Longitudinal data analysis for rare variants detection with penalized quadratic inference function.
Longitudinal data analysis for rare variants detection with penalized quadratic inference function.
复制标题
利用惩罚二次推理函数进行纵向下一代测序数据分析，用于罕见变异检测
DOI：
10.1038/s41598-017-00712-9
复制
发表时间：
2017-04-05
影响因子：
4.6
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Cao H;Li Z;Yang H;Cui Y;Zhang Y
Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.
影响因子：
3.7
作者：
Zhan H;Xu S
通讯作者：
Xu S