New computational methods to dynamically pinpointing the subregions carrying disease-associated rare variants

项目总结/摘要 高通量测序技术使我们能够查询常见和罕见的复杂变异， 人类疾病。当变异罕见时，单变异关联分析的功效较低。增加 现有的全外显子组测序研究通常将整个基因中的罕见变异（RV）聚合在一起 来研究它们的集体效应。据推测，当一个基因携带许多致病性RV时，聚集将 提高信噪比，从而提高功率。然而，一个基因往往携带许多突变，而只有 一个子集将导致新的或改变的活动。这些突变通常不均匀地分布在整个细胞中。 整个基因或结构域。对于其功能突变局限于或集中于特定区域的基因， 如果在整个基因或结构域上聚集所有RV，则会稀释信号，导致信号丢失。 动力.此外，即使基于基因或结构域的分析可以识别致病基因， 精确定位致病亚区精确定位致病亚区是优选的，因为它通常 在功能上更加统一，对下游疾病机制和翻译提供更多信息， 问题研究为了解决这些问题和需求，我们提出了一种新的统计和计算方法， 具有三个主要特征的稀有变异关联分析。首先，它会自动搜索GVS， 不同的大小，以优化其疾病关联的功率。第二，它可以确定与疾病相关的 高分辨率的GVS有助于下游疾病机制的研究。第三，它可以很容易地 定制以满足特殊需求，例如保护数据隐私，纳入功能注释，以及 调整混合种群的不同祖先负荷。我们将建立一个严格的数学和 GVS分析的统计基础并开发软件以实现其在高层的实施 通量测序研究。我们将把我们的方法应用于正在进行的全外显子组测序研究， 肌萎缩侧索硬化症（ALS），以确定ALS相关的基因组亚区。

PROJECT SUMMARY/ABSTRACT The high-throughput sequencing technology allows us to query both common and rare variants for complex human diseases. When variants are rare, single variant association analyses suffer from low power. To increase power, existing whole-exome sequencing studies often aggregate the rare variants (RVs) across an entire gene to study their collective effect. Presumably, when a gene harbors many pathogenic RVs, the aggregation will increase the signal-to-noise ratio and thus the power. However, a gene often carries many mutations, while only a subset will lead to novel or altered activities. These mutations usually do not distribute uniformly across the entire gene or domain. For genes whose functional mutations are localized or concentrated to the specific subregions, aggregating all the RVs across the entire gene or domain will dilute the signal, resulting in a loss of power. Besides, even if the gene- or domain-based analysis can identify the pathogenic genes, they cannot pinpoint the pathogenic subregions. Pinpointing the pathogenic subregions is preferred because it is usually more unified in function and will be more informative to the downstream disease mechanism and translational studies. To address these concerns and needs, we propose a novel statistical and computational method for rare-variant association analysis with the three main features. First, it automatically searches the GVSs with different sizes for their disease associations to optimize power. Second, it can pinpoint the disease-associated GVSs with high resolution to facilitate the downstream disease mechanism studies. Third, it can be easily customized to fit the special needs, such as preserving data privacy, incorporating functional annotations, and adjusting for varying ancestry loadings for admixed populations. We will establish a rigorous mathematical and statistical foundation for the GVS analysis and develop the software to realize its implementation on high- throughput sequencing studies. We will apply our method to an ongoing whole-exome sequencing study of amyotrophic lateral sclerosis (ALS) to identify ALS-related genomic subregions.