VCSEL: PRIORITIZING SNP-SET BY PENALIZED VARIANCE COMPONENT SELECTION.
VCSEL: PRIORITIZING SNP-SET BY PENALIZED VARIANCE COMPONENT SELECTION.
复制标题
DOI：
10.1214/21-aoas1491
复制
发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Zhou H
中科院分区：
文献类型：
--
作者：
Kim J;Shen J;Wang A;Mehrotra DV;Ko S;Zhou JJ;Zhou H
单核苷酸多态性 (SNP) 集分析聚集了常见和罕见的变异，并测试感兴趣的表型与集合之间的关联。然而，通常会在整个基因组中研究多个 SNP 集，例如基因、通路或滑动窗口，其中所有组都单独进行测试，然后进行多次测试调整。我们提出了一种在联合多元方差分量模型中对 SNP 集进行优先级排序的新方法。每个 SNP 集对应一个方差分量（或核），模型选择是通过合并凸或非凸惩罚来实现的。这种方差分量选择框架（我们称之为 VCSEL）的独特之处在于它自然地包含多变量特征 (VCSEL-M) 和 SNP 组治疗或环境相互作用 (VCSEL-I)。我们基于主要最小化（MM）原理设计了一种可扩展到许多方差分量的优化算法。模拟研究证明了我们的方法在模型选择性能方面的优越性，通过精确召回（PR）曲线下的面积来衡量，与常用的边际测试和群体惩罚方法相比。最后，我们将我们的方法应用于真正的药物基因组学研究和真正的全外显子组测序研究。通过边缘测试方法，VCSEL 排名靠前的一些基因被检测为不显着，边缘测试方法强调具有严格的显着性阈值的单个基因的正式推断。这为生物学家提供了替代见解，以优先考虑后续研究并开发多基因风险评分模型。
Single nucleotide polymorphism (SNP) set analysis aggregates both common and rare variants and tests for association between phenotype(s) of interest and a set. However, multiple SNP-sets, such as genes, pathways, or sliding windows are usually investigated across the whole genome in which all groups are tested separately, followed by multiple testing adjustments. We propose a novel method to prioritize SNP-sets in a joint multivariate variance component model. Each SNP-set corresponds to a variance component (or kernel), and model selection is achieved by incorporating either convex or nonconvex penalties. The uniqueness of this variance component selection framework, which we call VCSEL, is that it naturally encompasses multivariate traits (VCSEL-M) and SNP-set-treatment or -environment interactions (VCSEL-I). We devise an optimization algorithm scalable to many variance components, based on the majorization-minimization (MM) principle. Simulation studies demonstrate the superiority of our methods in model selection performance, as measured by the area under the precision-recall (PR) curve, compared to the commonly used marginal testing and group penalization methods. Finally, we apply our methods to a real pharmacogenomics study and a real whole exome sequencing study. Some top ranked genes by VCSEL are detected as insignificant by the marginal test methods which emphasizes formal inference of individual genes with a strict significance threshold. This provides alternative insights for biologists to prioritize follow-up studies and develop polygenic risk score models.
登录
查看更多内容
影响因子：
1.2
作者：
Cingolani, Pablo;Platts, Adrian;Ruden, Douglas M.
通讯作者：
Ruden, Douglas M.
影响因子：
2.1
作者：
Broadaway, K. Alaine;Duncan, Richard;Conneely, Karen N.;Almli, Lynn M.;Bradley, Bekh;Ressler, Kerry J.;Epstein, Michael P.
通讯作者：
Epstein, Michael P.
影响因子：
2.7
作者：
Chen, Jiahua;Chen, Zehua
通讯作者：
Chen, Zehua
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
4.5
作者：
Fan Y;Li R
通讯作者：
Li R