Detecting Evolution of Amino-Acid Fitness in Vertebrate Genomes

数百种脊椎动物的基因组测序现已接近完成，还有近10，000种将在未来几年内确定（“Genome10k”）。这项工作的主要动机是增加比较分析的力量，以阐明基因和基因组功能如何进化（以及它是如何进化的）。虽然在脊椎动物生物多样性的主要谱系中进行密集的分类学采样有望大大提高对进化过程中“重要”的遗传变化进行统计推断的能力，但仍存在大量的计算和分析障碍。目标 ** 该项目的主要目标是利用计算和建模创新来可靠地检测多达数百至数千个脊椎动物基因组的蛋白质编码区多个位置上氨基酸适应度的协调变化（“适应度变化”）。这种转变是通过定向选择改变蛋白质功能需求的预期结果，因此可能意味着功能分化或适应。为了表征可靠检测的限度，在替代实验设计（物种数量，分歧水平等）下详细计算统计信息。将被执行以确定比较数据能够多好地将适应性变化与其他现象区分开（例如，人口规模缩小）。快速马尔可夫链蒙特卡罗方法推断大型比较数据集的适应度变化将开发和评估。* * 科学方法 ** 我们最近开发了几种通用方法，用于对大型基因组数据集进行快速贝叶斯分析，这有助于消除计算瓶颈，并在某些情况下将数据分析时间从数月缩短到几分钟。在这个项目中，我们将整合这些技术，沿着未发表的改进，与算法，利用大规模并行的廉价，众核协处理器的新兴重要性，在科学计算。我们将使用这些方法来实现离散的空间和时间异质性的选择性约束和人口规模的模型，并将检查其性能的一个大型的脊椎动物单拷贝基因。在整个过程中，计算的可扩展性和时间复杂性的降低（即使以明显温和的近似为代价）将被优先考虑，以最大限度地利用大型数据集。我们最近开始开发（未发表）的渐近功效分析方法将被详细阐述，并用于表征实验设计对功效的影响，并评估推断的限制。预期意义 ** 随着越来越多的脊椎动物基因组，现在出现了巨大的机会，以推进知识的遗传基础的基本进化过程，包括功能的分歧和适应。用于检测功能分歧的统计方法是从基因组数据进行功能推断的最广泛使用的方法之一。尽管人们普遍认为这种方法过于简单，在重要方面存在缺陷，但由于其计算方便，它们被容忍。通过集中在这里的算法快速和可扩展的推理，它是希望在分子进化的最新进展可以“扩大规模”，使比较基因组学的原则性更强的方法。通过实验设计的定量框架的发展，将出现更合理的方法来选择需要哪些物种和多少物种来解决特定问题。因此，这里开发的工具将促进大规模比较基因组研究的合理设计和执行。

The genomes of hundreds of vertebrate species have now been sequenced to near-completion, and nearly 10,000 more are slated to be determined over the next several years ("Genome10k"). A major motivation for this work is to increase the power of comparative analysis to illuminate how gene and genome function evolves (and how it has evolved). Although dense taxonomic sampling across the major lineages of vertebrate biodiversity is expected to substantially increase the ability to make statistical inferences about the genetic changes that "mattered" during evolution, substantial computational and analytic barriers to progress exist.**Objectives**The primary objective of this project is to exploit computational and modeling innovations to reliably detect coordinated changes in amino-acid fitness ("fitness shifts") across multiple positions in the protein-coding regions of up to hundreds to thousands of vertebrate genomes. Such shifts are an expected outcome of changes in the functional requirements of a protein by directional selection, and thus may imply functional divergence or adaptation. To characterize the limits of reliable detection, detailed calculations of statistical information under alternative experimental designs (numbers of species, divergence levels, etc.) will be performed to determine how well comparative data can distinguish fitness shifts from other phenomena (e.g., reductions in population size). Fast Markov Chain Monte Carlo methods of inferring fitness shifts in large comparative datasets will be developed and evaluated. * *Scientific approach **We recently developed several general approaches for rapid, Bayesian analysis of large phylogenomic datasets, which can help eliminate computational bottlenecks and in some cases reduce data analysis times from months to minutes. In this project, we will integrate these techniques, along with unpublished improvements, with algorithms that exploit the massive parallelism of inexpensive, many-core coprocessors of emerging importance in scientific computing. We will use these approaches to implement models of discrete spatial and temporal heterogeneity in selective constraints and population size, and will examine their performance on a large set of vertebrate single-copy genes. Throughout, scalability of computations and reductions in time-complexity (even at the expense of demonstrably mild approximations) will be prioritized to maximize utility in large datasets. Asymptotic power analysis methods that we have recently begun developing (unpublished) will be elaborated and used to characterize the impact of experimental design on power and to evaluate the limits of inference.**Expected significance**With increasingly large numbers of vertebrate genomes now available, tremendous opportunities are emerging to advance knowledge of the genetic basis for fundamental evolutionary processes including functional divergence and adaptation. Statistical methods for detecting functional divergence are among the most widely used ways that functional inferences are made from genomic data. Although it is widely appreciated that such approaches are oversimplified and flawed in important ways, they are tolerated because of their computational convenience. By focusing here on algorithms for fast and scalable inference, it is hoped that recent progress in molecular evolution can be "scaled up" to enable more principled approaches in comparative genomics. Through the development of a quantitative framework for experimental design, more reasoned methods for selecting which and how many species are needed to address a particular question will emerge. Thus, the tools developed here will facilitate advances in both the rational design and execution of large-scale comparative genomic studies.