III: Small: Inference of Causal Regulatory Relationships from Genetic Studies

III：小型：从高通量基因组数据中推断生物网络是生物信息学中的一个核心问题，其中已经提出了许多不同类型的方法并应用于各种各样的数据集。 最近的几项研究已经收集了包含遗传变异信息以及来自一组遗传上不同的生物体菌株的基因表达信息的数据，这些生物体菌株具有用于推断基因之间的因果调节关系的几个有利特性。表示因果关系的一个原则性方法是使用图形因果模型，并且已经开发了从观察数据和干预措施推断这种模型的丰富理论。 然而，这个理论假设联合分布的全部知识，这相当于具有非常大的样本，因此只能保证渐近工作。 在这项提案中，该团队将在几个方向上扩展因果推理方法，其动机是将其应用于样本相对较小的基因组学数据集的遗传观点。 特别是，他们将应用他们的新方法来检测酵母基因之间因果关系的存在和不存在。虽然本提案的重点是将开发的技术应用于生物信息学中的特定问题，但本提案中解决的因果推理问题是将因果推理应用于有限样本时面临的一般问题。 本提案中提出的许多办法将适用于各种各样的问题。 本提案中所制定的方法将通过公开软件提供给科学界，该项目涉及培训一名研究生和一名本科生。 该项目的协作性质将使学生接触医学和遗传学世界，同时，它将提高他们设计和实施复杂算法和统计问题解决方案的能力。 这项研究将被转换为跨学科课程的课程材料，计算遗传学，这是由本科生和研究生以及医学院的学生采取。

III:Small: Inference of Causal Regulatory Relationships from Genetic StudiesInference of biological networks from high-throughput genomic data is a central problem in bioinformatics where many different types of methods have been proposed and applied to a wide diversity of datasets. Several recent studies have collected data which contain both genetic variation information as well as gene expression information from a set of genetically distinct strains of an organism which have several advantageous properties for inferring causal regulatory relationships between genes. A principled way of representing causal relationships is using graphical causal models and a rich theory of inference of such models from observational data and interventions has been developed. However, this theory assumes full knowledge of the joint distribution which is equivalent to having very large samples and so is only guaranteed to work asymptotically. In this proposal, the team will extend causal inference methods in several directions motivated by applications to genetic views of genomics datasets where there are relatively small samples. In particular they will apply their new methods to detecting the presence and absence of causal relationships between yeast genes. While the focus of this proposal is on applying the developed techniques to a specific problem in bioinformatics, the causal inference issues addressed in this proposal are the general issues faced when applying causal inference to finite samples. Many of the approaches developed in this proposal will be applicable to a wide range of problems. The resulting methods developed in this proposal will be made available to the scientific community through publicly available software.The project involves the training of a graduate and undergraduate students. The collaborative nature of the project will expose the students to the medical and genetics worlds, and at the same time, it will improve their abilities to design and implement solutions to complex algorithmic and statistical problems. The research will be converted into course materials for the interdisciplinary course, Computational Genetics, which is taken by both undergrad and graduate students as well as students from the medical school.