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: 9143009
• 负责人: Kirk Lohmueller
• 金额: $ 30.88万
• 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 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9143009
• 关键词: Address; Affect; Amino Acids; Biological Process; Canis familiaris; Complex; Computing Methodologies; Data; Data Set; Disease; Environment; Evolution; Future; Genetic; Genetic Epistasis; Genetic Polymorphism; Genetic Variation; Genetic study; Genome; Genomics; Heart; Homo; Human; Human Genome; Individual; Knowledge; Medical Genetics; Methods; Modeling; Mutation; Natural Selections; Nature; Population; Population Genetics; Public Health; Research; Risk; Running; Sampling; Site; Sum; Testing; Variant; Wolves; Work; base; cohort; computerized tools; exome sequencing; fitness; genetic pedigree; genetic variant; improved; next generation sequencing; novel; pressure; public health relevance; response; trait; transmission process

PROJECT SUMMARY (DESCRIPTION) How new mutations affect an individual's fitness lies at the heart of many questions in evolutionary genetics and is highly relevant for elucidating the genetic basis of complex traits. Current evidence suggests that while many mutations are deleterious, selection coefficients for particular mutations come from a distribution of se- lective effects (DSE). Although the DSE has been subjected to intense study over the past decade, many open questions about this distribution and the importance of deleterious mutations in evolution remain unanswered. The widespread use of next-generation sequencing has provided a plethora of new polymorphism datasets that are potentially informative about the nature of fitness effects of mutations. However, existing computational approaches make simplifying assumptions and lack statistical power to extract the full potential of these data. This project will develop and apply new computational approaches to integrate these emerging polymorphism datasets from multiple species together in a rigorous statistical framework to estimate fundamental parameters relating to natural selection across genomes. We will develop novel computational methods that use very large samples of human genomes to better estimates selective effects. First, we propose to leverage runs of homo- zygosity within an individual to co-estimates dominance coefficients and the DSE. We will develop a second set of methods that utilizes transmission patters within large numbers of pedigrees to estimate selective ef- fects. Third, previous studies of the DSE have assumed all sites are independent of each other and have ig- nored epistasis. We will develop a composite likelihood approach to estimate the degree of epistasis between many pairs of SNPs. We will then apply these methods to the large cohorts of human genomes that are being sequenced to obtain the most detailed estimates of the DSE, dominance coefficients, and nature of epistasis from human populations. Next, these new computational tools will be combined with data from a variety of dif- ferent species to address longstanding questions concerning selective effects. We will investigate the evolution of the DSE itself by testing whether it has changed in response to recent shifts in the environment. As an ex- ample, we will test whether the change in selective pressure associated with dog domestication has led to dif- ferences in the DSE between dogs and wolves. This will be accomplished by generating exome sequencing data from of dogs and wolves. Finally, we will investigate why different species appear to show disparate amounts of amino acid adaptation. This work will resolve a current paradox in population genetics and will con- tribute to understanding the different modes of adaptation across different species. In sum, successful comple- tion of this research will provide the most comprehensive estimates of dominance, the DSE, and the amount of epistasis in natural populations to date. These estimates will dramatically improve the interpretation of genetic variation in future evolutionary and medical genetic studies.

项目总结(说明) 新的突变如何影响个体的适应性是进化遗传学中许多问题的核心 对于阐明复杂性状的遗传基础具有很高的相关性。目前的证据表明，虽然 许多突变是有害的，特定突变的选择系数来自硒和硒的分布。 集体效应(DSE)。尽管DSE在过去十年中受到了密集的研究，但许多开放的 关于这种分布和有害突变在进化中的重要性的问题仍然没有答案。 下一代测序的广泛使用提供了过多的新的多态数据集 这对突变的适合性影响的性质有潜在的信息。然而，现有的计算 这些方法简化了假设，缺乏充分挖掘这些数据潜力的统计能力。 这个项目将开发和应用新的计算方法来整合这些新兴的多态 在一个严格的统计框架中一起使用来自多个物种的数据集来估计基本参数 与跨基因组的自然选择有关的。我们将开发新的计算方法，使用非常大的 人类基因组样本，以更好地估计选择性效应。首先，我们建议利用同性恋群体- 个体内的合子来共同估计优势系数和DSE。我们将开发第二个 一套利用大量家系中的传递模式来估计选择性效应的方法。 影响很大。第三，以前对DSE的研究假设所有站点都是相互独立的，并且具有 诺瑞德上位论。我们将开发一种复合似然方法来估计上位性程度 许多对SNPs。然后，我们将把这些方法应用于人类基因组的大群组 测序以获得DSE、显性系数和上位性的最详细估计 从人类人口中分离出来。接下来，这些新的计算工具将与各种不同的数据相结合-- 不同的物种来解决长期存在的关于选择效应的问题。我们将研究其进化过程 通过测试DSE是否随着最近环境的变化而发生变化来评估DSE本身。作为一名前任- 我们将测试与狗驯化相关的选择压力的变化是否导致了不同的- 在DSE中狗和狼之间的比喻。这将通过产生外显子组测序来完成 来自狗和狼的数据。最后，我们将调查为什么不同的物种表现出不同的 氨基酸适应的量。这项工作将解决当前群体遗传学中的一个悖论，并将与 致敬于了解不同物种的不同适应模式。总而言之，成功完成了- 这项研究将提供对支配地位、DSE和 到目前为止，自然种群中的上位性疾病。这些估计将极大地改善对基因的解释 未来进化和医学遗传学研究中的变异。