Evolutionary inferences from protein-coding genes

描述（由申请人提供）：自然选择的研究可能具有挑战性。太小而无法直接测量的适应度差异可能会对进化产生深远的影响。这推动了统计工具的创建，用于从自然发生的遗传变异数据集描述自然选择。选择作用于表型，但这些统计工具往往忽略了这一点。相反，与每个等位基因或基因型相关的适应度通常被视为一个自由参数。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能够从数据集中提取更多关于选择的信息，并有助于研究表型对基因型进化的影响。我们研究的另一个非常规特征是，我们分析种间数据，但根据群体遗传学进行框架估计。我们这样做是因为大多数的进化历史只能通过种间比较来研究，因为群体遗传学是研究选择的自然框架。我们的研究主要集中在维持蛋白质结构的自然选择，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结果将是从种间数据中更好地推断自然选择的种群遗传，但这些模型也有可能帮助从祖先序列重建到推断适应性景观的应用。模拟将有助于从种间数据评估我们的种群遗传推断的质量，并将让我们确定如何改进这些推断。我们将特别关注人群同时具有影响健康的多态性的情况，这些多态性通过希尔-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，我们可以以一种合理的方式将种间和种内数据结合起来。这种明确的进化观点将导致改进已经成功的方法来预测哪些非同义变异对人类健康有影响。

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.