III: Small: Computational Methods for Ancestry Inference In Genetics

许多人对他们的祖先感兴趣。传统上，历史记录是了解一个人祖先的主要信息来源。在基因组学时代，分析一个人的基因组正在成为最流行的祖先测试方法。公司现在为数百万客户提供此类测试。DNA祖先测试的涌入现在使人们不仅了解自己，而且更多地了解他们的祖先。人们提出了一个有趣的问题，即一个人能从自己的DNA中了解他或她最近的祖先多少。最近，研究人员一直在与一位人口遗传学家合作，开发从个体基因组推断最近祖先的计算方法。想象一下，一个小女孩，爱丽丝，有欧洲和美洲土著的遗产，想知道一些关于她最近的祖先。现有的商业祖先测试提供了对Alice基因组的遗传组成的估计（例如，爱丽丝的DNA或长基因组片段的百分比可以追溯到美洲原住民起源）。另一方面，研究者和他的合作者考虑的问题涉及从爱丽丝的基因组推断最近祖先的遗传组成。也就是说，研究者的目标是回答诸如“我只有自己的基因组，但我想知道我最近的祖先。我的父母是50%-50%的欧洲人和美洲原住民吗？或者一个是未混合的欧洲人，另一个是未混合的美洲土著人？我的祖父母呢？“这些问题在以前的文献中没有得到严格的解决，即使这些问题可能对遗传学家和基因检测的消费者都感兴趣。为了解决这些问题，研究人员将致力于基于DNA的祖先推断的新计算方法。研究人员计划建立在他最近对这个主题的研究基础上，PedMix方法，它可以推断最近祖先的祖先（例如，父母和祖父母）从焦点个体的基因组。目前，PedMix是唯一一种从单个个体的基因组推断最近祖先的方法。在本研究计画中，研究者计画针对一般性的祖先推论主题进行研究。第一个目标是提高PedMix的性能，以获得更准确的推理结果。这可以使PedMix更适用于真实的基因测试。第二个目标是开发祖先推理方法，可以学习祖先信息更远的祖先。目前，由于计算困难，PedMix最多只能用于祖父母推理。 最后，本计画也致力于研究新的祖先推理公式，这些公式在过去并未被严格研究过。该项目的关键技术是计算效率。该项目研究的成功完成将产生新的高效和准确的算法，这些算法将在实用的软件工具中实现，并能够从大规模基因组学数据中进行新的祖先推断。开发的软件工具将免费提供给多学科研究社区，并有望在基于DNA的祖先推断中实现新的生物学应用。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Many people are interested in their ancestry. Traditionally, historical records are the main sources of information for knowing one's ancestry. In the age of Genomics, analyzing one's genome is becoming the most popular way of ancestry test. Companies now offer such tests to millions of customers. The influx of DNA ancestry tests now make people know not only something about themselves, but also more about their ancestors. Interesting questions have been raised about how much one can learn about his or her recent ancestors from one's own DNA. Recently, the investigator has been working with a population geneticist on developing computational methods for inferring recent ancestors from an individual's genome. Imagine that a little girl, Alice, has both European and Native American heritage and wants to know something about her recent ancestors. Existing commercial ancestry tests provide estimates of genetic composition on Alice's genome (e.g., percentage of Alice's DNAs or long genomic segments that can be traced to Native American origin). The problem considered by the investigator and his collaborators, on the other hand, concerns the inference of the genetic composition of recent ancestors from Alice's genome. That is, the investigator aims to answer questions such as "I only have my own genome but I want to know about my recent ancestors. Are my parents 50%-50% European and Native American? Or one is unadmixed European and the other is unadmixed Native American? How about my grandparents?" Such questions have not been rigorously addressed in the literature before, even though these questions may be of interests to both geneticists and consumers of genetic tests. To address these questions, the investigator will work on new computational methods for DNA-based ancestry inference. The investigator plans to build on his recent research on this subject, the PedMix approach, which can infer the ancestry of recent ancestors (e.g., parents and grandparents) from a focal individual's genome. At present, PedMix is the only publically available method for inferring recent ancestors from a single individual's genome. In this project, the investigator plans to conduct research on the general subject of ancestry inference. The first objective is to improve the performance of PedMix to obtain more accurate inference results. This can make PedMix more applicable and practical to real genetic tests. The second objective is developing ancestry inference methods which can learn ancestry information for more distant ancestors. At present, due to computational difficulty, PedMix can only work for great grandparental inference at most. Finally, this project also aims to study new ancestry inference formulations which haven't been rigorously studied before. The key technical aspect of this project is computational efficiency. Successful completion of the research in this project will produce new efficient and accurate algorithms that are implemented in practical software tools and enable new ancestry inference from large scale genomics data. Developed software tools will be made available freely to the multidisciplinary research community, and are expected to enable novel biological applications in DNA-based ancestry inference.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.