CAREER: Deciphering the human regulome: omics-based analysis of intergenic genotype-to-trait associations, made accessible and powerful

该项目的研究活动将通过创建一种结合来自全基因组关联研究的不同类型信息的计算方法来推动生物信息学领域的发展。遗传关联研究测量整个基因组的序列差异，以确定哪些变异导致身高和疾病易感性等特征的发生和可变性。这个项目的目标是开发出精确定位基因调控变异的方法，这些变异现在只知道在一个大区域的某个地方，并用它们来了解一个群体中的特征是如何变化的。这项研究中开发的方法旨在结合多种类型的信息，如细胞的基因表达水平，许多个体的特征测量，以及与其他物种的特征比较，以确定因果调控变异。该项目的课程开发活动将通过创建和分享研究领域的基因组生物信息学实践讲习班，为STEM教育做出贡献。通过整合研究和教育活动，学生将(I)获得遗传学和生物信息学领域的科学素养；(Ii)展示亲身实践方法如何在遗传学教育中发挥作用；以及(Iii)利用已经进行的遗传关联研究获得生物学和生物医学的新知识。这项研究将创建和评估一个综合的机器学习模型，用于识别人类基因间GWAs区域内的调控变异。该模型的输入将包括参考基因组、本地DNA三维形状、系统发育保守以及转录和表观基因组测量。该模型的输出将被预测为具有显著意义的监管变体。该模型将以已公布的方法为基准，使用基本真实的监管变体。机器学习模型的各种预测将被合并到一个开源的、基于网络的软件工具中，用于对全球气候变化分析数据进行综合的后期分析。与云计算框架的兼容性将使该工具发挥最大影响。通过与项目研究活动相结合的教育活动，拉姆齐博士将为高中教育工作者和学生创建、评估和传播基因组生物信息学研讨会单元。学员将学习使用该工具分析和探索人类基因组学模型特征(身高)确定的区域；通过这一点，他们将有望更好地了解个人基因组学领域的潜力。研讨会的材料将在跨学科研讨会孵化器中开发，该孵化器由STEM代表不足的CS和生物学本科生组成的暑期学生对组成。我们将与三个针对STEM代表不足的学生的外展计划合作，创建、评估和传播研讨会单元。项目结果将在项目网站上公布：Lab.saramsey.org/Regulome

This project's research activities will advance the field of bioinformatics by creating a computational method that combines different types of information from genome-wide association studies. Genetic association studies measure sequence differences across an entire genome in order to identify what variants cause the occurrence and variability of traits like height and disease susceptibility. The goal of this project is to develop methods to precisely locate gene regulatory variants, that now are only known to be somewhere in a large region, and use them to understand how traits vary in a population. The methods developed in this research aim to combine a number of types of information, like gene expression levels for cells, measurement of traits in many individuals, and comparisons with traits in other species, in order to identify causal regulatory variants. The project's curriculum development activities will contribute to STEM education by creating and sharing a hands-on workshop on genome bioinformatics in the research area. By integrating research and educational activities students will (i) gain science literacy in the areas of genetics and bioinformatics; (ii) show how well hands-on methods work in genetics education; and (iii) use already performed genetic association studies to gain new knowledge in biology and in biomedicine.This research will create and evaluate an integrative machine-learning model for identifying regulatory variants within human intergenic GWAS regions. The model's inputs will include the reference genome, the local DNA 3-D shape, phylogenetic conservation, and transcriptomic and epigenomic measurements. The model's output will be predicted regulatory variants with significance scores. The model will be benchmarked against published methods using ground-truth regulatory variants. The machine-learning model's variant predictions will be incorporated into an open-source, web-based software tool for integrative post-analysis of GWAS data. Compatibility with a cloud-computing framework will position the tool for maximum impact. Through educational activities that are integrated with the project's research activities, Dr. Ramsey will create, evaluate, and disseminate a Genome Bioinformatics Workshop unit for high school educators and students. Participants will learn to use the tool to analyze and explore human GWAS-identified regions for a model trait (height); through this they would be expected to gain a better understanding of the potential of the field of personal genomics. The workshop's materials will be developed within an interdisciplinary workshop incubator consisting of pairs of STEM-underrepresented CS and biology undergraduate summer students. We will create, evaluate, and disseminate the workshop unit in partnership with three outreach programs for STEM-underrepresented students. Project results will be made available at the project website: lab.saramsey.org/regulome