How does the genetic architecture of quantitative traits evolve over time? Answering this question is crucial for many applied fields such as human genetics and plant or animal breeding. In the last decades, high‐throughput genome techniques have been used to better understand links between genetic information and quantitative traits. Recently, high‐throughput phenotyping methods are also being used to provide huge information at a phenotypic scale. In particular, these methods allow traits to be measured over time, and this, for a large number of individuals. Combining both information might provide evidence on how genetic architecture evolves over time. However, such data raise new statistical challenges related to, among others, high dimensionality, time dependencies, time varying effects. In this work, we propose a Bayesian varying coefficient model allowing, in a single step, the identification of genetic markers involved in the variability of phenotypic traits and the estimation of their dynamic effects. We evaluate the use of spike‐and‐slab priors for the variable selection with either P‐spline interpolation or non‐functional techniques to model the dynamic effects. Numerical results are shown on simulations and on a functional mapping study performed on an Arabidopsis thaliana (L. Heynh) data which motivated these developments.

中文翻译：

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选择的贝叶斯变系数模型：在函数映射中的应用

数量性状的遗传结构如何随时间演变？回答这个问题对于人类遗传学和动植物育种等许多应用领域至关重要。在过去的几十年中，高通量基因组技术已被用来更好地理解遗传信息与定量性状之间的联系。最近，高通量表型分析方法也被用于以表型规模提供大量信息。特别地，这些方法允许随时间测量特征，并且对于大量个体而言。结合这两种信息可能会提供有关遗传结构如何随时间演变的证据。但是，此类数据提出了新的统计挑战，其中尤其涉及高维度，时间依赖性，时变效应。在这项工作中 我们提出了一种贝叶斯变化系数模型，该模型允许在单个步骤中识别涉及表型性状变异性的遗传标记并评估其动态效应。我们使用P样条插值法或非功能性技术对动态效果进行建模，评估了先验先验和先验先验用于变量选择。数值结果显示在仿真和在计算机上执行的功能映射研究中促使这些发展的拟南芥（L. Heynh）数据。