Dep. of Animal Science, Univ. of California Davis, Davis, CA, 95616, USA.
Dep. of Animal and Poultry Sciences, Virginia Polytechnic Institute and State Univ., Blacksburg, VA, 24061, USA.
Plant Genome. 2022 Sep;15(3):e20228. doi: 10.1002/tpg2.20228. Epub 2022 Jul 29.
The recent advancement in image-based phenotyping platforms enables the acquisition of large-scale nondestructive crop phenotypes measured at frequent intervals. To further understand the underlying genetic basis over a physiological process and improve plant breeding programs, the question of how to efficiently utilize these time-series measurements in genome-enabled analysis including genomic prediction and genome-wide association studies (GWASs) should be considered. In this paper, a Bayesian random regression model with mixture priors is developed to introduce more meaningful biological assumptions to the analysis of longitudinal traits. The mixture prior for marker effects in Bayes Cπ is implemented in our developed model (RR-BayesC) for demonstration purpose. The estimation of single-nucleotide polymorphism-specific effects that are related to the dynamic performance of crops and the accuracy of genomic prediction by RR-BayesC were studied through both simulated and real rice (Oryza sativa L.) data. For genomic prediction, three predictive scenarios were studied. In the simulated study, RR-BayesC showed a significantly higher prediction accuracy than that obtained by single-trait analysis, especially for days when heritability were low. In real data analysis, RR-BayesC showed relatively high prediction accuracy when forecast is required for phenotypes at later period (e.g., from 0.94 to 0.98 for lines with observations at an earlier period and from 0.65 to 0.67 for lines without any observations). For GWASs, inference of single markers and inference of genomic windows were conducted. In the simulated study, RR-BayesC showed its promising ability to distinguish quantitative trait loci (QTL) that are invariant to temporal covariates and QTL that interact with time. An association study of real data was also presented to demonstrate the application of RR-BayesC in real data analysis. In this paper, we develop a Bayesian random regression model that is able to incorporate mixture priors to marker effects and show improved performance of genomic prediction and GWASs for longitudinal data analysis based on both simulated and real data. The software tool JWAS offers routines to perform our proposed random regression analysis.
基于图像的表型平台的最新进展使得能够以频繁的时间间隔获取大规模的无损作物表型。为了进一步了解生理过程的潜在遗传基础，并改进植物育种计划，应该考虑如何在包括基因组预测和全基因组关联研究（GWAS）在内的基于基因组的分析中有效地利用这些时间序列测量值。在本文中，开发了一种具有混合先验的贝叶斯随机回归模型，以便为纵向特征分析引入更有意义的生物学假设。用于展示目的，在我们开发的模型（RR-BayesC）中实现了 BayesCπ 中标记效应的混合先验。通过 RR-BayesC 研究了与作物动态性能相关的单核苷酸多态性特异性效应的估计以及基因组预测的准确性，研究中使用了模拟和真实水稻（Oryza sativa L.）数据。对于基因组预测，研究了三种预测方案。在模拟研究中，RR-BayesC 显示出比单一性状分析获得的更高的预测准确性，特别是在遗传力低的情况下。在真实数据分析中，当需要预测后期表型时，RR-BayesC 显示出相对较高的预测准确性（例如，对于在早期阶段有观测的品系，从 0.94 到 0.98，对于没有任何观测的品系，从 0.65 到 0.67）。对于 GWAS，进行了单标记和基因组窗口的推断。在模拟研究中，RR-BayesC 显示出区分与时间协变量不变的数量性状基因座（QTL）和与时间相互作用的 QTL 的有希望的能力。还提出了真实数据的关联研究，以证明 RR-BayesC 在真实数据分析中的应用。在本文中，我们开发了一种贝叶斯随机回归模型，该模型能够将混合先验纳入标记效应，并基于模拟和真实数据显示出对纵向数据分析的基因组预测和 GWAS 的改进性能。JWAS 软件工具提供了执行我们提出的随机回归分析的例程。
