Systems Immunogenetics and Bioinformatics

摘要 系统遗传学研究利用各种各样的实验，计算和统计方法， 将基因型和表型相互关联，以提供潜在遗传学的全面观点。 复杂特征的结构。系统遗传学和生物信息学核心（核心C）作为催化剂 用于跨数据类型、病毒和物种集成U19，以识别候选病毒 与人类疾病有关的基因、途径和网络，并为在 全基因组关联研究（GWAS）就其对疾病易感性的贡献。芯C 将专注于强大的和可重复的方法，以促进候选基因和基因的识别 参与先天免疫、适应性免疫和记忆免疫的网络，以及优先排序和候选人细化 与所有项目和核心B（小鼠遗传学）密切协调。该核心的目标是提供焦点 进行实证调查和验证的计算预测。具体而言，通过使用 综合的，系统水平的方法解剖免疫性状，我们能够阐明的关键驱动因素， 免疫宿主的反应超出了传统的遗传关联研究本身所能实现的。核心 C将促进假设驱动（候选基因）和假设生成（网络）分析，以解决 U19的目标在某些情况下，潜在的候选基因在动物模型和人类中可能是相同的 并将优先进行进一步研究。在其他情况下，动物模型研究或我们对人类的分析 反应和指导这些反应的遗传变体可以鉴定与人类相关的基因网络， 用一个可以在老鼠模型中操作的集线器来检查网络效应。两者的分析 候选基因和网络将比任何一个单独提供更强大的连锁水平， 从基因/网络到机制的证明。成功实施和执行拟议的 每个项目的研究都需要强大的尖端分析方法和计算工作流程。 核心C人员在系统遗传学（统计遗传学和系统遗传学）方面具有丰富的经验 生物学），包括计算建模和网络推理，除了检测QTL在一个 高度复杂的遗传背景，以及管理和传播大规模 遗传和基因组资源。

ABSTRACT Systems genetics studies utilize a diverse array of experimental, computational and statistical approaches to interrelate genotypes and phenotypes in order to provide a comprehensive view of the underlying genetic architecture of complex traits. The Systems Genetics and Bioinformatics Core (Core C) serves as the catalyst for integration for the U19 across data types, viruses and species to enable the identification of candidate genes, pathways and networks implicated in human diseases and provides context for the loci identified in genome-wide association studies (GWAS) with regard to their contribution to disease susceptibility. Core C will focus on robust and reproducible approaches to facilitate identification of candidate genes and gene networks involved in innate, adaptive, and memory immunity, as well as prioritization and candidate refinement in close coordination with all projects and Core B (Mouse Genetics). The goal of this Core is to provide focus for empirical investigation and validation of the computational predictions. Specifically, by employing integrative, systems-level approaches dissecting immunological traits, we are able to elucidate key drivers of immune host response beyond what could be achieved by traditional genetic association studies alone. Core C will facilitate hypothesis-driven (candidate gene) and hypothesis-generating (network) analyses to address the U19 goals. In some cases an underlying candidate gene may be the same in animal models and humans and will be prioritized for further study. In other cases, animal model research or our analysis of the human responses and genetic variants that direct these responses may identify a gene network relevant in humans, with a hub that could be manipulated in the mouse model to examine network effects. Analyses of both candidate genes and networks will be more powerful than either alone to provide the interlocking levels of proof to move from gene/network to mechanism. Successful implementation and execution of the proposed research in each project necessitates robust cutting-edge analytical methods and computational workflows. Core C personnel have significant experience in systems genetics (both statistical genetics and systems biology) that includes computational modeling and network inference, in addition to the detection of QTLs in a highly complex genetic background, as well as expertise in management and dissemination of large scale genetic and genomic resources.