Evolutionary inferences from protein-coding genes

描述(由申请者提供)：自然选择可能是一项具有挑战性的研究。体型差异太小，无法直接测量，可能会产生深远的进化后果。这推动了从自然发生的遗传变异的数据集中创建统计工具来描述自然选择的特征。选择是根据表型进行的，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相关的适合度通常被视为一个自由参数。我们的研究使用计算方法从DNA序列数据中预测表型。这使得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进化的影响。我们研究的另一个非常规特征是，我们分析物种间的数据，但框架与群体遗传学有关的估计。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，也因为群体遗传学是研究选择的自然框架。我们的研究侧重于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，我们将同时检查上下文相关突变、密码子使用和信使核糖核酸丰度的进化作用。我们更现实的进化模型的主要结果将是从物种间数据中更好地做出关于自然选择的种群遗传推断，但这些模型也有可能辅助从祖先序列重建到推断适应景观的各种应用。模拟将有助于从种间数据评估我们群体遗传推断的质量，并将使我们确定如何改进这些推断。我们将特别关注通过Hill-Robertson效应相互干扰的种群同时具有影响适应度的多态的情况。因为我们的种间模型是以种群遗传学为框架的，所以我们可以合理地将种间和种内数据结合起来。这种明确的进化论观点将导致对预测哪些非同义变异对人类健康产生影响的已经成功的方法的改进。 公共卫生相关性：该项目将有助于更好地理解自然选择在塑造遗传变异中的作用。通过这种更好的理解，我们将开发统计技术来确定蛋白质编码基因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.