Powerful Simulation Tools for the Genomics Age

基因组时代的强大模拟工具

• 批准号: 8673650
• 负责人: Ryan D. Hernandez
• 金额: $ 39.02万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-19 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8673650
• 关键词: Adopted; Age; Alleles; Architecture; Back; Base Sequence; Bioinformatics; Biological Testing; Boxing; Clinical; Code; Communities; Complex; Computer Simulation; Computer software; DNA Resequencing; Data; Development; Disease; Disease susceptibility; Drosophila genome; Drosophila genus; Educational process of instructing; Evaluation; Frequencies; Functional RNA; Future; Gene Frequency; Genes; Genetic; Genetic Recombination; Genetic Variation; Genome; Genomics; Goals; Haplotypes; Heritability; Human; Human Genetics; Large-Scale Sequencing; Learning; Maintenance; Meta-Analysis; Methods; Modeling; Mutation; Natural Selections; Pathway interactions; Pattern; Phase; Phenotype; Population; Population Genetics; Positioning Attribute; Process; Publishing; Radio; Recording of previous events; Relative (related person); Research; Research Design; Running; Sample Size; Sensitivity and Specificity; Sex Chromosomes; Shapes; Site; Specific qualifier value; Statistical Methods; Students; Techniques; Testing; Time; Variant; analytical method; base; cohort; computer based statistical methods; demographics; design; driving force; empowered; exome; experience; flexibility; genetic association; genome sequencing; genome wide association study; graphical user interface; human data; lectures; method development; novel; programs; public health relevance; rare variant; response; simulation; simulation software; theories; tool; trait; user-friendly

DESCRIPTION (provided by applicant): Genome-wide association studies have been incredibly successful at identifying novel genes and pathways associated with a wide array of complex diseases. However, despite the formation of large consortia to perform meta-analyses across cohorts, only a small fraction of the expected heritability of most common, complex diseases has been explained. The human genetics community is now adopting large-scale sequencing approaches (e.g., exome and whole genome) to identify rare variants that potentially have larger phenotypic effects. In response, statistical geneticists have created a litany of tests for geared toward associating rare variants with disease. We hypothesize that the most parsimonious explanation for an inverse relationship between the frequency of causal alleles and their effect size is that many diseases are caused by an influx of newly arising deleterious mutations that are continually removed from the population due to natural selection. We therefore propose to develop simulation software that will integrate what we know about how allele frequencies change over time from the theory-rich field of population genetics into the data-rich field of human genetics. Our resulting software will be used to develop strategies for sequencing global cohorts with high discovery power, and to aid in the evaluation of existing/future statistical tests. To achieve broad impact, we will create a graphical user interface (GUI) that produces effective figures, and apply our tool to compare and contrast a wide variety of existing statistical tests. We will then revamp our population genetic simulator to become the first population genetic simulator based on the heterogeneous computing architecture of both CPUs and graphical processing units (GPUs). Through intensive parallelization, our software will achieve disruptive efficiency. Using this approach, we will develop a platform for simulation-based inference that can accommodate complex evolutionary models. We will apply this approach to analyze forthcoming whole genome sequencing data from humans and Drosophila. Finally, we aim to return cutting-edge research to the classroom by developing simulation-based teaching tools. Our teaching tool will be in the form of a GUI that enables hands-on learning of complex concepts.

描述（由申请人提供）：全基因组关联研究在识别与各种复杂疾病相关的新基因和途径方面取得了极大的成功。然而，尽管形成了大型财团以跨人类进行荟萃分析，但已经解释了最常见的复杂疾病的预期遗传力的一小部分。现在，人类遗传学界正在采用大规模测序方法（例如外显子和整个基因组），以识别稀有的变体，这些变体可能具有更大的表型效应。作为回应，统计遗传学家为将稀有变体与疾病相关联的旨在构成了一系列测试。我们假设，关于因果等位基因频率及其效果大小之间存在反比关系的最简约的解释是，许多疾病是由于自然选择引起的自然选择而不断从人群中移除的新出现的有害突变引起的。因此，我们建议开发模拟软件，以整合我们对等位基因频率如何随时间变化的知识从理论丰富的人群遗传学领域变化为人类遗传学数据丰富的领域。我们最终的软件将用于制定策略，以测序具有高发现能力的全球人群，并帮助评估现有/未来的统计测试。为了获得广泛的影响，我们将创建一个图形用户界面（GUI），该界面（GUI）产生有效的数字，并应用我们的工具比较和对比各种现有的统计测试。然后，我们将把我们的种群遗传模拟器改造为 基于CPU和图形处理单元（GPU）的异质计算体系结构，成为第一个种群遗传模拟器。通过密集的并行化，我们的软件将达到破坏性效率。使用这种方法，我们将开发一个基于仿真的推理的平台，可以容纳复杂的进化模型。我们将采用这种方法来分析人类和果蝇的整个基因组测序数据。最后，我们旨在通过开发基于模拟的教学工具将最先进的研究返回教室。我们的教学工具将以GUI的形式，可以实践复杂的概念。