Evolutionary inferences from protein-coding genes

描述（由申请人提供）：自然选择可以是具有挑战性的研究。太小而无法直接测量的适应性差异可能会产生深远的进化后果。这激发了统计工具的创建，用于从自然发生的遗传变异的数据集来表征自然选择。选择作用于表型，但这往往被这些统计工具所忽略。相反，与每个等位基因或基因型相关的适应度通常被视为自由参数。我们的研究采用计算方法从DNA序列数据预测表型。这使我们的统计程序，以提取更多的信息选择从数据集，它有利于研究的影响表型的基因型进化。我们研究的另一个非传统的特点是，我们分析种间数据，但框架估计与人口遗传学。我们这样做是因为大多数进化史只能通过种间比较来研究，而且群体遗传学是研究选择的自然框架。 我们的研究重点是自然选择来维持蛋白质结构，但我们的推理策略可以评估其他表型的进化影响。为了更好地理解三级结构的影响，我们将同时研究上下文依赖性突变，密码子使用和mRNA丰度的进化作用。我们更现实的进化模型的主要后果将是更好的种群遗传推断从种间数据的自然选择，但该模型也有潜力，以协助从祖先序列重建推断适应性景观的应用。模拟将有助于评估我们的种群遗传推断的质量从种间数据，并让我们确定如何改善这些推断。我们将特别关注的情况下，人口有并发的健身影响多态性，通过希尔罗伯逊效应相互干扰。由于我们的种间模型是根据种群遗传学构建的，因此我们可以以一种合理的方式将种间和种内数据联合收割机结合起来。这种明确的进化观点将导致一个已经成功的方法来预测非同义变异对人类健康的影响的改进。 公共卫生相关性：该项目将导致更好地理解自然选择在形成遗传变异中的作用。通过这种更好的理解，我们将开发统计技术来识别蛋白质编码基因中的哪些变异可能对人类健康有害。

DESCRIPTION (provided by applicant): Natural selection can be challenging to study. Fitness differences that are too small to directly measure can have profound evolutionary consequences. This has motivated the creation of statistical tools for characterizing natural selection from data sets of naturally occurring genetic variation. Selection operates on phenotype but this tends to be ignored by these statistical tools. Instead, the fitness associated with each allele or genotype is often treated as a free parameter. Our research employs computational methods for predicting phenotype from DNA sequence data. This enables our statistical procedures to extract more information about selection from data sets and it facilitates studies of the impact of phenotype on evolution of the genotype. Another unconventional feature of our research is that we analyze interspecific data but frame estimates with respect to population genetics. We do this because most of evolutionary history can be studied only through interspecific comparisons and because population genetics is the natural framework within which to study selection. Our research focuses on natural selection to maintain protein structure, but our inference strategies can assess the evolutionary impact of other phenotypes. To better understand the influence of tertiary structure, we will simultaneously examine the evolutionary roles of context-dependent mutation, codon usage, and mRNA abundance. The main consequence of our more realistic evolutionary models will be better population genetic inferences about natural selection from interspecific data, but the models also have the potential to assist with applications ranging from ancestral sequence reconstruction to inferring adaptive landscapes. Simulation will help to evaluate the quality of our population genetic inferences from interspecific data and will let us determine how to improve these inferences. We will devote particular attention to the situation where populations have concurrent fitness-affecting polymorphisms that interfere with each other via the Hill-Robertson effect. Because our interspecific models are framed with respect to population genetics, we can combine interspecific and intraspecific data in a sensible way. This explicit evolutionary perspective will lead to improvement of an already successful approach for predicting which nonsynonymous variation has effects on human health. PUBLIC HEALTH RELEVANCE: This project will lead to improved understanding of the role that natural selection has in shaping genetic variation. Via this improved understanding, we will develop statistical techniques for identifying which variation in protein-coding genes is likely to be deleterious to human health.