A Fast and Robust Strategy to Remove Variant-Level Artifacts in Alzheimer Disease Sequencing Project Data.
A Fast and Robust Strategy to Remove Variant-Level Artifacts in Alzheimer Disease Sequencing Project Data.
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DOI：
10.1212/nxg.0000000000200012
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发表时间：
2022-10
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预计外显子组测序（ES）和基因组测序（GS）对于通过识别导致阿尔茨海默病（AD）发病机制的罕见编码和/或非编码变体来进一步阐明阿尔茨海默病（AD）风险缺失的遗传遗传性至关重要。在美国，阿尔茨海默病测序项目（ADSP）在大规模测序AD相关样本方面发挥了主导作用，研究人员可以公开获得由此产生的数据，从而对AD的遗传病因学产生新的见解。为了获得足够的把握度，ADSP采用了一种研究设计，其中收集了较大AD队列的子集，并使用各种测序平台在多个中心进行测序。这种方法可能导致测序中心和/或平台之间的变异质量不同。在这项研究中，我们试图实现和评估过滤器，可以快速应用，以鲁棒地去除ADSP数据中的变量级伪影。我们实施了一个强大的质量控制程序来处理ADSP数据。我们在使用最新的ADSP全ES（WES）和全GS（WGS）数据发布（NG00067.v5）对AD风险进行全外显子组和全基因组关联分析时评估了该程序。我们观察到，许多变体在测序中心/平台之间的等位基因频率中显示出很大的变化，并导致了与AD风险的虚假关联信号。我们还观察到关联模型中的测序平台/中心调整不能完全解释这些虚假信号。为了解决这个问题，我们设计并实现了变体过滤器，可以捕获和删除这些中心特定/平台特定的伪影变体。我们推导出一种快速而稳健的方法来过滤代表测序中心相关或平台相关伪影的变体，这些伪影是ADSP WES和WGS数据中与AD风险的虚假关联的基础。这种方法将是重要的，以支持未来强大的遗传关联研究的ADSP数据，以及其他研究具有类似的设计。
Exome sequencing (ES) and genome sequencing (GS) are expected to be critical to further elucidate the missing genetic heritability of Alzheimer disease (AD) risk by identifying rare coding and/or noncoding variants that contribute to AD pathogenesis. In the United States, the Alzheimer Disease Sequencing Project (ADSP) has taken a leading role in sequencing AD-related samples at scale, with the resultant data being made publicly available to researchers to generate new insights into the genetic etiology of AD. To achieve sufficient power, the ADSP has adapted a study design where subsets of larger AD cohorts are collected and sequenced across multiple centers, using a variety of sequencing platforms. This approach may lead to variable variant quality across sequencing centers and/or platforms. In this study, we sought to implement and evaluate filters that can be applied fast to robustly remove variant-level artifacts in the ADSP data. We implemented a robust quality control procedure to handle ADSP data. We evaluated this procedure while performing exome-wide and genome-wide association analyses on AD risk using the latest ADSP whole ES (WES) and whole GS (WGS) data releases (NG00067.v5). We observed that many variants displayed large variation in allele frequencies across sequencing centers/platforms and contributed to spurious association signals with AD risk. We also observed that sequencing platform/center adjustment in association models could not fully account for these spurious signals. To address this issue, we designed and implemented variant filters that could capture and remove these center-specific/platform-specific artifactual variants. We derived a fast and robust approach to filter variants that represent sequencing center-related or platform-related artifacts underlying spurious associations with AD risk in ADSP WES and WGS data. This approach will be important to support future robust genetic association studies on ADSP data, as well as other studies with similar designs.