Powerful Simulation Tools for the Genomics Age

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，但只有一小部分最常见的复杂疾病的预期遗传性得到了解释。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）以鉴定可能具有较大表型效应的罕见变体。作为回应，统计遗传学家创造了一系列测试，旨在将罕见变异与疾病联系起来。我们假设，对于致病等位基因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去除。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着时间的推移而变化，从理论丰富的群体遗传学领域整合到人类遗传学的数据丰富领域。我们得到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，我们将创建一个图形用户界面（GUI），产生有效的数字，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，我们将改造我们的人口遗传模拟器， 成为第一个基于CPU和图形处理单元（GPU）的异构计算架构的种群遗传模拟器。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，可以容纳复杂的进化模型。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回归课堂。我们的教学工具将以GUI的形式，使动手学习复杂的概念。

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.