CRII: AF: Novel evolutionary models and algorithms to connect genomic sequence and phenotypic data

生物体的基因组是其所有DNA的集合，可以写成一个字符串。生物体的所有生物复杂性都编码在其基因组中。当今科学的最大挑战之一是理解基因组的“句法”如何产生生物功能的“语义学”，实际上，地球上的所有生命都是如此。进化论提供了一条前进的道路。这些看似异质的生物学特征只是它们产生的共同进化过程的一个方面。通过查询它们的遗传或进化历史，我们可以开始破译基因组书写的活语言，并最终掌握它，为我们自己的目的服务。至关重要的是，最先进的算法将基因组视为一个无序的观察包，而不是一个有序的观察序列。这一假设纯粹是为了数学上的方便。相比之下，DNA是线性分子，基因组中编码的信息被认为是连续的，其顺序非常重要。进化是随着时间的推移重新排列基因组的主要进化过程之一，最终塑造了信息的顺序。重组引起的序列依赖性（或缺乏）是系统发育推断的计算问题的一个重要方面，然而，（基因组中的位置）是独立和相同分布的仍然是一个主要的方法学差距。为了解决这一关键需求，该项目将创建新的进化模型和算法，用于从基因组推断物种的遗传，同时考虑点突变，基因漂移和重组。然后，通过将新的进化模型构建成一种新的计算方法，用于绘制复杂表型（可观察性状）的基因组结构，从而与系统生物学建立了联系。新的方法将使用一个广泛的性能研究，结合经验和合成数据进行验证。对实验数据的分析预计将导致新的生物学发现，例如了解家鼠适应特性的遗传基础，家鼠是最广泛使用的实验室生物。研究目标中提出的新的数学模型、算法和工具将成为两个系列研讨会的基础：一个针对进化计算研究人员，另一个针对进化生物学家。本科和研究生一级的跨学科培训包括代表性不足的少数民族学生。所有方法和数据的开放实现将通过一个协作在线社区公开提供。该项目包括三个综合研究目标。首先，将开发一种新的点突变、遗传漂变和重组组合模型下的物种遗传推断算法。该组合模型将群体遗传学的结合模型与隐马尔可夫模型结合起来，以捕获由于重组而导致的相邻基因座之间不同程度的序列依赖性。一个关键的挑战是可扩展性，这是解决使用新的近似算法。其次，新的进化模型将与线性混合模型融合，以捕获基因组基因座和基因组内因果基因座编码的性状之间的依赖关系。新模型将成为新算法的基础，解决功能基因组学中的几个相关问题。一个应用是关联作图，其寻求基于等位基因频率和观察到的性状值之间的显著相关性来推断因果基因座。第三，将进行性能研究，以验证新的计算方法。

An organism's genome is the collection of all of its DNA, which can be written as a single string. All of the biological complexity of an organism is encoded within its genome. One of the greatest challenges in science today is understanding how the "syntax" of the genome gives rise to the "semantics" of biological function and, indeed, all life on Earth. The theory of evolution offers a way forward. These seemingly heterogeneous biological features are merely facets of the common evolutionary process by which they arose. By querying their phylogeny, or evolutionary history, we can begin to decipher the living language in which genomes are written and, ultimately, master it for our own purposes.Today, phylogenies are primarily reconstructed by computational analysis of biomolecular sequence data. Crucially, state-of-the-art algorithms treat genomes as an unordered bag of observations, not as an ordered sequence of observations. This assumption is made for pure mathematical convenience. In contrast, DNA is a linear molecule, the information encoded in the genome is understood to be sequential, and its order matters greatly. Recombination is one of the major evolutionary processes that rearranges genomes over time, ultimately shaping the sequential ordering of information. Sequence dependence due to recombination (or the lack thereof) is an essential aspect of the computational problem of phylogenetic inference, and yet the common assumption that loci (positions in the genome) are independent and identically distributed remains a major methodological gap.To address this critical need, this project will create new evolutionary models and algorithms for inferring species phylogenies from genomes while accounting for point mutations, genetic drift, and recombination. A connection is then forged to systems biology by building the new evolutionary models into a new computational method for mapping the genomic architecture of complex phenotypes (observable traits). The new methods will be validated using an extensive performance study incorporating empirical and synthetic data. Analyses of the empirical data are anticipated to result in new biological discoveries such as understanding the genetic basis of adaptive traits in house mouse, the most widely used laboratory organism.This project incorporates significant educational and outreach components. The new mathematical models, algorithms, and tools proposed in the research objectives will be the basis for two workshop series: one targeted to evolutionary computation researchers and the other to evolutionary biologists. Interdisciplinary training at the undergraduate and graduate level includes underrepresented minority students. Open implementations of all methods and data will be publicly available through a collaborative online community. This project entails three integrated research objectives. First, algorithms for inferring species phylogenies under a new combined model of point mutations, genetic drift, and recombination will be developed. The combined model unites the coalescent model of population genetics with a hidden Markov model to capture varying degrees of sequence dependence among neighboring loci due to recombination. A key challenge is scalability, which is addressed using new approximation algorithms. Second, the new evolutionary models will be fused with a linear mixed model to capture dependence between genomic loci and a trait encoded by causal loci within the genome. The new models will be the basis for new algorithms that address several related problems in functional genomics. One application is association mapping, which seeks to infer causal loci based upon significant correlation between allele frequencies and observed trait values. Third, a performance study will be conducted to validate the new computational methodologies.