Burden Testing of Rare Variants Identified through Exome Sequencing via Publicly Available Control Data
Burden Testing of Rare Variants Identified through Exome Sequencing via Publicly Available Control Data
复制标题
DOI：
10.1016/j.ajhg.2018.08.016
复制
发表时间：
2018-10-04
影响因子：
9.8
通讯作者：
Lippincott, Margaret F.
中科院分区：
文献类型：
--
作者：
Guo, Michael H.;Plummer, Lacey;Lippincott, Margaret F.
许多孟德尔疾病的遗传原因仍未确定。缺乏大型多基因家系、基因座异质性和不完全外显性等因素阻碍了许多疾病的治疗。以前的工作表明，基于基因的负担测试--在病例和对照受试者之间比较每个基因中罕见的、改变蛋白质的变异的总负担--可能会克服其中的一些限制。大规模公共测序数据库(如基因组聚合数据库(GnomAD))的可用性不断提高，可以使用这些数据库作为对照进行负担测试，从而消除了对每项研究进行额外对照测序的需要。然而，使用公共数据库作为对照存在各种挑战，包括缺乏个人层面的数据、祖先的差异以及测序平台和数据处理的差异。为了说明使用公开数据作为对照的方法，我们分析了393例特发性低促性腺激素减退症(IHH)患者的全外显子测序数据，IHH是一种罕见的疾病，具有显著的基因异质性和与来自gnomAD的对照对象的不完全外显(n=123,136)。我们利用可能是良性的同义变体来校准我们的方法。通过迭代分析，我们系统地解决并克服了在使用公共控制数据时可能出现的各种伪像来源。特别是，我们介绍了一种高度适应性的可变质量过滤方法，该方法导致了很好的校准结果。我们的方法“重新发现”了以前与IHH(FGFR1、TACR3、GNRHR)有关的基因。此外，我们还发现了与小鼠性腺激素低下症有关的基因Tyro3的显著负担。最后，我们开发了一个用户友好的软件包TRAPD(Test Rare Variants With Public Data)，用于在公共数据库中进行基于基因的负担测试。
The genetic causes of many Mendelian disorders remain undefined. Factors such as lack of large multiplex families, locus heterogeneity, and incomplete penetrance hamper these efforts for many disorders. Previous work suggests that gene-based burden testing-where the aggregate burden of rare, protein-altering variants in each gene is compared between case and control subjects-might overcome some of these limitations. The increasing availability of large-scale public sequencing databases such as Genome Aggregation Database (gnomAD) can enable burden testing using these databases as controls, obviating the need for additional control sequencing for each study. However, there exist various challenges with using public databases as controls, including lack of individual-level data, differences in ancestry, and differences in sequencing platforms and data processing. To illustrate the approach of using public data as controls, we analyzed whole-exome sequencing data from 393 individuals with idiopathic hypogonadotropic hypogonadism (IHH), a rare disorder with significant locus heterogeneity and incomplete penetrance against control subjects from gnomAD (n = 123,136). We leveraged presumably benign synonymous variants to calibrate our approach. Through iterative analyses, we systematically addressed and overcame various sources of artifact that can arise when using public control data. In particular, we introduce an approach for highly adaptable variant quality filtering that leads to well-calibrated results. Our approach "re-discovered" genes previously implicated in IHH (FGFR1, TACR3, GNRHR). Furthermore, we identified a significant burden in TYRO3, a gene implicated in hypogonadotropic hypogonadism in mice. Finally, we developed a user-friendly software package TRAPD (Test Rare vAriants with Public Data) for performing gene-based burden testing against public databases.