A Global-Local Approach for Detecting Hotspots in Multiple-Response Regression.
A Global-Local Approach for Detecting Hotspots in Multiple-Response Regression.
复制标题
DOI：
10.1214/20-aoas1332
复制
发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
Bottolo L
中科院分区：
文献类型：
--
作者：
Ruffieux H;Davison AC;Hager J;Inshaw J;Fairfax BP;Richardson S;Bottolo L
我们处理具有许多预测变量和许多响应的回归问题中变量选择的建模和推理。我们专注于检测热点，即与多个响应相关的预测变量。这样的任务在统计遗传学中至关重要，因为热点遗传变异通过控制许多基因的表达来塑造基因组的结构，并可能启动疾病终点的决定性功能机制。现有的用于对热点建模的分层回归方法存在两个局限性：它们对热点的区分对预测变量成为热点倾向的顶级尺度参数的选择很敏感，并且它们不能扩展到大型预测变量和响应向量，例如遗传应用中维度 103-105 的向量。我们通过引入灵活的分层回归框架来解决这些缺点，该框架专为热点检测而定制，并可扩展至上述维度。我们的建议基于马蹄收缩先验实现了完全贝叶斯热点模型。其全局-局部公式在全局范围内缩小了噪声，因此适应了遗传分析的高度稀疏性质，同时对个体信号具有鲁棒性，从而使热点的影响不被缩小。使用快速变分算法结合新颖的模拟退火程序进行推理，可以有效地探索多模态分布。
We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, as hotspot genetic variants shape the architecture of the genome by controlling the expression of many genes and may initiate decisive functional mechanisms underlying disease endpoints. Existing hierarchical regression approaches designed to model hotspots suffer from two limitations: their discrimination of hotspots is sensitive to the choice of top-level scale parameters for the propensity of predictors to be hotspots, and they do not scale to large predictor and response vectors, for example, of dimensions 103–105 in genetic applications. We address these shortcomings by introducing a flexible hierarchical regression framework that is tailored to the detection of hotspots and scalable to the above dimensions. Our proposal implements a fully Bayesian model for hotspots based on the horseshoe shrinkage prior. Its global-local formulation shrinks noise globally and, hence, accommodates the highly sparse nature of genetic analyses while being robust to individual signals, thus leaving the effects of hotspots unshrunk. Inference is carried out using a fast variational algorithm coupled with a novel simulated annealing procedure that allows efficient exploration of multimodal distributions.