Rare Variant Analysis of Next Generation Sequencing Studies with the Variable Binning / Variable Threshold (VB/VT) Collapsing Algorithm

使用可变分箱/可变阈值 (VB/VT) 折叠算法进行下一代测序研究的罕见变异分析

• 批准号: 279728737
• 负责人: Professor Dr. Michael Nothnagel, Ph.D., since 6/2016
• 金额: --
• 依托单位: Abteilung Study of Health in Pomerania (SHIP)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/279728737?language=en
• 关键词: Rare; Variant; Analysis; Next; Generation

Rare variants derived from next generation sequencing (NGS) studies are prime candidates to explain relevant portions of the so-called missing heritability of complex diseases. Respective statistical methods typically address multiple variants simultaneously and rely on a twofold parameterization of the analysis dataset: first, the choice of the threshold minor allele frequency to define rare and common variants, and, second, the definition of the SNP sets that shall be analyzed together (analysis bins). While the optimal definition of rareness can be obtained with the established variable threshold (VT) approach, a respective method for the choice of analysis bins was not proposed until now. This is an important methodological gap, since power is limited when selected analysis bins (exons, genes, haplotype blocks etc.) do not exactly correspond to associated regions. In a pilot study, we investigated the possibility to analyze all possible contiguous bins, i.e. bins with all possible combinations of start and end positions. While the theoretical number is high, in practice, the effective number of analysis bins is substantially lower for burden tests such as the collapsing method. We give the outline of an algorithm that efficiently identifies all effective bins, the variable binning (VB) algorithm. In addition, we plan to combine the VB algorithm with the VT approach. Adjustment for multiple testing shall be achieved within a Monte-Carlo simulation framework, adjustment for population stratification will be performed by localized genetic matching of cases and controls. Our goal is to provide an efficient implementation of the VB/VT algorithm that enables application to NGS studies as well as to combined GWAS/NGS studies on a Genome-wide scale. Power and validity of the algorithm will be evaluated via a simulation study and application to Exome-chip data and NGS data in collaboration with external partners.

从下一代测序（NGS）研究中获得的罕见变异是解释复杂疾病的所谓缺失遗传性相关部分的主要候选者。各自的统计方法通常同时处理多个变异，并依赖于分析数据集的双重参数化：首先，选择阈值小等位基因频率来定义罕见和常见的变异，其次，定义需要一起分析的SNP集（分析箱）。虽然用已建立的变阈值方法可以得到稀缺性的最优定义，但目前还没有相应的分析箱选择方法。这是一个重要的方法差距，因为当选择的分析箱（外显子，基因，单倍型块等）不完全对应于相关区域时，功率是有限的。在一项初步研究中，我们调查了分析所有可能的连续箱的可能性，即具有所有可能的开始和结束位置组合的箱。虽然理论数量很高，但在实践中，对于诸如崩溃方法之类的负荷测试，分析箱的有效数量要低得多。我们给出了一种有效识别所有有效箱的算法的概要，即变量箱（VB）算法。此外，我们计划将VB算法与VT方法相结合。多重检测的调整在蒙特卡罗模拟框架内完成，群体分层的调整通过病例与对照的局部基因匹配进行。我们的目标是提供VB/VT算法的有效实现，使其能够应用于NGS研究以及全基因组范围内的GWAS/NGS联合研究。该算法的能力和有效性将通过与外部合作伙伴合作对exome芯片数据和NGS数据进行模拟研究和应用来评估。