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多种复杂疾病相关的新基因和途径方面取得了令人难以置信的成功。然而，尽管形成了大型联盟来进行跨队列的荟萃分析，但最常见、复杂疾病的预期遗传力只有一小部分得到了解释。人类遗传学界现在正在采用大规模测序方法（例如外显子组和全基因组）来识别可能具有更大表型效应的罕见变异。作为回应，统计遗传学家创建了一系列测试，旨在将罕见变异与疾病联系起来。我们假设，对于因果等位基因的频率与其效应大小之间的反比关系，最简单的解释是，许多疾病是由新出现的有害突变的涌入引起的，这些突变由于自然选择而不断从人群中消失。因此，我们建议开发模拟软件，将我们所知道的等位基因频率如何随时间变化的知识从理论丰富的群体遗传学领域整合到数据丰富的人类遗传学领域。我们最终的软件将用于开发具有高发现能力的全球队列测序策略，并帮助评估现有/未来的统计测试。为了实现广泛的影响，我们将创建一个生成有效数据的图形用户界面 (GUI)，并应用我们的工具来比较和对比各种现有的统计测试。然后我们将改进我们的群体遗传模拟器 成为第一个基于CPU和图形处理单元（GPU）异构计算架构的群体遗传模拟器。通过密集的并行化，我们的软件将实现颠覆性的效率。使用这种方法，我们将开发一个基于模拟的推理平台，可以适应复杂的进化模型。我们将应用这种方法来分析即将到来的人类和果蝇的全基因组测序数据。最后，我们的目标是通过开发基于模拟的教学工具，将前沿研究带回课堂。我们的教学工具将采用图形用户界面的形式，可以让您动手学习复杂的概念。