NSF EAGER: Topic Models for Population Genetics

该项目通过揭示自然语言分析和遗传学之间令人信服的相似性来开辟新天地。在文本分析中，文档被建模为讨论不同的主题，每个主题都有其特征词汇。同样，现代的个体可以被认为是在多个种群中有祖先，每个种群都有其特有的遗传模式。应用于当代个人和考古遗迹的最先进的基因组数据，该项目提出的统一框架有望解决重大的历史谜团，如玛雅人的衰落，农业的传播，或印度种姓制度的演变。该项目预计将采用主题建模技术，自然语言处理的一个框架，它采用了潜在的狄利克雷分配人口遗传学。该项目将追求三个目标：1。将群体遗传学中现有的分析方法制定为主题模型，利用其他领域的现有框架来提高基因组分析的效率和准确性2。引入特定领域的时间和空间概念，人口以理论上理解的方式进化。3.整合模型的组成部分，创建一个祖先种群的图形模型，该模型描述了基因组已被测序的当代和历史种群的遗传历史。该项目将比较模型与该领域现有标准的准确性和效率。 作为该项目一部分开发的所有软件工具都将提供给研究界。

The project breaks new ground by revealing the compelling analogy between analysis of natural language and genetics. In text analysis, documents are modeled as discussing different topics, each with its characteristic vocabulary. Similarly, modern day individuals can be thought of as having ancestry in multiple populations, each with its characteristic genetic patterns. Applied to state-of-the-art genomic data from contemporary individuals and archaeological remains, the unified framework proposed by this project is expected to resolve great historical mysteries, such as the decline of the Mayans, the spread of agriculture, or the evolution of the Indian caste system.The project is expected to adapt Topic Modeling techniques, a framework from Natural Language Processing which employs Latent Dirichlet Allocation to population genetics. The project will pursue three goals:1. Formulate existing analysis methods in population genetics as Topic Models, leveraging the existing framework in other domains to improve efficiency and accuracy of genomic analysis2. Introduce the domain-specific concepts of time and space, across which populations evolve in a theoretically understood way. 3. Integrate the components of the model to create a graphical model of ancestral populations, which describes the genetic history of contemporary and historical populations whose genomes had been sequenced.The project will compare accuracy and efficiency of models vs. the existing standards in the field. All software tools that will be developed as part of the project will be made available to the research community.