高遗传作图能力的新方法可以 对公共卫生产生变革性的影响。遗传疾病风险通过复杂的相互作用表现出来 基因调控和组织结构之间的联系，最终影响器官功能。然而，量化组织 用于定量遗传作图的结构尚未被广泛采用。这部分是因为组织学 评分传统上是劳动密集型的并且容易出错，并且限于粗略的测量（例如，离散 类别），这是次优的关联测试。相比之下，深度神经网络（DNN）现在通常 自动化组织病理学的繁重图像量化任务，使其成为集成的理想平台 组织学转化为基因分析此外，与人类定义的组织学评分不同，DNN读数使 作为遗传、分子和生理变异的函数的客观组织学性状发现。在这 项目，组织学特征将使用DNN进行严格和稳健的量化，这些数据将被 整合到新的多尺度统计模型中，将遗传、分子和组织学变异联系起来， 到生理结果。特别是，将开发新的方法，将组织学整合到三个主要的 系统遗传分析的类别，即，遗传性状推断、因果中介分析和分子生物学 数量性状基因座（mQTL）作图。这些方法将使用以下数据集进行开发和验证： 来自一群多样性远交小鼠的遗传、组织学、转录组、蛋白质组和生理学数据 用于研究与年龄相关的肾脏疾病。通过使用模型系统，复杂的遗传效应和因果关系 调解假设可以直接测试，以验证和完善分析框架。的具体目标 该提案包括：目标1：通过对配对的深度学习来识别最大可遗传的组织学特征 基因型和组织学图像。目的2：遗传图谱复杂生理性状的组织介质 在组织学图像上使用深度学习。目的3：确定连接分子QTL（mQTL）与 结果通过组织学介质。这项研究的结果将是一个经过验证的方法框架 对于组织学系统遗传学，它是模块化的，使广泛的用户能够将适当的 将计算机视觉工具应用于任何复杂疾病的最先进系统遗传学工作流程。