Population genomics of the selective effects of new mutations

新突变选择性效应的群体基因组学

• 批准号: 10402242
• 负责人: Kirk Lohmueller
• 金额: $ 37.98万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10402242
• 关键词: Address; Biological; Biological Process; Code; Complex; Copy Number Polymorphism; Data; Disease; Evolution; Funding; Genetic Models; Genetic Polymorphism; Genetic Risk; Genetic Variation; Genome; Genomics; Human; Individual; Knowledge; Laboratories; Longevity; Methods; Modeling; Mutation; Natural Selections; Nucleotides; Paint; Pathogenicity; Population; Population Genetics; Population Sizes; Process; Public Health; Research; Risk; Role; Short Tandem Repeat; Testing; Time; Untranslated RNA; Variant; Work; Yeasts; fitness; functional genomics; genetic variant; genomic data; public health relevance; risk prediction; simulation; tool; trait

PROJECT SUMMARY (DESCRIPTION) Deleterious mutations are ubiquitous in genomes. However, the manner in which they impact evolution and complex traits remains unclear. My laboratory has focused on understanding the role of deleterious mutations in evolution by combining polymorphism data from multiple species with population genetic models. During the previous funding period, we have developed tools to infer the distribution of fitness effects (DFE) and domi- nance coefficients from polymorphism data in natural populations and made several discoveries as to how pu- rifying selection acts in different species. In particular, we have learned from our recent work that the DFE and selection coefficients at individual mutations differ across species, many deleterious mutations are recessive, the fate of deleterious mutations in populations and their effects on genome variation heavily depend on specif- ic demographic and biological parameters, strongly deleterious recessive mutations determine the fitness of a population on short timescales more than weakly deleterious mutations do, and deleterious mutations contrib- ute to poor transferability of genetic risk prediction between populations. Despite this progress, critical gaps in knowledge remain. Much of the existing work on deleterious variation has focused on single nucleotide muta- tions in coding regions in a limited subset of species. Further, there has been limited work on testing the ex- planatory power of inferred model parameters. Here we propose to expand our knowledge of deleterious varia- tion across genomes by addressing five new questions. First, we will combine large-scale functional genomic data with polymorphism data to infer a DFE for noncoding regulatory mutations, which is vital for understanding complex traits as the vast majority of disease-associated mutations are non-coding variants. Second, we will develop new computational approaches to infer a DFE for complex mutations, such as short tandem repeats and copy number variants. These new methods and inferences will enable direct quantitative comparison of the fitness effects of different types of mutations in different parts of the genome. Third, by combining polymor- phism data from multiple species with different population sizes, complexity, and lifespan, we will test how the- se factors influence the DFE over evolutionary time. Fourth, we will use both human polymorphism data as well as yeast functional genomic data to test our previously developed model for the existence of dominance. Final- ly, we will use detailed forward simulations to establish whether state-of-the-art population genetic models of multiple evolutionary forces occurring simultaneously can explain genetic variation across genomes. Success- ful completion of this research will paint a more complete picture of how evolutionary processes influence ge- netic variation and the causes and consequences of deleterious variation.

项目概要（描述） 有害突变在基因组中普遍存在。然而，它们影响进化的方式， 复杂的特征仍不清楚。我的实验室致力于了解有害突变的作用 通过将多个物种的多态性数据与种群遗传模型相结合，期间 上一个资助期，我们已经开发了工具来推断健身效果（DFE）和domi的分布， nance系数从多态性数据在自然人群中，并作出了几项发现，如何浦- 进化选择作用于不同的物种。特别是，我们从最近的工作中了解到，DFE和 个体突变的选择系数在物种间不同，许多有害突变是隐性的， 有害突变在种群中的命运及其对基因组变异的影响在很大程度上取决于特定的 根据人口统计学和生物学参数，强烈有害的隐性突变决定了一个 在短时间尺度上的人口比弱有害突变做，和有害突变贡献- 遗传风险预测在群体间的传递性差。尽管取得了这一进展， 知识仍然存在。现有的有害变异研究大多集中在单核苷酸穆塔上， 在一个有限的物种子集的编码区的连接。此外，在测试前- 推断模型参数的平射功率。在这里，我们建议扩大我们的知识有害的变量- 通过解决五个新的问题，首先，我们将联合收割机大规模的功能基因组 多态性数据来推断非编码调控突变的DFE，这对于理解 复杂的性状，因为绝大多数疾病相关的突变是非编码变异。二是 开发新的计算方法来推断复杂突变的DFE，如短串联重复序列 和拷贝数变体。这些新的方法和推论将使直接定量比较 基因组不同部分不同类型突变的适应性效应。第三，通过聚合物- 来自不同种群规模，复杂性和寿命的多个物种的phism数据，我们将测试- 这些因素会在进化过程中影响DFE。第四，我们也将使用人类多态性数据 作为酵母功能基因组数据来测试我们先前开发的显性存在模型。最后- ly，我们将使用详细的前向模拟，以建立是否国家的最先进的人口遗传模型， 同时发生的多种进化力量可以解释基因组间的遗传变异。成功- 这项研究的全面完成将为进化过程如何影响基因描绘一幅更完整的图景， 遗传变异以及有害变异的原因和后果。